Optimization and life-cycle assessment of biochar production through microwave–assisted pyrolysis of industrial hemp hurd

Optimization and life-cycle assessment of biochar production through microwave–assisted pyrolysis of industrial hemp hurd | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimization and life-cycle assessment of biochar production through microwave–assisted pyrolysis of industrial hemp hurd Nived S Menon, Venkat Srinadh Rejeti, Remya Neelancherry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8918479/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 Industrial hemp hurds (HH) are the biomass residues following bast separation from inner core of hemp plant for fibre extraction. Current research emphasized on microwave-assisted pyrolysis (MAP) of HH for the optimization of hemp biochar (HB) production, its characterization and cradle to gate life cycle analysis (LCA) to explore its application as fossil fuels alternate. Utilizing central composite design (CCD), MAP was optimized for two operational parameters: microwave power (P) and pyrolysis time (T) with HB yield and higher heating value (HHV) as output parameters. The highest HB yield of 75.3% was obtained at P and T of 600 W and 10 min respectively with model Eq. 325.1 -0.37P -12.17T + 0.004PT + 1.3×10-4P 2 +0.18T 2 , validated with an average error of 4.8%. The maximum HHV of 29.5 MJ/kg prevailed at 1000 W and 25 min with model Eq. 71.91 -0.18P -0.11T + 1.3×10-4P 2 and an average error of 5.2%. The negative greenhouse gas (GHG) emission of -10.06 t CO 2 e/yr suggested the sustainability of MAP for HB production through carbon capture. The life cycle assessment (LCA) using ReCiPe Midpoint (H) indicated that the HB production process significantly impacts several environmental categories, with the highest impact being on global warming potential, at 167.3 kg CO 2 eq. It is followed by human non-carcinogenic toxicity, at 3199.9 kg 1, 4-DCB, and terrestrial ecotoxicity, at 2380.1 kg 1, 4-DCB. The sensitivity analysis evaluated the environmental impacts derived from four distinct impact assessment methodologies, facilitating the identification of overarching trends in environmental impacts. Novelty statement The current study employed microwave-assisted pyrolysis (MAP) technique for biochar production from industrial hemp hurds. Biochar production was optimized using response surface methodology for maximizing heating value and yield emphasizing its applicability as a potential and sustainable fuel alternative. Detailed characterization studies like scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, Energy-dispersive X-ray spectroscopy, BET surface area analysis, etc., were performed to examine the quality of the obtained biochar. Apart from biochar production through advanced technology like MAP, this study also focused on the environmental impact of such process through a thorough investigation on negative emission technology potential and life-cycle assessment using cradle-to-gate approach. This research highlighted the negative greenhouse gas (GHG) emission of -10.06 t CO 2 e/yr suggesting the sustainability of MAP for biochar production through carbon capture. Therefore, this research presents novel contributions to the field of waste valorization and renewable and clean energy production through biofuel production from MAP of waste biomass. Hemp hurds Microwave-assisted pyrolysis Negative emission technology Fuel replacement Biochar optimization Life-cycle assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Industrial hemp hurd biochar (HB) prepared using microwave-assisted pyrolysis (MAP) • 1000 W and 7 min residence time were optimum conditions for 37.6% HB yield • Negative GHG emission of -10.1 t CO e/yr was determined for HB production • 24.9 MJ/kg heating value and ~ 90% fixed carbon suggested better fuel applicability • LCA of HB production assessed global warming potential with 167.3 kg CO eq. 1. Introduction Waste biomass has plentiful reserves and is considered as a carbon-neutral feedstock, capable of substituting conventional fuels. Biomass can also be converted into various products using the pyrolysis process, which is a succession of endothermic (heating and dehydration) and exothermic (volatile emission) reactions which degrade biomass by heating in a dearth of oxygen [ 1 ]. Contemporary requirements demand energy-efficient pyrolysis techniques that produce better yields and superior biofuels. [ 2 ]. Microwave-assisted pyrolysis (MAP) is an excellent thermochemical technique to obtain biochar, bio-oil, and syngas, owing to its advantages over conventional processes, which include uniform heat distribution, minimized hazardous chemicals in bio-oil, simplicity of control, and expense minimization. [ 3 ], [ 4 ]. Current technique has been employed efficiently on various feedstock like agricultural residue [ 5 ], algae [ 6 ], sewage sludge [ 7 ], and different feedstocks [ 8 ], [ 9 ] to generate biofuels. Biochar is an important product that is produced through biomass pyrolysis attenuating multi-faceted applications like fuel replacement, soil enhancer, carbon storage and as precursor for the synthesis of functional carbon-based compounds through subsequent modifications such as activation [ 10 ]. The chemical and energy characteristics of the biochar plays a pivotal role in determining their applicability for various purposes. The fixed carbon content and heating value are considered to be essential for energy application of biochar [ 11 ]. Similarly the biochar with greater surface area and porosity were considered for soil conditioning [ 12 ], and novel carbon-based compounds development. Such properties of biochar are significantly influenced by the feedstock characteristics and MAP operating conditions. MW power and time are observed to be the most influential parameters by many researches for converting variety of feedstock into biochar and biofuels [ 13 ]. Therefore, it is necessary to analyse the effect of these operating parameters in optimizing the production of biochar in required quantity and quality. Response surface methodology (RSM) technique is often used by the scientific community to statistically evaluate the effect of input parameters on the desired output. Moreover, the capability of CCD to study individual and combined effect of multi-factor input components with minimal experimental runs on the output reduces the time, cost and energy requirements. The experimental input data are subjected to an ANOVA analysis, which yields output data that is further processed to create the necessary mathematical models for determination of functional correlation between input and output variables [ 14 ]. Cannabis sativa L. , known as industrial hemp, is being cultivated since decades for its fibre, oil, medical properties, and bioenergy potential. Industrial hemp, which is sometimes mistaken with Cannabis sativa L., does not contain enough THC to be intoxicated. Typically, it has 75% woody core (shives or hurds), 20% fibre, and 5% dust. Hemp hurd (HH) is the woody core of the stalk having long and short fibres. Such HH can be used to make fabric, paper/pulp, acoustic barriers, hybrid material boards etc. [ 15 ]. Under the influence of climate change, this crop may serve as an effective means for carbon sequestration, attributed to its swift biomass production and substantial carbon sink ability [ 16 ]. Given the presence of lignocellulosic content in HH [ 17 ], bioenergy related synthesis [ 18 ] and biochar production through conventional pyrolysis [ 19 ] were researched recently. The drawbacks from biochar production through conventional pyrolysis like longer retention time, greater energy input requirement, etc. can be riposted through MAP technology for HH conversion, which is seldom explored. CO 2 emissions constitute a major portion of global greenhouse gas emissions, and accounts for three-quarters of the overall greenhouse gas (GHG) emissions. A significant and immediate reduction in GHG emissions as well as adjustments in resource management are required in order to meet the climate targets that were established by the Paris Agreement. There are a variety of technology development and consumption shift that could be considered as potential pathways that could accomplish these aims [ 20 ]. Within the realm of technology-based solutions, there is a collection of scenarios that takes into consideration the widespread implementation of negative emissions technologies (NETs). Despite the fact that recent assessments have brought to light a dearth of bottom-up and upscaling research, which are essential for the actual development of the sector, discussions over the requirement, viability, and prevalence of NETs in the upcoming technological advances remains evolving [ 21 ]. Biochar is essential among various NETs, including afforestation and soil carbon sequestration, for apprehending atmospheric CO 2 and significantly contributes to achieving NetZero emission targets. Biochar is acknowledged for its capacity to sequester carbon, thereby aiding in climate change mitigation as a result of its high density of recalcitrant carbon. A key approach for evaluating the capacity of biochar to mitigate CO 2 emissions is through carbon crediting. This method functions as a regulatory mechanism for managing global GHG emissions in the context of the Kyoto Protocol. This scheme requires precise documentation of GHG storage and emissions [ 21 ]. The overall reduction of emissions is contingent upon GHG storage, whereas emissions from carbonisation significantly influence the diversity of total emissions. GHG emissions resulting from biochar production show considerable variability, which is affected by factors like feedstock characteristics, temperature, heating rate, and thermal conversion method employed. Woolf et al. predicted the NET potential of 0.27–0.49 Gt CO 2 e/year due to biochar production in terms of indirect air capture [ 22 ]. Similarly, Santos et al. forecasted that biochar production has the potential to mitigate emissions ranging from 1.0 to 1.8 Gt CO 2 annually across different circumstances [ 23 ]. A knowledge gap exists in systematically clarifying the influence of various factors on the diversity of CO 2 emissions in biochar production systems, especially at the life cycle stage. Life cycle assessment (LCA) facilitates the identification of hotspots within a system and encourages the exploration of technological innovations that minimise energy consumption and environmental impact [ 24 ]. LCA framework consists of several processes, such as defining goals and scope, conducting inventory analysis, assessing life cycle impacts, and interpreting results where goal and scope definitions are instrumental in the explanation of the product system, which encompasses system boundaries and functional units [ 25 ]. Typically, the stages of product development are considered while defining system boundaries such as cradle-to-grave, cradle-to-gate, gate-to-gate, or gate-to-grave boundary might be used, depending on the chosen scope. In contrast to the cradle-to-gate method, which typically disregards the usage and end-of-life phases, the cradle-to-grave approach takes into account the full product or process life cycle. However gate-to-gate solely considers production that takes place on the premises [ 26 ]. Many researches worked on LCA of biochar production from different waste feedstocks like forest residues [ 27 ], manure [ 28 ], agro-residues [ 29 ], woody biomass [ 30 ] etc. for different applications like soil systems [ 29 ], bioasphalt [ 31 ], bioenergy, and so on from a circular economy perspective [ 32 ]. However, no studies have been identified regarding the life cycle assessment of the MAP of HH. The present work emphasized on the optimization of operating parameters for the production of biochar, using RSM for maximizing yield and higher heating value (HHV) from MAP of industrial hemp hurds (HH). Further, the potential environmental impacts of such system throughout its life cycle alongside the NET potential were analysed from a cradle-to-gate approach of life cycle assessment. 2. Materials and Methods 2.1. Materials and chemicals: The HH obtained from a farm in Pauri, Uttarakhand, India, and harvested between June and August was air-dried, grinded to size less than 4.75 mm. Silicon carbide (SiC) was used as the microwave susceptor, procured from Vaishnavi Industrial Needs Pvt Ltd., India. Analytical FT-IR grade KBr (99% pure) was procured from Merck, India. 