Enhancing Biodegradability of Coffee Husk and Water Hyacinth using Food Waste: Synergistic and Kinetic Evaluation under Co-digestion

Enhancing Biodegradability of Coffee Husk and Water Hyacinth using Food Waste: Synergistic and Kinetic Evaluation under Co-digestion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Biodegradability of Coffee Husk and Water Hyacinth using Food Waste: Synergistic and Kinetic Evaluation under Co-digestion mohammed Kelif Ibro, Venkata Ramayya Ancha, Dejene Beyene Lemma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3880494/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2024 Read the published version in BioEnergy Research → Version 1 posted 4 You are reading this latest preprint version Abstract Coffee husk (CH) and water hyacinth (WH) are seen as environmental challenges causing eutrophication of water streams and infestation of water bodies. These biomass resources, available in plenty with high organic content can be considered for anaerobic digestion. However, their high lignin content poses a challenge to their biodegradability in which case co-digestion with easily degradable food waste (FW) could alleviate this problem. Thus, the synergistic effect with co-digestion of CH and WH employing increasing FW levels on biogas yield, biodegradability (BD fpc ), and biodegradation rate (η BD ) were investigated in this work. Experimental studies were conducted with a varied mixtures of CH/WH/FW (100:0:0, 0:100:0, 35:35:30, 30:30:40, 25:25:50, 20:20:60 and 0:0:100) at constant temperature (38 ± 1°C). The results indicated that addition of FW significantly enhanced WH and CH digestion performance, with the maximum biogas yield of 572.60 ml/gVS, highest BD fpc of 89.22% and η BD of 57.82% obtained at a mix ratio of 25:25:50, which was improved by 194.98% compared to CH mono-digestion. The co-digestion tests exhibited strong synergy due to their nutritional balance and other interactive effects promoting stability. Maximum synergy was 1.72 for a mix of 20:20:60. The modified Gompertz, logistic, and first-order kinetic models were used to simulate the experimental data to portray the biodegradation and kinetics involved. The modified logistic equation was seen to be the best fit to elucidate biogas production. The current findings highlighted the importance of increasing the easily biodegradable waste fractions in the co-digestion of lignocellulosic biomass for enhanced biodegradability. Coffee husk water hyacinth food waste biodegradability co-digestion synergistic effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlight The feasibility of coffee husk, water hyacinth, and food waste co-digestion was studied. An optimum mix of 25% CH, 25% WH, and 50% FW produced 572.60 ml/gVS biogas. Co-digestion tests exhibited a strong synergistic effect. Adding food waste enhanced biodegradability and biogas production. The kinetics of biogas production follows modified logistic model. 1. Introduction Valorization of organic wastes is instrumental in decreasing the environmental and economic burden and transitioning to a bio-based circular economy. Coffee husk is the main by-product generated in huge volumes during dry coffee processing [ 1 ]. In Ethiopia, coffee husks are discarded in streams and open dumps. This improper disposal of coffee waste causes environmental harm through eutrophication of water bodies, salinization of soils, and poisonous effects on some biological processes. These aspects have limited its application in agriculture [ 2 ]. In addition, caffeine and transition metals in coffee husks can cause DNA damage and present toxicity to aquatic organisms [ 3 ]. On the other hand, coffee husk is a valuable feedstock for the biogas production process due to its excellent chemical composition, elemental composition, and existence of proteins, carbohydrates, and bioactive compounds [ 4 ]. It also has high volatile solids [ 5 ], cellulose, and hemicelluloses that make the coffee husk a good feedstock for biogas production. However, concerns like the high lignin content and caffeine, tannin, and phenols might inhibit microbial activities [ 6 ]; as a result, AD of this residue needs long-term stabilization [ 7 ]. The lignin is highly recalcitrant and might be toxic to some microorganisms, which hinders microbial degradation [ 8 ]. In addition, in AD settings, lignin hydrolysis is not only a rate-limiting and stage-limiting reaction; it leads to blocking the anaerobic fermentation process due to low mass transfer [ 9 ], and deficiency of anaerobic bacteria that decompose lignin is another limitation. Aquatic plants [ 8 ] and food waste [ 10 ] were reported as good substrates to enhance biomethane production through co-digestion with lignocellulosic biomass. Combining the aquatic biomass and food waste with lignocellulose might optimize the initial pH and carbon-nitrogen ratio (C/N) ratio for proficient AD. The WH is an invasive species that grows quickly. It can simply out-compete native plant life after it grows in the region, being thus very problematic to remove or control. These water plants have penetrated water bodies and decreased dissolved oxygen. Water hyacinth threatens water streams, tourism, transportation networks, fisheries, power plants, agriculture, residents, and living conditions [ 11 ]. In Ethiopia, the plant has infiltrated and spread throughout different water bodies. Lake Ziway (known as Batu Dembel) is one of the lakes infected with the water hyacinth, which covers 434 square kilometers. Freshwater systems are known for their abundant birdlife and fish wildlife, which are exposed to the conquest of aggressive water hyacinths [ 12 ]. Thus, control of water hyacinths on Batu Dembel Lake needs the attention of the concerned bodies. There are different methods to expel the aquatic plant at present, such as biological, chemical, and physical control approaches [ 13 ]. These methods have respective limitations, but the physical approaches appear to be the best alternative if collected waste is turned into energy production. Water hyacinth is a nutrient-rich substrate and contains easily biodegradable organic matter, which makes it excellent for biogas production [ 14 ]. It has a comparatively high C/N ratio, a high cellulose and hemicellulose content, and a low lignin content, suggesting that it could be used as a feedstock in biogas generation [ 11 ]. Although water hyacinth has low lignin content, its lignocellulosic compositions may be a difficulty in biomethane production by decelerating the hydrolysis process and final conversion to biomethane [ 11 ]. Water hyacinths grown in different locations may have varying nutrient components like nitrogen and phosphorus that may affect biogas production and quality. Besides, environmental factors like temperature, humidity, and sunlight exposure may also influence the chemical composition of water hyacinths. Water hyacinth grown in regions of harsh climates may have poor cellulose and hemicellulose content, making it less suitable for biogas production [ 15 ]. AcoD of lignocellulosic materials with highly biodegradable feedstocks is an effective approach for improving lignocellulose biodegradation. Food waste is one such feedstock because it is highly degradable[ 16 ]. In Ethiopia, huge amounts of food waste (food leftovers, fruit, and vegetable waste) are generated in higher education institutions. For example, an average of 5876.5kg of food leftovers are being disposed off per week at Jimma Institute of Technology [ 17 ]. The FW naturally decomposes quickly a few days after collection due to the high moisture content, which presents health hazards, social challenges, and environmental issues (foul odors, potential spread of hazardous microbes) [ 18 ]. If food waste were reused as the substrate for biogas production, it would not only decrease environmental impact but also bring a considerable volume of renewable energy. Despite the high potential of FW for biogas generation, it also has some limitations for anaerobic digestion (AD), mostly the production of high volatile fatty acid (VFA) concentration, which can disturb the pH and become toxic for microbial growth [ 19 ]. Consequently, when choosing suitable feedstocks for anaerobic digestion, it is important to take into account their limits, even though these feedstocks have the potential to contribute to renewable energy production [ 20 ]. Therefore, to prevent the probable failure of FW digestion, two-stage anaerobic digestion and co-digestion are two countermeasures that have been proposed to address the problem of reactor inhibition, as Ding et al. highlighted [ 21 ]. Liu et al.'s investigation revealed that AcoD was a feasible option for the anaerobic digestion of FW as two-stage treatment and pretreatment procedures were costly and time-consuming [ 22 ]. Compared with single substrate treatment, AcoD facilitates the breakdown of recalcitrant complex polymeric substrates for augmented biodegradability, accelerates the start-up rate, increases process efficiency, alleviates pH, balances the C/N ratio, and stability of the digestion process to maximize productivity [ 23 ]. In addition, it reduces the effect of inhibitors and toxins, delivers the required nutrients for microbial growth, reduces retention time and lag time, and increases loading rate and methane yields [ 24 ]. On the other hand, the previous studies also presented the role of FW as an acid pretreatment method to delignify lignocellulose. For instance, Ma et al. observed the improvement of the hydrolysis kinetic constant of the co-digestion group by 4.1 times over the control group [ 10 ]. Similarly, Zou and co-workers used food waste as a chemical pretreatment mediator to accelerate lignocellulose hydrolysis and improved hydrolysis efficiency by 28% over control trials [ 25 ]. Several studies in co-digesting lignocellulosic biomass and food waste were also carried out by Panigrahi et al. [ 26 ] and Begum et al. [ 27 ]. They evaluated the biodegradability of the substrate based on their elemental compositions and reported some positive results. However, determining the biodegradability of the substrate based on their elemental composition may have limitations, as it does not provide a complete understanding of the organic fractions and does not consider the specific chemical structure and their specific biodegradability, which can lead to the wrong estimation of biogas yield. Organic fractions (carbohydrates, protein, and fat) provide a more accurate biogas potential prediction. Thus, it is better to evaluate biodegradability based on carbohydrates, protein, and fat, as this approach affords a more comprehensive analysis of the substrate's potential for biodegradation. Among operating parameters, optimization of co-digestion mixing ratios is important in maximizing the efficiency and productivity of the AcoD process. Different substrates have different biodegradation rates, and characteristics, which can affect the microbial community and overall biogas production [ 24 ]. In AcoD systems, different organic materials are treated by varying their fractions to solve their limitations faced during AD and find the best composition that maximizes biogas production [ 28 ]. Despite the increasing interest in using the AcoD process for waste management and biogas production, there is a research gap in the specific area of enhancing the biodegradability of water hyacinth and coffee husks using food waste. The existing literature primarily focuses on the biodegradability of water hyacinth and food waste in their co-digestion. However, there is a lack of comprehensive research on the potential benefits and challenges of co-digesting these lignocellulose materials together with food waste to determine the optimal mix ratio that promotes methane-rich biogas production from their AcoD. Understanding the synergistic effects of blending WH and CH with food waste in the AD process could provide valuable insights into the optimization of co-digestion systems, leading to enhanced biogas production and waste management strategies. Moreover, there is a need for detailed kinetic evaluation studies to understand the behavior of the co-digestion process. The objective of this study is to investigate the synergistic effects of co-digestion on biodegradability, and biogas production, as well as to conduct a detailed kinetic evaluation to understand the process dynamics using modified Gompertz, logistic, and first-order kinetic models. 2. Materials and method 2.1. Substrate and inoculum preparation The three feedstocks were used as the substrate in a batch experiment. In this study, water hyacinth was collected from Lake Ziway, around Batu, Oromia, Ethiopia. The stems and leaves of water hyacinths were cut into smaller pieces of roughly less than 2.5 cm and then opened to dry in the sun for six days. A coffee husk sample was obtained from a dry coffee processing Machine found in Agaro, Jimma zone, Oromia, Ethiopia. Then water hyacinth and coffee husk were separately grounded to fine particles using a coffee crusher to increase their surface areas for easy microbial degradation in the biodigester. The food waste was sampled from Jimma Institute of Technology students' cafeterias for two consecutive days to get mixtures of Injera leftovers, pasta, potato, meat, and rice. The segregated food waste was homogenized to achieve a uniform mixture of organic contents after impurities were removed. Collected materials were kept in a plastic bag and transported to the Addis Ababa Institute of Technology. The anaerobically digested cow dung was selected as an inoculum to initiate the AD process based on its accessibility, nutrient content, stability, microbial activity, and compatibility with different waste materials [ 29 ]. The inoculum was obtained from cow dung that had been fermented in a mesophilic anaerobic environment before this study began [ 30 ]. After collection, the inoculum was then filtered through a sieve to remove coarse particulates. The inoculum was prepared at the Biochemical Engineering Laboratory in the mesophilic digester, following the guidelines specified in VDI 4630 guidelines [ 31 ], to degas and induce a starvation phase for the microorganisms. All prepared samples were stored at four degrees Celsius in a cold condition until used for further biogas production. 2.2. Experimental design and procedure The plastic bottle of 500 ml was used for the anaerobic fermentation processes with a working volume of 350 ml. A series of batch tests were carried out with varying coffee husk and water hyacinth in similar proportions as well as increasing the quantity of food waste proportions in the different digesters labeled as R 1 – R 7 (CH/WH/FW, i.e., 100:0:0, 0:100:0, 35:35:30, 30:30:40, 25:25:50, 20:20:60 and 0:0:100), respectively, to get the best composition that allows the maximum biogas production. The inoculum was also treated alone to subtract the volume of biogas produced from the endogenous respiration of the inoculum. To prevent inhibition in the fermentation process, 10.75 grams of feedstock were added to each bottle, based on volatile solids (VS), to avoid overdosing on the seeding slurry. Thus, the anaerobic inoculum was added to all bottles, which provided an inoculum-feedstock ratio of 2 on a VS basis, as described elsewhere [ 31 ]. Then distilled water was added to obtain the final working volume. After each sample was added to all bottles, the pH of the blended liquor was checked and neutralized to around seven. The bottles were connected to a gas-tight plastic airbag with gas-tight plastic tubes and a rubber stopper for collecting biogas samples. Then, they were placed inside a water bath to maintain the desired temperature (38 ± 1°C). Biogas production and its compositions were measured in five-day intervals using a water displacement method and the calibrated portable biogas analyzer until the insignificant or ceased biogas production for 40 days. The gas collection bag that the biogas filled during the volume measurement was directly connected to the calibrated gas analyzer, and the gas analyzer displayed gas compositions in percentage. The volume of produced biogas then was reported as the biogas produced per gram of substrate volatile solids introduced to the flasks (ml/gVS), and gas compositions were reported in percentages (%). Besides, to identify the co-digestion synergistic effects on energy recovery, the biogas evolution in each bottle was assessed following Eq. (1) according to Zhen et al. [ 32 ] based on the fractions of coffee husk, water hyacinth, and food waste introduced in the co-digestion and their separate biogas yield during the mono-digestion process. \({EBY}_{est\left(i\right)}={\text{E}\text{B}\text{Y}}_{FW\left(i\right)}*{X}_{1}+{\text{E}\text{B}\text{Y}}_{WH\left(i\right)}*{X}_{2}+{\text{E}\text{B}\text{Y}}_{CH\left(i\right)}*{X}_{3}\) (1) Where i is the instant digestion time (day), EBY est(i) is the estimated biogas yield at ith day (ml/gVS), EBY FW(i) is the experimental biogas yield of food waste alone at the ith day (ml/g VS), EBY WH(i) is the experimental biogas yield of WH alone at the ith day (ml/gVS), EBY CH(i) is the experimental methane yield of CH alone at the ith day (ml/g VS) X 1 is the percentage of FW in the co-substrates (%), X 2 is the percentage of WH in the co-substrates (%) and X 3 is the percentage of CH in the co-substrates (%) [ 32 ]. To improve biogas production of lignocellulosic materials by co-digestion with food waste, Zhen et al. mixed microalgae and food waste with varying mixing proportions (MA/FW, i.e., 100:0, 60:40, 50:50, 40:60, 20:80, and 0:100) % [ 32 ]. In addition, Zala et al. co-digested WH with FW at ratios of (100:0, 0:100, 30:80, 10:75)% [ 33 ], and Ma et al. combined Sophora flavescens residues (SFR) with FW at different ratios of 10:0, 7:3, 5:5, 3:7, 0:10 [ 10 ]. They reported maximum biogas yields when the FW proportions were high in mixtures. Henceforth, based on these works, this research mixed CH and WH with FW, as mentioned above, since these ratios were close to the different types of studies that were carried out using at least one of the same feedstocks used in this study. 