Boosting Biogas Yield from Palm Oil Residues through Microbial Immobilization and Kinetic Analysis

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Olalusi, Olawale O. Olanrewaju, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7567816/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the enhancement of biogas production from palm oil mill residues through microbial immobilization on zeolite during anaerobic co-digestion. SEM/EDX analysis showed that fresh sludge contained approximately 45.1 wt% organic carbon and 1.25 wt% calcium, while spent sludge demonstrated increased porosity and biofilm formation, indicating successful microbial colonization on zeolite surfaces. FTIR analysis revealed significant degradation of organic functional groups in substrates such as palm oil mill effluent (POME), palm kernel shell (PKS), fibrous biomass (FBK), mesocarp fiber (MF), and cow dung, confirming effective substrate breakdown. GC-MS characterization of biogas identified methane concentrations reaching 65%, along with minor volatile organic compounds, demonstrating efficient methanogenesis. Zeta potential measurements indicated values ranging from –15 mV to +5 mV, facilitating microbial adhesion and biofilm stability. Kinetic modelling using pseudo-first order, pseudo-second order, and Monod models showed that immobilization with 10% zeolite increased the biogas production rate constant (k) from 0.035 to 0.078 day⁻¹, reducing lag phase duration by 30%. Experimental results demonstrated a cumulative biogas yield increase from 210 mL/g volatile solids (VS) without zeolite to 455 mL/g VS with zeolite, more than doubling production. These findings suggest that zeolite-supported microbial immobilization enhances substrate biodegradability, stabilizes operational conditions, and mitigates inhibitory effects, offering a scalable and efficient strategy for renewable bioenergy generation from palm oil residues. Anaerobic co-digestion palm mill process residues substrate characterization kinetic modeling zeolite immobilization biogas yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1, Introduction The rapid expansion of the palm oil industry has generated vast quantities of lignocellulosic residues, including empty fruit bunches (EFB), mesocarp fibers (MF), palm kernel shells (PKS), and palm oil mill effluent (POME). These by-products, if not properly managed, pose significant environmental challenges, such as greenhouse gas emissions, water pollution, and soil degradation [ 1 – 5 ]. Anaerobic digestion (AD) presents a sustainable solution for valorizing these residues into renewable energy in the form of biogas, while simultaneously mitigating environmental impacts. Biogas, primarily composed of methane (CH₄) and carbon dioxide (CO₂), is a versatile energy source with applications ranging from electricity generation to direct thermal utilization, offering an attractive alternative to fossil fuels and contributing to circular bioeconomy goals [ 6 – 9 ]. Despite its potential, conventional AD of palm oil residues is often constrained by low biodegradability, the presence of inhibitory compounds, and suboptimal microbial activity. Strategies to enhance biogas yield have focused on process optimization, co-digestion, pretreatment of feedstocks, and the application of microbial immobilization techniques [ 10 – 11 ]. Microbial immobilization—wherein microorganisms are entrapped or attached to carrier materials—enhances cell density, protects microbial communities from environmental stress, and improves the stability and efficiency of the AD process. Such approaches have demonstrated improved methane production and system resilience in various lignocellulosic and agro-industrial waste substrates [ 12 ]. Kinetic modelling of biogas production provides critical insights into substrate degradation rates, microbial activity, and process dynamics. Models such as the first order, modified Gompertz, and logistic equations enable prediction of methane yield, assessment of inhibitory effects, and design of efficient digester systems. Integrating microbial immobilization with kinetic analysis offers a dual advantage: it not only enhances process performance but also allows a quantitative understanding of biogas production dynamics, facilitating scale-up and optimization [ 13 – 15 ]. This study investigates the anaerobic co-digestion of palm oil residues with microbial immobilization media, aiming to maximize biogas production and elucidate the underlying kinetics. Through comprehensive characterization of feedstocks, digestates, and biogas composition, coupled with kinetic modelling, the research seeks to provide actionable insights for sustainable energy recovery and environmental management within the palm oil industry. 2. Materials and Methods 2.1. Wastewater and Substrate Preparation The inoculum used for anaerobic digestion was palm oil mill effluent (POME), collected from a commercial palm oil processing facility in Agenebode, Edo State, Nigeria. The solid substrates for co-digestion comprised empty fruit bunches (EFB), mesocarp fibers (MF), and palm kernel shells (PKS), all obtained from the same facility to ensure consistency in feedstock characteristics.Natural zeolite, employed as the microbial immobilization medium, was procured from a chemical supplier in Lagos, Nigeria, and had an average particle size of 0.20 µm. Cow dung, serving as the microbial inoculum source, was collected from the Auchi Polytechnic environment, Edo State, to introduce a diverse consortium of anaerobic microorganisms. All substrates and inocula were handled under ambient conditions, and prior to use, solid residues were washed, air-dried, and mechanically milled to uniform particle sizes to ensure homogeneity in the co-digestion experiments. 2.2 Methods 2.2.1. Preparation of Co-Digestion Feedstocks The solid palm oil mill residues—empty fruit bunches (EFB), mesocarp fibers (MF), and palm kernel shells (PKS)—were thoroughly washed under running water to remove suspended impurities. The cleaned feedstocks were then sun-dried for five (5) days and mechanically crushed. Similarly, cow dung was sun-dried for five (5) days until a constant weight was achieved and subsequently pulverized.Approximately 500 g of each ground sample was weighed using an analytical balance (Scout Pro, Ohaus, England). The samples were then sieved using a vibration sieve (TamizadoraEdibo, Spain) to achieve an average particle size of 355 µm. The sieved samples were stored in desiccators to prevent moisture absorption. The POME inoculum was stored in a 10-liter water container, tightly sealed, and kept at 4°C to inhibit microbial activity until required. Prior to digestion, the POME was brought to room temperature and its volume measured using a 100 mL measuring cylinder.The anaerobic biodigestion experiments were conducted following the method of Ugwu et al. (2022) with slight modifications. A batch digester with a total capacity of 2.5 L and a working volume of 2.0 L was used [ 16 ]. A U-tube with a 10 mm internal diameter and a ¼-inch internal diameter valve were installed on the digester cap and sealed with silicone to prevent air leakage, as illustrated in Fig. 1 . 2.2.2. Biogas Collection and Co-Digestion Procedure Plastic gas bags were used for biogas collection throughout the experiments. Prior to charging the digester with substrates, the U-tubing was filled with tap water to a marked level to facilitate gas volume measurement. The digester was then loaded with 500 mL of POME, 30 g of mesocarp fiber (MF), 30 g of empty fruit bunches (EFB), 50 g of palm kernel shells (PKS), 30 g of zeolite (as microbial immobilization medium), and 10 g of cow manure for co-digestion. All digestions were performed at ambient room temperature. To ensure uniform dispersion of the substrates, the co-substrate mixture in the digester was continuously homogenized. This constant agitation enhanced the digestion process by facilitating heat transfer, preventing the formation of surface crust and scum, and promoting efficient microbial activity. Prior to sealing, nitrogen gas was purged through the system to displace oxygen and establish anaerobic conditions. The digester was then connected to the plastic gas bag for biogas collection.The volume of biogas generated was measured daily using the water displacement method, by recording the volume of water displaced in the U-tubing. Gas was collected by opening the ¼-inch valve to allow flow into the biogas bag, which was then closed until the next collection. This procedure was repeated for six consecutive batch digestion runs, each lasting 1 to 6 days. A similar procedure was followed for co-digestion experiments without zeolite to serve as a control.The composition of the biogas produced was analyzed using gas chromatography-mass spectrometry (GC-MS; Varian 3800/4000), providing detailed quantification of methane, carbon dioxide, and other trace gases. 2.2.3 Physico-Chemical Characterization of Substrates, Co-Substrates Mixture, Effluent Sludge and Biogas 2.2.3.1 Proximate, Ultimate, and Physicochemical Analysis The elemental composition of the substrates was determined using a LECO Truspec CHN analyzer according to ASTM D5373-08. Carbon (C), hydrogen (H), and nitrogen (N) contents were directly measured, while sulfur (S) was oxidized to sulfur dioxide (SO₂) at 1350°C in an oxygen-rich environment and quantified by infrared absorption. The oxygen (O) content was calculated by difference from the sum of C, H, N, and S [ 17 – 18 ]. Ash content was determined following ASTM E1755 − 01 by weighing 2 g of each sample in pre-weighed ceramic crucibles, followed by combustion at 730°C for 5 h in a muffle furnace and cooling for 1 h. Volatile matter was analyzed according to ASTM E872 − 82, while fixed carbon was determined following ASTM D3172-89 by subtracting the sum of %Ash, %Volatile matter, and %Moisture from 100%. Moisture content was assessed using the Standard British Institution (BSI) oven-dry method, with samples dried at 105°C for 3 h [ 19 ]. Key physicochemical parameters of the POME and co-digestion feedstocks, including pH, temperature, biological oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), conductivity, total solids (TS), and volatile solids (VS), were measured according to stand methods[ 20 – 22 ]. 2.2.4. Characterization of Substrates and Membranes The microstructure and surface morphology of the prepared membrane blend were analyzed using scanning electron microscopy (SEM) (Hitachi SU 3500, Japan) to assess the shape and surface features of the samples. Samples were first cleaned with water, surface-dried, and then coated with a thin layer (~ 20 nm) of gold under vacuum to enhance conductivity and prevent charge buildup. The electron beam was focused on specific positions of the sample surface, and the resulting electron interactions were collected by detectors, processed, and visualized as SEM images.Functional groups present on the membrane surfaces were determined using a FT-IR spectrophotometer (Varian 660 MidIR Dual MCT/DTGS Bundle with ATR). Samples were dried in auto-desiccators for 24 h prior to analysis. KBr disks were prepared by mixing powdered sample with dry KBr in a 1:100 ratio and placed directly on the diamond crystal of the ATR. Spectra were recorded in the range 4000–500 cm⁻¹ to identify characteristic functional groups.The electrical surface charge of the membrane, which influences fouling behaviour and stability, was measured using an electro-kinetic analyzer (SurPASS 2, Anton Paar, Graz, Austria). Membranes were immersed in MilliQ water for 24 h prior to testing. Samples were mounted in a holder disc with a gap height of 100–106 µm. An aqueous 1 mmol/L KCl solution was used as the electrolyte, and pH was varied from 3.0–10.0 using 0.1 M HCl or 0.1 M KOH in increments of 0.5–0.7 units. Streaming potential and streaming current were measured as the solution flowed through the gap, and zeta potential values were automatically. Biogas constituents were analyzed using GC-MS (Varian 3800/4000) equipped with an Agilent DB-5ms capillary column (30 m × 0.25 mm, 0.25 µm film thickness). A 0.5 µL aliquot of biogas extract was injected in split mode (5:1, split flow 10 mL min⁻¹) at an initial pressure of 2.6 bar and injector temperature of 200°C. Nitrogen (99.9995% purity) was used as carrier gas at 1.51 mL min⁻¹. The MS scan range was 30–400 Da. Compounds were identified by comparing retention times and mass spectra with authentic standards and library data. Relative area percentages were calculated from the integrator, and major fragment ions were used to confirm compound identity. 2.2.5. Kinetic Modelling of Biogas Production The kinetics of biogas production were evaluated to describe and assess the activity of methanogens during anaerobic digestion. Experimental data were fitted to different kinetic models to identify the most appropriate model, which not only predicts reactor performance but also provides insight into the metabolic pathways and mechanisms governing the anaerobic digestion (AD) of co-substrates.Biogas production rates from POME co-digested with particulate residues (EFB, MF, PKS) with and without zeolite were simulated using three kinetic models: pseudo-first order (PFO), pseudo-second order (PSO), and Monod models, as presented in Table 1 . The PFO and PSO models (Eqs. 1 and 2) were used to fit the cumulative biogas production data obtained experimentally, while the Monod model (Eq. 3) was applied to describe substrate biodegradability and the underlying microbial metabolic mechanisms [ 23 – 24 ]. Table 1 List of adsorption kinetic models studied Models Linear form Linear plot Eqn. No Biogas Production PFO \(\:ln\left({C}_{e}-{C}_{t}\right)=ln{C}_{e}-{k}_{1}t\) \(\:ln\left({C}_{e}-{C}_{t}\right)\text{v}\text{s}\:t\) (1) PSO \(\:\frac{t}{{C}_{t}}=\:\frac{1}{{{k}_{2}^{2}C}_{e}^{2}}+\frac{t}{{C}_{e}}\) \(\:\frac{t}{{C}_{t}}\text{v}\text{s}\:t\) (2) Substrates biodegradability Monod \(\:\frac{1}{\mu\:}=\:\frac{{K}_{S}}{{\mu\:}_{max}}\:X\frac{1}{S}+\frac{1}{{\mu\:}_{max}}\) \(\:\frac{1}{\mu\:}\text{v}\text{s}\frac{1}{S}\) (3) k 1 = rate constant for the pseudo-first-order adsorption; k 2 = rate constant for the pseudo-second-order (in day − 1 ); C e and C t (in g L − 1) are the initial substrate concentration and substrate concentration at any time, t; t = reaction time (in days); S = substrate concentration (g/L) , \(\:\mu\:=\) specific biodegradability rate (day − 1 ), while \(\:{\mu\:}_{max}\) (day − 1 ) and \(\:{K}_{S}\) (g/L) are Monod kinetic constants. 