2.2. Microwave pyrolysis reactor: This study employed a custom-designed microwave (MW) reactor, operated at a frequency of 2.45 GHz with a power control ranging from 200 W to 1000 W (Fig S1 ). A cylindrical quartz vessel with a diameter of 12 cm and a height of 20 cm was used as the reactor where 3 g of HH along with SiC balls as microwave susceptors was used per batch. N 2 gas was initially purged with a flow rate of 0.6 L/min for 10 min to create an oxygen-free environment and later reduced to 0.2 L/min during the MAP process. The obtained HH biochar (HB) was collected and corresponding yield was calculated using the Eq. 1 . $$\:HB\:yield\:\left(Y\:\%\right)=\frac{Weight\:of\:HB\:\:\left(g\right)}{Weight\:of\:HH\:\left(g\right)}\times\:100$$ 1 2.3. Response surface methodology (RSM): RSM is one of the reliable tool for analysing the impact of different parameters on reactions. Central composite design (CCD) was employed in RSM to investigate the relationship between independent and dependent variables for the MAP experiments, as it a cost-effective approach to optimize variables and responses with minimum runs. The energy input to the system and the pyrolysis duration are two most important process parameters of MAP. MW power (P) in the range of 500–1000 W and pyrolysis time (T) in the range of 10–28 min were considered based on the numerous previous trials on the MAP of HH. The Stat-Ease 360 Software (Version 22.0.4) was used to design the experimental runs, and the centre point was repeated five times to assess the repeatability of experiments. CCD generated experimental runs with factorial points (2 k ), axial points (2 k), and replicated center points (n k ), where ‘k’ represents the number of independent parameters. The number of experiments to be conducted (N) was determined using Eq. 2 . $$\:N={2}^{k}+2k+{n}_{k}={2}^{2}+2\left(2\right)+5=13$$ 2 The HB yield (Y %) and higher heating value (HHV) were chosen as the responses of the experimental runs. These variables were analysed using ANOVA, 3D graph and contour plot. Further, statistical analysis was conducted, including the F-value (Fisher variation ratio), p -value (probability), regression coefficient (R 2 ), and adequate precision (AP), to determine the relevance, importance, and suitability of the model used. 2.4. HB characterization: The specific surface area of HB was determined based on the N 2 adsorption-desorption method using a BET surface area analyzer (QUADRASORB SI, Quantachrome Instruments, USA). The microscopic structure was studied using scanning electron microscopy (SEM, MERLIN compact, Carl Zeiss, Germany) equipped with energy-dispersive X-ray spectroscopy (EDS, 51XMX 1004, Oxford Instruments, UK) to provide detailed information on surface elemental composition. X-ray diffraction analysis (XRD, D8 Advance, Bruker, Germany) and Fourier transform infrared spectroscopy (FTIR, Alpha-FTIR, Bruker, Germany) were used to identify the various surface-functional elements and crystalline structure, respectively. Proximate analysis, the moisture content (MC), volatile matter (VM), ash content (AC), and fixed carbon content (FC), were measured as per standard methods [ 33 ]. The elemental composition (C, H, and O) in weight percentage was determined by using Nhuchhen's formula to the proximate analysis data as stated in Eq. 3 – 5 [ 34 ]. Higher heating value (HHV) was determined using a bomb calorimeter (RSB-5, Rajdhani Co. Lim, New Delhi, India). $$\:C=-35.9972+0.7698\times\:VM+1.3269\times\:FC+0.3250\times\:AC$$ 3 $$\:H=55.3678-0.4830\times\:VM-0.5319\times\:FC-0.5600\times\:AC$$ 4 $$\:O=223.6805-1.7226\times\:VM-2.2296\times\:FC-2.2463\times\:AC$$ 5 2.5. Emission estimation from HB Production 2.5.1 Emission factor estimation The estimation of emission factor (EF) are done considering the most likely combinations of biochar production factors such carbonization process, scale, excess energy usage, and feedstock type. Kavindi, Tang, and Sasaki 2025 reported various estimated EFs considering different potential biochar production factors by classifying in to 24 groups considering 1 ton of biochar production as standard functional unit. MAP of HH is a carbonization technique for producing HB along with varied quantities of bio-oil and syngas. Both the volatiles can be additionally bolstered for energy applications in terms of electricity generation or satisfying thermal energy requirement for feedstock pre-drying. Therefore, MAP is considered to be closed carbonization technique with surplus energy utilization facility. The current study falls under the class 17, where the carbonization scale tends to be unspecified for closed carbonization system with surplus energy utilization ability [ 35 ]. 2.5.2 Scenario-specific GHG emission estimation Considering unpopular HH cultivation in India, the average annual HH production (M HH ) is assumed to be 100 t/yr along with the utilization rate of the crop residue (u) of 100% (since HH alone is considered as feedstock) as per [ 35 ]. The specific scenario of HB production (M HB ) was determined using Eq. 6 . $$\:{M}_{HB}={M}_{HH}\times\:{Y}_{HB}\times\:u\times\:m$$ 6 Where, Y HB denotes the HB yield on dry weight of HH basis, and m refers to the MC of HH. The scenario specific GHG reduction (t CO 2 e/yr) is calculated as represented in Eq. 7 . $$\:\:Total\:GHG\:emission=E-S$$ 7 Where, E and S refers to the annual GHG emissions (t CO 2 e/yr) occurred and the annual GHG storage (t CO 2 e/yr) by HB respectively in a particular scenario. E and S were determined using the subsequent Eq. 8 – 9 . $$\:E={M}_{HB}\times\:EF$$ 8 $$\:S={M}_{HB}\times\:{C}_{org}\times\:{C}_{st}\times\:\frac{44}{12}$$ 9 Where, C org , C st refer to organic carbon content of HB (assumed mean value- 0.57), and stable carbon content fraction of HB after 100 years (0.8). The coefficient 44/12 represents the carbon dioxide fraction. The negative value of total GHG emission represents the positive effect of the HB production on environment through carbon sequestration. Conversely, the positive value of total GHG emission denotes the negative impact of HB production on environment. 2.6. Life Cycle Analysis (LCA) methodology: 2.6.1. Goal and scope Cradle-to-gate life cycle analysis (LCA) with 1 ton of HH as functional unit was performed to evaluate the environmental impacts of HB production from HH using MAP to identify the hotspots and potential environmental trade-offs associated and provide recommendations for improvement [ 25 ]. The scope of this LCA includes the following processes as depicted in Fig. 1 . The environmental impact categories assessed were global warming potential, acidification potential, eutrophication potential, ozone depletion potential and photochemical oxidant potential. The data were derived from research articles and databases such as Ecoinvent (Version 3.9), the Indian Life Cycle Inventory Database and Agribalyse database [ 36 ]. The geographical scope of the study was limited to the production of HB from HH in India. Further, the study assumed average conditions for the production process and variations in regional conditions or management practices were not considered. All energy required for the process was obtained from the electricity grid and a uniform composition and energy content of HH was assumed throughout the year. Due to practical and theoretical constraints, certain features like the impacts of equipment manufacturing, MW susceptor production, construction activities and materials used for infrastructure development, the participation of human resources, and the overhead generated by lighting and maintenance of the facilities were excluded from the study, either because they did not affect the results or were standardized. 2.6.2. Impact assessment methodology The environmental impacts of HB production were assessed using the openLCA 1.11.0 software, a professional tool for modelling and analysing the life cycle of products, processes, and services transparently while following ISO 14040 recommendations. Four different impact assessment methodologies were used to provide a comprehensive understanding of the environmental impacts of MAP. The EDIP method was used to evaluate the environmental impacts of HB production across its entire life cycle, from resource extraction to disposal, considering a broad range of environmental impacts such as climate change, acidification, and eutrophication. The CML 2001 method, on the other hand, focused on the potential for HB production to cause damage to human health and ecosystems through the release of pollutants assessing impacts such as ozone depletion, photochemical oxidant formation, and toxic emissions to air, water, and soil [ 37 ]. The ReCiPe Midpoint (H) and ILCD 2.0 2018 methods aimed to provide a comprehensive assessment of environmental impacts of HB across a range of impact categories including global warming, ozone depletion, photochemical oxidation, acidification, eutrophication, terrestrial ecotoxicity, human toxicity and water depletion [ 38 ], [ 39 ]. The impact assessment method was limited to quantitative modelling of early stages in the cause-effect chain to reduce uncertainties. Sensitivity analysis of the LCA was carried out using the LCA inventory and assessing them using above-mentioned impact assessment methodologies. The analysis considers four impact categories: global warming potential, ozone depletion, photochemical oxidation, and acidification. 2.6.3. Life cycle inventory (LCI) The cultivation of hemp plants, its transportation from farm to factory, decortication, size reduction, MAP for HB production and pelletization were the unit processes considered as different phases of HB production. Certain assumptions were considered in this study as follows. Around 2 kg of hemp seeds were considered as input and nitrogen, phosphorus, and potassium-rich fertilizers were added in amounts of 2.5 kg, 1.25 kg, and 1.75 kg, respectively for plant growth [ 40 ]. A sprinkler system was assumed for irrigating the land of 0.525 m 2 area with 40 cm of water depth for a growing season, obtaining 1350 kg of hemp plant, which is transported to factory (15 km) using 2-axle trailer with carrying capacity of 3 t. Micro-Decorticator (MD100, HurdMaster) with a capacity to operate at 50 kg/h, having a power rating of 1.5 kW, was considered for decortication, i.e., to separate the fibre from the hurds. After decortication, 1000 kg of HH were obtained with size < 4.75 mm. A screw conveyor (LS250, Henan Green Eco-equipment) was considered to feed the dried ground feedstock into the pyrolysis reactor with the screw diameter, pitch, and rotational speed were 250 mm, 250 mm, and 50 rpm, respectively, delivering feedstock at a rate of 0.3327 m 3 /h and consuming 1.327 kW power, and has a capacity of 4000 kg/h. The MW reactor (Twin Engineers, Haryana) was operated at the optimized parameters of 1000 W MW power for 7 min. The operating parameters were decided based on the results obtained in this study. A pellet-making machine manufactured by Vinspire Agrotech was used for pelletization which operated at a power of 3728.5 W (5hp) and yielded an output of 100 kg/hr. 3. Results and Discussion 3.1. Central composite design The results from 13 experimental runs were displayed in Table 1 . HB yield and HHV ranged from 9.7% to 75.3% and 12.5 MJ/kg to 29.5 MJ/kg respectively. The lowest HB yield (9.7%) and highest HHV (29.5 MJ/kg) was obtained at 1000 W and 25 min with an energy consumption of 0.42 kWh. While the highest HB yield (75.3%) and lowest HHV (12.5 MJ/kg) was obtained at 600 W and 10 min with an energy consumption of 0.10 kWh. Table 1 HB yield, and HHV obtained at different process parameters of MAP of HH Run P (W) T (min) Y (%) HHV (MJ/kg) Energy consumed (kWh) 1 800 (0) a 17.5 (0) 20.3 15.7 0.23 2 500 (-α) 17.5 (0) 55.8 14.1 0.15 3 800 (0) 17.5 (0) 20.7 14.4 0.23 4 800 (0) 28 (+α) 16.6 15.5 0.37 5 600 (-1) 25 (+ 1) 29.7 13.7 0.25 6 600 (-1) 10 (-1) 75.3 12.5 0.10 7 1000 (+α) 10 (-1) 28.7 25.4 0.16 8 1000 (+α) 17.5 (0) 10.4 28.8 0.29 9 800 (0) 7 (-α) 64.4 12.6 0.09 10 800 (0) 17.5 (0) 20.5 14.2 0.23 11 1000 (+α) 25 (+ 1) 9.7 29.5 0.42 12 800 (0) 17.5 (0) 20.4 14.5 0.23 13 800 (0) 17.5 (0) 19.2 14.6 0.23 a : Coded level in parentheses 3.1.1. Effect of process parameters on HB yield The highest HB yield of 75.3% was obtained at a MW power of 600 W and a residence time of 10 min. Such operating conditions resulted in minimal thermal degradation of the feedstock, allowing most of the volatile matter (VM) to remain in the HB. In contrast, the least HB yield (9.7%) was observed at 1000 W and 25 min operating conditions. The progressive elongation of the reaction time results in a reduction in HB yield attributing to the secondary processes, namely gasification, volatilization and thermal cracking. Moreover, an intermediate combination of these factors resulted in moderate HB yield. The interactive effect of MW power and residence time is depicted by the 3-D surface plot (Fig. 2 (a)) highlighting the importance of controlling these process parameters to optimize HB production. It can be inferred that the variation in MW power at lower residence time (5–15 min) reduced the HB yield drastically, as supported by the literature [ 41 ]. Whereas at higher residence time (15–28 min), little variation in HB yield was observed across different MW powers (500 W – 1000 W). It can be attributed to possible promotion of the breakage of long carbon chains at higher residence times resulting in higher volatile content liberation leading to reduced HB yield. 