2.3. Analytical methods Moisture, ash, total solid (TS), and volatile solid (VS) were analyzed using the water and wastewater standard examination method [ 34 ]. The Kjeldahl method was utilized to compute the Kjeldahl nitrogen and protein contents [ 35 ]. The total carbon and sulfur (CS) contents of the samples were determined using an EA-1112 Flash elemental analyzer (combustion method) at 900°C, according to Kahassay and Bogale [ 36 ]. The crude fiber determination was performed by gravimetric technique following chemical digestion and solubilization of other constituents available in the sample [ 37 ]. Diethyl ether was used to remove the powdered samples, which were subsequently evaporated from the fat solution to estimate the crude fat content. The resulting rest was balanced and obtained as crude fat [ 38 ]. The carbohydrate percentage was determined with the subtraction method, and the formula is % Carbohydrate = % 100 - % (crude protein + crude fat + crude fiber + ash) [ 39 ]. The pH was measured with a pH meter. The C/N ratio of the feedstock mixture was determined using Eq. (2) [ 40 ]. \(C/N ratio=\frac{\sum {C}_{i}*{X}_{i}}{\sum {N}_{i}*{X}_{i}}\) (2) Where C i is the total organic carbon and N i is the Kjeldahl nitrogen content of each feedstock. X i is the percentage of WH, FW, or CH contents supplemented in the mixture to attain the targeted C/N value. 2.4. Fiber Content Determination The lignocellulosic compositions of the water hyacinth and coffee husk were determined following the method described here [ 41 ]. To determine the lignocellulosic components, 5g of sample was boiled in ethanol four times for 15 minutes. After that, it was carefully cleaned with distilled water and kept in an oven furnace for 12 hours at 40°C. The dry sample was weighed as A before being treated with 24% KOH for 4 hours at 25°C. Then it was thoroughly washed with distilled water, dried at 80°C overnight, and then weighed as B. The sample was then refluxed with 5% H 2 SO 4 for two hours after being exposed to 72% H 2 SO 4 for three hours to disintegrate the cellulose. Again, after being washed in distilled water to remove H 2 SO4, the sample was dried in an oven at 80 degrees for 24 hours before the dry weight was calculated as C. Calculations were performed, according to the following relations defined in Eq. (3). \(Cellulose = B-C; Hemicellulose = A-B; Lignin = C\) (3) 2.5. Biodegradability and co-digestion impacts (CI) Equation ( 4 ) was employed to compute the maximum theoretical biogas potential (TBP fpc , ml/gVS), assuming that the feedstock is fully converted into biogas as defined by VDI 4630 guidelines, based on the concentration of carbohydrates (c), proteins (p), and fat (f) in the substrates; thus, the BD fpc of the substrate was calculated with Eq. ( 5 ) [ 31 ]. $${TBP}_{fpc}=\% (750*c+1390*f+800*p)/100$$ 4 $${BD}_{fpc}\left(\%\right)=\frac{CBY}{{TBP}_{CH}*{X}_{CH}+{TBP}_{WH}*{X}_{WH}+{TBP}_{FW}*{X}_{FW}}*100$$ 5 CBY is the cumulative biogas yield, TBP CH, TBP WH, and TBP FW are the theoretical biogas potential of CH, WH, and FW, respectively, that have been calculated based on the lipids, proteins, and carbohydrates compositions of the organic substrates. X CH , X WH , and X FW are the fractions of CH, WH, and FW utilized in the co-digester, respectively. The biodegradation degree (η BD ) of organic waste of anaerobic digestion was assessed by dividing the mass of measured biogas (M Biogas ) by the organic substrate (gVS) added to the digester (Eq. (6)) [ 42 ]. \({\eta }_{BD}=\frac{{M}_{B}}{gVS }*100\) (6) The M B was calculated by the molar masses of the produced cumulative biogas compositions (MCH 4 – a molar mass of biomethane, and MCO 2 – a molar mass of carbon dioxide) with their respective percentage compositions, assuming the produced gas was only composed of CH 4 , and CO 2 (Eq. (7)). \({M}_{B}={V}_{B}*\left(\frac{{M}_{{CH}_{4}}*{cCH}_{4}}{22400*100}+\frac{{M}_{{CO}_{2}}*{cCO}_{2}}{22400*100}\right)\) (7) Where 22400 ml/mol is the volume of ideal gas per mole; cCO 2 –is the carbon dioxide percentage, cCH 4 –is the methane percentage and V B is the measured biogas volume. The synergistic effect was calculated according to Yu et al. [ 43 ] using Eq. (8). \(\text{C}\text{I}\left(\%\right)=\frac{{CBY}_{co}}{{CBY}_{CH}{*X}_{i}+{CBY}_{WH}\text{*}{X}_{j}+{CBY}_{FW}*{X}_{k}}*100\) (8) Where CBY co is the cumulative biogas yield of co-digestion, CBY FW is the cumulative biogas yield of FW mono digestion, CBY WH is the cumulative biogas yield of WH mono digestion, and CBYCH is the cumulative biogas yield of CH mono digestion. X ijk is the percentage of CH, WH, and FW utilized in the co-digester. 2.6. Biogas production kinetics The biogas production rates were simulated using the measured data obtained from batch experiments with the sigmoid-modified Gompertz equation (MGE) (9), a modified logistic equation (MLE) (10) [ 44 ], and a first-order kinetic equation (FOKE) (11) [ 45 ]. \(\text{B}={B}_{o}exp\left\{-exp\left[\frac{{R}_{m}}{{B}_{o}}*e\left(\lambda -t\right)+1\right]\right\}\) (9) \(\text{B}=\frac{{B}_{o}}{1+exp\left[\frac{{4R}_{m}}{{B}_{o}}\left(\lambda -t\right)+2\right]}\) (10) \(\text{B}={B}_{o}\left[1-exp\left(-k*t\right)\right]\) (11) Where B is cumulative specific biogas yield (ml/gVS), B o is the predicted maximum specific biogas yield potential (ml/gVS), R m is the maximum specific biogas rate (ml/gVS.d), e is equal to 2.718282, 𝜆 is the lag time in days, t is incubation period (d) and k (/d) is hydrolysis kinetic constant. The following parameters, B o , k, R m , 𝜆, and correlation coefficient (R 2 ), were estimated with these models using the SPSS software version 26. The statistical indicators R 2 , Root mean square error (RMSE), and the difference between predicted and actual values (ϒ) were applied to evaluate the validity of the kinetic models. Microsoft Excel software was used to evaluate those statistical parameters and plot graphs. The MGE and MLE are S-shape functions and are usually compared to elucidate exponential bacterial growth. Although both models appear to be similar, the major difference between the two models is that the curve of the MGE is symmetrical, and that of the MLE is asymmetric [ 30 ]. The MLE fits biogas production and assumes that the rate of biomethane production is directly proportional to the volume of gas produced and the maximum quantity of methane that may be produced [ 46 ]. 2.7. Statistical analysis Data were processed using Microsoft Excel 2013 and SPSS (version 26). All experiments were carried out in duplicate to ensure the efficiency of the results. The average values were reported. A one-way analysis of variance (ANOVA) with a single factor was employed to detect significant variation between the treatments, with a p < 0.05 being considered significant. In addition, Microsoft Office Excel was used to generate plots and calculate mathematical relations. 3. Results and discussion 3.1. Characterization of the feed materials The moisture, VS, TS, and ash content of the inoculum were 91.39, 73.98, 8.6, and 26%, respectively, suggesting its possibility for initiating an anaerobic digestion process. Moisture is an essential factor in biogas production as it affects microbial activity responsible for organic waste decomposition and produces gas. At the same time, ash provides minerals and other nutrients that are crucial for their metabolism and growth. Additionally, TS and VS results are in line with the VDI4630 guidelines requirement that the inoculum contains a minimum of 50% organic fractions [ 31 ]. Table 1 shows the physiochemical characterization of substrates used in this study. Biomethanation is correlated with VS conversion, which indicates that organic matter is the main content of the dried samples. Biodegradation efficiency is also closely linked to mass transfer for one anaerobic system. The maximum biogas production rate would usually follow more mass conversion, which could be represented by TS, VS, and COD conversions [ 47 ]. The VS of each substrate is more than 85%, which makes it the preferable substrate for renewable energy recovery through AD. Like other lignocellulosic materials, CH and WH comprise lignin, cellulose, and hemicelluloses. CH contains a high content of lignin (23.16%), cellulose (24.88%), and hemicellulose (28.96%), while the lignin, cellulose, and hemicellulose contents of WH are 8.31, 41.39, 19.33, respectively. A high lignin content recorded for CH could cause a low conversion rate. The Lignin component of biomass is not water-soluble; hence, anaerobic microbes require more time to adhere to the substrate to start the hydrolysis [ 44 ]. However, the lignin concentration for WH is less than a third of that of common wood, about 25–36%, which implies that it is more suitable for energy recovery through the AD system compared to CH [ 37 ]. Besides, cellulose and hemicellulose contents of each substrate figure their suitability for bacterial anaerobic degradation and biogas production. The lignocellulose contents of WH and CH are shown in Table 1 . The C/N ratio is a key factor that needs optimization, which specifies the nutrient level in the substrate. A high C/N ratio above the optimum range shows a low nitrogen concentration, while a low C/N below the optimum range indicates a high nitrogen composition of organic matter. Both ranges of C/N levels are not suitable for AD because they lead to low total ammonia nitrogen and fatty acid accumulations [ 26 ]. In this study, the C/N ratio of WH is 25.5, which is within the optimum range for the methanation process. FW has a C/N ratio (19.94), which is very close to the range better for microbial conditions. The C/N ratio of CH is comparatively high, 34.46 (Table 1 ), which is greater than the range of the suggested optimal ratio of 20–30 of the feed substrate feasibility for Ch 4 -rich biogas production [ 20 ]. Thus, it demanded easily digestible nutrient-rich material for better performance. Albeit the C contents make the CH a good substrate for biogas production, the lignin content and bioactive chemical compositions might inhibit the anaerobic microbial activities [ 5 ]. Organic fractions like carbohydrates, fat, and protein are the main energy and nutrient sources that are important for the metabolism and growth of microorganisms. The carbohydrate component of FW was higher than that of CH and WH, indicating the higher biodegradability of FW. This leads to fast hydrolysis reactions, with this facilitating the biodegradation of lignocellulosic components. Thus, the organic fractions of each substrate, presented in Table 1 suggest their potential for biogas production. To recover hydrolysis efficiency and boost final products (CH 4 and effluent), introducing food waste as co-substrate to lignocellulose digester creates plentiful senses, as this process may disintegrate lignin and improve biodegradability [ 20 ]. Thus, a mixture of CH with WH and FW can transform into a superior substrate compared to their mono-digestions. Table 1 Compositions of the feedstocks. Parameters Inoculum WH CH FW Moisture (%) 91.39 13.37 11.1 69.60 VS (%) 73.98 87.24 89.82 90.25 TS (%) 8.6 86.63 88.88 30.40 Ash (%) 26 12.75 10.17 9.75 C (%) 38.8 48.93 47.66 N (%) 1.52 1.42 2.39 C/N 25.53 34.46 19.94 S (%) 0.64 0.074 0. 46 Lignin (%) 8.31 23.16 Cellulose (%) 41.39 24.88 Hemicellulose (%) 19.33 28.96 Crude fiber (%) 19.0 32.67 Crude fat (%) 14.35 0.9 11.81 Crude protein (%) 9.50 8.56 14.93 Carbohydrate (%) 44.40 47.70 63.51 3.2. Impact of FW addition on CH and WH for biogas production The co-digestion of FW with lignocellulosic wastes was carried out in batch experiments with varying CH and WH in similar proportions and increasing the quantity of FW to attain the best C/N ratio that allows achieving the methane-rich biogas at different mixtures. The biogas profiles were measured every five days for 40 days. Biogas yields are presented in Fig. 1 . After the first day of the test, the biogas production started to vary and eventually decreased to zero after the 40th day of the incubation time. As viewed in Fig. 1 a, the maximum peak biogas yield produced daily from R 1 , R 2 , R 3 , R 4 , R 5 , R 6 , and R 7 occurred on the 10th day (45.68 ml/gVS), the 15th day (63.35 ml/gVS), the 20th day (92.81 ml/gVS), the 10th day (99.25 ml/gVS), the 15th day (104.3 ml/gVS), the 15th day (94.70 ml/gVS) and day 10th (94.32 ml/gVS), respectively. Compared to other digesters, R 7 daily biogas was more fluctuated, which may be attributed to digester instability. Analysis of variance also indicated that an increase in FW from 30–60% had a significant effect on daily biogas production compared to mono-digestion of WH and CH (p < 0.05). For all trials, the daily biogas production occurred during the first weeks of the experiments due to easily digestible organic matter and the degrading process that followed for the used substrate (solid concentration, fats, proteins, and carbohydrates). The maximum peak value of the daily biogas of FW is greater than that of the CH and WH, which might be attributed to the easy digestibility of FW, which is simple to digest for anaerobic microbes [ 20 ]. In addition, the maximum biogas yield was noted almost up to 25 days in all vials, which might indicate that the reactors were working steadily. These results suggest that microorganisms rapidly convert readily digested food waste into biogas at the onset of AD and then slowly digest the lignocellulosic materials, including hemicellulose, cellulose, and, or lignin, to form gas [ 48 ]. The highest peaks of biogas production were obtained at R 5 . These maximum peaks are higher than the peak values of other digesters. This may be due to the balanced micro/macro-nutrients in the substrate at C/N ratios of 23.7, which improved the biomethanation process and microbial growth (diversity) [ 20 ]. After 25 days of fermentation in each bottle, the biogas production rate decreased and rose slowly until the end of the biogas production. The biogas production rate during the incubation time was very low for R 1 (C/N-34.46), which treats CH solely. These findings were in line with Prabhu et al. [ 48 ], who clarified that hydrolytic microbes had limited access to carbohydrates because lignin is strictly connected to hemicellulose and cellulose. Moreover, after the start of biogas generation, it kept growing until it reached its peak point on the 10th day and then slowly declined. After 30 days of digestion, biogas is produced very slowly, consistent with the reduction trend of the biogas production rate. In contrast, the introduction of FW improved not only the peak intensity but also the peak point of the biogas production rate. Almost a two-peak value was observed for co-digestion tests. As projected, at the end of the experiment (Fig. 1 b and Table 2 ), CH biomass showed the lowest total biogas yield of 204.71 ml/gVS, which is comparable with those reported by Wang et al. [ 49 ] with CH (241.3 ml/gVS) and lower than that achieved by Zhang et al. [ 7 ] for CH (335.96 ml/gVS). The difference in biogas production efficiency might be a consequence of various growth conditions, chemical compositions of biomass [ 32 ], as well as experimental conditions where an experiment is done and used inoculum [ 7 ]. At the termination of the anaerobic incubation period, the cumulative biogas yields (CBY) observed from mono digestion of FW (480.74 ml/gVS) were about 2.34 times greater than that obtained from CH (204.71ml/gVS) and about 1.27 times greater than that observed from WH (378.28 ml/gVS) (Table 2 ). The result obtained from the digestion of FW alone was greater than the report of Zala et al. [ 33 ], in which the CBY of 370.85 ml/gVS and lower than those obtained by Ma et al. [ 10 ], who reported 588 ml/gVS. In addition, CBY attained from WH was about 1.8-fold higher than that obtained from CH (p < 0.02). Indeed, CH had a low content of easily biodegradable organic fractions like carbohydrates, protein, and fat (Table 1 ), which can explain the results of its mono-digestion. The limited biomethane potential of CH mono-digestion was most likely due to the lignin's resistance to hydrolytic enzyme [ 34 ]. When digesters were substituted with CH, biogas generation declined, in keeping with the BD, emphasizing the beneficial impact of co-digestion on bioenergy conversion [ 32 ]. In addition, this lowest biogas recovered from CH may be attributed to the deficiency of trace elements frequently causing a failure in gas production owing to the miss of a stable AD process. Real-scale AD of CH thus needs trace element supplementation through co-digestion with materials rich in trace elements [ 5 ]. From Table 1 , CBYs from co-digestion at mix ratios of R 3 , R 4 , R 5 , and R 6 were 515.70, 526.40, 572.60, and 532.29 ml/gVS, respectively, which presented a higher biogas production of 2.5, 2.57, 2.79, and 2.6 times than digesting CH alone, respectively, and higher biogas yield of 1.36, 1.39, 1.50, and 1.4 times than digesting WH alone, respectively (p < 0.02) showing that the mixture of CH, WH, and FW in R 4 showed a best performance in biogas production. Increasing the easily degradable organic waste during lignocellulosic co-digestion would be essential when choosing substrates. According to Fig. 1 , the cumulative biogas yield is increased with increasing the concentration of FW to 50%, which is responsible for the optimum C/N ratio. The peak biogas yield of 572.60 ml/gVS was obtained at R 5 , which was up to 2.8-fold higher respective to the CH alone (p-value < 0.05). This result closely agrees with the results reported by Budiarti et al. [ 50 ], who found the highest CBY of 584.49 ml/gVS from co-digestion of corn stover with food waste at a mix ratio of 8:2. They also proved the enhancement of biogas yields with the growth of FW fraction in the mixture. Cumulative biogas yields from co-digestion studies (Table 2 ) demonstrated that all co-substrate investigated effectively enhanced biogas production from lignocellulosic biowaste. Co-digestion of substrate has a positive effect on the pros and cons of each of the substrates, leading to