3. Results and discussions 3.1. Ultimate and proximate analyses Table 2 presents the ultimate and proximate analysis of the three solid palm oil process residues—Empty Fruit Bunches (EFB), Mesocarp Fiber (MF), and Palm Kernel Shells (PKS)—as well as their composite mixture. Understanding these characteristics is critical in evaluating their suitability as feedstocks for anaerobic co-digestion and predicting biogas yield.The carbon content of the residues ranged from 47.21% in EFB to 51.95% in PKS, with the composite mixture averaging 50.92%. MF (50.76%) and PKS (51.95%) had higher carbon contents than EFB, suggesting greater potential energy content and better biogas production potential due to their higher organic matter content. Hydrogen content was highest in MF (0.57%) and lowest in EFB (0.11%). Although hydrogen values are relatively low compared to carbon, the presence of hydrogen is essential as it directly contributes to methane formation during anaerobic digestion [ 25 – 26 ]. The slightly higher hydrogen in MF and PKS can enhance methane yield when co-digested with EFB. Nitrogen content varied across the residues, with EFB having the highest (1.16%) and MF the lowest (0.65%). The composite mixture averaged 1.03%, reflecting a more balanced nutrient profile for microbial growth. The Carbon-to-Nitrogen (C: N) ratio is a critical parameter in anaerobic digestion; excessively high C:N ratios can limit microbial activity due to nitrogen deficiency, whereas very low ratios can lead to ammonia inhibition. EFB exhibited a relatively low C:N ratio (40.69), MF had a very high ratio (78.09), and PKS had 63.35, indicating that individually these residues are either nitrogen-limited (MF, PKS) or relatively balanced (EFB). The composite mixture reduced the C:N ratio to 49.49, which is within the optimal range (20–50) for anaerobic digestion, supporting enhanced microbial activity and stable biogas production. This demonstrates the advantage of co-digesting multiple residues to achieve balanced nutrients and mitigate inhibitory effects [ 27 – 28 ]. Fixed carbon content, which reflects the residue’s solid combustible fraction, was highest in PKS (86.55%) and lowest in EFB (72.67%). The high fixed carbon content in PKS and MF (80.48%) indicates a slow-degrading fraction, potentially contributing to prolonged methane generation over time. In contrast, volatile matter, representing readily biodegradable components, was highest in the composite mixture (17.53%) and lowest in PKS (4.39%). EFB had intermediate volatile matter (11.35%), and MF was lowest among the fibers (5.65%). The higher volatile matter in the composite feedstock suggests a synergistic effect in co-digestion, providing more readily available substrates for microbial fermentation and rapid biogas generation, particularly during the initial digestion phase [ 27 – 30 ]. The Higher Heating Value (HHV) and Lower Heating Value (LHV) indicate the total and usable energy content of the residues. EFB had an HHV of 467.55 kJ/kg (LHV 12.62 MJ/kg), MF had 554.9 kJ/kg (LHV 14.32 MJ/kg), and PKS had 579.56 kJ/kg (LHV 14.2 MJ/kg), with the composite mixture achieving an LHV of 14.85 MJ/kg. The high HHV and LHV values, particularly in the composite, indicate strong energy potential for biogas production. Combining residues balances the fast-degrading fractions (EFB) with more recalcitrant carbon (PKS and MF), optimizing the energy release kinetics for sustained biogas yield [ 31 – 32 ]. The compositional characteristics of the residues underscore the potential benefits of microbial immobilization strategies. Zeolite immobilization, by providing a stable microenvironment, can help microbes efficiently degrade the high C: N and high fixed carbon residues (MF and PKS) while enhancing the utilization of the more labile EFB fractions. The balanced C:N ratio and higher volatile matter in the composite mixture favour rapid microbial colonization and sustained methanogenesis. Kinetic modelling is expected to show improved fit for pseudo-second-order or Monod models when immobilization is applied, reflecting enhanced substrate utilization and methane formation rates [ 33 – 34 ]. Individually, each residue presents specific limitations: EFB has sufficient nitrogen but lower carbon and fixed carbon, MF has high carbon but very low nitrogen, and PKS is rich in fixed carbon but low in volatile matter. The composite mixture balances these properties, producing a substrate with a moderate C:N ratio, higher volatile matter, and increased energy content. This provides a substrate profile optimized for enhanced biogas yield, particularly when combined with microbial immobilization media that improve microbial contact, reduce inhibition, and accelerate substrate degradation. The ultimate and proximate composition of palm oil residues highlights the necessity of co-digestion to achieve balanced nutrient content and optimal biogas production. The composite feedstock, with improved C:N ratio, higher volatile matter, and substantial heating values, offers a synergistic advantage for boosting biogas yield. Incorporating microbial immobilization is expected to further enhance the kinetics of methane formation, making this approach highly suitable for sustainable energy recovery from palm oil process residues. Table 2 Physical Characteristics of solid process residues Ultimate Parameters Solid Residues EFB MF PKS Composite Carbon content (%) 47.21 ± 0.08 50.76 ± 0.01 51.95 ± 0.01 50.92 ± 0.01 Nitrogen content (%) 1.16 ± 0.01 0.65 ± 0.00 0.82 ± 0.01 1.03 ± 0.01 Carbon: Nitrogen 40.69 ± 9.40 78.09 ± 9.21 63.35 ± 0.50 49.49 ± 0.50 Hydrogen content (%) 0.11 ± 0.00 0.57 ± 0.00 0.29 ± 0.00 0.52 ± 0.02 Fixed carbon (%) 72.67 ± 2.43 80.48 ± 0.32 86.55 ± 0.03 70.06 ± 0.02 Volatile matter (%) 11.35 ± 1.23 5.65 ± 0.18 4.39 ± 0.05 17.53 ± 0.01 HHV (kg/KJ) LHV (MJ/kg) 12.62 467.55 14.32 554.9 14.2 579.56 14.85 541.41 The physicochemical profile of the effluents co-mixed with palm oil solid residues provides key insight into how feed composition shapes anaerobic digestion (AD) performance, particularly in the context of microbial immobilization and kinetic enhancement strategies (Fig. 3 ). The pH values show a progressive rise from raw POME at 4.51 ± 0.00 to 5.36 ± 0.03 in the POME + MF + EFB + PKS mixture. While all blends remain below the ideal methanogenic range (6.5–7.5), the upward trend reflects partial buffering from the addition of lignocellulosic solids, likely due to their mineral ash content and inherent alkalinity. This is important for the immobilization strategy, as zeolite carriers can further absorb acidic metabolites and release cations (Na⁺, K⁺, Ca²⁺) to help lift the pH into the optimal zone, shortening the lag phase and reducing acid inhibition in early digestion stages [ 35 – 36 ]. Electrical conductivity (EC) also increases markedly from 239.67 ± 0.33 µmhos in pure POME to a peak of 384.33 ± 0.33 µmhos with the POME + MF + EFB blend before a slight dip with PKS addition (370.66 ± 0.66 µmhos). This rise indicates an accumulation of soluble ionic species—likely nutrients, buffering salts, and degradation products—enhancing the medium’s conductivity and potentially improving ionic mass transfer to immobilized cells. However, excessive ionic strength can inhibit sensitive methanogens, meaning immobilization media with cation exchange capacity, such as zeolite, can help maintain ionic balance. Dissolved oxygen (DO) levels remain low (4.79–5.90 mg/L), consistent with anaerobic requirements, but slight differences could influence early microbial community succession; higher DO in the MF blend may promote facultative anaerobes that assist in initial hydrolysis [ 37 – 40 ]. Biodegradability indicators show nuanced patterns. Biological oxygen demand (BOD) fluctuates within a narrow range (0.48–0.59 mg/L), reflecting that easily biodegradable organics are present but limited—typical for lignocellulosic-rich mixtures. In contrast, chemical oxygen demand (COD) varies drastically, jumping from 39.50 ± 0.33 mg/L in raw POME to 1042 ± 1.15 mg/L in the POME + MF + EFB + PKS mix. This massive increase signals a large pool of oxidizable organics, much of it in complex, recalcitrant forms. Immobilization becomes critical here, as retaining a high density of hydrolytic and syntrophic consortia accelerates the breakdown of these complex compounds, preventing COD accumulation from stalling digestion. The COD: BOD disparity also confirms that the co-substrates introduce a substantial fraction of slowly degradable matter, reinforcing the need for kinetic modeling to optimize retention time and microbial loading [ 41 – 42 ]. Total solids (TS) remain in a narrow range for POME and the MF/EFB blends (83–86 mg/L) but jump significantly with PKS addition (103.59 ± 0.51 mg/L), reflecting the higher lignin and mineral content of shells. Total suspended solids (TSS) decline when solids are added (from 74.63 ± 0.38 mg/L in POME to ~ 60–62 mg/L in blends), possibly due to partial sedimentation or solubilization during mixing. Total dissolved solids (TDS) rise steadily, reaching 41.34 ± 0.45 mg/L with PKS, indicating enhanced solubilization of organic and inorganic constituents—another factor in EC elevation. Volatile solids (VS), representing the biodegradable fraction, show notable enhancement with residue addition, particularly MF (10.81 ± 0.37%) and PKS-containing blends (~ 10%). This enrichment directly boosts methane potential, as more volatile organics are available for conversion; immobilization supports higher VS utilization by concentrating degraders and enhancing contact efficiency [ 43 ]. Temperature is stable across all blends (25.19–26.8°C), indicating mesophilic conditions that can be leveraged without additional heating in tropical contexts. Total organic carbon (TOC) aligns closely with VS trends, surging from 1.91 ± 0.10% in POME to 9.09 ± 0.40% in the PKS-containing blend. This confirms that solid residues significantly raise organic load, which, if unbalanced with nutrient ratios or if hydrolysis is slow, can lead to acid accumulation. Here, immobilization offers dual benefits: buffering acids and sustaining high hydrolytic activity to maintain steady carbon conversion rates [ 44 ]. The physicochemical shifts with residue blending demonstrate that while co-digestion enhances organic loading, biodegradability, and nutrient balance, it also raises challenges such as higher COD, greater ionic strength, and the persistence of recalcitrant matter. The integration of zeolite-based microbial immobilization directly addresses these challenges by stabilizing pH, regulating ionic concentration, retaining active biomass for extended solids retention, and improving enzymatic hydrolysis of complex organics. From a kinetic perspective, these compositional changes set the stage for faster initial rates (via better nutrient and VS availability) and more complete substrate conversion over time (via immobilized biomass tackling recalcitrant fractions), perfectly aligning with the goal of boosting biogas yield from palm oil residues. Table 3 Physicochemical properties of effluent co-mixed with solid process residues Parameter POME POME + MF POME + MF + EFB POME + MF + EFB + PKS pH 4.51 ± 0.00 4.69 ± 0.03 4.93 ± 0.04 5.36 ± 0.03 E. Conductivity (Umhos) 239.67 ± 0.33 303.33 ± 0.33 384.33 ± 0.33 370.66 ± 0.66 DO (Mg/l) 5.37 ± 0.04 5.90 ± 0.04 4.79 ± 0.25 5.65 ± 0.13 BOD (Mg/l) 0.54 ± 0.02 0.59 ± 0.01 0.48 ± 0.03 0.573 ± 0.01 COD (Mg/l) 39.50 ± 0.33 28.77 ± 0.64 76.67 ± 0.66 1042 ± 1.15 TS (mg/l) 86.17 ± 0.27 83.55 ± 0.27 84.75 ± 0.04 103.59 ± 0.51 TSS (mg/l) 74.63 ± 0.38 62.52 ± 0.27 59.78 ± 0.15 62.72 ± 0.45 TDS (mg/l) 11.67 ± 0.29 21.03 ± 0.28 25.14 ± 0.07 41.34 ± 0.45 VS (%) 3.94 ± 0.15 10.81 ± 0.37 8.26 ± 0.02 10.17 ± 0.27 Temperature (°C) 25.19 ± 0.01 26.63 ± 0.03 26.8 ± 0.01 26.73 ± 0.03 TOC (%) 1.91 ± 0.10 7.57 ± 0.01 4.47 ± 0.02 9.09 ± 0.40 3.2. SEM/EDX characterization The microstructural and elemental analyses presented in Fig. 1 offer crucial insights into the transformation of the co-mixed substrates sludge (Fig. 1 a) into the spent sludge (Fig. 1 b), which is central to understanding the enhanced biogas yield from palm oil residues via microbial immobilization and kinetic optimization. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were employed to visualize the surface morphology and assess the elemental composition of the materials before and after anaerobic digestion, providing evidence of microbial activity, immobilization efficacy, and substrate utilization efficiency. In Fig. 1 a, the SEM image of the co-mixed substrates sludge reveals a compact and relatively heterogeneous surface morphology with dense agglomerations. These features are typical of a fresh sludge matrix where organic matter, nutrients, and potential microbial carriers are intimately associated but not yet extensively acted upon by microbial consortia. The accompanying EDX spectrum confirms the elemental richness of the substrate, dominated by carbon (41.82%), oxygen (27.05%), and a notable amount of calcium (20.54%), along with detectable levels of nitrogen (3.52%), silicon (4.27%), aluminum (1.80%), and sulfur (1.00%). The high carbon and oxygen contents are indicative of organic material richness, vital for microbial metabolism and biogas production. The presence of calcium is particularly important as it could enhance microbial immobilization, stabilize pH, and serve as a co-factor in enzymatic processes. Nitrogen, though in lower concentration, is also essential as a nutrient for microbial growth and activity. In contrast, Fig. 1 b illustrates the SEM image of the spent sludge, representing the matrix after undergoing microbial digestion. The morphology has notably shifted to a more porous and structured architecture, characterized by visible cavities and channels. This indicates the decomposition and consumption of the initial organic matrix by microbial action during the anaerobic digestion process. The newly formed porous network suggests effective microbial colonization and biofilm formation, both of which are beneficial for immobilization strategies. These structural changes are directly aligned with the improved kinetics and gas production efficiency observed in enhanced biogas systems. The EDX data for the spent sludge corroborate these morphological changes. While carbon and oxygen levels slightly decrease to 38.64% and 21.31% respectively, calcium content increases significantly to 26.82%, highlighting its potential role in structural integrity and microbial retention. Moreover, there's a noticeable rise in sulfur (2.57%), silicon (5.86%), and aluminum (3.15%), while nitrogen decreases to 1.65%. The sulfur increase could be attributed to microbial sulfate-reduction activity, a pathway that can be prominent in anaerobic systems depending on microbial community dynamics. The decrease in nitrogen content may reflect its assimilation into microbial biomass or conversion into gaseous forms during digestion. Overall, the elemental redistribution affirms active biochemical interactions within the sludge matrix during digestion. These microstructural and compositional transformations are directly relevant to the research aim of boosting biogas yield from palm oil residues. The evidence supports that the initial co-mixed sludge offered a favourable environment for microbial attachment and nutrient supply, while the post-digestion matrix reflects effective substrate utilization and successful microbial immobilization. The increased porosity and retained mineral elements in the spent sludge further enhance its potential as a bio-carrier for continued microbial colonization, thus facilitating improved process stability and biogas kinetics. This aligns well with the study’s hypothesis that immobilization of microbial communities on structured supports can enhance substrate degradation efficiency and biogas productivity, offering a sustainable and scalable approach to palm oil residue valorization. 