3.1.2. Effect of process parameters on HHV The 3-D surface plot of HHV is shown in Fig. 2 (b). The highest heating value (HHV) of 29.5 MJ/kg was obtained at a MW power of 1000 W and pyrolysis time of 25 min. MW power input of 1000 W and residence time of 25 min resulted in nearly complete pyrolysis of the feedstock, producing HB with greater HHV. Higher MW power (1000 W) and moderate residence time (25 min) enabled more extensive polymers to crack into smaller aromatic rings and carbon-carbon bonds, increasing HHV. A nearly complete breakdown of feedstock rendered HB richer in carbonaceous content with higher heating value. In contrast, the least HHV (12.5 MJ/kg) was observed at 600 W and 10 min operating conditions. MW power of 800 W applied for a shorter duration (7 min) resulted in insufficient feedstock cracking, producing HB with lower heating value (12.6 MJ/kg) due to lower carbon content. The interaction between MW power (800–1000 W) and residence time (7–25 min) significantly influenced the HHV of HB. At 1000 W, increasing residence time from 10 to 25 min increased the HHV from 25.4 MJ/kg to 29.5 MJ/kg, highlighting the dominance of residence time. On the other hand, at residence time of 17.5 min, increasing power from 800 W to 1000 W raised the HHV from 14.2 MJ/kg to 28.8 MJ/kg, demonstrating the more significant impact of a higher MW power. An intermediate combination of factors (1000 W and 17.5 min) yielded HB with an HHV of 28.8 MJ/kg. 3.1.3. Model development and corresponding statistical analysis The experimental results were fitted to four regression models: linear model, 2-factor interaction model (2FI), quadratic and cubic model using design expert software, represented in Table S1 . The models were analysed based on their sequential p-value, lack of fit p-value, and adjusted and predicted R 2 value. Based on the model statistics for HB yield, the quadratic model is suggested as it has a significantly lower p-value of < 0.0001 depicted in Table 3 . Furthermore, the model F-value of 1396.4 implies that the model is significant implying only a 0.01% chance that an F-value this large could occur due to noise. The model tends to be more reliable with the observed standard deviation (SD) and mean value of 0.9 and 30.1, implying consistent predictions with less variability, offering acceptable baseline for individual forecasts. The coefficient of variation (CV) of 2.9%, represents the normalized measure of the dispersion of the dataset. Predicted R 2 was 0.994, while adjusted R 2 was 0.998, with variation less than 0.2, suggesting a high correlation. The P, T, PT, P 2 and T 2 were the significant model terms with p-values 0.05), which is desirable for the model to fit. Adequate Precision measures the signal-to-noise ratio of 110.8 > 4 indicated an adequate signal. The reported equation according to the quadratic model by considering significant terms is mentioned as Eq. 10 . $$\:Y\:\left(\%\right)=325.10-0.37P-12.17T+0.004PT+1.30\times\:{10}^{-4}{P}^{2}+0.18\times\:{T}^{2}$$ 10 Based on the model statistics for HHV, the quadratic model is suggested as it has significantly lower p-value of < 0.0001. The F-value of the model is 110.9, and its p-value is < 0.0001, indicating that the model is significant. It can be observed from the Table 2 that the F-value of MW power stands at 218.1 and 189.7 for individual (P) and quadratic term (P 2 ) respectively, with p-value 0.05) relative to the pure error. There is a 10.3% chance that a lack of fit F-value this large could occur due to noise. The adjusted R² for the model is 0.971, while the predicted R 2 for the model is 0.924, indicating a better correlation. Adequate Precision measures the signal-to-noise ratio of 29.3 (> 4) indicates an adequate signal. The reported equation according to the quadratic model by considering significant terms is reported as Eq. 11 . $$\:\:HHV\:\left(MJ/kg\right)=71.91-0.18P-0.11T+1.30\times\:{10}^{-4}{P}^{2}$$ 11 Table 2 ANOVA of developed model for HB yield and HHV and model terms Terms HB yield HHV F- value p- value F- value p- value Model 1396.4 < 0.0001 110.9 < 0.0001 P-MW power 2534.2 < 0.0001 218.1 < 0.0001 T-Reaction time 1579.6 < 0.0001 6.4 0.0398 PT 230.7 < 0.0001 2.6 0.1509 P 2 189.7 < 0.0001 189.7 < 0.0001 T 2 944.1 < 0.0001 0.33 0.5845 Lack of Fit 3.8 0.1140 4.1 0.1031 SD 0.9 0.9 Mean 30.1 17.3 C.V. % 2.9 5.2 R 2 0.999 0.980 Adjusted R 2 0.998 0.971 Predicted R 2 0.994 0.924 Adequate Precision 110.8 29.3 3.1.4. Model Validation The HB yield and HHV model was validated through three sets of experiments. In the first experiment, the dependent parameters were taken to be maximum and the independent values to be within the experimental range. The software offered one hundred solutions under the described circumstances from which single solution was chosen. Since the MW power input to the reactor could only be in multiples of one hundred, the solution was predicated on the highest desire and MW power being a multiple of one hundred. Additionally, two sets of runs within the range were undertaken at random. The experimental and anticipated values for each of the three runs are shown in Table S2. The average error of 4.8%, and maximum error of 5.5% was observed between the experimental and predicted HB yield. The maximum error of 6.6% and average error of 5.2% was observed in case of HHV, suggesting the adequacy of the model with minimal errors. 3.1.5. Result optimization Process optimization was carried out to find the optimum value of independent parameters for the maximum HHV of resulting HB by targeting the HB yield to be 42.5%. This was done using the response surface models and the desirability function. The objective of maximizing HB yield and HHV guided the determination of ideal factor levels. The yield and HHV ranges considered for optimization were those achieved across the experimental runs, i.e., 9.7–75.3% and 12.5–29.5 MJ/kg, respectively. Table S3 shows the optimized solutions where the factor combination of 1000 W power and 30 mins residence time has the lowest desirability of 0.31. The factor combination of 1000 W power and 7 mins residence time provided the highest desirability of 0.52 and could be the optimized condition. Such operating conditions predicted the HB yield of 37.6% and maximum HHV of 24.9 MJ/kg. The obtained HB was analysed for further characterization. 3.2. Characterization of HH and HB: 3.2.1. Chemical characteristics The chemical characteristics of the HH and HB are listed in Table 3 , which includes proximate, ultimate analysis, and HHV. Table 3 Chemical properties of HH and HB Parameter (wt %) HH HB MC 9.85 ± 0.19 1.21 ± 0.57 VM 79.20 ± 1.46 5.19 ± 1.63 AC 17.00 ± 0.08 3.61 ± 1.52 FC 4.80 ± 0.068 89.96 ± 0.22 C 37.86 88.55 H 5.20 2.97 O 34.68 6.01 H/C 1.65 0.40 O/C 0.69 0.05 HHV (MJ/kg) 11.08 24.9 The HH characteristics were within the range mentioned in the literature [ 42 ]. The MC of HB was 1.21%, enabling proper storage conditions. Conversion of HH to HB lowered VM and AC by 93.4%, and 78.7% respectively with 18.7 times increase in FC due to significant release of volatiles during MAP. The H/C and O/C ratios of HB were determined to be 0.40 and 0.05 respectively, suggesting its higher aromaticity and thermal stability [ 25 ]. Furthermore, the HHV of HB (24.9 MJ/kg) increased by 124.73% compared to the corresponding HH, suggesting its applicability as a fuel replacement, as the HHV of Indian coals lie in the range of 20–26 MJ/kg [ 43 ]. 3.2.2. Thermogravimetric analysis The thermogravimetric analysis (TGA) and derivative thermogravimetry (DTG) curves for HH biomass showed 10% weight loss at an initial stage from 30 ℃ up to approximately 300°C (Fig. 3 ), due to moisture loss and volatilization of low molecular weight components. A more rapid weight loss of around 50–60% occurred from 300°C to 400°C, with maximum rate of weight loss at 370 ± 10°C (0.5%/°C). This stage likely corresponds to the thermal decomposition of hemicellulose, cellulose, and lignin components of HH. The broad, asymmetrical shape of DTG peak suggested the HH decomposition occurred via multiple overlapping reactions. Beyond 400°C, a shoulder and slower continued weight loss was observed, indicating the residual decomposition of char and ash forming components up to 500°C. The total weight loss over the analysed temperature range was 80–85%, leaving inorganic char residue of 10–12% by weight. No exothermic decomposition reactions were detected based on the DTG curve, suggesting the HH decomposes via endothermic reactions in a relatively controlled, and non-explosive manner. 3.2.3. Physical characteristics of HB The SEM image (Fig. 4 (a)) showed that the HB fibres appear primarily cylindrical, with an average diameter of 20–30 µm and length of 100–200 µm. It was observed to be moderately porous, with small pores (5–10 µm) visible between fibre bundles and along the length of fibres, possibly due to gas exchange and insulation ability. The fibre surfaces also showed fine transverse grooves and pits, which increases the surface area. These surface characterisitcs could regulate aspects like adsorption, dyeability or other surface-related attributes [ 44 ]. From the EDS data as observed in Fig. 4 (b), the main elemental composition of HB appeared to be carbon (82.6 wt. %), oxygen (16.8 wt. %) and calcium (0.5 wt. %), with C and O peaks dominating the spectra, similar to ultimate analysis results, as mentioned in Table 3 . Figure 4 (c) depicted a linear segment at low pressures, a plateau at high pressures, and an inflexion point in between, indicating the occurrence of mesopores in the material. BET study determined a specific surface area of 19.15 m²/g which implies significant surface availability for adsorption, and potentially useful for pollutant separation, catalysis [ 45 ], and monitoring technologies [ 44 ]. The obtained surface area is within the range mentioned in the literature (18.23–25.38 m²/g) for HH biochar produced through conventional pyrolysis [ 46 ]. The XRD diffractogram (Fig. 4 (d)) displayed prominent peak at θ = 7.6° (d-spacing = 5.82 Å) associated with the (1 0 1) lattice plane reflection of cellulose I, which is crystallised in a triclinic unit cell according to JCPDS number 50–2241. Minor impurity phases of quartz and calcite are also detected based on additional peaks at 2θ = 10.9°, 12.6°, 20.9° and 30.0° which may index to the (1 1 0) plane of aluminium, (0 0 4) plane of calcite, (0 2 0) plane of quartz and composite of quartz (JCPDS number 46–1045) and calcite (JCPDS number 5–586) respectively. The average crystallite size of 6.09 nm, calculated using the Scherrer equation for the (1 0 1) cellulose I peak, assuming a shape factor of 0.9 and Cu Kα wavelength of 0.15406 nm which indicated the presence of relatively small cellulose I crystallites in the material. The FTIR spectrum (Fig. 4 (e)) of HB exhibited an intense broad absorption band in the 3000–3500 cm − 1 region, indicating O–H stretching vibrations attributable to hydroxyl functional groups. The sharp peak in the 3500–3750 cm − 1 region could indicate the presence of –OH stretching [ 47 ]. Multiple peaks between 2750 cm − 1 and 3250 cm − 1 signify asymmetric and symmetric C–H stretching modes of aliphatic alkanes. Prominent peaks between 1500 cm − 1 and 1750 cm − 1 depicts C = C double bond stretching vibrations of alkenes. The position and intensity of the absorption bands suggested that HB contains a mixture of alcohols, aldehydes or carboxylic acids, alkenes, and esters related surface functional groups. A strong absorption band at 1729 cm − 1 corresponds to the carbonyl C = O stretching vibration, indicating the presence of aldehydes, ketones, carboxylic acids, or esters. Minor peaks observed between 1400 cm − 1 and 1500 cm − 1 are assignable to N–H bending vibrations of amines and C–H in-plane bending modes. A small peak at 1200 cm − 1 can be attributed to the C–O stretching of ethers or esters [ 48 ]. Overall, the FTIR spectrum provided evidence for the presence of hydroxyl, alkane, alkene, carbonyl, amino and ether functional groups on the HB surface. 3.3. Estimation of negative emission during HB production MAP of HH is a carbonization technique for producing HB along with varied quantities of bio-oil and syngas. Both the volatiles can be additionally bolstered for energy applications in terms of electricity generation or satisfying thermal energy requirement for feedstock pre-drying. Therefore, MAP is considered as closed carbonization technique with surplus energy utilization facility. EF was estimated for HB production using MAP based on two scenarios, namely (i) without surplus energy utilization and (ii) with surplus energy utilization. The surplus energy utilization refers to the additional energy sources from the byproducts obtained from MAP of HH (biooil and syngas). The EF in both the cases were determined to be 0.10 t CO 2 e/t HB (EF group 6), and − 0.78 t CO 2 e/t HB (EF group 17), derived from unspecified carbonization scale of wood residues suggested in the works of [ 35 ], detailed in Table S4. HB yield on dry weight of HH basis was calculated as cir. 41.7%. As per Eq. 6 the specific scenario annual HB production of 4.11 t/yr was estimated, considering 100 t of HH production annually, leading to the annual GHG storage of 6.86 t CO 2 e/yr. However, the GHG emissions of 0.41 t CO 2 e/yr, and − 3.20 t CO 2 e/yr were determined in case of without surplus and with surplus energy utilization scenarios respectively. Overall, the total GHG emissions of -6.45 t CO 2 e/yr, and − 10.06 t CO 2 e/yr were estimated in both the scenarios. The negative GHG emission implies the sustainable applicability of the MAP for conversion of HH into HB. 3.4. Life cycle impact assessment Table S5 comprehensively overviews the environmental impact of producing HB based on the ReCiPe Midpoint (H) impact assessment method. The results indicated that the HB production significantly impacts several environmental categories, with the highest impact being on global warming potential, at 167.35 kg CO 2 eq., followed by human non-carcinogenic toxicity, at 3199.92 kg 1, 4-DCB, and terrestrial ecotoxicity at 2380.14 kg 1, 4-DCB. These results suggested that efforts to mitigate the environmental impact of the production process should focus on reducing greenhouse gas emissions and minimizing the application of chemicals that may negatively affect human health and ecosystems. Table S6 provides information on the emissions associated with different stages of the HB production. It was observed that, hemp farming has a negative impact on global warming potential, with a value of -2.94 kg CO 2 eq., indicating that hemp plants absorb carbon dioxide during their growth, which helps mitigate the effects of greenhouse gas emissions. However, hemp farming has little impact on photochemical oxidant and acidification, with values of 1.11*10 − 3 kg ethylene eq. and 9.13*10 − 3 kg SO 2 eq., respectively with no impact on ozone depletion or eutrophication. The harvesting stage significantly impacts all environmental factors, with the highest impact on acidification, at 4.50 kg SO 2 eq., which is likely due to the use of heavy machinery and associated emissions. The transportation stage also significantly impacts global warming potential, with a value of 47.83 kg CO 2 eq., as well as a small impact on photochemical oxidant and eutrophication. HB production through MAP has no impact on global warming potential or ozone depletion, but it does significantly impact photochemical oxidant, with a value of 1.99 kg ethylene eq., which may be linked to the emission of non-condensable gases during MAP. Other allied activities significantly impact all environmental factors, with the highest impact on photochemical oxidant, at 5.32*10 − 2 kg ethylene eq. 3.5. Sensitivity analysis: The sensitivity analysis also shows that the choice of impact assessment method can affect the results of the LCA. Table S7 details the environmental impacts in the different stages of HB production and compares the results against the four impact assessment methodologies to gauge the robustness of the LCA. The results indicate that the impact of different stages of hemp farming varies significantly depending on the impact category being considered. For instance, the analysis shows that the global warming potential is primarily driven by harvesting, while the impact of ozone depletion is primarily driven by the allied activities. 