the higher biogas output realized. The easily biodegradable portion of the food waste that was directly exposed to the subsequent acidification to the biomethane production process without the requirement of solubilization-hydrolysis step [ 32 ] and raised the microbial load that stimulated the breakdown/ solubilization of CH and WH cell wall structure [ 20 ] might be responsible for the enriched biogas yield. This could be attributed to the synergy resulting from coupling effects of enhancing nutritional balance, dilution of hazardous chemicals, increased buffering capacity, and detoxification through co-metabolism, which finally manifested in the functions of the microbial community [ 51 ]. Additionally, the joint treatment of substrate helps to regulate the monitoring parameters, such as pH, alkalinity, and volatile fatty acids, which are responsible for digester stability and leading to enhanced biogas yields [ 52 ]. Optimal substrate mix ratios realized from the experiment led to optimal C/N ratio within the substrate blend. For this study, the optimal C/N ratio was found to be 23.70. This value agrees with the ranges reported in the former review [ 24 ]. Using different feedstocks in anaerobic digestion, depending on their C/N ratios, improves anaerobic digestion performance and significantly increases biogas generation and biodegradability [ 53 ]. Furthermore, biogas production was reduced at the highest/lowest percentage of the C/N ratio due to a higher/lower nitrogen consumption rate from acid-producing microbes than from methanogenic microorganisms [ 20 ]. In this study, the maximum CBY recovered at the optimum C/N ratio was improved by 179.7 and 51.36% when compared with the C/N ratios of 34.46 (mono digestion of CH) and 25.53 (mono digestion of WH), respectively. This improvement is comparable with the study of Kunatsa et al. [ 53 ], who achieved the best improvement of biogas yield by 157.11% at WH, municipal solid waste (MSW), and cow manure (CM) mixture of (53.27:24.64:22.09) over water hyacinth alone. Table 2 Summary of biomethane production potential and kinetic parameters. Key parameters R 1 R 2 R 3 R 4 R 5 R 6 R 7 Mix ratios (%) CH/WH/FW 100:0:0 0:100:0 35:35:30 30:30: 40 25:25:50 20:20:60 0:0:100 C/N (/) 34.46 25.53 25.80 24.70 23.70 22.80 19.94 CBY (ml/gVS) 204.71 378.28 515.70 526.40 572.60 532.29 480.74 TBP (ml/gVS) 438.74 608.46 685.76 618.13 641.76 665.39 759.92 % CH 4 49.80 52.50 61.50 62.60 66.30 58.30 55.16 CI (%) - - 1.48 1.43 1.50 1.72 - BD fpc (%) 46.66 62.17 75.20 85.16 89.22 79.99 63.26 η BD (%) 25.64 41.39 55.96 53.24 57.82 56.20 52.57 Models MGE B o (ml/gVS) 214.12 436.34 560.82 558.90 606.40 559.43 523.81 R m (ml/gVS.d) 8.26 13.22 20.02 21.60 23.60 22.21 17.23 λ (d) 2.65 3.12 3.05 3.3 3.6 3.2 1.4 R 2 0.994 0.994 0.995 0.996 0.992 0.997 0.980 ϒ (%) 4.59 15.32 8.74 6.0 5.90 5.09 8.95 RMSE 5.44 6.49 6.10 7.38 5.66 6.13 8.67 MLE B o (ml/gVS) 202.75 380.94 519.08 525.15 570.96 528.63 489.52 R m (ml/gVS.d) 8.53 15.49 21.59 23.03 24.98 23.46 17.75 λ (d) 3.6 3.30 3.16 3.50 3.8 3.21 2.5 R 2 0.987 0.994 0.996 0.995 0.994 0.991 0.985 ϒ (%) 0.96 0.7 0.65 0.23 0.28 0.68 1.82 RMSE 8.10 0.99 0.86 1.02 0.69 7.8 4.50 FOKE B o (ml /gVS) 225.15 450.47 576.28 598.32 612.43 600.48 530.97 k(/d) 0.043 0.034 0.029 0.035 0.034 0.037 0.042 R 2 0.968 0.978 0.972 0.974 0.972 0.974 0.985 ϒ (%) 9.98 19.08 11.73 13.65 6.95 12.81 10.44 RMSE 9.16 9.58 25.21 32.35 34.20 26.74 19.85 3.3. Biogas compositions The main energy source in biogas is the biomethane (CH 4 ) constituent, thus displaying that the more the CH 4 content the better the biogas quality is. Biogas production potentials through co-digestion were investigated to identify the optimum mixing ratio of food waste in AcoD concerning their C/N proportions (Table 2 ). Among single substrate treatments, the CH showed the lowest percentages of average CH 4 content (49.8%) with a similar trend to that of biogas yield (Table 1 ). The low biogas yield and CH 4 percentage in coffee husk treatment may be directly due to the lack of necessary nutrients and high lignin content. Increasing the FW concentration to 50% in the co-digester increased the average percentages of CH 4 , 1.40-fold higher over CH mono digestion. The percentages of CH 4 compositions were obtained to be 49.8, 52.53, 61.5, 62.60, 68.30, 58.30, and 55.16%, respectively, for experiments performed at mix ratios of (100:0:0, 0:100:0, 35:35:30, 30:30:40, 25:25:50, 20:20:60 and 0:0:100). The average CH 4 content recorded from co-digestions is shown in Table 2 and Fig. 2 . As shown in the above line, the maximum percentage of average CH 4 was observed at R 5 (25:25:50). This provides a high enough calorific value to find use in several energy technology applications since the CH 4 content is significantly high at 60% for energy utilizations. In this study, it was noted that supplementation of FW improved the percentage of CH 4 content in biogas compositions, which contributed to robust methanogen activity and growth [ 54 ]. Figure 2 also presents the correlations of biogas quality (CH 4 ) with lignin levels in co-digestion systems. As addressed in Fig. 2 b, the best CH 4 concentrations were observed at lignin levels of 7.86. The observation implied that the enhancement of biogas quality was almost related to the lignin concentrations in the co-mixtures. It was increased with decreasing lignin levels in mixtures. It was reported that the CH 4 /CO 2 fraction applied as a pointer exemplifies methanogenic action [ 55 ]. A lower CH 4 /CO 2 ratio was observed from mono digestion trails compared with the co-treatment of feedstocks, which proves the low bacterial activities (Fig. 2 a). Co-digestion resulted in the maximum average CH 4 /CO 2 ratio of 3.30 at R 5 , followed by R 3 and R 4 with their respective results of 2.99 and 2.30, respectively, which shows the adequate microbial activity responsible for the high CH 4 production. The lowest CH 4 /CO 2 value indicates poor microbial activity, which is responsible for the minimal CH4 production [ 55 ]. Therefore, these findings prove that AcoD is just more than treating multiple substrates at once. It may improve process stabilization, reduce inhibitory constituents, balance micro/macro-nutrients and C/N, disintegrate lignin, and enhance CH 4 content even when co-treating substrate of high lignin content with organic wastes. Hence, these wastes may be value-added through the anaerobic co-digestion system to generate biogas, thereby decreasing direct emissions of CH 4 and CO 2 into the atmosphere. 3.4. Biodegradability Based on the organic compositions of protein, crude fat, and carbohydrate (Table 1 ), the theoretical biogas yields of each sample were evaluated using equations ( 4 ). The biodegradability of the substrates was determined with the aid of Eq. ( 5 ). Their corresponding values are shown in Table 2 and Fig. 3 . The anaerobic biodegradability is used to evaluate the suitability of organic material for AD; substrates having high biodegradability indicate low lignin content and availability of organic matter to microbes for subsequent biogas production. On the other hand, the low biodegradability of a substrate indicates an unavailability of biodegradable organic components, which might be due to a high lignin percentage. The biodegradability of the CH, WH, and FW were 46.65, 62.17, and 63.26%, respectively. These results indicate that the BD fpc from individual digestion of FW alone was greater than that of digesting CH and WH digestion alone. This can show the easy digestibility of FW in anaerobic digestion. Compared to their mono-digestions, the CH-WH-FW co-digestions had greater biodegradability. The maximum BD fpc rate for the CH, WH, and FW anaerobic co-treatment was achieved at R 5 with values of 89.22%, which was 1.9 and 1.4 times higher than that of CH and WH digesting alone (Table 2 ), which specified that the co-digestion of feedstocks increased the biodegradability rate due to the synergistic effect resulting from the balanced nutrients provision of co-substrates, which provided augmented available organic matter for consequent methane-producing microorganisms. These results revealed that the increase in the appropriate content of food waste in co-digestion accelerates the biodegradability of lignocellulosic materials. The minimum BD fpc rate was produced from CH mono-digestion, which was attributed to the barrier lignin content of CH. These values support the findings of this study regarding the least and highest values of CBYs observed from R 1 and R 5 , respectively. In addition, the low theoretical biogas potential obtained for CH indicates an insufficiency of easily degradable organic matter in CH that may also be responsible for the lower biodegradability of CH. Food waste has a better biodegradability (in this test, 63.26%), which is closely in line with the result of Beniche et al. [ 56 ], who found a biodegradability of 60% for food waste fermentation. It was reported in the literature that the biodegradability of food waste is mostly 50–90% [ 10 ], which proves that the values of this work are reasonable. On the other hand, the biodegradation rate of feed materials was assessed based on their molar masses, gas volume, and gas compositions as defined in Eq. (6). As presented in Table 2 and Fig. 3 , the η BD ranges from 25.64% (CH alone) to 57.82% (R 5 ), while FW and WH digestion alone produced 52.57 and 41.39%, respectively. The lowest rate was estimated from CH owing to the high recalcitrant lignin content, which caused a justly low gas profile. A higher degradation rate was found in R 5 compared to other tested co-digestions, which may be attributed to the higher biogas volume and compositions. The maximum biogas volume and its quality can be attributed to good nutrient balance in terms of C and N concentration for microbial diversity and easy exposure of the cell structure of substrates to enzymatic attack [ 20 ]. The η BD of CH was comparable to those obtained by Chala et al. [ 5 ], who found 35.3%. Generally, anaerobic biodegradability shows a similar trend to that of biogas production. Generally, The increased biodegradability in AcoD compared to their mono digestion is recognized to have synergistic effects [ 56 ]. Co-digestion seems to be a stabilized C/N ratio and nutrients, which makes a more suitable condition for anaerobic microbial growth to decompose the organic material and speed up the entire degradation process. Table 3 Comparison of the current study’s result with pieces of literature. Co-substrates IFR C/N BD fpc (%) Synergistic effect CBY (ml/gVS) CH 4 (%) Reference CH/WH/FW (25:25:50) 2 23.70 89.22 1.50 572.60 66.3 This study CS/FW (8:2) 2 - - - 584.49 - [ 50 ] SFR/FW (3:7) 4 25.8 58.83 1.19 640 - [ 10 ] WH/FW(55:45) 1 13.95 - 548.91 68 [ 54 ] IFR – Inoculum-feedstock ratio, CH – coffee husk, WH – water hyacinth, FW – food waste, CS – corn stover, and SFR – Sophora flavescens residues. 3.5. Co-digestion synergistic effect To detail the co-digestion synergetic effect on gas production, measured and weighted values of biogas yields, synergistic effects, and increments between them in R 3 -R 6 are presented in Fig. 4 . The estimated data was calculated using Eq. (1) from daily measured biogas. As displayed in Fig. 4 a, for all co-digestion tested, the actual biogas profiles were always above the estimated levels, verifying the happening of the positive interaction effects. An increase in the measured cumulative biogas yields compared to the CH digestion alone is presented in Fig. 6 b, which indicates the enhancement of biogas production in co-digestion when compared with mono-digestion. In addition, to describe how co-digestion functioned in improving biogas production, the actual biogas profiles were simulated using three kinetic models, and the kinetic parameters, including B o , R m , and k, are listed in Table 2 . As can be shown, the B o and R m showed notable differences in each co-digestion test while the k remained particularly constant (Table 2 ). This revealed that the improvement in biogas productivity (B o ) rather than in hydrolysis kinetic efficiency (k) was mostly responsible for the synergistic effect engaged in the co-digestion process, dealing with the fact elucidated in Section 3.5, the like for the results observed by Astals et al. [ 57 ], who seen that substrate biodegradation kinetics were not affected by co-digestion. The observation implied that the enhancement of biogas production was simply proportional to the quantity of FW in the mixture. Following the above-stated concepts, the synergic effects of co-substrates were evaluated using Eq. (8). The following values were recommended as cutoff thresholds for evaluating the effects of co-digestion: If CI > 1 – the co-digested material presents synergic effects; 0.9 ≤ CI ≤ 1 – indicates an insignificant effect or an additive effect; whereas if CI < 1 – the co-digested material shows antagonistic effects [ 58 ]. Evaluation of CI also proves that the co-digestion tests showed synergic effects (Table 2 and Fig. 4 b), and the highest CI value was obtained from R 6 . As shown in Table 2 & Fig. 4 b, the synergistic effects observed from R 3 (C/N-25.8), R 4 (C/N-24.67), R 5 (C/N-23.7), and R 6 (C/N-22.8) were 1.48, 1.43, 1.50, and 1.72, respectively, which reasonable for the improvement of cumulative biogas yields. This result proves the elucidations directed that the maximum synergistic effect is not always responsible for maximum cumulative biogas production. According to these results, all co-digesters exerted a positive synergistic effect on improving the biogas production performance of each substrate, probably due to the co-digestion of the WH, CH, and FW forming the stable anaerobic condition (balanced nutrient and C/N ratio, improved buffering capacity of pH, relieved build-up of VFAs and ammonia toxicity [ 20 ]. These results proved that the co-digestion of FW could positively affect the degradation of lignocellulosic materials (WH and CH). Comparable results were obtained by Cucina et al. [ 59 ], who showed high values of CI (ranging from 1.33 to 3.17) in a study where buckwheat hulls were combined with dairy manure and fruit waste separately. The co-digestion of various substrates may result in positive synergistic effects in terms of trace elements, alkalinity, nutrients, or any other forms that the feedstock itself is missing. Conversely, negative synergistic effects can be caused due to ammonia toxicity, a drop in pH, or excess volatile fatty acid concentrations [ 24 ]. In the current work, the synergic effects were linked to the substrate characteristics presented in Table 1 . These findings verify that increasing the amount of easily biodegradable organic substances increases the digestibility of the lignocellulosic materials. These results also prove that co-substrates for lignocellulosic waste digestion must be preferred to increase the quantity of the bacterial action and the biomethane potential [ 59 ]. 3.6. Co-digestion kinetics The accumulated biogas production curve was evaluated by using three kinetic models, such as MGE, MLE, and FOKE, to estimate the best mixture. The kinetic parameters, such as maximum biogas production rate, cumulative biogas production potential, lag phase, and hydrolysis kinetic constant, were investigated; the suitability of these kinetic models depends on kinetic parameters (B o , R m , λ, k, R 2 , ϓ, and RMSE), which are shown in Table 2 . The fitting of CBYs with predicted results was plotted in Fig. 5 . The maximum biogas yield estimated by models follows the experimental results and reaches the maximum value at R 5 . The B o values of the MGE and FOKE appeared to be slightly greater than the actual biogas yield, while the B o values of the MLE were close to the actual data. The R m obtained from the simulation of experimental data with MGE and MLE models exhibited a similar discrepancy trend as the biogas production rate. CH mono-digestion produced the lowest R m (8.26 ml/gVS.d) for the MGE and 8.53 ml/gVS.d for the MLE, linked to the complex nature of lignin structure and its compositions [ 59 ], which caused the slow digestibility of CH. Similar to CBYs, the overall R m was enhanced with the increased FW levels and reached a peak point of 23.60 ml/gVS.d for MGE and 24.98 ml/gVS.d for MLE and at a CH/WH/FW ratio of 0:0:100. When FW was digested with lignocellulosic materials (CH and WH), the observed biogas production rate was greater than the biogas production rate observed for each substrate digestion. This might be attributed to the complementing effect of digested substrates. Thus, co-digestion of these materials comparatively enhanced biomethane production and rate (Table 3 ). This is an agreement with Cucina et al. [ 59 ], who demonstrated that co-digestion was tangible to the rise of the biomethane generation from buckwheat hull, brewery trub, fruit wastes, and slaughterhouse wastes. Furthermore, the lag phase suggests the time that methanogenic microbial require to produce biomethane. According to these results, the lag phase varies with an increase in food waste concentrations in the mixtures. The short lag stage times (1.4 to 3.6 days) for MGE and (2.5 to 3.8 days) for MLE were obtained in this study. The lowest λ values were observed at R 7 for both models, which indicates the readily accessibility of organic fractions of FW for microorganisms, while the highest λ was found at R 5 for MGE and MLE. The shorter lag stages may be attributed to the conducive conditions provided by the co-digestion of lignocellulosic materials with easily biodegradable food waste [ 60 ]. Remarkably, the k values observed in this study exhibited a reverse change feature (Table 2 ). The rise of FW to 50% in the co-digesters almost resulted in maximizing CBY, B o , and R m , but k values fluctuated with an increase in the FW fraction in co-digestion. The highest k value was 0.043/d for CH alone, while the lowest k value of 0.029 was obtained from R 3 . The k almost dropped in co-digesters compared to the mono-digestion of each substrate. The current results imply that the enhancement in CBY and B o was independent of