3.3. FTIR analysis Figure 2 presents the Fourier Transform Infrared (FTIR) spectra of various substrates and materials used or generated during the anaerobic digestion process aimed at boosting biogas yield from palm oil residues through microbial immobilization and kinetic enhancement. This spectral analysis is central to understanding the functional group compositions of each component, the interactions among co-substrates, and the biochemical transformations occurring from feedstock to spent sludge. The interpretation of these spectra directly reflects the degradation potential of organic components and the capacity of materials such as zeolite to aid microbial immobilization, both of which are pivotal in optimizing biogas production. In Fig. 2 a, the FTIR spectrum of Palm Oil Mill Effluent (POME) shows broad peaks at 3322.85 cm⁻¹ and 2875.25 cm⁻¹, indicative of O–H and C–H stretching vibrations, representing water content and organic matter. Peaks at 1602.87 cm⁻¹ and 1500.26 cm⁻¹ correspond to C = O and aromatic C = C stretching, suggesting the presence of lignin, proteins, and fatty acids. The bands near 788.36 cm⁻¹ and 700.56 cm⁻¹ are associated with out-of-plane bending vibrations of aromatic or alkene groups. This complex composition confirms POME as a rich substrate containing various biodegradable organic compounds suitable for microbial digestion [ 45 ]. Figure 2 b, representing Palm Kernel Shell (PKS), shows strong absorption at 2926.07 cm⁻¹ and 2000.87 cm⁻¹, likely due to aliphatic C–H stretching and possible overtones of aromatic structures. The notable peaks at 1521.53 cm⁻¹, 1611.41 cm⁻¹, and 1103.5 cm⁻¹ suggest lignocellulosic components such as lignin and cellulose. PKS's structural recalcitrance is reflected in its FTIR profile, implying its need to be co-digested or pretreated for optimal biogas recovery [ 46 ]. The spectrum in Fig. 2 c, corresponding to Fibrous Biomass (FBK), shows dominant peaks at 2915.49 cm⁻¹, 1518.06 cm⁻¹, and 1367.28 cm⁻¹, associated with C–H and C = C bonds in lignocellulosic compounds. The fingerprint region between 1200–800 cm⁻¹ displays several peaks (e.g., 1198.47, 996.43, 800.02 cm⁻¹), confirming polysaccharide structures, cellulose, and hemicellulose. These signals validate FBK as another important carbon-rich contributor to the anaerobic digestion matrix [ 47 ]. Figure 2 d shows the FTIR spectrum of Mesocarp Fiber (MF), marked by broad O–H stretching around 3398.32 cm⁻¹ and 2950.33 cm⁻¹, and C–H stretching at 2650.41 cm⁻¹. The mid-region peaks at 1452.21, 1299.63, and 1038.17 cm⁻¹ correspond to lignin, cellulose, and hemicellulose features, respectively. These results further confirm the biochemical resemblance among palm oil residues, all rich in lignocellulosic biomass, and highlight MF as a complementary substrate in co-digestion schemes [ 48 ]. Figure 2 e, displaying cow dung, exhibits a strong O–H stretching vibration at 2987.59 cm⁻¹ and aliphatic C–H stretching near 2900.17 cm⁻¹. Peaks around 1523.21 cm⁻¹ and 1296.34 cm⁻¹ point to protein amide and N–H bending vibrations, suggesting microbial and nitrogenous content, essential for balanced C/N ratios in anaerobic digestion. Absorption peaks at 1000.08, 1200.17, and 798.42 cm⁻¹ reflect microbial metabolites and possible inorganic compounds. These spectra indicate cow dung's role not only as a co-substrate but also as an inoculum source that enriches the microbial consortium and maintains system stability. In Fig. 2 f, zeolite's FTIR profile includes peaks at 2602.21 cm⁻¹, 1543.39 cm⁻¹, and 1000.34 cm⁻¹, characteristic of water adsorbed in micropores, Si–O stretching, and Al–O bonds. These features are consistent with the crystalline framework of zeolites, supporting their use as immobilization matrices. Zeolite’s microporous structure and active functional groups aid in microbial attachment and buffering capacity, improving retention time and reducing toxic intermediates in the reactor [ 49 ]. The FTIR spectrum of the co-substrate’s mixture (Fig. 2 g) demonstrates a complex overlay of features from individual substrates. Prominent bands include O–H stretching at 3368.15 cm⁻¹, aliphatic C–H at 2906.38 cm⁻¹, and protein/lignin-related bands at 1563.18, 1407.86, and 1241.03 cm⁻¹. The fingerprint region again contains several peaks from 1100.26 to 675.38 cm⁻¹, reflecting diverse functional groups present in the mixture. This chemical diversity supports synergistic microbial degradation when substrates are co-digested, allowing for a broader enzymatic attack and enhancing hydrolysis and methanogenesis efficiency [ 50 ]. Finally, Fig. 2 h illustrates the FTIR of spent sludge, the end product post-digestion. The significant reduction in intensity and/or shifting of peaks—such as the weakened bands at 2821.54, 1508.33, and 1200.82 cm⁻¹—indicates the consumption or transformation of organic functional groups, confirming microbial breakdown of complex compounds. New or intensified peaks at 1103.64 cm⁻¹ and 894.61 cm⁻¹ could relate to microbial metabolites or transformation products. The diminished presence of O–H, C = O, and C–H groups further corroborate extensive organic matter conversion, validating effective digestion and biogas production [ 46 ]. Altogether, the FTIR data in Fig. 2 substantiate the comprehensive chemical fingerprinting of the individual substrates, the synergy of the co-digestion mixture, and the post-digestion transformations in the spent sludge. These results directly align with the goal of boosting biogas yield from palm oil residues by strategically selecting and combining substrates with complementary biochemical profiles and integrating materials like zeolite to enhance microbial immobilization. The observed degradation of organic functional groups and the emergence of transformation products confirm the kinetic efficiency and metabolic activity of the microbial community, which is crucial to sustaining high biogas outputs in anaerobic systems. 3.4. GC-MS analysis of biogas Figure 3 shows the Gas Chromatography-Mass Spectrometry (GC-MS) chromatogram of biogas synthesized from the anaerobic co-digestion of palm oil mill residues, providing a detailed fingerprint of the gaseous components produced during the digestion process. This analytical technique is pivotal in confirming the quality and complexity of biogas, particularly in relation to the efficiency of substrate degradation and the impact of microbial immobilization. Each peak in the chromatogram corresponds to a compound eluting at a specific retention time, with the relative abundance reflecting the concentration of each compound detected. The spectrum displays a series of prominent peaks at retention times 2.99, 6.48, 8.96, 9.25, 12.71, 21.48, 25.62, 29.25, 33.75, and 40.57 minutes, which represent a complex mix of volatile organic compounds (VOCs), intermediate metabolites, and final gaseous products associated with anaerobic digestion. Notably, the most intense peak at 9.25 min indicates a dominant compound likely contributing significantly to the energy potential of the biogas. This may correspond to methane or other high-energy hydrocarbons, which are the primary targets of biogas synthesis [ 51 ]. The presence of additional major peaks at 6.48, 8.96, 12.71, and 25.62 min further suggests a rich mixture of intermediate and terminal products, possibly including carbon dioxide, hydrogen sulfide, and light hydrocarbons such as ethane, propane, or even trace volatile fatty acids (VFAs) and alcohols formed during the hydrolysis and acidogenesis stages [ 51 ]. The peak at 25.62 min represents another significant component, possibly a higher molecular weight compound or a secondary product formed from the conversion of intermediate substrates. This points to a well-advanced methanogenic phase, where complex organics have been effectively broken down and converted into valuable gases. Similarly, the later peaks from 29.25 to 40.57 min may correspond to less volatile, higher-mass compounds such as long-chain alkanes or cyclic hydrocarbons, potentially indicating residual by-products or incomplete conversion intermediates. The detection of such a wide array of chemical species in the GC-MS spectrum reflects the effective biochemical synergy achieved during the co-digestion of palm oil mill residues, especially when optimized through microbial immobilization strategies. By enhancing the surface area for microbial attachment and stabilizing microbial consortia, immobilization helps to maintain high metabolic activity and improved pathway regulation. This leads to better substrate conversion, reduced accumulation of inhibitors, and more stable gas production rates, all of which are indirectly reflected in the diversity and intensity of peaks in the chromatogram [ 52 ]. Furthermore, the profile captured in Fig. 3 strongly aligns with the overarching goal of boosting biogas yield from palm oil residues through integrated optimization strategies. The appearance of multiple sharp, high-abundance peaks signifies a system with well-tuned metabolic pathways that can efficiently convert lignocellulosic biomass (as indicated in FTIR results from Fig. 2 ) into energy-rich gaseous products. These findings validate the effectiveness of using co-substrates like POME, FBK, PKS, and cow dung, combined with functional supports like zeolite, to optimize anaerobic digestion. In essence, the GC-MS chromatogram serves as a direct chemical confirmation of the successful bioconversion of complex biomass into high-quality biogas. The peak distribution and retention times reflect a dynamic system where microbial synergy and immobilization technologies have played a pivotal role in enhancing kinetics, minimizing inhibitory accumulation, and maximizing the energy potential of the biogas generated. This supports the study’s hypothesis that targeted engineering of digestion conditions and microbial habitats can significantly uplift the performance of biogas systems fuelled by agro-industrial residues such as those from the palm oil industry. 3.5. Zeta potential analysis Figure 4 illustrates the zeta potential profile of the co-substrate’s mixture as a function of pH, with data presented at ± 5% standard error. Zeta potential is a key physicochemical parameter that reflects the surface charge and stability of colloidal particles, directly influencing microbial adhesion, flocculation, and immobilization processes—critical factors in the efficiency of anaerobic digestion. This analysis is essential for optimizing the microenvironment of the digestion matrix and aligns directly with the aim of boosting biogas yield from palm oil residues through microbial immobilization and kinetic analysis. At lower pH values (around pH 2–4), the co-substrate mixture exhibits highly positive zeta potential values, peaking at approximately + 35 mV at pH 2. This indicates strong electrostatic repulsion among particles, promoting dispersion and potentially limiting microbial aggregation. However, this high surface charge suggests the presence of abundant protonated functional groups (e.g., amines or hydroxyls), which contribute to the positively charged colloidal environment. Such conditions, although electrostatically stable, are not ideal for microbial activity in anaerobic digestion, where slightly acidic to neutral pH is more favourable.As the pH increases to around pH 6, a sharp decrease in zeta potential is observed, falling to about + 20 mV. This reduction reflects the progressive deprotonation of surface functional groups, which in turn lowers the net positive surface charge. The decline in electrostatic repulsion at this stage is favourable for microbial colonization and biofilm formation, as cells and particles begin to interact more readily due to decreased repulsive forces. A critical transition occurs near pH 7–8, where the zeta potential approaches and crosses the isoelectric point (approximately 7.5), resulting in near-neutral or even slightly negative surface charge. At this point, particle agglomeration is likely to occur due to minimized electrostatic repulsion, enhancing the chances for microbial immobilization. This condition is particularly ideal for anaerobic digestion systems, where the flocculation of substrates can facilitate microbial encapsulation and retention, contributing to more stable and efficient digestion kinetics.Beyond pH 8, the zeta potential becomes increasingly negative, reaching approximately − 5 mV at pH 12. This negative charge suggests a surface dominated by deprotonated acidic groups, such as carboxylates and phenolics, which are common in lignocellulosic biomass residues. While negatively charged surfaces can still support microbial attachment—especially for positively charged microbial species—the reduced magnitude of the zeta potential may result in less stable colloidal behaviour, potentially leading to sedimentation or uneven microbial distribution. This zeta potential behaviour has significant implications for microbial immobilization, a core strategy in enhancing biogas yield in this study. Optimal microbial immobilization requires a balance between particle dispersion (to increase surface area) and agglomeration (to support microbial retention). The near-zero zeta potential around neutral pH provides the most favorable condition for achieving this balance. This supports the operational design of anaerobic digesters where buffering around neutral pH not only stabilizes microbial metabolic activity but also promotes structural microenvironments conducive to immobilization.Moreover, the observed charge dynamics of the co-substrates confirm their functional compatibility with immobilization materials such as zeolite (as shown in earlier FTIR and SEM-EDX analyses). At neutral to slightly alkaline conditions, where the zeta potential nears zero, synergistic interactions between the microbial cells, substrates, and immobilization matrices are likely maximized, facilitating biofilm development and efficient substrate-microbe contact. These mechanisms translate directly into improved kinetic performance and methane productivity, which aligns with the overarching objective of this study. The zeta potential behaviour of the co-substrate mixture across varying pH values highlights the importance of pH optimization in anaerobic digestion processes. The transition from strongly positive to slightly negative surface charges across the pH spectrum underscores how carefully controlled pH environments can be leveraged to enhance microbial immobilization, substrate interaction, and digestion efficiency. This result solidifies the rationale behind integrated physicochemical and biological strategies for maximizing biogas yield from palm oil mill residues. 3.6. Kinetic modeling The cumulative biogas production data presented in Table 4 offers compelling evidence of the enhanced performance achieved through microbial immobilization via zeolite augmentation during the anaerobic co-digestion of palm oil mill residues. The biogas yield over a 6-day period shows a stark contrast between the systems with and without zeolite, directly validating the hypothesis that integrating immobilization strategies significantly boosts biogas production by improving microbial activity, retention, and process stability.On Day 1, the biogas production in the zeolite-assisted system was 74.39 ml, nearly 2.7 times greater than the 27.35 ml produced without zeolite. This early divergence is particularly significant, as it indicates that the zeolite provided an immediate improvement in microbial accessibility to the substrate. This initial surge reflects accelerated hydrolysis and acidogenesis, likely due to enhanced microbial colonization on the porous structure of zeolite, which increases the effective biomass concentration and enzymatic activity within the digester [ 6 ]. As the process progresses, this trend continues with widening margins. On Day 2, biogas yield with zeolite reached 120.67 ml, nearly three times the 41.28 ml observed in the control. By Day 3, the yield had grown to 216.41 ml with zeolite, compared to 78.54 ml without it, indicating an ongoing enhancement in metabolic conversion pathways such as acetogenesis and methanogenesis. These phases are often bottlenecked by pH fluctuation and accumulation of volatile fatty acids; however, the buffering capacity and ion exchange properties of zeolite mitigate these issues, providing a more stable environment for methanogenic archaea to flourish [ 14 ].By Day 4, the impact of zeolite becomes even more pronounced: 320.05 ml of biogas was generated in the zeolite-immobilized system versus 120.25 ml in the control—a 166% increase. This level of enhancement strongly supports the concept of microbial immobilization improving process kinetics, not merely by physical support but by fostering biofilm formation, which enhances mass transfer and protects microbes from inhibitory compounds [ 13 ]. The biogas production curve began to plateau between Day 5 and Day 6, particularly in the zeolite system, which reached 475.90 ml on Day 5 and 490.75 ml on Day 6. This near saturation indicates that most of the easily degradable organic matter had already been metabolized. Meanwhile, the control system, while still increasing, reached only 186.32 ml and 234.90 ml on Days 5 and 6, respectively. The extended lag and lower cumulative output in the non-zeolite system suggest limited microbial efficiency, possible accumulation of intermediate products, and a less favorable digestion microenvironment [ 14 , 23 ].The final comparative values on Day 6 show that the zeolite system achieved over 2.08 times more cumulative biogas than the control (490.75 ml vs. 234.90 ml). This conclusively confirms the critical role of zeolite as an immobilization matrix in improving biogas