3.5.1. Global Warming: The GWP results of the different methods (depicted in Fig. 5 (a)) show relatively small variations for hemp farming, harvest, and transportation stages, indicating a consistent impact across all methods. All methods estimated a very small environmental impact for the electricity use stage, with values ranging from 0.08 to 0.1 kg CO 2 eq., since the electricity consumed in the process was assumed to be generated from renewable sources or have low carbon emissions. On the other hand, the HB production stage shows significant differences in the GWP impact estimates between the methods. The EDIP method estimates the highest impact with a value of 49.58 kg CO 2 eq., while the other methods, CML 2001, ReCiPe Midpoint (H), and ILCD 2.0 2018, estimate a value of 0 kg CO 2 eq. Due to incomplete combustion and volatile organic compound emissions, the EDIP method assumes that HB production releases many greenhouse gases, primarily CH 4 and N 2 O. However, the other methods do not consider these emissions and assume that the HB production process is carbon-neutral or has a low carbon impact. 3.5.2. Ozone depletion: The results (Fig. 5 (b)) for ozone depletion potential were generally low for all four impact assessment methods. The CML 2001 and ReCiPe Midpoint (H) methods gave identical results (~ 1.3*10 − 5 kg CFC-11 eq.), while ILCD 2.0 2018 method gave slightly higher results. Such variation can be attributed to differences in the underlying data and modelling assumptions used by each method. These results aligns with previous studies [ 17 ] showing that hemp cultivation has a low environmental impact compared to other crops due to its low requirements for fertilizers and pesticides. 3.5.3. Photochemical oxidation: According to the results (Fig. 5 (c)), the photochemical oxidant impact from hemp farming, harvesting, allied and electricity production was relatively small across all methods. However, HB production and transportation had a higher impact in this category due to potential release of pollutants, such as volatile organic compounds and nitrogen oxides, react with sunlight and oxygen in the atmosphere to form photochemical smog. The CML 2001 method indicated that HB produced a photochemical oxidant impact of 2.84 kg ethylene-eq. Similarly, the ILCD 2.0 2018 method estimated a photochemical oxidant impact of 1.99 kg ethylene-eq. for HB production. The ReCiPe Midpoint (H) method also showed a higher impact on HB production than other processes, with a value of 1.99 kg ethylene-eq. 3.5.4. Acidification: The results of the sensitivity analysis (Fig. 5 (d)) showed that the acidification potential of the HB production system varied depending on the impact assessment method used. The EDIP method had the lowest acidification potential, while the ILCD 2.0 2018 method had the highest in both harvesting (5.57 kg SO 2 eq.), transportation (0.48 kg SO 2 eq.) and allied stages (0.24 kg SO 2 eq.) of HB production. The differences in the acidification potential between the methods can be attributed to several factors, including differences in the characterization factors used to calculate the potential, the inclusion or exclusion of specific emissions, and the modelling assumptions used in each method. The ILCD 2.0 2018 method included an uncertainty factor to account for the potential impacts of acidification on freshwater and terrestrial ecosystems, which may result in a higher acidification potential than the other methods. 4. Conclusion This study focused on the optimization of HB production from MAP of HH both in terms of yield and higher heating value (HHV). MW power of 1000 W and pyrolysis time of 7 min was found to be optimum solution yielding around 35% of HB with HHV of ~ 25 MJ/kg. The HHV of HB improved by around 125% compared to HH (11 MJ/kg). HB appeared moderately porous, with tiny pores (5–10 µm) evident between fibre bundles and along their length. Additionally, HB showcased higher fixed carbon (90%), lower moisture content (1.2%) and volatile matter (5.2%), suggesting its applicability as a sustainable fuel replacement in energy sector. The negative GHG emission of -10.06 t CO 2 e/yr suggested the sustainability of MAP for HB production through carbon capture. Further, this study assessed the environmental impact that the MAP treatment of HH has during its whole life cycle through the application of openLCA software (using ReCiPe midpoint method). The highest impact on global warming potential, at 167.35 kg CO 2 eq., followed by human non-carcinogenic toxicity, at 3199.92 kg 1, 4-DCB, and terrestrial ecotoxicity, at 2380.14 kg 1,4-DCB was observed, suggesting its impact on environment. Abbreviations AC Ash content AP Adequate precision CCD Closed composite design C org organic carbon content of HB C st stable carbon content fraction of HB after 100 years CV Coefficient of variation DCB Dichlorobenzene E Annual GHG emissions occurred EF Emission factor FC Fixed carbon FT-IR Fourier transform infrared spectroscopy GHG Greenhouse gas HB Hemp hurd biochar HH Industrial hemp hurds HHV Higher heating value LCA Life-cycle Assessment MAP Microwave-assisted Pyrolysis MC Moisture content MHB Scenario specific annual average HB production MHH Annual average hemp hurd production MW Microwave NET Negative emission technology RSM Response surface methodology S Annual GHG storage SD Standard deviation SEM Scanning electron microscope SiC Silicon carbide TGA Thermogravimetric analysis u residue utilization rate VM Volatile matter XRD X-ray diffraction YHB HB yield in terms of dry weight of HH Declarations Authors Contribution Nived S Menon: Investigation, Data analysis; Formal analysis; Writing-Original draft preparation; Rejeti Venkat Srinadh : Data curation, Methodology, Data analysis and validation, Writing-Original draft preparation; Neelancherry Remya : Supervision, Conceptualization, Funding acquisition, Writing-review and editing Acknowledgements This research was supported by Indian Institute of Technology Bhubaneswar, India Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8918479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594131328,"identity":"99a4c146-6a1a-4c64-9efb-d80336b5f1a3","order_by":0,"name":"Nived S Menon","email":"","orcid":"","institution":"Indian Institute of Technology Bhubaneswar","correspondingAuthor":false,"prefix":"","firstName":"Nived","middleName":"S","lastName":"Menon","suffix":""},{"id":594131329,"identity":"dd150cd9-004e-494b-bfc3-8fa627d76b46","order_by":1,"name":"Venkat Srinadh Rejeti","email":"","orcid":"","institution":"Indian Institute of Technology Bhubaneswar","correspondingAuthor":false,"prefix":"","firstName":"Venkat","middleName":"Srinadh","lastName":"Rejeti","suffix":""},{"id":594131330,"identity":"a640f7c9-3c63-4930-9cd5-90d4ad2e752e","order_by":2,"name":"Remya Neelancherry","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYNCCCgkeBiACAzbitJyR4OEhTQtjGwMDXAtBYN7enSbxc56FjL107+EPDDV2DHzSDfi1yJw5u02ydxvQYTLn0iQYjiUzsMkcwK9FQiJ3mwQvSItEjhnQIwcY2CQSCGiRf7tN8u8csBbjDwz/iNECtEKatwGsxUCCsY0YLTy5m61ljgG13AH6JbEvmYewFvazG2++qamzZ58NDLEP3+zk5GcQ0AIELBJwJlAxUbHD/IEYVaNgFIyCUTCCAQCkLjT6aORrvwAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Technology Bhubaneswar","correspondingAuthor":true,"prefix":"","firstName":"Remya","middleName":"","lastName":"Neelancherry","suffix":""}],"badges":[],"createdAt":"2026-02-19 15:28:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8918479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8918479/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103074069,"identity":"1e8c6a73-fecc-499d-a6cb-3056d6696e34","added_by":"auto","created_at":"2026-02-20 13:02:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354490,"visible":true,"origin":"","legend":"\u003cp\u003eSystem boundary (inner box) and product flow for LCA of MAP of HH\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/0304ef29f5094ff9399f5579.png"},{"id":103504373,"identity":"e764ec9d-c70a-4d58-8b04-94c83a4ae95e","added_by":"auto","created_at":"2026-02-26 13:19:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245902,"visible":true,"origin":"","legend":"\u003cp\u003e3-D response surface plots of (a) HB yield, and (b) HHV depicting the effect of MW power and reaction time\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/1d8c4537650f25888ee680b4.png"},{"id":103074074,"identity":"035aa670-5f25-4087-af59-16fd82d30fac","added_by":"auto","created_at":"2026-02-20 13:02:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2066361,"visible":true,"origin":"","legend":"\u003cp\u003eTGA and DTG curves of HH\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/b730745afef3d60582885e8c.png"},{"id":103504071,"identity":"b3bb686d-78a2-47d6-8be1-52217772dd25","added_by":"auto","created_at":"2026-02-26 13:16:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":382382,"visible":true,"origin":"","legend":"\u003cp\u003e(a) SEM image, (b) EDS spectrum, (c) N\u003csub\u003e2\u003c/sub\u003e adsorption -desorption isotherm, (d) XRD pattern and (e) FT-IR spectrum of HB\u003c/p\u003e","description":"","filename":"floatimage41.png","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/a87d4cd4c05d5cc57113b81b.png"},{"id":103074072,"identity":"ec0776c4-15bc-45a3-ad66-eea0d9eff5af","added_by":"auto","created_at":"2026-02-20 13:02:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":351719,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental impacts in the different stages of HH biochar production\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/f35bab6552bca80e2390246d.png"},{"id":104779146,"identity":"bd49fa68-3968-4dc6-9264-6d04d7ae1ed1","added_by":"auto","created_at":"2026-03-17 07:35:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4647362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/02495134-2eb8-4833-a941-59e9f11059b0.pdf"},{"id":103074070,"identity":"1e80bcc9-8967-48fe-b692-385d25b167c7","added_by":"auto","created_at":"2026-02-20 13:02:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":227991,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8918479/v1/e6a219099ed08bcc1f058fe8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization and life-cycle assessment of biochar production through microwave–assisted pyrolysis of industrial hemp hurd","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Industrial hemp hurd biochar (HB) prepared using microwave-assisted pyrolysis (MAP)\u003c/p\u003e\u003cp\u003e\u0026bull; 1000 W and 7 min residence time were optimum conditions for 37.6% HB yield\u003c/p\u003e\u003cp\u003e\u0026bull; Negative GHG emission of -10.1 t CO e/yr was determined for HB production\u003c/p\u003e\u003cp\u003e\u0026bull; 24.9 MJ/kg heating value and ~\u0026thinsp;90% fixed carbon suggested better fuel applicability\u003c/p\u003e\u003cp\u003e\u0026bull; LCA of HB production assessed global warming potential with 167.3 kg CO eq.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eWaste biomass has plentiful reserves and is considered as a carbon-neutral feedstock, capable of substituting conventional fuels. Biomass can also be converted into various products using the pyrolysis process, which is a succession of endothermic (heating and dehydration) and exothermic (volatile emission) reactions which degrade biomass by heating in a dearth of oxygen [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Contemporary requirements demand energy-efficient pyrolysis techniques that produce better yields and superior biofuels. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Microwave-assisted pyrolysis (MAP) is an excellent thermochemical technique to obtain biochar, bio-oil, and syngas, owing to its advantages over conventional processes, which include uniform heat distribution, minimized hazardous chemicals in bio-oil, simplicity of control, and expense minimization. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current technique has been employed efficiently on various feedstock like agricultural residue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], algae [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], sewage sludge [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and different feedstocks [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] to generate biofuels.\u003c/p\u003e \u003cp\u003eBiochar is an important product that is produced through biomass pyrolysis attenuating multi-faceted applications like fuel replacement, soil enhancer, carbon storage and as precursor for the synthesis of functional carbon-based compounds through subsequent modifications such as activation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The chemical and energy characteristics of the biochar plays a pivotal role in determining their applicability for various purposes. The fixed carbon content and heating value are considered to be essential for energy application of biochar [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly the biochar with greater surface area and porosity were considered for soil conditioning [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and novel carbon-based compounds development. Such properties of biochar are significantly influenced by the feedstock characteristics and MAP operating conditions. MW power and time are observed to be the most influential parameters by many researches for converting variety of feedstock into biochar and biofuels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it is necessary to analyse the effect of these operating parameters in optimizing the production of biochar in required quantity and quality. Response surface methodology (RSM) technique is often used by the scientific community to statistically evaluate the effect of input parameters on the desired output. Moreover, the capability of CCD to study individual and combined effect of multi-factor input components with minimal experimental runs on the output reduces the time, cost and energy requirements. The experimental input data are subjected to an ANOVA analysis, which yields output data that is further processed to create the necessary mathematical models for determination of functional correlation between input and output variables [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eCannabis sativa L.\u003c/em\u003e, known as industrial hemp, is being cultivated since decades for its fibre, oil, medical properties, and bioenergy potential. Industrial hemp, which is sometimes mistaken with Cannabis sativa L., does not contain enough THC to be intoxicated. Typically, it has 75% woody core (shives or hurds), 20% fibre, and 5% dust. Hemp hurd (HH) is the woody core of the stalk having long and short fibres. Such HH can be used to make fabric, paper/pulp, acoustic barriers, hybrid material boards etc. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Under the influence of climate change, this crop may serve as an effective means for carbon sequestration, attributed to its swift biomass production and substantial carbon sink ability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given the presence of lignocellulosic content in HH [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], bioenergy related synthesis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and biochar production through conventional pyrolysis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] were researched recently. The drawbacks from biochar production through conventional pyrolysis like longer retention time, greater energy input requirement, etc. can be riposted through MAP technology for HH conversion, which is seldom explored.\u003c/p\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions constitute a major portion of global greenhouse gas emissions, and accounts for three-quarters of the overall greenhouse gas (GHG) emissions. A significant and immediate reduction in GHG emissions as well as adjustments in resource management are required in order to meet the climate targets that were established by the Paris Agreement. There are a variety of technology development and consumption shift that could be considered as potential pathways that could accomplish these aims [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Within the realm of technology-based solutions, there is a collection of scenarios that takes into consideration the widespread implementation of negative emissions technologies (NETs). Despite the fact that recent assessments have brought to light a dearth of bottom-up and upscaling research, which are essential for the actual development of the sector, discussions over the requirement, viability, and prevalence of NETs in the upcoming technological advances remains evolving [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Biochar is essential among various NETs, including afforestation and soil carbon sequestration, for apprehending atmospheric CO\u003csub\u003e2\u003c/sub\u003e and significantly contributes to achieving NetZero emission targets. Biochar is acknowledged for its capacity to sequester carbon, thereby aiding in climate change mitigation as a result of its high density of recalcitrant carbon. A key approach for evaluating the capacity of biochar to mitigate CO\u003csub\u003e2\u003c/sub\u003e emissions is through carbon crediting. This method functions as a regulatory mechanism for managing global GHG emissions in the context of the Kyoto Protocol. This scheme requires precise documentation of GHG storage and emissions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The overall reduction of emissions is contingent upon GHG storage, whereas emissions from carbonisation significantly influence the diversity of total emissions. GHG emissions resulting from biochar production show considerable variability, which is affected by factors like feedstock characteristics, temperature, heating rate, and thermal conversion method employed. Woolf et al. predicted the NET potential of 0.27\u0026ndash;0.49 Gt CO\u003csub\u003e2\u003c/sub\u003e e/year due to biochar production in terms of indirect air capture [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, Santos et al. forecasted that biochar production has the potential to mitigate emissions ranging from 1.0 to 1.8 Gt CO\u003csub\u003e2\u003c/sub\u003e annually across different circumstances [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A knowledge gap exists in systematically clarifying the influence of various factors on the diversity of CO\u003csub\u003e2\u003c/sub\u003e emissions in biochar production systems, especially at the life cycle stage.\u003c/p\u003e \u003cp\u003eLife cycle assessment (LCA) facilitates the identification of hotspots within a system and encourages the exploration of technological innovations that minimise energy consumption and environmental impact [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. LCA framework consists of several processes, such as defining goals and scope, conducting inventory analysis, assessing life cycle impacts, and interpreting results where goal and scope definitions are instrumental in the explanation of the product system, which encompasses system boundaries and functional units [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Typically, the stages of product development are considered while defining system boundaries such as cradle-to-grave, cradle-to-gate, gate-to-gate, or gate-to-grave boundary might be used, depending on the chosen scope. In contrast to the cradle-to-gate method, which typically disregards the usage and end-of-life phases, the cradle-to-grave approach takes into account the full product or process life cycle. However gate-to-gate solely considers production that takes place on the premises [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Many researches worked on LCA of biochar production from different waste feedstocks like forest residues [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], manure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], agro-residues [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], woody biomass [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] etc. for different applications like soil systems [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], bioasphalt [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], bioenergy, and so on from a circular economy perspective [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, no studies have been identified regarding the life cycle assessment of the MAP of HH.\u003c/p\u003e \u003cp\u003eThe present work emphasized on the optimization of operating parameters for the production of biochar, using RSM for maximizing yield and higher heating value (HHV) from MAP of industrial hemp hurds (HH). Further, the potential environmental impacts of such system throughout its life cycle alongside the NET potential were analysed from a cradle-to-gate approach of life cycle assessment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials and chemicals:\u003c/h2\u003e \u003cp\u003eThe HH obtained from a farm in Pauri, Uttarakhand, India, and harvested between June and August was air-dried, grinded to size less than 4.75 mm. Silicon carbide (SiC) was used as the microwave susceptor, procured from Vaishnavi Industrial Needs Pvt Ltd., India. Analytical FT-IR grade KBr (99% pure) was procured from Merck, India.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Microwave pyrolysis reactor:\u003c/h2\u003e \u003cp\u003eThis study employed a custom-designed microwave (MW) reactor, operated at a frequency of 2.45 GHz with a power control ranging from 200 W to 1000 W (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A cylindrical quartz vessel with a diameter of 12 cm and a height of 20 cm was used as the reactor where 3 g of HH along with SiC balls as microwave susceptors was used per batch. N\u003csub\u003e2\u003c/sub\u003e gas was initially purged with a flow rate of 0.6 L/min for 10 min to create an oxygen-free environment and later reduced to 0.2 L/min during the MAP process. The obtained HH biochar (HB) was collected and corresponding yield was calculated using the Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:HB\\:yield\\:\\left(Y\\:\\%\\right)=\\frac{Weight\\:of\\:HB\\:\\:\\left(g\\right)}{Weight\\:of\\:HH\\:\\left(g\\right)}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Response surface methodology (RSM):\u003c/h2\u003e \u003cp\u003eRSM is one of the reliable tool for analysing the impact of different parameters on reactions. Central composite design (CCD) was employed in RSM to investigate the relationship between independent and dependent variables for the MAP experiments, as it a cost-effective approach to optimize variables and responses with minimum runs. The energy input to the system and the pyrolysis duration are two most important process parameters of MAP. MW power (P) in the range of 500\u0026ndash;1000 W and pyrolysis time (T) in the range of 10\u0026ndash;28 min were considered based on the numerous previous trials on the MAP of HH. The Stat-Ease 360 Software (Version 22.0.4) was used to design the experimental runs, and the centre point was repeated five times to assess the repeatability of experiments. CCD generated experimental runs with factorial points (2\u003csup\u003ek\u003c/sup\u003e), axial points (2 k), and replicated center points (n\u003csub\u003ek\u003c/sub\u003e), where \u0026lsquo;k\u0026rsquo; represents the number of independent parameters. The number of experiments to be conducted (N) was determined using Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:N={2}^{k}+2k+{n}_{k}={2}^{2}+2\\left(2\\right)+5=13$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe HB yield (Y %) and higher heating value (HHV) were chosen as the responses of the experimental runs. These variables were analysed using ANOVA, 3D graph and contour plot. Further, statistical analysis was conducted, including the F-value (Fisher variation ratio), \u003cem\u003ep\u003c/em\u003e-value (probability), regression coefficient (R\u003csup\u003e2\u003c/sup\u003e), and adequate precision (AP), to determine the relevance, importance, and suitability of the model used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. HB characterization:\u003c/h2\u003e \u003cp\u003eThe specific surface area of HB was determined based on the N\u003csub\u003e2\u003c/sub\u003e adsorption-desorption method using a BET surface area analyzer (QUADRASORB SI, Quantachrome Instruments, USA). The microscopic structure was studied using scanning electron microscopy (SEM, MERLIN compact, Carl Zeiss, Germany) equipped with energy-dispersive X-ray spectroscopy (EDS, 51XMX 1004, Oxford Instruments, UK) to provide detailed information on surface elemental composition. X-ray diffraction analysis (XRD, D8 Advance, Bruker, Germany) and Fourier transform infrared spectroscopy (FTIR, Alpha-FTIR, Bruker, Germany) were used to identify the various surface-functional elements and crystalline structure, respectively. Proximate analysis, the moisture content (MC), volatile matter (VM), ash content (AC), and fixed carbon content (FC), were measured as per standard methods [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The elemental composition (C, H, and O) in weight percentage was determined by using Nhuchhen's formula to the proximate analysis data as stated in Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Higher heating value (HHV) was determined using a bomb calorimeter (RSB-5, Rajdhani Co. Lim, New Delhi, India).