k and that the enlarged k does not always provide high gas productivity. This remark well agreed with Zhen et al. [ 32 ], who noted that the rise of FW in the co-digestion significantly increased the total degradability of microalgae; however, it decreased the hydrolysis rate (k). This finding indicated that investigators could have overemphasized the significance of the hydrolysis stage in the biogas production process; hydrolysis is significant for efficient AD, but it is not the only main factor governing biomethane productivity. However, the efforts dedicated before [ 32 ] and here, the reason for low k but better Rm is yet to be well recognized, and more work in investigating the real hydrolysis character and other leading parameters is still needed in future efforts. Besides, positive k values observed in this work correspond with Ugwu and Enweremadu's [ 61 ] findings that positive k values suggest a faster biogas production rate. Moreover, the reliability and accuracy of the three models were assessed by comparing the R 2 , ϒ, and RMSE. All models demonstrated a significant linear relationship, and R 2 ranged from 0.980 to 0.997, 0.985 to 0.996, and 0.968 to 0.985, respectively, for MGE, MLE, and FOKE that specifying the biogas production might be well reproduced by these kinetic models. The difference in predicted value from the measured one elucidates the degree of model fitting, of which a lower difference suggests precise biogas yield predictability. Thus, in this work, one value of ϒ was more than 10%; that is why MGE was not considered the best fit [ 44 ], and ϒ values ranged from 6.95–19.08% for FOKE. The ϒ values ranged from 0.23 to 1.82% for MLE. This low result validates that the MLE foresees the efficiency of digesters accurately as the estimated values were in line with the measured data compared with others. RMSE ranged from 5.44 to 8.67, 0.69 to 8.10, and 9.16 to 34.20, respectively, for MGE, MLE, and FOKE that identifying the biogas production might be reproduced by MGE and MLE. Thus, based on the overhead stated values, it is clear that the MLE might explain the actual reaction process more precisely and followed by MGE in this work. These findings were consistent with those of Meraj and colleagues [ 44 ], who reported the good fitness of experimental results to MLE over MGE in the co-fermentation of rice straw, wheat straw, and sugarcane bagasse. As can be displayed in Fig. 6 , the correlation plot showed a smaller dispersion of biogas data, which justifies the values closer to the normal line that indicates the accuracy of the model. Furthermore, the B o profiles predicted with MLE have significant positive relationships with experimental data. The statics of kinetic model parameters used in this work compared to works of literature for their reliability (Table 5 ). According to Table 5 , the statistical results of these studied kinetic models were comparable with the pieces of literature. Therefore, the reported values of statistical parameters of these kinetic models in the literature suggest that these models are well-fitted with the measured experimental data and make these results reasonable. As a result, these models can provide a preliminary estimate of the maximal biogas production. Indeed, the MLE is strongly recommended for more precise calculation and reactor design. MGE was also competitive in forecasting AcoD process behavior. However, it had lower accuracy than MLE and then followed by FOKE. Table 5 Comparison of the statistical results of three kinetic models with previous studies. Models Previous study This study MGE Co-mixtures Parameters Results Co-mixtures Results YW/FW [ 26 ] R 2 0.9–0.99 [ 32 ], 0.994–0.996 [ 26 ] 0.980–0.997 MA/FW [ 32 ] γ (%) 0.5–22.2 [ 32 ], 3.7–15.4 [ 26 ] CH/WH/FW 4.59–15.32 RMSE 3.7–15.4 [ 26 ] 5.44–8.67 MLE R 2 0.983–0.999 [ 26 ] 0.985–0.996 γ (%) 4.9–18.9 [ 26 ] 0.23–1.82 RMSE 6.5-22.28 [ 26 ] 0.69–8.10 FOKE R 2 0.992–0.997 [ 32 ] 0.968–0.985 γ (%) 0.6–19.7 [ 32 ] 6.95–19.08 RMSE 6-22.9 [ 32 ] 9.16–34.20 YW – yard waste, MA – microalgae. 4. Conclusion This study examined the opportunities for enhancing biogas production from co-digestion of coffee husk and water hyacinth in varying mix ratios using food waste to find the optimal mix, and the corresponding kinetics were evaluated through model simulation. Experimental results showed that adding FW upgraded WH and CH digestion performance, with the highest biogas yield of 572.60 ml/gVS and the maximum biodegradability of 89.22% for BD fpc and 57.82% for η BD with CH/WH/FW at 25:25:50. These optimal conditions increased biogas production by 194.98% when compared to the CH mono-digestion that gave the lowest biogas yield. In each co-digestion mode tested, a positive synergistic effect was observed. The highest synergy was 1.72 for a mix of 20:20:60, indicating the improvement of biogas production with increased fraction of FW under co-digestion. Among the three kinetic models, the modified logistic function fitted well with the evolution of biogas production, supported by the lowest ϒ and RMSE, as well as with a high correlation between the actual and predicted values. Furthermore, the kinetic parameter analysis revealed that, the improvement in biogas productivity (B o ) rather than that in hydrolysis rate efficiency (k) was mainly responsible for the positive synergistic effect in the co-digestion process. The strong synergy achieved in this experiment may result frominteractive effects such as improved nutrient balance and increased buffering capacity in co-metabolism. The results suggested that the mono-digestion of coffee husk is not feasible for biogas production. The study's findings suggested that increasing FW proportions in digesting lignocellulosic biomass improved biogas yield and biodegradability. Renewable methane-rich bioenergy generation through co-digestion of lignocellulosic materials like water hyacinth and coffee husk employing food waste might be an attractive proposition for reducing environmental pollution by diverting the huge amount of organic wastes from landfills. Further study may be essential to enhance the digester performance for stable operation and point out the pattern of intermediate reactions under co-digestion. Declarations Acknowledgments: Partial support from Jimma Institute of Technology Center of Excellence is gratefully acknowledged. Author contribution statement Mohammed Kelif Ibro: Conceptualization, Methodology, Investigation, data collection and organization, analysis, Writing - the original draft of the manuscript. Venkata Ramayya Ancha: Supervised, conceptualization, analysis, and prepared the final manuscript. Dejene Beyene Lemma: Supervised, conceptualization, and drafted the final manuscript. Marcel Pohl : overview and consistency checks. All authors revised and approved the final manuscript before submission. Funding Not applicable Data availability statement Data will be made available on request. Declaration of interests The authors declare no competing interests in submitting to and publishing in this journal. 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1","display":"","copyAsset":false,"role":"figure","size":62101,"visible":true,"origin":"","legend":"\u003cp\u003e(a) daily biogas production and (b) cumulative biogas production over time at different mixture ratios.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3880494/v1/2878ee203d6ec28e84787b85.jpg"},{"id":50021329,"identity":"deb02c96-ec4b-45bc-ab05-fdbfd8c7ced2","added_by":"auto","created_at":"2024-01-23 08:36:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64523,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Percentages of average gas compositions (CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e) and CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e proportions, and (b) percentages of average CH\u003csub\u003e4\u003c/sub\u003e content versus lignin fraction at various mixing 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of co-digestion over mono-digestion.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3880494/v1/a66c0386d20e742060b4e723.jpg"},{"id":50021333,"identity":"7a6f2b3e-45ac-45e4-a255-6d74503faf11","added_by":"auto","created_at":"2024-01-23 08:36:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92340,"visible":true,"origin":"","legend":"\u003cp\u003eRegression fitting of CBYs following MGE, MLE, and FOKEs under different co-digestion ratios.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3880494/v1/669f8b999df2891ce28dbd85.jpg"},{"id":50021330,"identity":"7e6938d0-deaf-4117-9d8c-0cccf7ab2eda","added_by":"auto","created_at":"2024-01-23 08:36:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50514,"visible":true,"origin":"","legend":"\u003cp\u003eModified logistic model parameters: (a) B\u003csub\u003eo\u003c/sub\u003e versus CBY at different mix ratios and (b) correlations of CBY and B\u003csub\u003eo\u003c/sub\u003e versus R\u003csub\u003em\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3880494/v1/9d0e931111c7ebc297802dd9.jpg"},{"id":55689413,"identity":"92a7ca94-ff75-4f1e-9ae4-bef9477c1100","added_by":"auto","created_at":"2024-05-01 21:58:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1331269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3880494/v1/b533c2cd-5e90-4dcd-be4a-d691a5c3e9e0.pdf"},{"id":50021332,"identity":"423a9ecb-6e9f-4f91-b85e-b355f6fbcd3d","added_by":"auto","created_at":"2024-01-23 08:36:40","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":87391,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3880494/v1/430ebca2e8941a14349a1bdd.jpg"}],"financialInterests":"","formattedTitle":"Enhancing Biodegradability of Coffee Husk and Water Hyacinth using Food Waste: Synergistic and Kinetic Evaluation under Co-digestion","fulltext":[{"header":"Highlight","content":"\u003cul\u003e\n \u003cli\u003eThe feasibility of coffee husk, water hyacinth, and food waste co-digestion was studied.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAn optimum mix of 25% CH, 25% WH, and 50% FW produced 572.60 ml/gVS biogas.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCo-digestion tests exhibited a strong synergistic effect.\u003c/li\u003e\n \u003cli\u003eAdding food waste enhanced biodegradability and biogas production.\u003c/li\u003e\n \u003cli\u003eThe kinetics of biogas production follows modified logistic model.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eValorization of organic wastes is instrumental in decreasing the environmental and economic burden and transitioning to a bio-based circular economy. Coffee husk is the main by-product generated in huge volumes during dry coffee processing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Ethiopia, coffee husks are discarded in streams and open dumps. This improper disposal of coffee waste causes environmental harm through eutrophication of water bodies, salinization of soils, and poisonous effects on some biological processes. These aspects have limited its application in agriculture [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, caffeine and transition metals in coffee husks can cause DNA damage and present toxicity to aquatic organisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On the other hand, coffee husk is a valuable feedstock for the biogas production process due to its excellent chemical composition, elemental composition, and existence of proteins, carbohydrates, and bioactive compounds [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It also has high volatile solids [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], cellulose, and hemicelluloses that make the coffee husk a good feedstock for biogas production. However, concerns like the high lignin content and caffeine, tannin, and phenols might inhibit microbial activities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; as a result, AD of this residue needs long-term stabilization [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The lignin is highly recalcitrant and might be toxic to some microorganisms, which hinders microbial degradation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, in AD settings, lignin hydrolysis is not only a rate-limiting and stage-limiting reaction; it leads to blocking the anaerobic fermentation process due to low mass transfer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and deficiency of anaerobic bacteria that decompose lignin is another limitation. Aquatic plants [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and food waste [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] were reported as good substrates to enhance biomethane production through co-digestion with lignocellulosic biomass. Combining the aquatic biomass and food waste with lignocellulose might optimize the initial pH and carbon-nitrogen ratio (C/N) ratio for proficient AD.\u003c/p\u003e \u003cp\u003eThe WH is an invasive species that grows quickly. It can simply out-compete native plant life after it grows in the region, being thus very problematic to remove or control. These water plants have penetrated water bodies and decreased dissolved oxygen. Water hyacinth threatens water streams, tourism, transportation networks, fisheries, power plants, agriculture, residents, and living conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Ethiopia, the plant has infiltrated and spread throughout different water bodies. Lake Ziway (known as Batu Dembel) is one of the lakes infected with the water hyacinth, which covers 434 square kilometers. Freshwater systems are known for their abundant birdlife and fish wildlife, which are exposed to the conquest of aggressive water hyacinths [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, control of water hyacinths on Batu Dembel Lake needs the attention of the concerned bodies. There are different methods to expel the aquatic plant at present, such as biological, chemical, and physical control approaches [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These methods have respective limitations, but the physical approaches appear to be the best alternative if collected waste is turned into energy production. Water hyacinth is a nutrient-rich substrate and contains easily biodegradable organic matter, which makes it excellent for biogas production [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It has a comparatively high C/N ratio, a high cellulose and hemicellulose content, and a low lignin content, suggesting that it could be used as a feedstock in biogas generation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although water hyacinth has low lignin content, its lignocellulosic compositions may be a difficulty in biomethane production by decelerating the hydrolysis process and final conversion to biomethane [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Water hyacinths grown in different locations may have varying nutrient components like nitrogen and phosphorus that may affect biogas production and quality. Besides, environmental factors like temperature, humidity, and sunlight exposure may also influence the chemical composition of water hyacinths. Water hyacinth grown in regions of harsh climates may have poor cellulose and hemicellulose content, making it less suitable for biogas production [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. AcoD of lignocellulosic materials with highly biodegradable feedstocks is an effective approach for improving lignocellulose biodegradation. Food waste is one such feedstock because it is highly degradable[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Ethiopia, huge amounts of food waste (food leftovers, fruit, and vegetable waste) are generated in higher education institutions. For example, an average of 5876.5kg of food leftovers are being disposed off per week at Jimma Institute of Technology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The FW naturally decomposes quickly a few days after collection due to the high moisture content, which presents health hazards, social challenges, and environmental issues (foul odors, potential spread of hazardous microbes) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. If food waste were reused as the substrate for biogas production, it would not only decrease environmental impact but also bring a considerable volume of renewable energy. Despite the high potential of FW for biogas generation, it also has some limitations for anaerobic digestion (AD), mostly the production of high volatile fatty acid (VFA) concentration, which can disturb the pH and become toxic for microbial growth [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, when choosing suitable feedstocks for anaerobic digestion, it is important to take into account their limits, even though these feedstocks have the potential to contribute to renewable energy production [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, to prevent the probable failure of FW digestion, two-stage anaerobic digestion and co-digestion are two countermeasures that have been proposed to address the problem of reactor inhibition, as Ding et al. highlighted [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Liu et al.'s investigation revealed that AcoD was a feasible option for the anaerobic digestion of FW as two-stage treatment and pretreatment procedures were costly and time-consuming [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompared with single substrate treatment, AcoD facilitates the breakdown of recalcitrant complex polymeric substrates for augmented biodegradability, accelerates the start-up rate, increases process efficiency, alleviates pH, balances the C/N ratio, and stability of the digestion process to maximize productivity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, it reduces the effect of inhibitors and toxins, delivers the required nutrients for microbial growth, reduces retention time and lag time, and increases loading rate and methane yields [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. On the other hand, the previous studies also presented the role of FW as an acid pretreatment method to delignify lignocellulose. For instance, Ma et al. observed