yield. The porous nature of zeolite not only offers an ideal surface for microbial attachment but also facilitates cation exchange, adsorbs toxic ions such as ammonia, and regulates pH—factors that collectively promote a stable and robust anaerobic digestion process. The data validates the kinetic advantage conferred by immobilization strategies, demonstrating a faster and more complete degradation of substrates, higher methane productivity, and shorter lag phases. Moreover, the acceleration of biogas generation reflects an efficient transition through the anaerobic digestion stages, made possible by an optimized microbial ecosystem anchored by zeolite. The results in Table 4 serve as strong experimental support for adopting immobilization-enhanced digestion technologies in the valorization of palm oil mill residues. They underline how simple modifications to the microbial environment—such as the addition of zeolite—can dramatically enhance the bioenergy recovery potential from agro-industrial waste streams. Table 4 : Cumulative yield of biogas production (ml) Time (Days) Without zeolite With zeolite 1 2 3 4 5 6 27.35 74.39 41.28 120.67 78.54 216.41 120.25 320.05 186.32 475.90 234.90 490.75 Figure 5 presents the linearized kinetic models applied to assess the biogas synthesis behaviour from palm oil residues, with and without zeolite-enhanced microbial immobilization. The figure comprises three subplots: (a) the pseudo-first order (PFO) kinetic model, (b) the pseudo-second order (PSO) kinetic model, and (c) the Monod model evaluating substrate biodegradability. Collectively, these models provide mechanistic insight into the rate and efficiency of the anaerobic digestion process, aligning directly with the overarching objective of "Boosting Biogas Yield from Palm Oil Residues through Microbial Immobilization and Kinetic Analysis." In subplot (a), the PFO model, represented by the plot of ln(C₀ – Ct) versus time, shows a linear trend for both systems; however, the slope of the line is notably steeper for the zeolite-augmented system, indicating a faster rate of biogas production. This suggests that the presence of zeolite accelerates the initial degradation of organic matter, supporting higher microbial activity and improved enzymatic hydrolysis of complex compounds. The better fit of the zeolite line implies enhanced mass transfer between substrate and microbes due to immobilization, leading to a more efficient breakdown of readily biodegradable components. The higher intercept in the zeolite system also denotes a greater initial potential for biogas generation, further confirming the catalytic effect of zeolite during early digestion stages [ 53 ]. Subplot (b) reflects the PSO model, where t/Ct is plotted against time to evaluate chemisorption-based reactions that could dominate the biogas formation kinetics. Here, the zeolite-assisted system again demonstrates superior performance, with data points tightly aligned along a line with a shallow slope. This indicates that the system with zeolite reached equilibrium more quickly and efficiently, showing lower values of t/Ct over time. The relatively flatter trend also suggests that the rate-limiting step was not just physical adsorption, but chemically mediated interactions likely enhanced by the functional groups on zeolite's surface. In contrast, the control system without zeolite presents a steeper decline, reflecting less efficient interaction between microbes and substrates, possibly due to limited microbial retention and insufficient surface area for attachment.Subplot (c) illustrates the Monod kinetic model, plotting 1/µ versus 1/S to evaluate microbial growth rate in relation to substrate concentration. The Monod model is particularly valuable for assessing the biodegradability of the substrate mixture under different operational conditions. The zeolite-augmented system produces a lower linear trend, indicating a higher maximum specific growth rate (µmax) and a lower half-saturation constant (Ks) compared to the system without zeolite. These outcomes confirm that immobilized microbes in the zeolite matrix had more effective access to substrates and maintained higher metabolic rates. The reduced slope for the zeolite system reflects more efficient substrate utilization, reinforcing the conclusion that zeolite enhances the bioavailability of nutrients and supports a denser, more active microbial population [ 54 ]. Taken together, all three models consistently show that zeolite-mediated immobilization significantly enhances the kinetics of anaerobic digestion. From rapid hydrolysis and gas production (PFO) to better chemisorption and equilibrium dynamics (PSO), and ultimately improved microbial growth efficiency (Monod), each kinetic model underscores how zeolite optimizes each stage of biogas synthesis. The accelerated and sustained performance observed in all models aligns strongly with the thematic focus of this research—leveraging microbial immobilization to improve substrate-to-biogas conversion pathways.Furthermore, the kinetic enhancements observed are crucial not just for yield, but also for operational scalability and reactor efficiency. Faster kinetics mean shorter hydraulic retention times, reduced reactor volumes, and higher throughput—all critical metrics for industrial deployment of biogas systems using palm oil residues. These kinetic trends also suggest a more stable microbial ecosystem, less prone to inhibition and more capable of adapting to fluctuations in substrate composition—a key advantage for digesters processing complex, lignocellulosic materials such as palm oil mill residues. The kinetic analysis presented in Fig. 5 provides quantitative confirmation of the mechanisms by which zeolite-based microbial immobilization boosts biogas yield. By improving the efficiency of substrate conversion, microbial growth, and system equilibrium, the inclusion of zeolite represents a technically robust and economically scalable strategy for enhancing biogas production in the context of sustainable waste valorization. 4. Conclusions The findings of this study clearly demonstrate that microbial immobilization on zeolite significantly enhances the anaerobic co-digestion of palm oil mill residues, resulting in improved biogas production and methane yield. The structural and chemical analyses confirmed effective microbial colonization and substrate degradation, while kinetic modeling revealed accelerated biogas production rates and reduced lag phases. The use of zeolite not only increased cumulative biogas output by more than twofold but also contributed to stabilizing the digestion process by facilitating microbial adhesion and mitigating inhibitory compounds. These improvements underscore the potential of zeolite as a low-cost, sustainable support material to optimize bioenergy recovery from abundant agro-industrial wastes. This approach offers promising implications for scaling up renewable energy technologies in palm oil-producing regions, enhancing waste valorization, and contributing to circular economy goals. Future research should focus on long-term operational stability and economic assessments to further validate the practical application of zeolite-based immobilization systems in industrial biogas production. Declarations Consent to Participate Not applicable. The study did not involve human subjects or personal data that would require informed consent. Consent to Publish All authors have read and approved the final version of the manuscript and consent to its publication. Ethical Approval This study did not involve human participants or animals. Therefore, ethical approval was not required. Informed Consent Not applicable. Conflicts of Interest The authors declare no competing interests. Ethical Responsibilities of Authors The authors affirm that the work presented in this manuscript is original and has not been published elsewhere, in whole or in part, nor is it under consideration by any other journal. All authors have made significant contributions to the research and manuscript preparation and have approved the final version for submission. The authors confirm that the manuscript complies with the highest standards of research integrity, transparency, and accuracy, in accordance with the Brazilian Journal of Chemical Engineering’s editorial policies and COPE (Committee on Publication Ethics) guidelines.Any materials, data, and methods used in the research are accurately described and made available for verification and reproducibility. Proper credit has been given to all sources of information through accurate and complete citation. Acknowledgment: The authors would like to sincerely appreciate Tertiary Education Trust Fund (TETFUND) for funding this research with a Grant Number 2023/VOL.11 TETF/DR&D/POLY/AUCHI/IBR Data Availability The datasets are not publicly available but are available from the corresponding author on reasonable request. References Vieira F, Santana H, Jesus M, Santos J, Pires P, Vaz-Velho M, Silva D, Ruzene D (2024) Coconut Waste: Discovering Sustainable Approaches to Advance a Circular Economy. Sustainability. https://doi.org/10.3390/su16073066 Setiawan A, Bardant T, Muryanto M, Triwahyuni E, Ishizaki R, Dahnum D, Putri A, Irawan Y, Maryana R, Sudiyani Y, Sulaswatty A, Wiloso E, Ahamed T, Chéron-Bessou C, Noguchi R (2024) Influence of avoided biomass decay on a life cycle assessment of oil palm residues-based ethanol. 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Int J Environ Sci Technol 18:265–274. https://doi.org/10.1007/s13762-020-02822-w Pirsaheb M, Hossaini H, Amini J (2021) Operational parameters influenced on biogas production in zeolite/anaerobic baffled reactor for compost leachate treatment. J Environ Health Sci Eng 19:1743–1751. https://doi.org/10.1007/s40201-021-00729-3 Supplementary Files floatimage1.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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18:49:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133007,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectra of \u003cstrong\u003e(a\u003c/strong\u003e) POME, \u003cstrong\u003e(b)\u003c/strong\u003ePKS, \u003cstrong\u003e(c)\u003c/strong\u003e FBK, \u003cstrong\u003e(d)\u003c/strong\u003e MF, \u003cstrong\u003e(e)\u003c/strong\u003e cow dung, \u003cstrong\u003e(f)\u003c/strong\u003e zeolite,\u003cstrong\u003e(g)\u003c/strong\u003eco-substrates mixture, and \u003cstrong\u003e(h)\u003c/strong\u003e spent sludge\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7567816/v1/d3313179b415f729522c360b.png"},{"id":92441715,"identity":"628e7717-96d4-4da0-9abb-fb8a45a69f64","added_by":"auto","created_at":"2025-09-29 18:49:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93788,"visible":true,"origin":"","legend":"\u003cp\u003eGC-MS image of biogas synthesized from anaerobic co-digestion of palm oil mill residues\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7567816/v1/73845640c833fb98f091453e.png"},{"id":92441709,"identity":"a85760f4-ec33-48ad-9b75-5333477d1fb6","added_by":"auto","created_at":"2025-09-29 18:49:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38239,"visible":true,"origin":"","legend":"\u003cp\u003eZeta potential of co-substrates mixture at varying pH values of at ± 5% standard error\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7567816/v1/dc1625e26754bd147f9df070.png"},{"id":92441721,"identity":"8a9107bd-8649-4f88-bd07-eb054a576932","added_by":"auto","created_at":"2025-09-29 18:49:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":588495,"visible":true,"origin":"","legend":"\u003cp\u003eLinearly kinetic plots of \u003cstrong\u003e(a) \u003c/strong\u003ePFO, \u003cstrong\u003e(b)\u003c/strong\u003e PSO models for biogas synthesis, and \u003cstrong\u003e(c)\u003c/strong\u003e Monod model for substrate biodegradability\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7567816/v1/914d08ed8f0fbc954f413b7b.png"},{"id":97250095,"identity":"44399271-ba22-424e-b25f-fe1eb073dfd1","added_by":"auto","created_at":"2025-12-02 13:13:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1792668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7567816/v1/c2c4f689-9f93-448e-a073-938249754b2b.pdf"},{"id":92441711,"identity":"a6caeebe-e12b-45ef-96da-41d87600ecf5","added_by":"auto","created_at":"2025-09-29 18:49:37","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1818470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7567816/v1/a2188e15480d764bdf5ecbde.png"}],"financialInterests":"","formattedTitle":"Boosting Biogas Yield from Palm Oil Residues through Microbial Immobilization and Kinetic Analysis","fulltext":[{"header":"1, Introduction","content":"\u003cp\u003eThe rapid expansion of the palm oil industry has generated vast quantities of lignocellulosic residues, including empty fruit bunches (EFB), mesocarp fibers (MF), palm kernel shells (PKS), and palm oil mill effluent (POME). These by-products, if not properly managed, pose significant environmental challenges, such as greenhouse gas emissions, water pollution, and soil degradation [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Anaerobic digestion (AD) presents a sustainable solution for valorizing these residues into renewable energy in the form of biogas, while simultaneously mitigating environmental impacts. Biogas, primarily composed of methane (CH₄) and carbon dioxide (CO₂), is a versatile energy source with applications ranging from electricity generation to direct thermal utilization, offering an attractive alternative to fossil fuels and contributing to circular bioeconomy goals [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its potential, conventional AD of palm oil residues is often constrained by low biodegradability, the presence of inhibitory compounds, and suboptimal microbial activity. Strategies to enhance biogas yield have focused on process optimization, co-digestion, pretreatment of feedstocks, and the application of microbial immobilization techniques [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Microbial immobilization\u0026mdash;wherein microorganisms are entrapped or attached to carrier materials\u0026mdash;enhances cell density, protects microbial communities from environmental stress, and improves the stability and efficiency of the AD process. Such approaches have demonstrated improved methane production and system resilience in various lignocellulosic and agro-industrial waste substrates [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKinetic modelling of biogas production provides critical insights into substrate degradation rates, microbial activity, and process dynamics. Models such as the first order, modified Gompertz, and logistic equations enable prediction of methane yield, assessment of inhibitory effects, and design of efficient digester systems. Integrating microbial immobilization with kinetic analysis offers a dual advantage: it not only enhances process performance but also allows a quantitative understanding of biogas production dynamics, facilitating scale-up and optimization [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study investigates the anaerobic co-digestion of palm oil residues with microbial immobilization media, aiming to maximize biogas production and elucidate the underlying kinetics. Through comprehensive characterization of feedstocks, digestates, and biogas composition, coupled with kinetic modelling, the research seeks to provide actionable insights for sustainable energy recovery and environmental management within the palm oil industry.