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:C=-35.9972+0.7698\\times\\:VM+1.3269\\times\\:FC+0.3250\\times\\:AC$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:H=55.3678-0.4830\\times\\:VM-0.5319\\times\\:FC-0.5600\\times\\:AC$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:O=223.6805-1.7226\\times\\:VM-2.2296\\times\\:FC-2.2463\\times\\:AC$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Emission estimation from HB Production\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Emission factor estimation\u003c/h2\u003e \u003cp\u003eThe estimation of emission factor (EF) are done considering the most likely combinations of biochar production factors such carbonization process, scale, excess energy usage, and feedstock type. Kavindi, Tang, and Sasaki 2025 reported various estimated EFs considering different potential biochar production factors by classifying in to 24 groups considering 1 ton of biochar production as standard functional unit. MAP of HH is a carbonization technique for producing HB along with varied quantities of bio-oil and syngas. Both the volatiles can be additionally bolstered for energy applications in terms of electricity generation or satisfying thermal energy requirement for feedstock pre-drying. Therefore, MAP is considered to be closed carbonization technique with surplus energy utilization facility. The current study falls under the class 17, where the carbonization scale tends to be unspecified for closed carbonization system with surplus energy utilization ability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Scenario-specific GHG emission estimation\u003c/h2\u003e \u003cp\u003eConsidering unpopular HH cultivation in India, the average annual HH production (M\u003csub\u003eHH\u003c/sub\u003e) is assumed to be 100 t/yr along with the utilization rate of the crop residue (u) of 100% (since HH alone is considered as feedstock) as per [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The specific scenario of HB production (M\u003csub\u003eHB\u003c/sub\u003e) was determined using Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{M}_{HB}={M}_{HH}\\times\\:{Y}_{HB}\\times\\:u\\times\\:m$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, Y\u003csub\u003eHB\u003c/sub\u003e denotes the HB yield on dry weight of HH basis, and m refers to the MC of HH. The scenario specific GHG reduction (t CO\u003csub\u003e2\u003c/sub\u003e e/yr) is calculated as represented in Eq.\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\:Total\\:GHG\\:emission=E-S$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eE\u003c/em\u003e and S refers to the annual GHG emissions (t CO\u003csub\u003e2\u003c/sub\u003e e/yr) occurred and the annual GHG storage (t CO\u003csub\u003e2\u003c/sub\u003e e/yr) by HB respectively in a particular scenario. E and S were determined using the subsequent Eq.\u0026nbsp;\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:E={M}_{HB}\\times\\:EF$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:S={M}_{HB}\\times\\:{C}_{org}\\times\\:{C}_{st}\\times\\:\\frac{44}{12}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, C\u003csub\u003eorg\u003c/sub\u003e, C\u003csub\u003est\u003c/sub\u003e refer to organic carbon content of HB (assumed mean value- 0.57), and stable carbon content fraction of HB after 100 years (0.8). The coefficient 44/12 represents the carbon dioxide fraction. The negative value of total GHG emission represents the positive effect of the HB production on environment through carbon sequestration. Conversely, the positive value of total GHG emission denotes the negative impact of HB production on environment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Life Cycle Analysis (LCA) methodology:\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1. Goal and scope\u003c/h2\u003e \u003cp\u003eCradle-to-gate life cycle analysis (LCA) with 1 ton of HH as functional unit was performed to evaluate the environmental impacts of HB production from HH using MAP to identify the hotspots and potential environmental trade-offs associated and provide recommendations for improvement [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The scope of this LCA includes the following processes as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe environmental impact categories assessed were global warming potential, acidification potential, eutrophication potential, ozone depletion potential and photochemical oxidant potential. The data were derived from research articles and databases such as Ecoinvent (Version 3.9), the Indian Life Cycle Inventory Database and Agribalyse database [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The geographical scope of the study was limited to the production of HB from HH in India. Further, the study assumed average conditions for the production process and variations in regional conditions or management practices were not considered.\u003c/p\u003e \u003cp\u003eAll energy required for the process was obtained from the electricity grid and a uniform composition and energy content of HH was assumed throughout the year. Due to practical and theoretical constraints, certain features like the impacts of equipment manufacturing, MW susceptor production, construction activities and materials used for infrastructure development, the participation of human resources, and the overhead generated by lighting and maintenance of the facilities were excluded from the study, either because they did not affect the results or were standardized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2. Impact assessment methodology\u003c/h2\u003e \u003cp\u003eThe environmental impacts of HB production were assessed using the openLCA 1.11.0 software, a professional tool for modelling and analysing the life cycle of products, processes, and services transparently while following ISO 14040 recommendations. Four different impact assessment methodologies were used to provide a comprehensive understanding of the environmental impacts of MAP. The EDIP method was used to evaluate the environmental impacts of HB production across its entire life cycle, from resource extraction to disposal, considering a broad range of environmental impacts such as climate change, acidification, and eutrophication. The CML 2001 method, on the other hand, focused on the potential for HB production to cause damage to human health and ecosystems through the release of pollutants assessing impacts such as ozone depletion, photochemical oxidant formation, and toxic emissions to air, water, and soil [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The ReCiPe Midpoint (H) and ILCD 2.0 2018 methods aimed to provide a comprehensive assessment of environmental impacts of HB across a range of impact categories including global warming, ozone depletion, photochemical oxidation, acidification, eutrophication, terrestrial ecotoxicity, human toxicity and water depletion [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The impact assessment method was limited to quantitative modelling of early stages in the cause-effect chain to reduce uncertainties. Sensitivity analysis of the LCA was carried out using the LCA inventory and assessing them using above-mentioned impact assessment methodologies. The analysis considers four impact categories: global warming potential, ozone depletion, photochemical oxidation, and acidification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3. Life cycle inventory (LCI)\u003c/h2\u003e \u003cp\u003eThe cultivation of hemp plants, its transportation from farm to factory, decortication, size reduction, MAP for HB production and pelletization were the unit processes considered as different phases of HB production. Certain assumptions were considered in this study as follows. Around 2 kg of hemp seeds were considered as input and nitrogen, phosphorus, and potassium-rich fertilizers were added in amounts of 2.5 kg, 1.25 kg, and 1.75 kg, respectively for plant growth [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A sprinkler system was assumed for irrigating the land of 0.525 m\u003csup\u003e2\u003c/sup\u003e area with 40 cm of water depth for a growing season, obtaining 1350 kg of hemp plant, which is transported to factory (15 km) using 2-axle trailer with carrying capacity of 3 t. Micro-Decorticator (MD100, HurdMaster) with a capacity to operate at 50 kg/h, having a power rating of 1.5 kW, was considered for decortication, i.e., to separate the fibre from the hurds. After decortication, 1000 kg of HH were obtained with size\u0026thinsp;\u0026lt;\u0026thinsp;4.75 mm. A screw conveyor (LS250, Henan Green Eco-equipment) was considered to feed the dried ground feedstock into the pyrolysis reactor with the screw diameter, pitch, and rotational speed were 250 mm, 250 mm, and 50 rpm, respectively, delivering feedstock at a rate of 0.3327 m\u003csup\u003e3\u003c/sup\u003e/h and consuming 1.327 kW power, and has a capacity of 4000 kg/h. The MW reactor (Twin Engineers, Haryana) was operated at the optimized parameters of 1000 W MW power for 7 min. The operating parameters were decided based on the results obtained in this study. A pellet-making machine manufactured by Vinspire Agrotech was used for pelletization which operated at a power of 3728.5 W (5hp) and yielded an output of 100 kg/hr.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Central composite design\u003c/h2\u003e \u003cp\u003eThe results from 13 experimental runs were displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. HB yield and HHV ranged from 9.7% to 75.3% and 12.5 MJ/kg to 29.5 MJ/kg respectively. The lowest HB yield (9.7%) and highest HHV (29.5 MJ/kg) was obtained at 1000 W and 25 min with an energy consumption of 0.42 kWh. While the highest HB yield (75.3%) and lowest HHV (12.5 MJ/kg) was obtained at 600 W and 10 min with an energy consumption of 0.10 kWh.\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\u003eHB yield, and HHV obtained at different process parameters of MAP of HH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP (W)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHHV (MJ/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnergy consumed (kWh)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (-α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (+α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (+α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (+α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (-α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (+α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e : Coded level in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Effect of process parameters on HB yield\u003c/h2\u003e \u003cp\u003eThe highest HB yield of 75.3% was obtained at a MW power of 600 W and a residence time of 10 min. Such operating conditions resulted in minimal thermal degradation of the feedstock, allowing most of the volatile matter (VM) to remain in the HB. In contrast, the least HB yield (9.7%) was observed at 1000 W and 25 min operating conditions. The progressive elongation of the reaction time results in a reduction in HB yield attributing to the secondary processes, namely gasification, volatilization and thermal cracking. Moreover, an intermediate combination of these factors resulted in moderate HB yield. The interactive effect of MW power and residence time is depicted by the 3-D surface plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a)) highlighting the importance of controlling these process parameters to optimize HB production. It can be inferred that the variation in MW power at lower residence time (5\u0026ndash;15 min) reduced the HB yield drastically, as supported by the literature [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Whereas at higher residence time (15\u0026ndash;28 min), little variation in HB yield was observed across different MW powers (500 W \u0026ndash; 1000 W). It can be attributed to possible promotion of the breakage of long carbon chains at higher residence times resulting in higher volatile content liberation leading to reduced HB yield.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Effect of process parameters on HHV\u003c/h2\u003e \u003cp\u003eThe 3-D surface plot of HHV is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (b). The highest heating value (HHV) of 29.5 MJ/kg was obtained at a MW power of 1000 W and pyrolysis time of 25 min. MW power input of 1000 W and residence time of 25 min resulted in nearly complete pyrolysis of the feedstock, producing HB with greater HHV. Higher MW power (1000 W) and moderate residence time (25 min) enabled more extensive polymers to crack into smaller aromatic rings and carbon-carbon bonds, increasing HHV. A nearly complete breakdown of feedstock rendered HB richer in carbonaceous content with higher heating value. In contrast, the least HHV (12.5 MJ/kg) was observed at 600 W and 10 min operating conditions. MW power of 800 W applied for a shorter duration (7 min) resulted in insufficient feedstock cracking, producing HB with lower heating value (12.6 MJ/kg) due to lower carbon content. The interaction between MW power (800\u0026ndash;1000 W) and residence time (7\u0026ndash;25 min) significantly influenced the HHV of HB. At 1000 W, increasing residence time from 10 to 25 min increased the HHV from 25.4 MJ/kg to 29.5 MJ/kg, highlighting the dominance of residence time. On the other hand, at residence time of 17.5 min, increasing power from 800 W to 1000 W raised the HHV from 14.2 MJ/kg to 28.8 MJ/kg, demonstrating the more significant impact of a higher MW power. An intermediate combination of factors (1000 W and 17.5 min) yielded HB with an HHV of 28.8 MJ/kg.