the improvement of the hydrolysis kinetic constant of the co-digestion group by 4.1 times over the control group [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, Zou and co-workers used food waste as a chemical pretreatment mediator to accelerate lignocellulose hydrolysis and improved hydrolysis efficiency by 28% over control trials [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Several studies in co-digesting lignocellulosic biomass and food waste were also carried out by Panigrahi et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and Begum et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. They evaluated the biodegradability of the substrate based on their elemental compositions and reported some positive results. However, determining the biodegradability of the substrate based on their elemental composition may have limitations, as it does not provide a complete understanding of the organic fractions and does not consider the specific chemical structure and their specific biodegradability, which can lead to the wrong estimation of biogas yield. Organic fractions (carbohydrates, protein, and fat) provide a more accurate biogas potential prediction. Thus, it is better to evaluate biodegradability based on carbohydrates, protein, and fat, as this approach affords a more comprehensive analysis of the substrate's potential for biodegradation. Among operating parameters, optimization of co-digestion mixing ratios is important in maximizing the efficiency and productivity of the AcoD process. Different substrates have different biodegradation rates, and characteristics, which can affect the microbial community and overall biogas production [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In AcoD systems, different organic materials are treated by varying their fractions to solve their limitations faced during AD and find the best composition that maximizes biogas production [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the increasing interest in using the AcoD process for waste management and biogas production, there is a research gap in the specific area of enhancing the biodegradability of water hyacinth and coffee husks using food waste. The existing literature primarily focuses on the biodegradability of water hyacinth and food waste in their co-digestion. However, there is a lack of comprehensive research on the potential benefits and challenges of co-digesting these lignocellulose materials together with food waste to determine the optimal mix ratio that promotes methane-rich biogas production from their AcoD. Understanding the synergistic effects of blending WH and CH with food waste in the AD process could provide valuable insights into the optimization of co-digestion systems, leading to enhanced biogas production and waste management strategies. Moreover, there is a need for detailed kinetic evaluation studies to understand the behavior of the co-digestion process. The objective of this study is to investigate the synergistic effects of co-digestion on biodegradability, and biogas production, as well as to conduct a detailed kinetic evaluation to understand the process dynamics using modified Gompertz, logistic, and first-order kinetic models.\u003c/p\u003e"},{"header":"2. Materials and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Substrate and inoculum preparation\u003c/h2\u003e \u003cp\u003eThe three feedstocks were used as the substrate in a batch experiment. In this study, water hyacinth was collected from Lake Ziway, around Batu, Oromia, Ethiopia. The stems and leaves of water hyacinths were cut into smaller pieces of roughly less than 2.5 cm and then opened to dry in the sun for six days. A coffee husk sample was obtained from a dry coffee processing Machine found in Agaro, Jimma zone, Oromia, Ethiopia. Then water hyacinth and coffee husk were separately grounded to fine particles using a coffee crusher to increase their surface areas for easy microbial degradation in the biodigester. The food waste was sampled from Jimma Institute of Technology students' cafeterias for two consecutive days to get mixtures of Injera leftovers, pasta, potato, meat, and rice. The segregated food waste was homogenized to achieve a uniform mixture of organic contents after impurities were removed. Collected materials were kept in a plastic bag and transported to the Addis Ababa Institute of Technology. The anaerobically digested cow dung was selected as an inoculum to initiate the AD process based on its accessibility, nutrient content, stability, microbial activity, and compatibility with different waste materials [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The inoculum was obtained from cow dung that had been fermented in a mesophilic anaerobic environment before this study began [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. After collection, the inoculum was then filtered through a sieve to remove coarse particulates. The inoculum was prepared at the Biochemical Engineering Laboratory in the mesophilic digester, following the guidelines specified in VDI 4630 guidelines [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], to degas and induce a starvation phase for the microorganisms. All prepared samples were stored at four degrees Celsius in a cold condition until used for further biogas production.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental design and procedure\u003c/h2\u003e \u003cp\u003eThe plastic bottle of 500 ml was used for the anaerobic fermentation processes with a working volume of 350 ml. A series of batch tests were carried out with varying coffee husk and water hyacinth in similar proportions as well as increasing the quantity of food waste proportions in the different digesters labeled as R\u003csub\u003e1\u003c/sub\u003e \u0026ndash; R\u003csub\u003e7\u003c/sub\u003e (CH/WH/FW, i.e., 100:0:0, 0:100:0, 35:35:30, 30:30:40, 25:25:50, 20:20:60 and 0:0:100), respectively, to get the best composition that allows the maximum biogas production. The inoculum was also treated alone to subtract the volume of biogas produced from the endogenous respiration of the inoculum. To prevent inhibition in the fermentation process, 10.75 grams of feedstock were added to each bottle, based on volatile solids (VS), to avoid overdosing on the seeding slurry. Thus, the anaerobic inoculum was added to all bottles, which provided an inoculum-feedstock ratio of 2 on a VS basis, as described elsewhere [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Then distilled water was added to obtain the final working volume. After each sample was added to all bottles, the pH of the blended liquor was checked and neutralized to around seven. The bottles were connected to a gas-tight plastic airbag with gas-tight plastic tubes and a rubber stopper for collecting biogas samples. Then, they were placed inside a water bath to maintain the desired temperature (38\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C). Biogas production and its compositions were measured in five-day intervals using a water displacement method and the calibrated portable biogas analyzer until the insignificant or ceased biogas production for 40 days. The gas collection bag that the biogas filled during the volume measurement was directly connected to the calibrated gas analyzer, and the gas analyzer displayed gas compositions in percentage. The volume of produced biogas then was reported as the biogas produced per gram of substrate volatile solids introduced to the flasks (ml/gVS), and gas compositions were reported in percentages (%). Besides, to identify the co-digestion synergistic effects on energy recovery, the biogas evolution in each bottle was assessed following Eq.\u0026nbsp;(1) according to Zhen et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] based on the fractions of coffee husk, water hyacinth, and food waste introduced in the co-digestion and their separate biogas yield during the mono-digestion process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({EBY}_{est\\left(i\\right)}={\\text{E}\\text{B}\\text{Y}}_{FW\\left(i\\right)}*{X}_{1}+{\\text{E}\\text{B}\\text{Y}}_{WH\\left(i\\right)}*{X}_{2}+{\\text{E}\\text{B}\\text{Y}}_{CH\\left(i\\right)}*{X}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere i is the instant digestion time (day), EBY\u003csub\u003eest(i)\u003c/sub\u003e is the estimated biogas yield at ith day (ml/gVS), EBY\u003csub\u003eFW(i)\u003c/sub\u003e is the experimental biogas yield of food waste alone at the ith day (ml/g VS), EBY\u003csub\u003eWH(i)\u003c/sub\u003e is the experimental biogas yield of WH alone at the ith day (ml/gVS), EBY\u003csub\u003eCH(i)\u003c/sub\u003e is the experimental methane yield of CH alone at the ith day (ml/g VS) X\u003csub\u003e1\u003c/sub\u003e is the percentage of FW in the co-substrates (%), X\u003csub\u003e2\u003c/sub\u003e is the percentage of WH in the co-substrates (%) and X\u003csub\u003e3\u003c/sub\u003e is the percentage of CH in the co-substrates (%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo improve biogas production of lignocellulosic materials by co-digestion with food waste, Zhen et al. mixed microalgae and food waste with varying mixing proportions (MA/FW, i.e., 100:0, 60:40, 50:50, 40:60, 20:80, and 0:100) % [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, Zala et al. co-digested WH with FW at ratios of (100:0, 0:100, 30:80, 10:75)% [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and Ma et al. combined Sophora flavescens residues (SFR) with FW at different ratios of 10:0, 7:3, 5:5, 3:7, 0:10 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. They reported maximum biogas yields when the FW proportions were high in mixtures. Henceforth, based on these works, this research mixed CH and WH with FW, as mentioned above, since these ratios were close to the different types of studies that were carried out using at least one of the same feedstocks used in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Analytical methods\u003c/h2\u003e \u003cp\u003eMoisture, ash, total solid (TS), and volatile solid (VS) were analyzed using the water and wastewater standard examination method [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The Kjeldahl method was utilized to compute the Kjeldahl nitrogen and protein contents [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The total carbon and sulfur (CS) contents of the samples were determined using an EA-1112 Flash elemental analyzer (combustion method) at 900\u0026deg;C, according to Kahassay and Bogale [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The crude fiber determination was performed by gravimetric technique following chemical digestion and solubilization of other constituents available in the sample [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Diethyl ether was used to remove the powdered samples, which were subsequently evaporated from the fat solution to estimate the crude fat content. The resulting rest was balanced and obtained as crude fat [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The carbohydrate percentage was determined with the subtraction method, and the formula is % Carbohydrate = % 100 - % (crude protein\u0026thinsp;+\u0026thinsp;crude fat\u0026thinsp;+\u0026thinsp;crude fiber\u0026thinsp;+\u0026thinsp;ash) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The pH was measured with a pH meter. The C/N ratio of the feedstock mixture was determined using Eq.\u0026nbsp;(2) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(C/N ratio=\\frac{\\sum {C}_{i}*{X}_{i}}{\\sum {N}_{i}*{X}_{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere C\u003csub\u003ei\u003c/sub\u003e is the total organic carbon and N\u003csub\u003ei\u003c/sub\u003e is the Kjeldahl nitrogen content of each feedstock. X\u003csub\u003ei\u003c/sub\u003e is the percentage of WH, FW, or CH contents supplemented in the mixture to attain the targeted C/N value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Fiber Content Determination\u003c/h2\u003e \u003cp\u003eThe lignocellulosic compositions of the water hyacinth and coffee husk were determined following the method described here [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To determine the lignocellulosic components, 5g of sample was boiled in ethanol four times for 15 minutes. After that, it was carefully cleaned with distilled water and kept in an oven furnace for 12 hours at 40\u0026deg;C. The dry sample was weighed as A before being treated with 24% KOH for 4 hours at 25\u0026deg;C. Then it was thoroughly washed with distilled water, dried at 80\u0026deg;C overnight, and then weighed as B. The sample was then refluxed with 5% H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e for two hours after being exposed to 72% H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e for three hours to disintegrate the cellulose. Again, after being washed in distilled water to remove H\u003csub\u003e2\u003c/sub\u003eSO4, the sample was dried in an oven at 80 degrees for 24 hours before the dry weight was calculated as C. Calculations were performed, according to the following relations defined in Eq.\u0026nbsp;(3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Cellulose = B-C; Hemicellulose = A-B; Lignin = C\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. \u003cb\u003eBiodegradability and co-digestion impacts (CI)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eEquation (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was employed to compute the maximum theoretical biogas potential (TBP\u003csub\u003efpc\u003c/sub\u003e, ml/gVS), assuming that the feedstock is fully converted into biogas as defined by VDI 4630 guidelines, based on the concentration of carbohydrates (c), proteins (p), and fat (f) in the substrates; thus, the BD\u003csub\u003efpc\u003c/sub\u003e of the substrate was calculated with Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e5\u003c/span\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${TBP}_{fpc}=\\% (750*c+1390*f+800*p)/100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${BD}_{fpc}\\left(\\%\\right)=\\frac{CBY}{{TBP}_{CH}*{X}_{CH}+{TBP}_{WH}*{X}_{WH}+{TBP}_{FW}*{X}_{FW}}*100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCBY is the cumulative biogas yield, TBP\u003csub\u003eCH,\u003c/sub\u003e TBP\u003csub\u003eWH,\u003c/sub\u003e and TBP\u003csub\u003eFW\u003c/sub\u003e are the theoretical biogas potential of CH, WH, and FW, respectively, that have been calculated based on the lipids, proteins, and carbohydrates compositions of the organic substrates. X\u003csub\u003eCH\u003c/sub\u003e, X\u003csub\u003eWH\u003c/sub\u003e, and X\u003csub\u003eFW\u003c/sub\u003e are the fractions of CH, WH, and FW utilized in the co-digester, respectively. The biodegradation degree (η\u003csub\u003eBD\u003c/sub\u003e) of organic waste of anaerobic digestion was assessed by dividing the mass of measured biogas (M\u003csub\u003eBiogas\u003c/sub\u003e) by the organic substrate (gVS) added to the digester (Eq.\u0026nbsp;(6)) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{BD}=\\frac{{M}_{B}}{gVS }*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe M\u003csub\u003eB\u003c/sub\u003e was calculated by the molar masses of the produced cumulative biogas compositions (MCH\u003csub\u003e4\u003c/sub\u003e \u0026ndash; a molar mass of biomethane, and MCO\u003csub\u003e2\u003c/sub\u003e \u0026ndash; a molar mass of carbon dioxide) with their respective percentage compositions, assuming the produced gas was only composed of CH\u003csub\u003e4\u003c/sub\u003e, and CO\u003csub\u003e2\u003c/sub\u003e (Eq.\u0026nbsp;(7)).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M}_{B}={V}_{B}*\\left(\\frac{{M}_{{CH}_{4}}*{cCH}_{4}}{22400*100}+\\frac{{M}_{{CO}_{2}}*{cCO}_{2}}{22400*100}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere 22400 ml/mol is the volume of ideal gas per mole; cCO\u003csub\u003e2\u003c/sub\u003e \u0026ndash;is the carbon dioxide percentage, cCH\u003csub\u003e4\u003c/sub\u003e \u0026ndash;is the methane percentage and V\u003csub\u003eB\u003c/sub\u003e is the measured biogas volume. The synergistic effect was calculated according to Yu et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] using Eq.\u0026nbsp;(8).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{C}\\text{I}\\left(\\%\\right)=\\frac{{CBY}_{co}}{{CBY}_{CH}{*X}_{i}+{CBY}_{WH}\\text{*}{X}_{j}+{CBY}_{FW}*{X}_{k}}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere CBY\u003csub\u003eco\u003c/sub\u003e is the cumulative biogas yield of co-digestion, CBY\u003csub\u003eFW\u003c/sub\u003e is the cumulative biogas yield of FW mono digestion, CBY\u003csub\u003eWH\u003c/sub\u003e is the cumulative biogas yield of WH mono digestion, and CBYCH is the cumulative biogas yield of CH mono digestion. X\u003csub\u003eijk\u003c/sub\u003e is the percentage of CH, WH, and FW utilized in the co-digester.