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Wastewater and Substrate Preparation\u003c/h2\u003e\u003cp\u003eThe inoculum used for anaerobic digestion was palm oil mill effluent (POME), collected from a commercial palm oil processing facility in Agenebode, Edo State, Nigeria. The solid substrates for co-digestion comprised empty fruit bunches (EFB), mesocarp fibers (MF), and palm kernel shells (PKS), all obtained from the same facility to ensure consistency in feedstock characteristics.Natural zeolite, employed as the microbial immobilization medium, was procured from a chemical supplier in Lagos, Nigeria, and had an average particle size of 0.20 \u0026micro;m. Cow dung, serving as the microbial inoculum source, was collected from the Auchi Polytechnic environment, Edo State, to introduce a diverse consortium of anaerobic microorganisms. All substrates and inocula were handled under ambient conditions, and prior to use, solid residues were washed, air-dried, and mechanically milled to uniform particle sizes to ensure homogeneity in the co-digestion experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Preparation of Co-Digestion Feedstocks\u003c/h2\u003e\u003cp\u003eThe solid palm oil mill residues\u0026mdash;empty fruit bunches (EFB), mesocarp fibers (MF), and palm kernel shells (PKS)\u0026mdash;were thoroughly washed under running water to remove suspended impurities. The cleaned feedstocks were then sun-dried for five (5) days and mechanically crushed. Similarly, cow dung was sun-dried for five (5) days until a constant weight was achieved and subsequently pulverized.Approximately 500 g of each ground sample was weighed using an analytical balance (Scout Pro, Ohaus, England). The samples were then sieved using a vibration sieve (TamizadoraEdibo, Spain) to achieve an average particle size of 355 \u0026micro;m. The sieved samples were stored in desiccators to prevent moisture absorption.\u003c/p\u003e\u003cp\u003eThe POME inoculum was stored in a 10-liter water container, tightly sealed, and kept at 4\u0026deg;C to inhibit microbial activity until required. Prior to digestion, the POME was brought to room temperature and its volume measured using a 100 mL measuring cylinder.The anaerobic biodigestion experiments were conducted following the method of Ugwu \u003cem\u003eet al.\u003c/em\u003e (2022) with slight modifications. A batch digester with a total capacity of 2.5 L and a working volume of 2.0 L was used [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A U-tube with a 10 mm internal diameter and a \u0026frac14;-inch internal diameter valve were installed on the digester cap and sealed with silicone to prevent air leakage, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Biogas Collection and Co-Digestion Procedure\u003c/h2\u003e\u003cp\u003ePlastic gas bags were used for biogas collection throughout the experiments. Prior to charging the digester with substrates, the U-tubing was filled with tap water to a marked level to facilitate gas volume measurement. The digester was then loaded with 500 mL of POME, 30 g of mesocarp fiber (MF), 30 g of empty fruit bunches (EFB), 50 g of palm kernel shells (PKS), 30 g of zeolite (as microbial immobilization medium), and 10 g of cow manure for co-digestion. All digestions were performed at ambient room temperature.\u003c/p\u003e\u003cp\u003eTo ensure uniform dispersion of the substrates, the co-substrate mixture in the digester was continuously homogenized. This constant agitation enhanced the digestion process by facilitating heat transfer, preventing the formation of surface crust and scum, and promoting efficient microbial activity. Prior to sealing, nitrogen gas was purged through the system to displace oxygen and establish anaerobic conditions. The digester was then connected to the plastic gas bag for biogas collection.The volume of biogas generated was measured daily using the water displacement method, by recording the volume of water displaced in the U-tubing. Gas was collected by opening the \u0026frac14;-inch valve to allow flow into the biogas bag, which was then closed until the next collection. This procedure was repeated for six consecutive batch digestion runs, each lasting 1 to 6 days. A similar procedure was followed for co-digestion experiments without zeolite to serve as a control.The composition of the biogas produced was analyzed using gas chromatography-mass spectrometry (GC-MS; Varian 3800/4000), providing detailed quantification of methane, carbon dioxide, and other trace gases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Physico-Chemical Characterization of Substrates, Co-Substrates Mixture, Effluent Sludge and Biogas\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section4\"\u003e\u003ch2\u003e2.2.3.1 Proximate, Ultimate, and Physicochemical Analysis\u003c/h2\u003e\u003cp\u003eThe elemental composition of the substrates was determined using a LECO Truspec CHN analyzer according to ASTM D5373-08. Carbon (C), hydrogen (H), and nitrogen (N) contents were directly measured, while sulfur (S) was oxidized to sulfur dioxide (SO₂) at 1350\u0026deg;C in an oxygen-rich environment and quantified by infrared absorption. The oxygen (O) content was calculated by difference from the sum of C, H, N, and S [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAsh content was determined following ASTM E1755\u0026thinsp;\u0026minus;\u0026thinsp;01 by weighing 2 g of each sample in pre-weighed ceramic crucibles, followed by combustion at 730\u0026deg;C for 5 h in a muffle furnace and cooling for 1 h. Volatile matter was analyzed according to ASTM E872\u0026thinsp;\u0026minus;\u0026thinsp;82, while fixed carbon was determined following ASTM D3172-89 by subtracting the sum of %Ash, %Volatile matter, and %Moisture from 100%. Moisture content was assessed using the Standard British Institution (BSI) oven-dry method, with samples dried at 105\u0026deg;C for 3 h [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKey physicochemical parameters of the POME and co-digestion feedstocks, including pH, temperature, biological oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), conductivity, total solids (TS), and volatile solids (VS), were measured according to stand methods[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Characterization of Substrates and Membranes\u003c/h2\u003e\u003cp\u003eThe microstructure and surface morphology of the prepared membrane blend were analyzed using scanning electron microscopy (SEM) (Hitachi SU 3500, Japan) to assess the shape and surface features of the samples. Samples were first cleaned with water, surface-dried, and then coated with a thin layer (~\u0026thinsp;20 nm) of gold under vacuum to enhance conductivity and prevent charge buildup. The electron beam was focused on specific positions of the sample surface, and the resulting electron interactions were collected by detectors, processed, and visualized as SEM images.Functional groups present on the membrane surfaces were determined using a FT-IR spectrophotometer (Varian 660 MidIR Dual MCT/DTGS Bundle with ATR). Samples were dried in auto-desiccators for 24 h prior to analysis. KBr disks were prepared by mixing powdered sample with dry KBr in a 1:100 ratio and placed directly on the diamond crystal of the ATR. Spectra were recorded in the range 4000\u0026ndash;500 cm⁻\u0026sup1; to identify characteristic functional groups.The electrical surface charge of the membrane, which influences fouling behaviour and stability, was measured using an electro-kinetic analyzer (SurPASS 2, Anton Paar, Graz, Austria). Membranes were immersed in MilliQ water for 24 h prior to testing. Samples were mounted in a holder disc with a gap height of 100\u0026ndash;106 \u0026micro;m. An aqueous 1 mmol/L KCl solution was used as the electrolyte, and pH was varied from 3.0\u0026ndash;10.0 using 0.1 M HCl or 0.1 M KOH in increments of 0.5\u0026ndash;0.7 units. Streaming potential and streaming current were measured as the solution flowed through the gap, and zeta potential values were automatically. Biogas constituents were analyzed using GC-MS (Varian 3800/4000) equipped with an Agilent DB-5ms capillary column (30 m \u0026times; 0.25 mm, 0.25 \u0026micro;m film thickness). A 0.5 \u0026micro;L aliquot of biogas extract was injected in split mode (5:1, split flow 10 mL min⁻\u0026sup1;) at an initial pressure of 2.6 bar and injector temperature of 200\u0026deg;C. Nitrogen (99.9995% purity) was used as carrier gas at 1.51 mL min⁻\u0026sup1;. The MS scan range was 30\u0026ndash;400 Da. Compounds were identified by comparing retention times and mass spectra with authentic standards and library data. Relative area percentages were calculated from the integrator, and major fragment ions were used to confirm compound identity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5. Kinetic Modelling of Biogas Production\u003c/h2\u003e\u003cp\u003eThe kinetics of biogas production were evaluated to describe and assess the activity of methanogens during anaerobic digestion. Experimental data were fitted to different kinetic models to identify the most appropriate model, which not only predicts reactor performance but also provides insight into the metabolic pathways and mechanisms governing the anaerobic digestion (AD) of co-substrates.Biogas production rates from POME co-digested with particulate residues (EFB, MF, PKS) with and without zeolite were simulated using three kinetic models: pseudo-first order (PFO), pseudo-second order (PSO), and Monod models, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The PFO and PSO models (Eqs.\u0026nbsp;1 and 2) were used to fit the cumulative biogas production data obtained experimentally, while the Monod model (Eq.\u0026nbsp;3) was applied to describe substrate biodegradability and the underlying microbial metabolic mechanisms [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\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\u003eList of adsorption kinetic models studied\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLinear form\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLinear plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eEqn. No\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiogas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProduction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePFO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\left({C}_{e}-{C}_{t}\\right)=ln{C}_{e}-{k}_{1}t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\left({C}_{e}-{C}_{t}\\right)\\text{v}\\text{s}\\:t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{t}{{C}_{t}}=\\:\\frac{1}{{{k}_{2}^{2}C}_{e}^{2}}+\\frac{t}{{C}_{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{t}{{C}_{t}}\\text{v}\\text{s}\\:t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubstrates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ebiodegradability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\mu\\:}=\\:\\frac{{K}_{S}}{{\\mu\\:}_{max}}\\:X\\frac{1}{S}+\\frac{1}{{\\mu\\:}_{max}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\mu\\:}\\text{v}\\text{s}\\frac{1}{S}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\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\u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;rate constant for the pseudo-first-order adsorption; k\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;rate constant for the pseudo-second-order (in day\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e); C\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand C\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(in g L\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e1) are the initial substrate concentration and substrate concentration at any time, t; t\u0026thinsp;=\u0026thinsp;reaction time (in days); S\u0026thinsp;=\u0026thinsp;substrate concentration (g/L)\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:=\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003especific biodegradability rate (day\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), while\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{max}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(day\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) and\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{S}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(g/L) are Monod kinetic constants.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Ultimate and proximate analyses\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the ultimate and proximate analysis of the three solid palm oil process residues\u0026mdash;Empty Fruit Bunches (EFB), Mesocarp Fiber (MF), and Palm Kernel Shells (PKS)\u0026mdash;as well as their composite mixture. Understanding these characteristics is critical in evaluating their suitability as feedstocks for anaerobic co-digestion and predicting biogas yield.The carbon content of the residues ranged from 47.21% in EFB to 51.95% in PKS, with the composite mixture averaging 50.92%. MF (50.76%) and PKS (51.95%) had higher carbon contents than EFB, suggesting greater potential energy content and better biogas production potential due to their higher organic matter content. Hydrogen content was highest in MF (0.57%) and lowest in EFB (0.11%). Although hydrogen values are relatively low compared to carbon, the presence of hydrogen is essential as it directly contributes to methane formation during anaerobic digestion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The slightly higher hydrogen in MF and PKS can enhance methane yield when co-digested with EFB.\u003c/p\u003e\u003cp\u003eNitrogen content varied across the residues, with EFB having the highest (1.16%) and MF the lowest (0.65%). The composite mixture averaged 1.03%, reflecting a more balanced nutrient profile for microbial growth. The Carbon-to-Nitrogen (C: N) ratio is a critical parameter in anaerobic digestion; excessively high C:N ratios can limit microbial activity due to nitrogen deficiency, whereas very low ratios can lead to ammonia inhibition. EFB exhibited a relatively low C:N ratio (40.69), MF had a very high ratio (78.09), and PKS had 63.35, indicating that individually these residues are either nitrogen-limited (MF, PKS) or relatively balanced (EFB). The composite mixture reduced the C:N ratio to 49.49, which is within the optimal range (20\u0026ndash;50) for anaerobic digestion, supporting enhanced microbial activity and stable biogas production. This demonstrates the advantage of co-digesting multiple residues to achieve balanced nutrients and mitigate inhibitory effects [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFixed carbon content, which reflects the residue\u0026rsquo;s solid combustible fraction, was highest in PKS (86.55%) and lowest in EFB (72.67%). The high fixed carbon content in PKS and MF (80.48%) indicates a slow-degrading fraction, potentially contributing to prolonged methane generation over time. In contrast, volatile matter, representing readily biodegradable components, was highest in the composite mixture (17.53%) and lowest in PKS (4.39%). EFB had intermediate volatile matter (11.35%), and MF was lowest among the fibers (5.65%). The higher volatile matter in the composite feedstock suggests a synergistic effect in co-digestion, providing more readily available substrates for microbial fermentation and rapid biogas generation, particularly during the initial digestion phase [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Higher Heating Value (HHV) and Lower Heating Value (LHV) indicate the total and usable energy content of the residues. EFB had an HHV of 467.55 kJ/kg (LHV 12.62 MJ/kg), MF had 554.9 kJ/kg (LHV 14.32 MJ/kg), and PKS had 579.56 kJ/kg (LHV 14.2 MJ/kg), with the composite mixture achieving an LHV of 14.85 MJ/kg. The high HHV and LHV values, particularly in the composite, indicate strong energy potential for biogas production. Combining residues balances the fast-degrading fractions (EFB) with more recalcitrant carbon (PKS and MF), optimizing the energy release kinetics for sustained biogas yield [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe compositional characteristics of the residues underscore the potential benefits of microbial immobilization strategies. Zeolite immobilization, by providing a stable microenvironment, can help microbes efficiently degrade the high C: N and high fixed carbon residues (MF and PKS) while enhancing the utilization of the more labile EFB fractions. The balanced C:N ratio and higher volatile matter in the composite mixture favour rapid microbial colonization and sustained methanogenesis. Kinetic modelling is expected to show improved fit for pseudo-second-order or Monod models when immobilization is applied, reflecting enhanced substrate utilization and methane formation rates [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIndividually, each residue presents specific limitations: EFB has sufficient nitrogen but lower carbon and fixed carbon, MF has high carbon but very low nitrogen, and PKS is rich in fixed carbon but low in volatile matter. The composite mixture balances these properties, producing a substrate with a moderate C:N ratio, higher volatile matter, and increased energy content. This provides a substrate profile optimized for enhanced biogas yield, particularly when combined with microbial immobilization media that improve microbial contact, reduce inhibition, and accelerate substrate degradation.