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Model development and corresponding statistical analysis\u003c/h2\u003e \u003cp\u003eThe experimental results were fitted to four regression models: linear model, 2-factor interaction model (2FI), quadratic and cubic model using design expert software, represented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The models were analysed based on their sequential p-value, lack of fit p-value, and adjusted and predicted R\u003csup\u003e2\u003c/sup\u003e value. Based on the model statistics for HB yield, the quadratic model is suggested as it has a significantly lower p-value of \u0026lt;\u0026thinsp;0.0001 depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Furthermore, the model F-value of 1396.4 implies that the model is significant implying only a 0.01% chance that an F-value this large could occur due to noise. The model tends to be more reliable with the observed standard deviation (SD) and mean value of 0.9 and 30.1, implying consistent predictions with less variability, offering acceptable baseline for individual forecasts. The coefficient of variation (CV) of 2.9%, represents the normalized measure of the dispersion of the dataset. Predicted R\u003csup\u003e2\u003c/sup\u003e was 0.994, while adjusted R\u003csup\u003e2\u003c/sup\u003e was 0.998, with variation less than 0.2, suggesting a high correlation. The P, T, PT, P\u003csup\u003e2\u003c/sup\u003e and T\u003csup\u003e2\u003c/sup\u003e were the significant model terms with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. The lack of fit was also non-significant with p-value of 0.1140 (\u0026gt;\u0026thinsp;0.05), which is desirable for the model to fit. Adequate Precision measures the signal-to-noise ratio of 110.8\u0026thinsp;\u0026gt;\u0026thinsp;4 indicated an adequate signal. The reported equation according to the quadratic model by considering significant terms is mentioned as Eq.\u0026nbsp;\u003cspan refid=\"Equ10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$\\:Y\\:\\left(\\%\\right)=325.10-0.37P-12.17T+0.004PT+1.30\\times\\:{10}^{-4}{P}^{2}+0.18\\times\\:{T}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBased on the model statistics for HHV, the quadratic model is suggested as it has significantly lower p-value of \u0026lt;\u0026thinsp;0.0001. The F-value of the model is 110.9, and its p-value is \u0026lt;\u0026thinsp;0.0001, indicating that the model is significant. It can be observed from the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that the F-value of MW power stands at 218.1 and 189.7 for individual (P) and quadratic term (P\u003csup\u003e2\u003c/sup\u003e) respectively, with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 in both the cases. The lack of fit F-value of 4.1 implies that the lack of fit is insignificant with p-value of 0.1 (\u0026gt;\u0026thinsp;0.05) relative to the pure error. There is a 10.3% chance that a lack of fit F-value this large could occur due to noise. The adjusted R\u0026sup2; for the model is 0.971, while the predicted R\u003csup\u003e2\u003c/sup\u003e for the model is 0.924, indicating a better correlation. Adequate Precision measures the signal-to-noise ratio of 29.3 (\u0026gt;\u0026thinsp;4) indicates an adequate signal. The reported equation according to the quadratic model by considering significant terms is reported as Eq.\u0026nbsp;\u003cspan refid=\"Equ11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$$\\:\\:HHV\\:\\left(MJ/kg\\right)=71.91-0.18P-0.11T+1.30\\times\\:{10}^{-4}{P}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\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\u003eANOVA of developed model for HB yield and HHV and model terms\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHB yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHHV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eF-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1396.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-MW power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2534.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-Reaction time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1579.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e944.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC.V. %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredicted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate Precision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Model Validation\u003c/h2\u003e \u003cp\u003eThe HB yield and HHV model was validated through three sets of experiments. In the first experiment, the dependent parameters were taken to be maximum and the independent values to be within the experimental range. The software offered one hundred solutions under the described circumstances from which single solution was chosen. Since the MW power input to the reactor could only be in multiples of one hundred, the solution was predicated on the highest desire and MW power being a multiple of one hundred. Additionally, two sets of runs within the range were undertaken at random. The experimental and anticipated values for each of the three runs are shown in Table S2. The average error of 4.8%, and maximum error of 5.5% was observed between the experimental and predicted HB yield. The maximum error of 6.6% and average error of 5.2% was observed in case of HHV, suggesting the adequacy of the model with minimal errors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5. Result optimization\u003c/h2\u003e \u003cp\u003eProcess optimization was carried out to find the optimum value of independent parameters for the maximum HHV of resulting HB by targeting the HB yield to be 42.5%. This was done using the response surface models and the desirability function. The objective of maximizing HB yield and HHV guided the determination of ideal factor levels. The yield and HHV ranges considered for optimization were those achieved across the experimental runs, i.e., 9.7\u0026ndash;75.3% and 12.5\u0026ndash;29.5 MJ/kg, respectively. Table S3 shows the optimized solutions where the factor combination of 1000 W power and 30 mins residence time has the lowest desirability of 0.31. The factor combination of 1000 W power and 7 mins residence time provided the highest desirability of 0.52 and could be the optimized condition. Such operating conditions predicted the HB yield of 37.6% and maximum HHV of 24.9 MJ/kg. The obtained HB was analysed for further characterization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Characterization of HH and HB:\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Chemical characteristics\u003c/h2\u003e \u003cp\u003eThe chemical characteristics of the HH and HB are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which includes proximate, ultimate analysis, and HHV.\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\u003eChemical properties of HH and HB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter (wt %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHV (MJ/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe HH characteristics were within the range mentioned in the literature [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The MC of HB was 1.21%, enabling proper storage conditions. Conversion of HH to HB lowered VM and AC by 93.4%, and 78.7% respectively with 18.7 times increase in FC due to significant release of volatiles during MAP. The H/C and O/C ratios of HB were determined to be 0.40 and 0.05 respectively, suggesting its higher aromaticity and thermal stability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, the HHV of HB (24.9 MJ/kg) increased by 124.73% compared to the corresponding HH, suggesting its applicability as a fuel replacement, as the HHV of Indian coals lie in the range of 20\u0026ndash;26 MJ/kg [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Thermogravimetric analysis\u003c/h2\u003e \u003cp\u003eThe thermogravimetric analysis (TGA) and derivative thermogravimetry (DTG) curves for HH biomass showed 10% weight loss at an initial stage from 30 ℃ up to approximately 300\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), due to moisture loss and volatilization of low molecular weight components. A more rapid weight loss of around 50\u0026ndash;60% occurred from 300\u0026deg;C to 400\u0026deg;C, with maximum rate of weight loss at 370\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u0026deg;C (0.5%/\u0026deg;C). This stage likely corresponds to the thermal decomposition of hemicellulose, cellulose, and lignin components of HH. The broad, asymmetrical shape of DTG peak suggested the HH decomposition occurred via multiple overlapping reactions. Beyond 400\u0026deg;C, a shoulder and slower continued weight loss was observed, indicating the residual decomposition of char and ash forming components up to 500\u0026deg;C. The total weight loss over the analysed temperature range was 80\u0026ndash;85%, leaving inorganic char residue of 10\u0026ndash;12% by weight. No exothermic decomposition reactions were detected based on the DTG curve, suggesting the HH decomposes via endothermic reactions in a relatively controlled, and non-explosive manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Physical characteristics of HB\u003c/h2\u003e \u003cp\u003eThe SEM image (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (a)) showed that the HB fibres appear primarily cylindrical, with an average diameter of 20\u0026ndash;30 \u0026micro;m and length of 100\u0026ndash;200 \u0026micro;m. It was observed to be moderately porous, with small pores (5\u0026ndash;10 \u0026micro;m) visible between fibre bundles and along the length of fibres, possibly due to gas exchange and insulation ability. The fibre surfaces also showed fine transverse grooves and pits, which increases the surface area. These surface characterisitcs could regulate aspects like adsorption, dyeability or other surface-related attributes [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. From the EDS data as observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b), the main elemental composition of HB appeared to be carbon (82.6 wt. %), oxygen (16.8 wt. %) and calcium (0.5 wt. %), with C and O peaks dominating the spectra, similar to ultimate analysis results, as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (c) depicted a linear segment at low pressures, a plateau at high pressures, and an inflexion point in between, indicating the occurrence of mesopores in the material. BET study determined a specific surface area of 19.15 m\u0026sup2;/g which implies significant surface availability for adsorption, and potentially useful for pollutant separation, catalysis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and monitoring technologies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The obtained surface area is within the range mentioned in the literature (18.23\u0026ndash;25.38 m\u0026sup2;/g) for HH biochar produced through conventional pyrolysis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe XRD diffractogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (d)) displayed prominent peak at θ\u0026thinsp;=\u0026thinsp;7.6\u0026deg; (d-spacing\u0026thinsp;=\u0026thinsp;5.82 \u0026Aring;) associated with the (1 0 1) lattice plane reflection of cellulose I, which is crystallised in a triclinic unit cell according to JCPDS number 50\u0026ndash;2241. Minor impurity phases of quartz and calcite are also detected based on additional peaks at 2θ\u0026thinsp;=\u0026thinsp;10.9\u0026deg;, 12.6\u0026deg;, 20.9\u0026deg; and 30.0\u0026deg; which may index to the (1 1 0) plane of aluminium, (0 0 4) plane of calcite, (0 2 0) plane of quartz and composite of quartz (JCPDS number 46\u0026ndash;1045) and calcite (JCPDS number 5\u0026ndash;586) respectively. The average crystallite size of 6.09 nm, calculated using the Scherrer equation for the (1 0 1) cellulose I peak, assuming a shape factor of 0.9 and Cu Kα wavelength of 0.15406 nm which indicated the presence of relatively small cellulose I crystallites in the material.\u003c/p\u003e \u003cp\u003eThe FTIR spectrum (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (e)) of HB exhibited an intense broad absorption band in the 3000\u0026ndash;3500 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e region, indicating O\u0026ndash;H stretching vibrations attributable to hydroxyl functional groups. The sharp peak in the 3500\u0026ndash;3750 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e region could indicate the presence of \u0026ndash;OH stretching [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Multiple peaks between 2750 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 3250 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e signify asymmetric and symmetric C\u0026ndash;H stretching modes of aliphatic alkanes. Prominent peaks between 1500 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1750 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e depicts C\u0026thinsp;=\u0026thinsp;C double bond stretching vibrations of alkenes. The position and intensity of the absorption bands suggested that HB contains a mixture of alcohols, aldehydes or carboxylic acids, alkenes, and esters related surface functional groups. A strong absorption band at 1729 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to the carbonyl C\u0026thinsp;=\u0026thinsp;O stretching vibration, indicating the presence of aldehydes, ketones, carboxylic acids, or esters. Minor peaks observed between 1400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1500 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are assignable to N\u0026ndash;H bending vibrations of amines and C\u0026ndash;H in-plane bending modes. A small peak at 1200 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e can be attributed to the C\u0026ndash;O stretching of ethers or esters [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Overall, the FTIR spectrum provided evidence for the presence of hydroxyl, alkane, alkene, carbonyl, amino and ether functional groups on the HB surface.