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Biogas production kinetics\u003c/h2\u003e \u003cp\u003eThe biogas production rates were simulated using the measured data obtained from batch experiments with the sigmoid-modified Gompertz equation (MGE) (9), a modified logistic equation (MLE) (10) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and a first-order kinetic equation (FOKE) (11) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{B}={B}_{o}exp\\left\\{-exp\\left[\\frac{{R}_{m}}{{B}_{o}}*e\\left(\\lambda -t\\right)+1\\right]\\right\\}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{B}=\\frac{{B}_{o}}{1+exp\\left[\\frac{{4R}_{m}}{{B}_{o}}\\left(\\lambda -t\\right)+2\\right]}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabi\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{B}={B}_{o}\\left[1-exp\\left(-k*t\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere B is cumulative specific biogas yield (ml/gVS), B\u003csub\u003eo\u003c/sub\u003e is the predicted maximum specific biogas yield potential (ml/gVS), R\u003csub\u003em\u003c/sub\u003e is the maximum specific biogas rate (ml/gVS.d), e is equal to 2.718282, \u0026#120582; is the lag time in days, t is incubation period (d) and k (/d) is hydrolysis kinetic constant. The following parameters, B\u003csub\u003eo\u003c/sub\u003e, k, R\u003csub\u003em\u003c/sub\u003e, \u0026#120582;, and correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e), were estimated with these models using the SPSS software version 26. The statistical indicators R\u003csup\u003e2\u003c/sup\u003e, Root mean square error (RMSE), and the difference between predicted and actual values (ϒ) were applied to evaluate the validity of the kinetic models. Microsoft Excel software was used to evaluate those statistical parameters and plot graphs. The MGE and MLE are S-shape functions and are usually compared to elucidate exponential bacterial growth. Although both models appear to be similar, the major difference between the two models is that the curve of the MGE is symmetrical, and that of the MLE is asymmetric [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The MLE fits biogas production and assumes that the rate of biomethane production is directly proportional to the volume of gas produced and the maximum quantity of methane that may be produced [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eData were processed using Microsoft Excel 2013 and SPSS (version 26). All experiments were carried out in duplicate to ensure the efficiency of the results. The average values were reported. A one-way analysis of variance (ANOVA) with a single factor was employed to detect significant variation between the treatments, with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 being considered significant. In addition, Microsoft Office Excel was used to generate plots and calculate mathematical relations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Characterization of the feed materials\u003c/h2\u003e \u003cp\u003eThe moisture, VS, TS, and ash content of the inoculum were 91.39, 73.98, 8.6, and 26%, respectively, suggesting its possibility for initiating an anaerobic digestion process. Moisture is an essential factor in biogas production as it affects microbial activity responsible for organic waste decomposition and produces gas. At the same time, ash provides minerals and other nutrients that are crucial for their metabolism and growth. Additionally, TS and VS results are in line with the VDI4630 guidelines requirement that the inoculum contains a minimum of 50% organic fractions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the physiochemical characterization of substrates used in this study. Biomethanation is correlated with VS conversion, which indicates that organic matter is the main content of the dried samples. Biodegradation efficiency is also closely linked to mass transfer for one anaerobic system. The maximum biogas production rate would usually follow more mass conversion, which could be represented by TS, VS, and COD conversions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The VS of each substrate is more than 85%, which makes it the preferable substrate for renewable energy recovery through AD. Like other lignocellulosic materials, CH and WH comprise lignin, cellulose, and hemicelluloses. CH contains a high content of lignin (23.16%), cellulose (24.88%), and hemicellulose (28.96%), while the lignin, cellulose, and hemicellulose contents of WH are 8.31, 41.39, 19.33, respectively. A high lignin content recorded for CH could cause a low conversion rate. The Lignin component of biomass is not water-soluble; hence, anaerobic microbes require more time to adhere to the substrate to start the hydrolysis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, the lignin concentration for WH is less than a third of that of common wood, about 25\u0026ndash;36%, which implies that it is more suitable for energy recovery through the AD system compared to CH [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Besides, cellulose and hemicellulose contents of each substrate figure their suitability for bacterial anaerobic degradation and biogas production. The lignocellulose contents of WH and CH are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The C/N ratio is a key factor that needs optimization, which specifies the nutrient level in the substrate. A high C/N ratio above the optimum range shows a low nitrogen concentration, while a low C/N below the optimum range indicates a high nitrogen composition of organic matter. Both ranges of C/N levels are not suitable for AD because they lead to low total ammonia nitrogen and fatty acid accumulations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, the C/N ratio of WH is 25.5, which is within the optimum range for the methanation process. FW has a C/N ratio (19.94), which is very close to the range better for microbial conditions. The C/N ratio of CH is comparatively high, 34.46 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is greater than the range of the suggested optimal ratio of 20\u0026ndash;30 of the feed substrate feasibility for Ch\u003csub\u003e4\u003c/sub\u003e-rich biogas production [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, it demanded easily digestible nutrient-rich material for better performance. Albeit the C contents make the CH a good substrate for biogas production, the lignin content and bioactive chemical compositions might inhibit the anaerobic microbial activities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Organic fractions like carbohydrates, fat, and protein are the main energy and nutrient sources that are important for the metabolism and growth of microorganisms. The carbohydrate component of FW was higher than that of CH and WH, indicating the higher biodegradability of FW. This leads to fast hydrolysis reactions, with this facilitating the biodegradation of lignocellulosic components. Thus, the organic fractions of each substrate, presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e suggest their potential for biogas production. To recover hydrolysis efficiency and boost final products (CH\u003csub\u003e4\u003c/sub\u003e and effluent), introducing food waste as co-substrate to lignocellulose digester creates plentiful senses, as this process may disintegrate lignin and improve biodegradability [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, a mixture of CH with WH and FW can transform into a superior substrate compared to their mono-digestions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompositions of the feedstocks.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInoculum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0. 46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLignin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemicellulose (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude fiber (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Impact of FW addition on CH and WH for biogas production\u003c/h2\u003e \u003cp\u003eThe co-digestion of FW with lignocellulosic wastes was carried out in batch experiments with varying CH and WH in similar proportions and increasing the quantity of FW to attain the best C/N ratio that allows achieving the methane-rich biogas at different mixtures. The biogas profiles were measured every five days for 40 days. Biogas yields are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After the first day of the test, the biogas production started to vary and eventually decreased to zero after the 40th day of the incubation time. As viewed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, the maximum peak biogas yield produced daily from R\u003csub\u003e1\u003c/sub\u003e, R\u003csub\u003e2\u003c/sub\u003e, R\u003csub\u003e3\u003c/sub\u003e, R\u003csub\u003e4\u003c/sub\u003e, R\u003csub\u003e5\u003c/sub\u003e, R\u003csub\u003e6\u003c/sub\u003e, and R\u003csub\u003e7\u003c/sub\u003e occurred on the 10th day (45.68 ml/gVS), the 15th day (63.35 ml/gVS), the 20th day (92.81 ml/gVS), the 10th day (99.25 ml/gVS), the 15th day (104.3 ml/gVS), the 15th day (94.70 ml/gVS) and day 10th (94.32 ml/gVS), respectively. Compared to other digesters, R\u003csub\u003e7\u003c/sub\u003e daily biogas was more fluctuated, which may be attributed to digester instability. Analysis of variance also indicated that an increase in FW from 30\u0026ndash;60% had a significant effect on daily biogas production compared to mono-digestion of WH and CH (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor all trials, the daily biogas production occurred during the first weeks of the experiments due to easily digestible organic matter and the degrading process that followed for the used substrate (solid concentration, fats, proteins, and carbohydrates). The maximum peak value of the daily biogas of FW is greater than that of the CH and WH, which might be attributed to the easy digestibility of FW, which is simple to digest for anaerobic microbes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, the maximum biogas yield was noted almost up to 25 days in all vials, which might indicate that the reactors were working steadily. These results suggest that microorganisms rapidly convert readily digested food waste into biogas at the onset of AD and then slowly digest the lignocellulosic materials, including hemicellulose, cellulose, and, or lignin, to form gas [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The highest peaks of biogas production were obtained at R\u003csub\u003e5\u003c/sub\u003e. These maximum peaks are higher than the peak values of other digesters. This may be due to the balanced micro/macro-nutrients in the substrate at C/N ratios of 23.7, which improved the biomethanation process and microbial growth (diversity) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. After 25 days of fermentation in each bottle, the biogas production rate decreased and rose slowly until the end of the biogas production. The biogas production rate during the incubation time was very low for R\u003csub\u003e1\u003c/sub\u003e (C/N-34.46), which treats CH solely. These findings were in line with Prabhu et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], who clarified that hydrolytic microbes had limited access to carbohydrates because lignin is strictly connected to hemicellulose and cellulose. Moreover, after the start of biogas generation, it kept growing until it reached its peak point on the 10th day and then slowly declined. After 30 days of digestion, biogas is produced very slowly, consistent with the reduction trend of the biogas production rate. In contrast, the introduction of FW improved not only the peak intensity but also the peak point of the biogas production rate. Almost a two-peak value was observed for co-digestion tests. As projected, at the end of the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), CH biomass showed the lowest total biogas yield of 204.71 ml/gVS, which is comparable with those reported by Wang et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] with CH (241.3 ml/gVS) and lower than that achieved by Zhang et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] for CH (335.96 ml/gVS). The difference in biogas production efficiency might be a consequence of various growth conditions, chemical compositions of biomass [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], as well as experimental conditions where an experiment is done and used inoculum [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the termination of the anaerobic incubation period, the cumulative biogas yields (CBY) observed from mono digestion of FW (480.74 ml/gVS) were about 2.34 times greater than that obtained from CH (204.71ml/gVS) and about 1.27 times greater than that observed from WH (378.28 ml/gVS) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The result obtained from the digestion of FW alone was greater than the report of Zala et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], in which the CBY of 370.85 ml/gVS and lower than those obtained by Ma et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], who reported 588 ml/gVS. In addition, CBY attained from WH was about 1.8-fold higher than that obtained from CH (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02). Indeed, CH had a low content of easily biodegradable organic fractions like carbohydrates, protein, and fat (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which can explain the results of its mono-digestion. The limited biomethane potential of CH mono-digestion was most likely due to the lignin's resistance to hydrolytic enzyme [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. When digesters were substituted with CH, biogas generation declined, in keeping with the BD, emphasizing the beneficial impact of co-digestion on bioenergy conversion [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, this lowest biogas recovered from CH may be attributed to the deficiency of trace elements frequently causing a failure in gas production owing to the miss of a stable AD process. Real-scale AD of CH thus needs trace element supplementation through co-digestion with materials rich in trace elements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. From Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, CBYs from co-digestion at mix ratios of R\u003csub\u003e3\u003c/sub\u003e, R\u003csub\u003e4\u003c/sub\u003e, R\u003csub\u003e5\u003c/sub\u003e, and R\u003csub\u003e6\u003c/sub\u003e were 515.70, 526.40, 572.60, and 532.29 ml/gVS, respectively, which presented a higher biogas production of 2.5, 2.57, 2.79, and 2.6 times than digesting CH alone, respectively, and higher biogas yield of 1.36, 1.39, 1.50, and 1.4 times than digesting WH alone, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02) showing that the mixture of CH, WH, and FW in R\u003csub\u003e4\u003c/sub\u003e showed a best performance in biogas production. Increasing the easily degradable organic waste during lignocellulosic co-digestion would be essential when choosing substrates. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the cumulative biogas yield is increased with increasing the concentration of FW to 50%, which is responsible for the optimum C/N ratio. The peak biogas yield of 572.60 ml/gVS was obtained at R\u003csub\u003e5\u003c/sub\u003e, which was up to 2.8-fold higher respective to the CH alone (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This result closely agrees with the results reported by Budiarti et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], who found the highest CBY of 584.49 ml/gVS from co-digestion of corn stover with food waste at a mix ratio of 8:2. They also proved the enhancement of biogas yields with the growth of FW fraction in the mixture. Cumulative biogas yields from co-digestion studies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated that all co-substrate investigated effectively enhanced biogas production from lignocellulosic biowaste. Co-digestion of substrate has a positive effect on the pros and cons of each of the substrates, leading to the higher biogas output realized. The easily biodegradable portion of the food waste that was directly exposed to the subsequent acidification to the biomethane production process without the requirement of solubilization-hydrolysis step [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and raised the microbial load that stimulated the breakdown/ solubilization of CH and WH cell wall structure [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] might be responsible for the enriched biogas yield. This could be attributed to the synergy resulting from coupling effects of enhancing nutritional balance, dilution of hazardous chemicals, increased buffering capacity, and detoxification through co-metabolism, which finally manifested in the functions of the microbial community [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Additionally, the joint treatment of substrate helps to regulate the monitoring parameters, such as pH, alkalinity, and volatile fatty acids, which are responsible for digester stability and leading to enhanced biogas yields [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOptimal substrate mix ratios realized from the experiment led to optimal C/N ratio within the substrate blend. For this study, the optimal C/N ratio was found to be 23.70. This value agrees with the ranges reported in the former review [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Using different feedstocks in anaerobic digestion, depending on their C/N ratios, improves anaerobic digestion performance and significantly increases biogas generation and biodegradability [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Furthermore, biogas production was reduced at the highest/lowest percentage of the C/N ratio due to a higher/lower nitrogen consumption rate from acid-producing microbes than from methanogenic microorganisms [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, the maximum CBY recovered at the optimum C/N ratio was improved by 179.7 and 51.36% when compared with the C/N ratios of 34.46 (mono digestion of CH) and 25.53 (mono digestion of WH), respectively. This improvement is comparable with the study of Kunatsa et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], who achieved the best improvement of biogas yield by 157.11% at WH, municipal solid waste (MSW), and cow manure (CM) mixture of (53.27:24.64:22.09) over water hyacinth alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of biomethane production potential and kinetic parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKey parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMix ratios (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH/WH/FW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100:0:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0:100:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35:35:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30:30: 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25:25:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20:20:60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0:0:100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/N (/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCBY (ml/gVS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e378.