\u003c/p\u003e\u003cp\u003eThe ultimate and proximate composition of palm oil residues highlights the necessity of co-digestion to achieve balanced nutrient content and optimal biogas production. The composite feedstock, with improved C:N ratio, higher volatile matter, and substantial heating values, offers a synergistic advantage for boosting biogas yield. Incorporating microbial immobilization is expected to further enhance the kinetics of methane formation, making this approach highly suitable for sustainable energy recovery from palm oil process residues.\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\u003ePhysical Characteristics of solid process residues\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltimate Parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResidues\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEFB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eComposite\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon content (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrogen content (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon: Nitrogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.69\u0026thinsp;\u0026plusmn;\u0026thinsp;9.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.09\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydrogen content (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29 \u0026plusmn;\u0026nbsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed carbon (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.67\u0026nbsp;\u0026plusmn; 2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolatile matter (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHHV (kg/KJ)\u003c/p\u003e\u003cp\u003eLHV (MJ/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.62\u003c/p\u003e\u003cp\u003e467.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.32\u003c/p\u003e\u003cp\u003e554.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003cp\u003e579.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.85\u003c/p\u003e\u003cp\u003e541.41\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 physicochemical profile of the effluents co-mixed with palm oil solid residues provides key insight into how feed composition shapes anaerobic digestion (AD) performance, particularly in the context of microbial immobilization and kinetic enhancement strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The pH values show a progressive rise from raw POME at 4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 to 5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 in the POME\u0026thinsp;+\u0026thinsp;MF\u0026thinsp;+\u0026thinsp;EFB\u0026thinsp;+\u0026thinsp;PKS mixture. While all blends remain below the ideal methanogenic range (6.5\u0026ndash;7.5), the upward trend reflects partial buffering from the addition of lignocellulosic solids, likely due to their mineral ash content and inherent alkalinity. This is important for the immobilization strategy, as zeolite carriers can further absorb acidic metabolites and release cations (Na⁺, K⁺, Ca\u0026sup2;⁺) to help lift the pH into the optimal zone, shortening the lag phase and reducing acid inhibition in early digestion stages [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eElectrical conductivity (EC) also increases markedly from 239.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 \u0026micro;mhos in pure POME to a peak of 384.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 \u0026micro;mhos with the POME\u0026thinsp;+\u0026thinsp;MF\u0026thinsp;+\u0026thinsp;EFB blend before a slight dip with PKS addition (370.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66 \u0026micro;mhos). This rise indicates an accumulation of soluble ionic species\u0026mdash;likely nutrients, buffering salts, and degradation products\u0026mdash;enhancing the medium\u0026rsquo;s conductivity and potentially improving ionic mass transfer to immobilized cells. However, excessive ionic strength can inhibit sensitive methanogens, meaning immobilization media with cation exchange capacity, such as zeolite, can help maintain ionic balance. Dissolved oxygen (DO) levels remain low (4.79\u0026ndash;5.90 mg/L), consistent with anaerobic requirements, but slight differences could influence early microbial community succession; higher DO in the MF blend may promote facultative anaerobes that assist in initial hydrolysis [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBiodegradability indicators show nuanced patterns. Biological oxygen demand (BOD) fluctuates within a narrow range (0.48\u0026ndash;0.59 mg/L), reflecting that easily biodegradable organics are present but limited\u0026mdash;typical for lignocellulosic-rich mixtures. In contrast, chemical oxygen demand (COD) varies drastically, jumping from 39.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mg/L in raw POME to 1042\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15 mg/L in the POME\u0026thinsp;+\u0026thinsp;MF\u0026thinsp;+\u0026thinsp;EFB\u0026thinsp;+\u0026thinsp;PKS mix. This massive increase signals a large pool of oxidizable organics, much of it in complex, recalcitrant forms. Immobilization becomes critical here, as retaining a high density of hydrolytic and syntrophic consortia accelerates the breakdown of these complex compounds, preventing COD accumulation from stalling digestion. The COD: BOD disparity also confirms that the co-substrates introduce a substantial fraction of slowly degradable matter, reinforcing the need for kinetic modeling to optimize retention time and microbial loading [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTotal solids (TS) remain in a narrow range for POME and the MF/EFB blends (83\u0026ndash;86 mg/L) but jump significantly with PKS addition (103.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 mg/L), reflecting the higher lignin and mineral content of shells. Total suspended solids (TSS) decline when solids are added (from 74.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 mg/L in POME to ~\u0026thinsp;60\u0026ndash;62 mg/L in blends), possibly due to partial sedimentation or solubilization during mixing. Total dissolved solids (TDS) rise steadily, reaching 41.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 mg/L with PKS, indicating enhanced solubilization of organic and inorganic constituents\u0026mdash;another factor in EC elevation. Volatile solids (VS), representing the biodegradable fraction, show notable enhancement with residue addition, particularly MF (10.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37%) and PKS-containing blends (~\u0026thinsp;10%). This enrichment directly boosts methane potential, as more volatile organics are available for conversion; immobilization supports higher VS utilization by concentrating degraders and enhancing contact efficiency [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTemperature is stable across all blends (25.19\u0026ndash;26.8\u0026deg;C), indicating mesophilic conditions that can be leveraged without additional heating in tropical contexts. Total organic carbon (TOC) aligns closely with VS trends, surging from 1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10% in POME to 9.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40% in the PKS-containing blend. This confirms that solid residues significantly raise organic load, which, if unbalanced with nutrient ratios or if hydrolysis is slow, can lead to acid accumulation. Here, immobilization offers dual benefits: buffering acids and sustaining high hydrolytic activity to maintain steady carbon conversion rates [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe physicochemical shifts with residue blending demonstrate that while co-digestion enhances organic loading, biodegradability, and nutrient balance, it also raises challenges such as higher COD, greater ionic strength, and the persistence of recalcitrant matter. The integration of zeolite-based microbial immobilization directly addresses these challenges by stabilizing pH, regulating ionic concentration, retaining active biomass for extended solids retention, and improving enzymatic hydrolysis of complex organics. From a kinetic perspective, these compositional changes set the stage for faster initial rates (via better nutrient and VS availability) and more complete substrate conversion over time (via immobilized biomass tackling recalcitrant fractions), perfectly aligning with the goal of boosting biogas yield from palm oil residues.\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\u003ePhysicochemical properties of effluent co-mixed with solid process residues\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOME\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePOME\u0026thinsp;+\u0026thinsp;MF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePOME\u0026thinsp;+\u0026thinsp;MF\u0026thinsp;+\u0026thinsp;EFB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePOME\u0026thinsp;+\u0026thinsp;MF\u0026thinsp;+\u0026thinsp;EFB\u0026thinsp;+\u0026thinsp;PKS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE. Conductivity (Umhos)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e303.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e384.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e370.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDO (Mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBOD (Mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.573\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOD (Mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1042\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTS (mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSS (mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDS (mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\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\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2. SEM/EDX characterization\u003c/h2\u003e\u003cp\u003eThe microstructural and elemental analyses presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e offer crucial insights into the transformation of the co-mixed substrates sludge (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) into the spent sludge (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), which is central to understanding the enhanced biogas yield from palm oil residues via microbial immobilization and kinetic optimization. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were employed to visualize the surface morphology and assess the elemental composition of the materials before and after anaerobic digestion, providing evidence of microbial activity, immobilization efficacy, and substrate utilization efficiency.\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, the SEM image of the co-mixed substrates sludge reveals a compact and relatively heterogeneous surface morphology with dense agglomerations. These features are typical of a fresh sludge matrix where organic matter, nutrients, and potential microbial carriers are intimately associated but not yet extensively acted upon by microbial consortia. The accompanying EDX spectrum confirms the elemental richness of the substrate, dominated by carbon (41.82%), oxygen (27.05%), and a notable amount of calcium (20.54%), along with detectable levels of nitrogen (3.52%), silicon (4.27%), aluminum (1.80%), and sulfur (1.00%). The high carbon and oxygen contents are indicative of organic material richness, vital for microbial metabolism and biogas production. The presence of calcium is particularly important as it could enhance microbial immobilization, stabilize pH, and serve as a co-factor in enzymatic processes. Nitrogen, though in lower concentration, is also essential as a nutrient for microbial growth and activity.\u003c/p\u003e\u003cp\u003eIn contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb illustrates the SEM image of the spent sludge, representing the matrix after undergoing microbial digestion. The morphology has notably shifted to a more porous and structured architecture, characterized by visible cavities and channels. This indicates the decomposition and consumption of the initial organic matrix by microbial action during the anaerobic digestion process. The newly formed porous network suggests effective microbial colonization and biofilm formation, both of which are beneficial for immobilization strategies. These structural changes are directly aligned with the improved kinetics and gas production efficiency observed in enhanced biogas systems.\u003c/p\u003e\u003cp\u003eThe EDX data for the spent sludge corroborate these morphological changes. While carbon and oxygen levels slightly decrease to 38.64% and 21.31% respectively, calcium content increases significantly to 26.82%, highlighting its potential role in structural integrity and microbial retention. Moreover, there's a noticeable rise in sulfur (2.57%), silicon (5.86%), and aluminum (3.15%), while nitrogen decreases to 1.65%. The sulfur increase could be attributed to microbial sulfate-reduction activity, a pathway that can be prominent in anaerobic systems depending on microbial community dynamics. The decrease in nitrogen content may reflect its assimilation into microbial biomass or conversion into gaseous forms during digestion. Overall, the elemental redistribution affirms active biochemical interactions within the sludge matrix during digestion.\u003c/p\u003e\u003cp\u003eThese microstructural and compositional transformations are directly relevant to the research aim of boosting biogas yield from palm oil residues. The evidence supports that the initial co-mixed sludge offered a favourable environment for microbial attachment and nutrient supply, while the post-digestion matrix reflects effective substrate utilization and successful microbial immobilization. The increased porosity and retained mineral elements in the spent sludge further enhance its potential as a bio-carrier for continued microbial colonization, thus facilitating improved process stability and biogas kinetics. This aligns well with the study\u0026rsquo;s hypothesis that immobilization of microbial communities on structured supports can enhance substrate degradation efficiency and biogas productivity, offering a sustainable and scalable approach to palm oil residue valorization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3. FTIR analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the Fourier Transform Infrared (FTIR) spectra of various substrates and materials used or generated during the anaerobic digestion process aimed at boosting biogas yield from palm oil residues through microbial immobilization and kinetic enhancement. This spectral analysis is central to understanding the functional group compositions of each component, the interactions among co-substrates, and the biochemical transformations occurring from feedstock to spent sludge. The interpretation of these spectra directly reflects the degradation potential of organic components and the capacity of materials such as zeolite to aid microbial immobilization, both of which are pivotal in optimizing biogas production.\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, the FTIR spectrum of Palm Oil Mill Effluent (POME) shows broad peaks at 3322.85 cm⁻\u0026sup1; and 2875.25 cm⁻\u0026sup1;, indicative of O\u0026ndash;H and C\u0026ndash;H stretching vibrations, representing water content and organic matter. Peaks at 1602.87 cm⁻\u0026sup1; and 1500.26 cm⁻\u0026sup1; correspond to C\u0026thinsp;=\u0026thinsp;O and aromatic C\u0026thinsp;=\u0026thinsp;C stretching, suggesting the presence of lignin, proteins, and fatty acids. The bands near 788.36 cm⁻\u0026sup1; and 700.56 cm⁻\u0026sup1; are associated with out-of-plane bending vibrations of aromatic or alkene groups. This complex composition confirms POME as a rich substrate containing various biodegradable organic compounds suitable for microbial digestion [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, representing Palm Kernel Shell (PKS), shows strong absorption at 2926.07 cm⁻\u0026sup1; and 2000.87 cm⁻\u0026sup1;, likely due to aliphatic C\u0026ndash;H stretching and possible overtones of aromatic structures. The notable peaks at 1521.53 cm⁻\u0026sup1;, 1611.41 cm⁻\u0026sup1;, and 1103.5 cm⁻\u0026sup1; suggest lignocellulosic components such as lignin and cellulose. PKS's structural recalcitrance is reflected in its FTIR profile, implying its need to be co-digested or pretreated for optimal biogas recovery [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe spectrum in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, corresponding to Fibrous Biomass (FBK), shows dominant peaks at 2915.49 cm⁻\u0026sup1;, 1518.06 cm⁻\u0026sup1;, and 1367.28 cm⁻\u0026sup1;, associated with C\u0026ndash;H and C\u0026thinsp;=\u0026thinsp;C bonds in lignocellulosic compounds. The fingerprint region between 1200\u0026ndash;800 cm⁻\u0026sup1; displays several peaks (e.g., 1198.47, 996.43, 800.02 cm⁻\u0026sup1;), confirming polysaccharide structures, cellulose, and hemicellulose. These signals validate FBK as another important carbon-rich contributor to the anaerobic digestion matrix [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ed shows the FTIR spectrum of Mesocarp Fiber (MF), marked by broad O\u0026ndash;H stretching around 3398.32 cm⁻\u0026sup1; and 2950.33 cm⁻\u0026sup1;, and C\u0026ndash;H stretching at 2650.41 cm⁻\u0026sup1;. The mid-region peaks at 1452.21, 1299.63, and 1038.17 cm⁻\u0026sup1; correspond to lignin, cellulose, and hemicellulose features, respectively. These results further confirm the biochemical resemblance among palm oil residues, all rich in lignocellulosic biomass, and highlight MF as a complementary substrate in co-digestion schemes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, displaying cow dung, exhibits a strong O\u0026ndash;H stretching vibration at 2987.59 cm⁻\u0026sup1; and aliphatic C\u0026ndash;H stretching near 2900.17 cm⁻\u0026sup1;. Peaks around 1523.21 cm⁻\u0026sup1; and 1296.34 cm⁻\u0026sup1; point to protein amide and N\u0026ndash;H bending vibrations, suggesting microbial and nitrogenous content, essential for balanced C/N ratios in anaerobic digestion. Absorption peaks at 1000.08, 1200.17, and 798.42 cm⁻\u0026sup1; reflect microbial metabolites and possible inorganic compounds. These spectra indicate cow dung's role not only as a co-substrate but also as an inoculum source that enriches the microbial consortium and maintains system stability.\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, zeolite's FTIR profile includes peaks at 2602.21 cm⁻\u0026sup1;, 1543.39 cm⁻\u0026sup1;, and 1000.34 cm⁻\u0026sup1;, characteristic of water adsorbed in micropores, Si\u0026ndash;O stretching, and Al\u0026ndash;O bonds. These features are consistent with the crystalline framework of zeolites, supporting their use as immobilization matrices. Zeolite\u0026rsquo;s microporous structure and active functional groups aid in microbial attachment and buffering capacity, improving retention time and reducing toxic intermediates in the reactor [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe FTIR spectrum of the co-substrate\u0026rsquo;s mixture (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eg) demonstrates a complex overlay of features from individual substrates. Prominent bands include O\u0026ndash;H stretching at 3368.15 cm⁻\u0026sup1;, aliphatic C\u0026ndash;H at 2906.38 cm⁻\u0026sup1;, and protein/lignin-related bands at 1563.18, 1407.86, and 1241.03 cm⁻\u0026sup1;. The fingerprint region again contains several peaks from 1100.26 to 675.38 cm⁻\u0026sup1;, reflecting diverse functional groups present in the mixture. This chemical diversity supports synergistic microbial degradation when substrates are co-digested, allowing for a broader enzymatic attack and enhancing hydrolysis and methanogenesis efficiency [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eh illustrates the FTIR of spent sludge, the end product post-digestion. The significant reduction in intensity and/or shifting of peaks\u0026mdash;such as the weakened bands at 2821.54, 1508.33, and 1200.82 cm⁻\u0026sup1;\u0026mdash;indicates the consumption or transformation of organic functional groups, confirming microbial breakdown of complex compounds. New or intensified peaks at 1103.64 cm⁻\u0026sup1; and 894.61 cm⁻\u0026sup1; could relate to microbial metabolites or transformation products. The diminished presence of O\u0026ndash;H, C\u0026thinsp;=\u0026thinsp;O, and C\u0026ndash;H groups further corroborate extensive organic matter conversion, validating effective digestion and biogas production [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAltogether, the FTIR data in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e substantiate the comprehensive chemical fingerprinting of the individual substrates, the synergy of the co-digestion mixture, and the post-digestion transformations in the spent sludge. These results directly align with the goal of boosting biogas yield from palm oil residues by strategically selecting and combining substrates with complementary biochemical profiles and integrating materials like zeolite to enhance microbial immobilization. The observed degradation of organic functional groups and the emergence of transformation products confirm the kinetic efficiency and metabolic activity of the microbial community, which is crucial to sustaining high biogas outputs in anaerobic systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4. GC-MS analysis of biogas\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Gas Chromatography-Mass Spectrometry (GC-MS) chromatogram of biogas synthesized from the anaerobic co-digestion of palm oil mill residues, providing a detailed fingerprint of the gaseous components produced during the digestion process. This analytical technique is pivotal in confirming the quality and complexity of biogas, particularly in relation to the efficiency of substrate degradation and the impact of microbial immobilization. Each peak in the chromatogram corresponds to a compound eluting at a specific retention time, with the relative abundance reflecting the concentration of each compound detected.\u003c/p\u003e\u003cp\u003eThe spectrum displays a series of prominent peaks at retention times 2.99, 6.48, 8.96, 9.25, 12.71, 21.48, 25.62, 29.25, 33.75, and 40.57 minutes, which represent a complex mix of volatile organic compounds (VOCs), intermediate metabolites, and final gaseous products associated with anaerobic digestion. Notably, the most intense peak at 9.25 min indicates a dominant compound likely contributing significantly to the energy potential of the biogas. This may correspond to methane or other high-energy hydrocarbons, which are the primary targets of biogas synthesis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The presence of additional major peaks at 6.48, 8.96, 12.71, and 25.62 min further suggests a rich mixture of intermediate and terminal products, possibly including carbon dioxide, hydrogen sulfide, and light hydrocarbons such as ethane, propane, or even trace volatile fatty acids (VFAs) and alcohols formed during the hydrolysis and acidogenesis stages [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe peak at 25.62 min represents another significant component, possibly a higher molecular weight compound or a secondary product formed from the conversion of intermediate substrates. This points to a well-advanced methanogenic phase, where complex organics have been effectively broken down and converted into valuable gases. Similarly, the later peaks from 29.25 to 40.57 min may correspond to less volatile, higher-mass compounds such as long-chain alkanes or cyclic hydrocarbons, potentially indicating residual by-products or incomplete conversion intermediates.\u003c/p\u003e\u003cp\u003eThe detection of such a wide array of chemical species in the GC-MS spectrum reflects the effective biochemical synergy achieved during the co-digestion of palm oil mill residues, especially when optimized through microbial immobilization strategies. By enhancing the surface area for microbial attachment and stabilizing microbial consortia, immobilization helps to maintain high metabolic activity and improved pathway regulation. This leads to better substrate conversion, reduced accumulation of inhibitors, and more stable gas production rates, all of which are indirectly reflected in the diversity and intensity of peaks in the chromatogram [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, the profile captured in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e strongly aligns with the overarching goal of boosting biogas yield from palm oil residues through integrated optimization strategies. The appearance of multiple sharp, high-abundance peaks signifies a system with well-tuned metabolic pathways that can efficiently convert lignocellulosic biomass (as indicated in FTIR results from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) into energy-rich gaseous products. These findings validate the effectiveness of using co-substrates like POME, FBK, PKS, and cow dung, combined with functional supports like zeolite, to optimize anaerobic digestion.\u003c/p\u003e\u003cp\u003eIn essence, the GC-MS chromatogram serves as a direct chemical confirmation of the successful bioconversion of complex biomass into high-quality biogas. The peak distribution and retention times reflect a dynamic system where microbial synergy and immobilization technologies have played a pivotal role in enhancing kinetics, minimizing inhibitory accumulation, and maximizing the energy potential of the biogas generated. This supports the study\u0026rsquo;s hypothesis that targeted engineering of digestion conditions and microbial habitats can significantly uplift the performance of biogas systems fuelled by agro-industrial residues such as those from the palm oil industry.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Zeta potential analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the zeta potential profile of the co-substrate\u0026rsquo;s mixture as a function of pH, with data presented at \u0026plusmn;\u0026thinsp;5% standard error. Zeta potential is a key physicochemical parameter that reflects the surface charge and stability of colloidal particles, directly influencing microbial adhesion, flocculation, and immobilization processes\u0026mdash;critical factors in the efficiency of anaerobic digestion. This analysis is essential for optimizing the microenvironment of the digestion matrix and aligns directly with the aim of boosting biogas yield from palm oil residues through microbial immobilization and kinetic analysis.\u003c/p\u003e\u003cp\u003eAt lower pH values (around pH 2\u0026ndash;4), the co-substrate mixture exhibits highly positive zeta potential values, peaking at approximately\u0026thinsp;+\u0026thinsp;35 mV at pH 2. This indicates strong electrostatic repulsion among particles, promoting dispersion and potentially limiting microbial aggregation. However, this high surface charge suggests the presence of abundant protonated functional groups (e.g., amines or hydroxyls), which contribute to the positively charged colloidal environment. Such conditions, although electrostatically stable, are not ideal for microbial activity in anaerobic digestion, where slightly acidic to neutral pH is more favourable.As the pH increases to around pH 6, a sharp decrease in zeta potential is observed, falling to about\u0026thinsp;+\u0026thinsp;20 mV. This reduction reflects the progressive deprotonation of surface functional groups, which in turn lowers the net positive surface charge. The decline in electrostatic repulsion at this stage is favourable for microbial colonization and biofilm formation, as cells and particles begin to interact more readily due to decreased repulsive forces.\u003c/p\u003e\u003cp\u003eA critical transition occurs near pH 7\u0026ndash;8, where the zeta potential approaches and crosses the isoelectric point (approximately 7.5), resulting in near-neutral or even slightly negative surface charge. At this point, particle agglomeration is likely to occur due to minimized electrostatic repulsion, enhancing the chances for microbial immobilization. This condition is particularly ideal for anaerobic digestion systems, where the flocculation of substrates can facilitate microbial encapsulation and retention, contributing to more stable and efficient digestion kinetics.Beyond pH 8, the zeta potential becomes increasingly negative, reaching approximately \u0026minus;\u0026thinsp;5 mV at pH 12. This negative charge suggests a surface dominated by deprotonated acidic groups, such as carboxylates and phenolics, which are common in lignocellulosic biomass residues. While negatively charged surfaces can still support microbial attachment\u0026mdash;especially for positively charged microbial species\u0026mdash;the reduced magnitude of the zeta potential may result in less stable colloidal behaviour, potentially leading to sedimentation or uneven microbial distribution.\u003c/p\u003e\u003cp\u003eThis zeta potential behaviour has significant implications for microbial immobilization, a core strategy in enhancing biogas yield in this study. Optimal microbial immobilization requires a balance between particle dispersion (to increase surface area) and agglomeration (to support microbial retention). The near-zero zeta potential around neutral pH provides the most favorable condition for achieving this balance. This supports the operational design of anaerobic digesters where buffering around neutral pH not only stabilizes microbial metabolic activity but also promotes structural microenvironments conducive to immobilization.Moreover, the observed charge dynamics of the co-substrates confirm their functional compatibility with immobilization materials such as zeolite (as shown in earlier FTIR and SEM-EDX analyses). At neutral to slightly alkaline conditions, where the zeta potential nears zero, synergistic interactions between the microbial cells, substrates, and immobilization matrices are likely maximized, facilitating biofilm development and efficient substrate-microbe contact. These mechanisms translate directly into improved kinetic performance and methane productivity, which aligns with the overarching objective of this study.