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e515.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e526.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e572.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e532.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e480.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBP (ml/gVS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e608.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e685.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e618.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e641.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e665.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e759.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBD\u003csub\u003efpc\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eη\u003csub\u003eBD\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003csub\u003eo\u003c/sub\u003e (ml/gVS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e560.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e558.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e606.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e559.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e523.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003em\u003c/sub\u003e (ml/gVS.d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλ (d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eϒ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003csub\u003eo\u003c/sub\u003e (ml/gVS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e519.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e525.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e570.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e528.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e489.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003em\u003c/sub\u003e (ml/gVS.d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλ (d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eϒ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003csub\u003eo\u003c/sub\u003e (ml /gVS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e576.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e598.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e612.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e600.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e530.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ek(/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eϒ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Biogas compositions\u003c/h2\u003e \u003cp\u003eThe main energy source in biogas is the biomethane (CH\u003csub\u003e4\u003c/sub\u003e) constituent, thus displaying that the more the CH\u003csub\u003e4\u003c/sub\u003e content the better the biogas quality is. Biogas production potentials through co-digestion were investigated to identify the optimum mixing ratio of food waste in AcoD concerning their C/N proportions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among single substrate treatments, the CH showed the lowest percentages of average CH\u003csub\u003e4\u003c/sub\u003e content (49.8%) with a similar trend to that of biogas yield (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The low biogas yield and CH\u003csub\u003e4\u003c/sub\u003e percentage in coffee husk treatment may be directly due to the lack of necessary nutrients and high lignin content. Increasing the FW concentration to 50% in the co-digester increased the average percentages of CH\u003csub\u003e4\u003c/sub\u003e, 1.40-fold higher over CH mono digestion. The percentages of CH\u003csub\u003e4\u003c/sub\u003e compositions were obtained to be 49.8, 52.53, 61.5, 62.60, 68.30, 58.30, and 55.16%, respectively, for experiments performed at mix ratios of (100:0:0, 0:100:0, 35:35:30, 30:30:40, 25:25:50, 20:20:60 and 0:0:100). The average CH\u003csub\u003e4\u003c/sub\u003e content recorded from co-digestions is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As shown in the above line, the maximum percentage of average CH\u003csub\u003e4\u003c/sub\u003e was observed at R\u003csub\u003e5\u003c/sub\u003e (25:25:50). This provides a high enough calorific value to find use in several energy technology applications since the CH\u003csub\u003e4\u003c/sub\u003e content is significantly high at 60% for energy utilizations. In this study, it was noted that supplementation of FW improved the percentage of CH\u003csub\u003e4\u003c/sub\u003e content in biogas compositions, which contributed to robust methanogen activity and growth [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also presents the correlations of biogas quality (CH\u003csub\u003e4\u003c/sub\u003e) with lignin levels in co-digestion systems. As addressed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, the best CH\u003csub\u003e4\u003c/sub\u003e concentrations were observed at lignin levels of 7.86. The observation implied that the enhancement of biogas quality was almost related to the lignin concentrations in the co-mixtures. It was increased with decreasing lignin levels in mixtures.\u003c/p\u003e \u003cp\u003eIt was reported that the CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e fraction applied as a pointer exemplifies methanogenic action [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. A lower CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e ratio was observed from mono digestion trails compared with the co-treatment of feedstocks, which proves the low bacterial activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Co-digestion resulted in the maximum average CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e ratio of 3.30 at R\u003csub\u003e5\u003c/sub\u003e, followed by R\u003csub\u003e3\u003c/sub\u003e and R\u003csub\u003e4\u003c/sub\u003e with their respective results of 2.99 and 2.30, respectively, which shows the adequate microbial activity responsible for the high CH\u003csub\u003e4\u003c/sub\u003e production. The lowest CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e value indicates poor microbial activity, which is responsible for the minimal CH4 production [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Therefore, these findings prove that AcoD is just more than treating multiple substrates at once. It may improve process stabilization, reduce inhibitory constituents, balance micro/macro-nutrients and C/N, disintegrate lignin, and enhance CH\u003csub\u003e4\u003c/sub\u003e content even when co-treating substrate of high lignin content with organic wastes. Hence, these wastes may be value-added through the anaerobic co-digestion system to generate biogas, thereby decreasing direct emissions of CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e into the atmosphere.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Biodegradability\u003c/h2\u003e \u003cp\u003eBased on the organic compositions of protein, crude fat, and carbohydrate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the theoretical biogas yields of each sample were evaluated using equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The biodegradability of the substrates was determined with the aid of Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Their corresponding values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The anaerobic biodegradability is used to evaluate the suitability of organic material for AD; substrates having high biodegradability indicate low lignin content and availability of organic matter to microbes for subsequent biogas production. On the other hand, the low biodegradability of a substrate indicates an unavailability of biodegradable organic components, which might be due to a high lignin percentage. The biodegradability of the CH, WH, and FW were 46.65, 62.17, and 63.26%, respectively. These results indicate that the BD\u003csub\u003efpc\u003c/sub\u003e from individual digestion of FW alone was greater than that of digesting CH and WH digestion alone. This can show the easy digestibility of FW in anaerobic digestion. Compared to their mono-digestions, the CH-WH-FW co-digestions had greater biodegradability. The maximum BD\u003csub\u003efpc\u003c/sub\u003e rate for the CH, WH, and FW anaerobic co-treatment was achieved at R\u003csub\u003e5\u003c/sub\u003e with values of 89.22%, which was 1.9 and 1.4 times higher than that of CH and WH digesting alone (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which specified that the co-digestion of feedstocks increased the biodegradability rate due to the synergistic effect resulting from the balanced nutrients provision of co-substrates, which provided augmented available organic matter for consequent methane-producing microorganisms.\u003c/p\u003e \u003cp\u003eThese results revealed that the increase in the appropriate content of food waste in co-digestion accelerates the biodegradability of lignocellulosic materials. The minimum BD\u003csub\u003efpc\u003c/sub\u003e rate was produced from CH mono-digestion, which was attributed to the barrier lignin content of CH. These values support the findings of this study regarding the least and highest values of CBYs observed from R\u003csub\u003e1\u003c/sub\u003e and R\u003csub\u003e5\u003c/sub\u003e, respectively. In addition, the low theoretical biogas potential obtained for CH indicates an insufficiency of easily degradable organic matter in CH that may also be responsible for the lower biodegradability of CH. Food waste has a better biodegradability (in this test, 63.26%), which is closely in line with the result of Beniche et al. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], who found a biodegradability of 60% for food waste fermentation. It was reported in the literature that the biodegradability of food waste is mostly 50\u0026ndash;90% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which proves that the values of this work are reasonable.\u003c/p\u003e \u003cp\u003eOn the other hand, the biodegradation rate of feed materials was assessed based on their molar masses, gas volume, and gas compositions as defined in Eq.\u0026nbsp;(6). As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the η\u003csub\u003eBD\u003c/sub\u003e ranges from 25.64% (CH alone) to 57.82% (R\u003csub\u003e5\u003c/sub\u003e), while FW and WH digestion alone produced 52.57 and 41.39%, respectively. The lowest rate was estimated from CH owing to the high recalcitrant lignin content, which caused a justly low gas profile. A higher degradation rate was found in R\u003csub\u003e5\u003c/sub\u003e compared to other tested co-digestions, which may be attributed to the higher biogas volume and compositions. The maximum biogas volume and its quality can be attributed to good nutrient balance in terms of C and N concentration for microbial diversity and easy exposure of the cell structure of substrates to enzymatic attack [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The η\u003csub\u003eBD\u003c/sub\u003e of CH was comparable to those obtained by Chala et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], who found 35.3%. Generally, anaerobic biodegradability shows a similar trend to that of biogas production. Generally, The increased biodegradability in AcoD compared to their mono digestion is recognized to have synergistic effects [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Co-digestion seems to be a stabilized C/N ratio and nutrients, which makes a more suitable condition for anaerobic microbial growth to decompose the organic material and speed up the entire degradation process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the current study\u0026rsquo;s result with pieces of literature.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-substrates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD\u003csub\u003efpc\u003c/sub\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSynergistic effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCBY (ml/gVS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH/WH/FW (25:25:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e572.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS/FW (8:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e584.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFR/FW (3:7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWH/FW(55:45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e548.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eIFR \u0026ndash; Inoculum-feedstock ratio, CH \u0026ndash; coffee husk, WH \u0026ndash; water hyacinth, FW \u0026ndash; food waste, CS \u0026ndash; corn stover,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eand SFR \u0026ndash; Sophora flavescens residues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Co-digestion synergistic effect\u003c/h2\u003e \u003cp\u003eTo detail the co-digestion synergetic effect on gas production, measured and weighted values of biogas yields, synergistic effects, and increments between them in R\u003csub\u003e3\u003c/sub\u003e-R\u003csub\u003e6\u003c/sub\u003e are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The estimated data was calculated using Eq.\u0026nbsp;(1) from daily measured biogas. As displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, for all co-digestion tested, the actual biogas profiles were always above the estimated levels, verifying the happening of the positive interaction effects. An increase in the measured cumulative biogas yields compared to the CH digestion alone is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, which indicates the enhancement of biogas production in co-digestion when compared with mono-digestion. In addition, to describe how co-digestion functioned in improving biogas production, the actual biogas profiles were simulated using three kinetic models, and the kinetic parameters, including B\u003csub\u003eo\u003c/sub\u003e, R\u003csub\u003em\u003c/sub\u003e, and k, are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As can be shown, the B\u003csub\u003eo\u003c/sub\u003e and R\u003csub\u003em\u003c/sub\u003e showed notable differences in each co-digestion test while the k remained particularly constant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This revealed that the improvement in biogas productivity (B\u003csub\u003eo\u003c/sub\u003e) rather than in hydrolysis kinetic efficiency (k) was mostly responsible for the synergistic effect engaged in the co-digestion process, dealing with the fact elucidated in Section 3.5, the like for the results observed by Astals et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], who seen that substrate biodegradation kinetics were not affected by co-digestion. The observation implied that the enhancement of biogas production was simply proportional to the quantity of FW in the mixture.\u003c/p\u003e \u003cp\u003eFollowing the above-stated concepts, the synergic effects of co-substrates were evaluated using Eq.\u0026nbsp;(8). The following values were recommended as cutoff thresholds for evaluating the effects of co-digestion: If CI\u0026thinsp;\u0026gt;\u0026thinsp;1 \u0026ndash; the co-digested material presents synergic effects; 0.9\u0026thinsp;\u0026le;\u0026thinsp;CI\u0026thinsp;\u0026le;\u0026thinsp;1 \u0026ndash; indicates an insignificant effect or an additive effect; whereas if CI\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026ndash; the co-digested material shows antagonistic effects [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Evaluation of CI also proves that the co-digestion tests showed synergic effects (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), and the highest CI value was obtained from R\u003csub\u003e6\u003c/sub\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, the synergistic effects observed from R\u003csub\u003e3\u003c/sub\u003e (C/N-25.8), R\u003csub\u003e4\u003c/sub\u003e (C/N-24.67), R\u003csub\u003e5\u003c/sub\u003e (C/N-23.7), and R\u003csub\u003e6\u003c/sub\u003e (C/N-22.8) were 1.48, 1.43, 1.50, and 1.72, respectively, which reasonable for the improvement of cumulative biogas yields. This result proves the elucidations directed that the maximum synergistic effect is not always responsible for maximum cumulative biogas production. According to these results, all co-digesters exerted a positive synergistic effect on improving the biogas production performance of each substrate, probably due to the co-digestion of the WH, CH, and FW forming the stable anaerobic condition (balanced nutrient and C/N ratio, improved buffering capacity of pH, relieved build-up of VFAs and ammonia toxicity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These results proved that the co-digestion of FW could positively affect the degradation of lignocellulosic materials (WH and CH). Comparable results were obtained by Cucina et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], who showed high values of CI (ranging from 1.33 to 3.17) in a study where buckwheat hulls were combined with dairy manure and fruit waste separately. The co-digestion of various substrates may result in positive synergistic effects in terms of trace elements, alkalinity, nutrients, or any other forms that the feedstock itself is missing. Conversely, negative synergistic effects can be caused due to ammonia toxicity, a drop in pH, or excess volatile fatty acid concentrations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the current work, the synergic effects were linked to the substrate characteristics presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These findings verify that increasing the amount of easily biodegradable organic substances increases the digestibility of the lignocellulosic materials. These results also prove that co-substrates for lignocellulosic waste digestion must be preferred to increase the quantity of the bacterial action and the biomethane potential [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Co-digestion kinetics\u003c/h2\u003e \u003cp\u003eThe accumulated biogas production curve was evaluated by using three kinetic models, such as MGE, MLE, and FOKE, to estimate the best mixture. The kinetic parameters, such as maximum biogas production rate, cumulative biogas