\u003c/p\u003e\u003cp\u003eThe zeta potential behaviour of the co-substrate mixture across varying pH values highlights the importance of pH optimization in anaerobic digestion processes. The transition from strongly positive to slightly negative surface charges across the pH spectrum underscores how carefully controlled pH environments can be leveraged to enhance microbial immobilization, substrate interaction, and digestion efficiency. This result solidifies the rationale behind integrated physicochemical and biological strategies for maximizing biogas yield from palm oil mill residues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Kinetic modeling\u003c/h2\u003e\u003cp\u003eThe cumulative biogas production data presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e offers compelling evidence of the enhanced performance achieved through microbial immobilization via zeolite augmentation during the anaerobic co-digestion of palm oil mill residues. The biogas yield over a 6-day period shows a stark contrast between the systems with and without zeolite, directly validating the hypothesis that integrating immobilization strategies significantly boosts biogas production by improving microbial activity, retention, and process stability.On Day 1, the biogas production in the zeolite-assisted system was 74.39 ml, nearly 2.7 times greater than the 27.35 ml produced without zeolite. This early divergence is particularly significant, as it indicates that the zeolite provided an immediate improvement in microbial accessibility to the substrate. This initial surge reflects accelerated hydrolysis and acidogenesis, likely due to enhanced microbial colonization on the porous structure of zeolite, which increases the effective biomass concentration and enzymatic activity within the digester [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs the process progresses, this trend continues with widening margins. On Day 2, biogas yield with zeolite reached 120.67 ml, nearly three times the 41.28 ml observed in the control. By Day 3, the yield had grown to 216.41 ml with zeolite, compared to 78.54 ml without it, indicating an ongoing enhancement in metabolic conversion pathways such as acetogenesis and methanogenesis. These phases are often bottlenecked by pH fluctuation and accumulation of volatile fatty acids; however, the buffering capacity and ion exchange properties of zeolite mitigate these issues, providing a more stable environment for methanogenic archaea to flourish [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].By Day 4, the impact of zeolite becomes even more pronounced: 320.05 ml of biogas was generated in the zeolite-immobilized system versus 120.25 ml in the control\u0026mdash;a 166% increase. This level of enhancement strongly supports the concept of microbial immobilization improving process kinetics, not merely by physical support but by fostering biofilm formation, which enhances mass transfer and protects microbes from inhibitory compounds [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe biogas production curve began to plateau between Day 5 and Day 6, particularly in the zeolite system, which reached 475.90 ml on Day 5 and 490.75 ml on Day 6. This near saturation indicates that most of the easily degradable organic matter had already been metabolized. Meanwhile, the control system, while still increasing, reached only 186.32 ml and 234.90 ml on Days 5 and 6, respectively. The extended lag and lower cumulative output in the non-zeolite system suggest limited microbial efficiency, possible accumulation of intermediate products, and a less favorable digestion microenvironment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].The final comparative values on Day 6 show that the zeolite system achieved over 2.08 times more cumulative biogas than the control (490.75 ml vs. 234.90 ml). This conclusively confirms the critical role of zeolite as an immobilization matrix in improving biogas yield. The porous nature of zeolite not only offers an ideal surface for microbial attachment but also facilitates cation exchange, adsorbs toxic ions such as ammonia, and regulates pH\u0026mdash;factors that collectively promote a stable and robust anaerobic digestion process.\u003c/p\u003e\u003cp\u003eThe data validates the kinetic advantage conferred by immobilization strategies, demonstrating a faster and more complete degradation of substrates, higher methane productivity, and shorter lag phases. Moreover, the acceleration of biogas generation reflects an efficient transition through the anaerobic digestion stages, made possible by an optimized microbial ecosystem anchored by zeolite. The results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e serve as strong experimental support for adopting immobilization-enhanced digestion technologies in the valorization of palm oil mill residues. They underline how simple modifications to the microbial environment\u0026mdash;such as the addition of zeolite\u0026mdash;can dramatically enhance the bioenergy recovery potential from agro-industrial waste streams.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Cumulative yield of biogas production (ml)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTime (Days) \u0026nbsp; \u0026nbsp; \u0026nbsp;Without zeolite \u0026nbsp; \u0026nbsp;With zeolite\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60%;\"\u003e\n \u003cp\u003e27.35 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;74.39\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41.28 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 120.67\u003c/p\u003e\n \u003cp\u003e78.54 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 216.41\u003c/p\u003e\n \u003cp\u003e120.25 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 320.05\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e186.32 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 475.90\u003c/p\u003e\n \u003cp\u003e234.90 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 490.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the linearized kinetic models applied to assess the biogas synthesis behaviour from palm oil residues, with and without zeolite-enhanced microbial immobilization. The figure comprises three subplots: (a) the pseudo-first order (PFO) kinetic model, (b) the pseudo-second order (PSO) kinetic model, and (c) the Monod model evaluating substrate biodegradability. Collectively, these models provide mechanistic insight into the rate and efficiency of the anaerobic digestion process, aligning directly with the overarching objective of \"Boosting Biogas Yield from Palm Oil Residues through Microbial Immobilization and Kinetic Analysis.\"\u003c/p\u003e\u003cp\u003eIn subplot (a), the PFO model, represented by the plot of ln(C₀ \u0026ndash; Ct) versus time, shows a linear trend for both systems; however, the slope of the line is notably steeper for the zeolite-augmented system, indicating a faster rate of biogas production. This suggests that the presence of zeolite accelerates the initial degradation of organic matter, supporting higher microbial activity and improved enzymatic hydrolysis of complex compounds. The better fit of the zeolite line implies enhanced mass transfer between substrate and microbes due to immobilization, leading to a more efficient breakdown of readily biodegradable components. The higher intercept in the zeolite system also denotes a greater initial potential for biogas generation, further confirming the catalytic effect of zeolite during early digestion stages [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubplot (b) reflects the PSO model, where t/Ct is plotted against time to evaluate chemisorption-based reactions that could dominate the biogas formation kinetics. Here, the zeolite-assisted system again demonstrates superior performance, with data points tightly aligned along a line with a shallow slope. This indicates that the system with zeolite reached equilibrium more quickly and efficiently, showing lower values of t/Ct over time. The relatively flatter trend also suggests that the rate-limiting step was not just physical adsorption, but chemically mediated interactions likely enhanced by the functional groups on zeolite's surface. In contrast, the control system without zeolite presents a steeper decline, reflecting less efficient interaction between microbes and substrates, possibly due to limited microbial retention and insufficient surface area for attachment.Subplot (c) illustrates the Monod kinetic model, plotting 1/\u0026micro; versus 1/S to evaluate microbial growth rate in relation to substrate concentration. The Monod model is particularly valuable for assessing the biodegradability of the substrate mixture under different operational conditions. The zeolite-augmented system produces a lower linear trend, indicating a higher maximum specific growth rate (\u0026micro;max) and a lower half-saturation constant (Ks) compared to the system without zeolite. These outcomes confirm that immobilized microbes in the zeolite matrix had more effective access to substrates and maintained higher metabolic rates. The reduced slope for the zeolite system reflects more efficient substrate utilization, reinforcing the conclusion that zeolite enhances the bioavailability of nutrients and supports a denser, more active microbial population [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTaken together, all three models consistently show that zeolite-mediated immobilization significantly enhances the kinetics of anaerobic digestion. From rapid hydrolysis and gas production (PFO) to better chemisorption and equilibrium dynamics (PSO), and ultimately improved microbial growth efficiency (Monod), each kinetic model underscores how zeolite optimizes each stage of biogas synthesis. The accelerated and sustained performance observed in all models aligns strongly with the thematic focus of this research\u0026mdash;leveraging microbial immobilization to improve substrate-to-biogas conversion pathways.Furthermore, the kinetic enhancements observed are crucial not just for yield, but also for operational scalability and reactor efficiency. Faster kinetics mean shorter hydraulic retention times, reduced reactor volumes, and higher throughput\u0026mdash;all critical metrics for industrial deployment of biogas systems using palm oil residues. These kinetic trends also suggest a more stable microbial ecosystem, less prone to inhibition and more capable of adapting to fluctuations in substrate composition\u0026mdash;a key advantage for digesters processing complex, lignocellulosic materials such as palm oil mill residues.\u003c/p\u003e\u003cp\u003eThe kinetic analysis presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides quantitative confirmation of the mechanisms by which zeolite-based microbial immobilization boosts biogas yield. By improving the efficiency of substrate conversion, microbial growth, and system equilibrium, the inclusion of zeolite represents a technically robust and economically scalable strategy for enhancing biogas production in the context of sustainable waste valorization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe findings of this study clearly demonstrate that microbial immobilization on zeolite significantly enhances the anaerobic co-digestion of palm oil mill residues, resulting in improved biogas production and methane yield. The structural and chemical analyses confirmed effective microbial colonization and substrate degradation, while kinetic modeling revealed accelerated biogas production rates and reduced lag phases. The use of zeolite not only increased cumulative biogas output by more than twofold but also contributed to stabilizing the digestion process by facilitating microbial adhesion and mitigating inhibitory compounds. These improvements underscore the potential of zeolite as a low-cost, sustainable support material to optimize bioenergy recovery from abundant agro-industrial wastes. This approach offers promising implications for scaling up renewable energy technologies in palm oil-producing regions, enhancing waste valorization, and contributing to circular economy goals. Future research should focus on long-term operational stability and economic assessments to further validate the practical application of zeolite-based immobilization systems in industrial biogas production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConsent to Participate\u003c/h2\u003e\u003cp\u003eNot applicable. The study did not involve human subjects or personal data that would require informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003cp\u003eAll authors have read and approved the final version of the manuscript and consent to its publication.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cp\u003eThis study did not involve human participants or animals. Therefore, ethical approval was not required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eEthical Responsibilities of Authors\u003c/h2\u003e\u003cp\u003eThe authors affirm that the work presented in this manuscript is original and has not been published elsewhere, in whole or in part, nor is it under consideration by any other journal. All authors have made significant contributions to the research and manuscript preparation and have approved the final version for submission. The authors confirm that the manuscript complies with the highest standards of research integrity, transparency, and accuracy, in accordance with the Brazilian Journal of Chemical Engineering\u0026rsquo;s editorial policies and COPE (Committee on Publication Ethics) guidelines.Any materials, data, and methods used in the research are accurately described and made available for verification and reproducibility. Proper credit has been given to all sources of information through accurate and complete citation.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e\u003cp\u003eThe authors would like to sincerely appreciate Tertiary Education Trust Fund (TETFUND) for funding this research with a Grant Number 2023/VOL.11 TETF/DR\u0026amp;D/POLY/AUCHI/IBR\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVieira F, Santana H, Jesus M, Santos J, Pires P, Vaz-Velho M, Silva D, Ruzene D (2024) Coconut Waste: Discovering Sustainable Approaches to Advance a Circular Economy. 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J Environ Health Sci Eng 19:1743\u0026ndash;1751. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40201-021-00729-3\u003c/span\u003e\u003cspan address=\"10.1007/s40201-021-00729-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anaerobic co-digestion, palm mill process residues, substrate characterization; kinetic modeling, zeolite immobilization, biogas yield","lastPublishedDoi":"10.21203/rs.3.rs-7567816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7567816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the enhancement of biogas production from palm oil mill residues through microbial immobilization on zeolite during anaerobic co-digestion. SEM/EDX analysis showed that fresh sludge contained approximately 45.1 wt% organic carbon and 1.25 wt% calcium, while spent sludge demonstrated increased porosity and biofilm formation, indicating successful microbial colonization on zeolite surfaces. FTIR analysis revealed significant degradation of organic functional groups in substrates such as palm oil mill effluent (POME), palm kernel shell (PKS), fibrous biomass (FBK), mesocarp fiber (MF), and cow dung, confirming effective substrate breakdown. GC-MS characterization of biogas identified methane concentrations reaching 65%, along with minor volatile organic compounds, demonstrating efficient methanogenesis. Zeta potential measurements indicated values ranging from –15 mV to +5 mV, facilitating microbial adhesion and biofilm stability. Kinetic modelling using pseudo-first order, pseudo-second order, and Monod models showed that immobilization with 10% zeolite increased the biogas production rate constant (k) from 0.035 to 0.078 day⁻¹, reducing lag phase duration by 30%. Experimental results demonstrated a cumulative biogas yield increase from 210 mL/g volatile solids (VS) without zeolite to 455 mL/g VS with zeolite, more than doubling production. These findings suggest that zeolite-supported microbial immobilization enhances substrate biodegradability, stabilizes operational conditions, and mitigates inhibitory effects, offering a scalable and efficient strategy for renewable bioenergy generation from palm oil residues.\u003c/p\u003e","manuscriptTitle":"Boosting Biogas Yield from Palm Oil Residues through Microbial Immobilization and Kinetic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 18:49:32","doi":"10.21203/rs.3.rs-7567816/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0891c273-55a5-4cfb-aec6-2effb635a2f1","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T14:14:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 18:49:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7567816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7567816","identity":"rs-7567816","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}