production potential, lag phase, and hydrolysis kinetic constant, were investigated; the suitability of these kinetic models depends on kinetic parameters (B\u003csub\u003eo\u003c/sub\u003e, R\u003csub\u003em\u003c/sub\u003e, λ, k, R\u003csup\u003e2\u003c/sup\u003e, ϓ, and RMSE), which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The fitting of CBYs with predicted results was plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The maximum biogas yield estimated by models follows the experimental results and reaches the maximum value at R\u003csub\u003e5\u003c/sub\u003e. The B\u003csub\u003eo\u003c/sub\u003e values of the MGE and FOKE appeared to be slightly greater than the actual biogas yield, while the B\u003csub\u003eo\u003c/sub\u003e values of the MLE were close to the actual data. The R\u003csub\u003em\u003c/sub\u003e obtained from the simulation of experimental data with MGE and MLE models exhibited a similar discrepancy trend as the biogas production rate. CH mono-digestion produced the lowest R\u003csub\u003em\u003c/sub\u003e (8.26 ml/gVS.d) for the MGE and 8.53 ml/gVS.d for the MLE, linked to the complex nature of lignin structure and its compositions [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which caused the slow digestibility of CH. Similar to CBYs, the overall R\u003csub\u003em\u003c/sub\u003e was enhanced with the increased FW levels and reached a peak point of 23.60 ml/gVS.d for MGE and 24.98 ml/gVS.d for MLE and at a CH/WH/FW ratio of 0:0:100. When FW was digested with lignocellulosic materials (CH and WH), the observed biogas production rate was greater than the biogas production rate observed for each substrate digestion. This might be attributed to the complementing effect of digested substrates. Thus, co-digestion of these materials comparatively enhanced biomethane production and rate (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This is an agreement with Cucina et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], who demonstrated that co-digestion was tangible to the rise of the biomethane generation from buckwheat hull, brewery trub, fruit wastes, and slaughterhouse wastes. Furthermore, the lag phase suggests the time that methanogenic microbial require to produce biomethane. According to these results, the lag phase varies with an increase in food waste concentrations in the mixtures. The short lag stage times (1.4 to 3.6 days) for MGE and (2.5 to 3.8 days) for MLE were obtained in this study. The lowest λ values were observed at R\u003csub\u003e7\u003c/sub\u003e for both models, which indicates the readily accessibility of organic fractions of FW for microorganisms, while the highest λ was found at R\u003csub\u003e5\u003c/sub\u003e for MGE and MLE. The shorter lag stages may be attributed to the conducive conditions provided by the co-digestion of lignocellulosic materials with easily biodegradable food waste [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Remarkably, the k values observed in this study exhibited a reverse change feature (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The rise of FW to 50% in the co-digesters almost resulted in maximizing CBY, B\u003csub\u003eo\u003c/sub\u003e, and R\u003csub\u003em\u003c/sub\u003e, but k values fluctuated with an increase in the FW fraction in co-digestion. The highest k value was 0.043/d for CH alone, while the lowest k value of 0.029 was obtained from R\u003csub\u003e3\u003c/sub\u003e. The k almost dropped in co-digesters compared to the mono-digestion of each substrate. The current results imply that the enhancement in CBY and B\u003csub\u003eo\u003c/sub\u003e was independent of k and that the enlarged k does not always provide high gas productivity. This remark well agreed with Zhen et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], who noted that the rise of FW in the co-digestion significantly increased the total degradability of microalgae; however, it decreased the hydrolysis rate (k). This finding indicated that investigators could have overemphasized the significance of the hydrolysis stage in the biogas production process; hydrolysis is significant for efficient AD, but it is not the only main factor governing biomethane productivity. However, the efforts dedicated before [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and here, the reason for low k but better Rm is yet to be well recognized, and more work in investigating the real hydrolysis character and other leading parameters is still needed in future efforts. Besides, positive k values observed in this work correspond with Ugwu and Enweremadu's [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] findings that positive k values suggest a faster biogas production rate.\u003c/p\u003e \u003cp\u003eMoreover, the reliability and accuracy of the three models were assessed by comparing the R\u003csup\u003e2\u003c/sup\u003e, ϒ, and RMSE. All models demonstrated a significant linear relationship, and R\u003csup\u003e2\u003c/sup\u003e ranged from 0.980 to 0.997, 0.985 to 0.996, and 0.968 to 0.985, respectively, for MGE, MLE, and FOKE that specifying the biogas production might be well reproduced by these kinetic models. The difference in predicted value from the measured one elucidates the degree of model fitting, of which a lower difference suggests precise biogas yield predictability. Thus, in this work, one value of ϒ was more than 10%; that is why MGE was not considered the best fit [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and ϒ values ranged from 6.95\u0026ndash;19.08% for FOKE. The ϒ values ranged from 0.23 to 1.82% for MLE. This low result validates that the MLE foresees the efficiency of digesters accurately as the estimated values were in line with the measured data compared with others. RMSE ranged from 5.44 to 8.67, 0.69 to 8.10, and 9.16 to 34.20, respectively, for MGE, MLE, and FOKE that identifying the biogas production might be reproduced by MGE and MLE. Thus, based on the overhead stated values, it is clear that the MLE might explain the actual reaction process more precisely and followed by MGE in this work. These findings were consistent with those of Meraj and colleagues [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], who reported the good fitness of experimental results to MLE over MGE in the co-fermentation of rice straw, wheat straw, and sugarcane bagasse. As can be displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the correlation plot showed a smaller dispersion of biogas data, which justifies the values closer to the normal line that indicates the accuracy of the model. Furthermore, the B\u003csub\u003eo\u003c/sub\u003e profiles predicted with MLE have significant positive relationships with experimental data. The statics of kinetic model parameters used in this work compared to works of literature for their reliability (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). According to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the statistical results of these studied kinetic models were comparable with the pieces of literature. Therefore, the reported values of statistical parameters of these kinetic models in the literature suggest that these models are well-fitted with the measured experimental data and make these results reasonable. As a result, these models can provide a preliminary estimate of the maximal biogas production. Indeed, the MLE is strongly recommended for more precise calculation and reactor design. MGE was also competitive in forecasting AcoD process behavior. However, it had lower accuracy than MLE and then followed by FOKE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the statistical results of three kinetic models with previous studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrevious study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo-mixtures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eParameters Results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCo-mixtures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYW/FW [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026ndash;0.99 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], 0.994\u0026ndash;0.996 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.980\u0026ndash;0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMA/FW [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eγ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u0026ndash;22.2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], 3.7\u0026ndash;15.4 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCH/WH/FW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.59\u0026ndash;15.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026ndash;15.4 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44\u0026ndash;8.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u0026ndash;0.999 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.985\u0026ndash;0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eγ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9\u0026ndash;18.9 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u0026ndash;1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5-22.28 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u0026ndash;8.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992\u0026ndash;0.997 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.968\u0026ndash;0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eγ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;19.7 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.95\u0026ndash;19.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6-22.9 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.16\u0026ndash;34.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eYW \u0026ndash; yard waste, MA \u0026ndash; microalgae.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study examined the opportunities for enhancing biogas production from co-digestion of coffee husk and water hyacinth in varying mix ratios using food waste to find the optimal mix, and the corresponding kinetics were evaluated through model simulation. Experimental results showed that adding FW upgraded WH and CH digestion performance, with the highest biogas yield of 572.60 ml/gVS and the maximum biodegradability of 89.22% for BD\u003csub\u003efpc\u003c/sub\u003e and 57.82% for η\u003csub\u003eBD\u003c/sub\u003e with CH/WH/FW at 25:25:50. These optimal conditions increased biogas production by 194.98% when compared to the CH mono-digestion that gave the lowest biogas yield. In each co-digestion mode tested, a positive synergistic effect was observed. The highest synergy was 1.72 for a mix of 20:20:60, indicating the improvement of biogas production with increased fraction of FW under co-digestion. Among the three kinetic models, the modified logistic function fitted well with the evolution of biogas production, supported by the lowest ϒ and RMSE, as well as with a high correlation between the actual and predicted values. Furthermore, the kinetic parameter analysis revealed that, the improvement in biogas productivity (B\u003csub\u003eo\u003c/sub\u003e) rather than that in hydrolysis rate efficiency (k) was mainly responsible for the positive synergistic effect in the co-digestion process. The strong synergy achieved in this experiment may result frominteractive effects such as improved nutrient balance and increased buffering capacity in co-metabolism. The results suggested that the mono-digestion of coffee husk is not feasible for biogas production. The study's findings suggested that increasing FW proportions in digesting lignocellulosic biomass improved biogas yield and biodegradability. Renewable methane-rich bioenergy generation through co-digestion of lignocellulosic materials like water hyacinth and coffee husk employing food waste might be an attractive proposition for reducing environmental pollution by diverting the huge amount of organic wastes from landfills. Further study may be essential to enhance the digester performance for stable operation and point out the pattern of intermediate reactions under co-digestion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePartial support from Jimma Institute of Technology Center of Excellence is gratefully acknowledged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMohammed Kelif Ibro:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Investigation, data collection and organization, analysis, Writing - the original draft of the manuscript.\u003cstrong\u003e\u0026nbsp;Venkata Ramayya Ancha:\u0026nbsp;\u003c/strong\u003eSupervised, conceptualization, analysis, and prepared the final manuscript.\u003cstrong\u003e\u0026nbsp;Dejene Beyene Lemma:\u0026nbsp;\u003c/strong\u003eSupervised, conceptualization, and drafted the final manuscript. \u003cstrong\u003eMarcel Pohl\u003c/strong\u003e: overview and consistency checks. All authors revised and approved the final manuscript before submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests in submitting to and publishing in this journal.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. E. 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Pezzolla, \u0026ldquo;Anaerobic co-digestion of a lignocellulosic residue with different organic wastes: Relationship between biomethane yield, soluble organic matter and process stability,\u0026rdquo; \u003cem\u003eBiomass and Bioenergy\u003c/em\u003e, vol. 153, p. 106209, 2021, doi: 10.1016/j.biombioe.2021.106209.\u003c/li\u003e\n\u003cli\u003eM. J. Sukhesh and P. V. Rao, \u0026ldquo;Synergistic effect in anaerobic co-digestion of rice straw and dairy manure - a batch kinetic study,\u0026rdquo; \u003cem\u003eEnergy Sources, Part A Recover. Util. Envir. Eff.\u003c/em\u003e, vol. 41, no. 17, pp. 2145\u0026ndash;2156, 2018, doi: 10.1080/15567036.2018.1550536.\u003c/li\u003e\n\u003cli\u003eS. N. Ugwu and C. C. Enweremadu, \u0026ldquo;Effects of pre-treatments and co-digestion on biogas production from Okra waste,\u0026rdquo; \u003cem\u003eJ. Renew. Sustain. Energy\u003c/em\u003e, vol. 11, p. 013101, 2019, doi: 10.1063/1.5049530.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bioenergy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bere","sideBox":"Learn more about [BioEnergy Research](https://www.springer.com/journal/12155)","snPcode":"12155","submissionUrl":"https://submission.nature.com/new-submission/12155/3","title":"BioEnergy Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coffee husk, water hyacinth, food waste, biodegradability, co-digestion, synergistic effect","lastPublishedDoi":"10.21203/rs.3.rs-3880494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3880494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoffee husk (CH) and water hyacinth (WH) are seen as environmental challenges causing eutrophication of water streams and infestation of water bodies. These biomass resources, available in plenty with high organic content can be considered for anaerobic digestion. However, their high lignin content poses a challenge to their biodegradability in which case co-digestion with easily degradable food waste (FW) could alleviate this problem. Thus, the synergistic effect with co-digestion of CH and WH employing increasing FW levels on biogas yield, biodegradability (BD\u003csub\u003efpc\u003c/sub\u003e), and biodegradation rate (η\u003csub\u003eBD\u003c/sub\u003e) were investigated in this work. Experimental studies were conducted with a varied mixtures of CH/WH/FW (100:0:0, 0:100:0, 35:35:30, 30:30:40, 25:25:50, 20:20:60 and 0:0:100) at constant temperature (38 ± 1°C). The results indicated that addition of FW significantly enhanced WH and CH digestion performance, with the maximum biogas yield of 572.60 ml/gVS, highest BD\u003csub\u003efpc\u003c/sub\u003e of 89.22% and η\u003csub\u003eBD\u003c/sub\u003e of 57.82% obtained at a mix ratio of 25:25:50, which was improved by 194.98% compared to CH mono-digestion. The co-digestion tests exhibited strong synergy due to their nutritional balance and other interactive effects promoting stability. Maximum synergy was 1.72 for a mix of 20:20:60. The modified Gompertz, logistic, and first-order kinetic models were used to simulate the experimental data to portray the biodegradation and kinetics involved. The modified logistic equation was seen to be the best fit to elucidate biogas production. The current findings highlighted the importance of increasing the easily biodegradable waste fractions in the co-digestion of lignocellulosic biomass for enhanced biodegradability.\u003c/p\u003e","manuscriptTitle":"Enhancing Biodegradability of Coffee Husk and Water Hyacinth using Food Waste: Synergistic and Kinetic Evaluation under Co-digestion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 08:36:35","doi":"10.21203/rs.3.rs-3880494/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-19T08:42:39+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-19T07:37:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-19T03:53:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioEnergy Research","date":"2024-01-18T16:17:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioenergy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bere","sideBox":"Learn more about [BioEnergy Research](https://www.springer.com/journal/12155)","snPcode":"12155","submissionUrl":"https://submission.nature.com/new-submission/12155/3","title":"BioEnergy Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f59e8cc1-014a-4755-87f2-b12df5292978","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T21:58:51+00:00","versionOfRecord":{"articleIdentity":"rs-3880494","link":"https://doi.org/10.1007/s12155-024-10750-7","journal":{"identity":"bioenergy-research","isVorOnly":false,"title":"BioEnergy Research"},"publishedOn":"2024-04-20 21:58:51","publishedOnDateReadable":"April 20th, 2024"},"versionCreatedAt":"2024-01-23 08:36:35","video":"","vorDoi":"10.1007/s12155-024-10750-7","vorDoiUrl":"https://doi.org/10.1007/s12155-024-10750-7","workflowStages":[]},"version":"v1","identity":"rs-3880494","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3880494","identity":"rs-3880494","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}