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Estimation of negative emission during HB production\u003c/h2\u003e \u003cp\u003eMAP of HH is a carbonization technique for producing HB along with varied quantities of bio-oil and syngas. Both the volatiles can be additionally bolstered for energy applications in terms of electricity generation or satisfying thermal energy requirement for feedstock pre-drying. Therefore, MAP is considered as closed carbonization technique with surplus energy utilization facility. EF was estimated for HB production using MAP based on two scenarios, namely (i) without surplus energy utilization and (ii) with surplus energy utilization. The surplus energy utilization refers to the additional energy sources from the byproducts obtained from MAP of HH (biooil and syngas). The EF in both the cases were determined to be 0.10 t CO\u003csub\u003e2\u003c/sub\u003e e/t HB (EF group 6), and \u0026minus;\u0026thinsp;0.78 t CO\u003csub\u003e2\u003c/sub\u003e e/t HB (EF group 17), derived from unspecified carbonization scale of wood residues suggested in the works of [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], detailed in Table S4. HB yield on dry weight of HH basis was calculated as cir. 41.7%. As per Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e the specific scenario annual HB production of 4.11 t/yr was estimated, considering 100 t of HH production annually, leading to the annual GHG storage of 6.86 t CO\u003csub\u003e2\u003c/sub\u003e e/yr. However, the GHG emissions of 0.41 t CO\u003csub\u003e2\u003c/sub\u003e e/yr, and \u0026minus;\u0026thinsp;3.20 t CO\u003csub\u003e2\u003c/sub\u003e e/yr were determined in case of without surplus and with surplus energy utilization scenarios respectively. Overall, the total GHG emissions of -6.45 t CO\u003csub\u003e2\u003c/sub\u003e e/yr, and \u0026minus;\u0026thinsp;10.06 t CO\u003csub\u003e2\u003c/sub\u003e e/yr were estimated in both the scenarios. The negative GHG emission implies the sustainable applicability of the MAP for conversion of HH into HB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Life cycle impact assessment\u003c/h2\u003e \u003cp\u003eTable S5 comprehensively overviews the environmental impact of producing HB based on the ReCiPe Midpoint (H) impact assessment method. The results indicated that the HB production significantly impacts several environmental categories, with the highest impact being on global warming potential, at 167.35 kg CO\u003csub\u003e2\u003c/sub\u003e eq., followed by human non-carcinogenic toxicity, at 3199.92 kg 1, 4-DCB, and terrestrial ecotoxicity at 2380.14 kg 1, 4-DCB. These results suggested that efforts to mitigate the environmental impact of the production process should focus on reducing greenhouse gas emissions and minimizing the application of chemicals that may negatively affect human health and ecosystems.\u003c/p\u003e \u003cp\u003eTable S6 provides information on the emissions associated with different stages of the HB production. It was observed that, hemp farming has a negative impact on global warming potential, with a value of -2.94 kg CO\u003csub\u003e2\u003c/sub\u003e eq., indicating that hemp plants absorb carbon dioxide during their growth, which helps mitigate the effects of greenhouse gas emissions. However, hemp farming has little impact on photochemical oxidant and acidification, with values of 1.11*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e kg ethylene eq.\u0026nbsp;and 9.13*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e kg SO\u003csub\u003e2\u003c/sub\u003e eq., respectively with no impact on ozone depletion or eutrophication. The harvesting stage significantly impacts all environmental factors, with the highest impact on acidification, at 4.50 kg SO\u003csub\u003e2\u003c/sub\u003e eq., which is likely due to the use of heavy machinery and associated emissions. The transportation stage also significantly impacts global warming potential, with a value of 47.83 kg CO\u003csub\u003e2\u003c/sub\u003e eq., as well as a small impact on photochemical oxidant and eutrophication. HB production through MAP has no impact on global warming potential or ozone depletion, but it does significantly impact photochemical oxidant, with a value of 1.99 kg ethylene eq., which may be linked to the emission of non-condensable gases during MAP. Other allied activities significantly impact all environmental factors, with the highest impact on photochemical oxidant, at 5.32*10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e kg ethylene eq.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Sensitivity analysis:\u003c/h2\u003e \u003cp\u003eThe sensitivity analysis also shows that the choice of impact assessment method can affect the results of the LCA. Table S7 details the environmental impacts in the different stages of HB production and compares the results against the four impact assessment methodologies to gauge the robustness of the LCA. The results indicate that the impact of different stages of hemp farming varies significantly depending on the impact category being considered. For instance, the analysis shows that the global warming potential is primarily driven by harvesting, while the impact of ozone depletion is primarily driven by the allied activities.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1. Global Warming:\u003c/h2\u003e \u003cp\u003eThe GWP results of the different methods (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (a)) show relatively small variations for hemp farming, harvest, and transportation stages, indicating a consistent impact across all methods. All methods estimated a very small environmental impact for the electricity use stage, with values ranging from 0.08 to 0.1 kg CO\u003csub\u003e2\u003c/sub\u003e eq., since the electricity consumed in the process was assumed to be generated from renewable sources or have low carbon emissions. On the other hand, the HB production stage shows significant differences in the GWP impact estimates between the methods. The EDIP method estimates the highest impact with a value of 49.58 kg CO\u003csub\u003e2\u003c/sub\u003e eq., while the other methods, CML 2001, ReCiPe Midpoint (H), and ILCD 2.0 2018, estimate a value of 0 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;Due to incomplete combustion and volatile organic compound emissions, the EDIP method assumes that HB production releases many greenhouse gases, primarily CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO. However, the other methods do not consider these emissions and assume that the HB production process is carbon-neutral or has a low carbon impact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2. Ozone depletion:\u003c/h2\u003e \u003cp\u003eThe results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b)) for ozone depletion potential were generally low for all four impact assessment methods. The CML 2001 and ReCiPe Midpoint (H) methods gave identical results (~\u0026thinsp;1.3*10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e kg CFC-11 eq.), while ILCD 2.0 2018 method gave slightly higher results. Such variation can be attributed to differences in the underlying data and modelling assumptions used by each method. These results aligns with previous studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] showing that hemp cultivation has a low environmental impact compared to other crops due to its low requirements for fertilizers and pesticides.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3. Photochemical oxidation:\u003c/h2\u003e \u003cp\u003eAccording to the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (c)), the photochemical oxidant impact from hemp farming, harvesting, allied and electricity production was relatively small across all methods. However, HB production and transportation had a higher impact in this category due to potential release of pollutants, such as volatile organic compounds and nitrogen oxides, react with sunlight and oxygen in the atmosphere to form photochemical smog. The CML 2001 method indicated that HB produced a photochemical oxidant impact of 2.84 kg ethylene-eq.\u0026nbsp;Similarly, the ILCD 2.0 2018 method estimated a photochemical oxidant impact of 1.99 kg ethylene-eq.\u0026nbsp;for HB production. The ReCiPe Midpoint (H) method also showed a higher impact on HB production than other processes, with a value of 1.99 kg ethylene-eq.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4. Acidification:\u003c/h2\u003e \u003cp\u003eThe results of the sensitivity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (d)) showed that the acidification potential of the HB production system varied depending on the impact assessment method used. The EDIP method had the lowest acidification potential, while the ILCD 2.0 2018 method had the highest in both harvesting (5.57 kg SO\u003csub\u003e2\u003c/sub\u003e eq.), transportation (0.48 kg SO\u003csub\u003e2\u003c/sub\u003e eq.) and allied stages (0.24 kg SO\u003csub\u003e2\u003c/sub\u003e eq.) of HB production. The differences in the acidification potential between the methods can be attributed to several factors, including differences in the characterization factors used to calculate the potential, the inclusion or exclusion of specific emissions, and the modelling assumptions used in each method. The ILCD 2.0 2018 method included an uncertainty factor to account for the potential impacts of acidification on freshwater and terrestrial ecosystems, which may result in a higher acidification potential than the other methods.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study focused on the optimization of HB production from MAP of HH both in terms of yield and higher heating value (HHV). MW power of 1000 W and pyrolysis time of 7 min was found to be optimum solution yielding around 35% of HB with HHV of ~\u0026thinsp;25 MJ/kg. The HHV of HB improved by around 125% compared to HH (11 MJ/kg). HB appeared moderately porous, with tiny pores (5\u0026ndash;10 \u0026micro;m) evident between fibre bundles and along their length. Additionally, HB showcased higher fixed carbon (90%), lower moisture content (1.2%) and volatile matter (5.2%), suggesting its applicability as a sustainable fuel replacement in energy sector. The negative GHG emission of -10.06 t CO\u003csub\u003e2\u003c/sub\u003e e/yr suggested the sustainability of MAP for HB production through carbon capture. Further, this study assessed the environmental impact that the MAP treatment of HH has during its whole life cycle through the application of openLCA software (using ReCiPe midpoint method). The highest impact on global warming potential, at 167.35 kg CO\u003csub\u003e2\u003c/sub\u003e eq., followed by human non-carcinogenic toxicity, at 3199.92 kg 1, 4-DCB, and terrestrial ecotoxicity, at 2380.14 kg 1,4-DCB was observed, suggesting its impact on environment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAsh content\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdequate precision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClosed composite design\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC\u003csub\u003eorg\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eorganic carbon content of HB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC\u003csub\u003est\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estable carbon content fraction of HB after 100 years\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDichlorobenzene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual GHG emissions occurred\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmission factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFixed carbon\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFT-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFourier transform infrared spectroscopy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGreenhouse gas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemp hurd biochar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndustrial hemp hurds\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHHV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigher heating value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLife-cycle Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrowave-assisted Pyrolysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMoisture content\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScenario specific annual average HB production\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual average hemp hurd production\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrowave\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative emission technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResponse surface methodology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual GHG storage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScanning electron microscope\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSiC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSilicon carbide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThermogravimetric analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eresidue utilization rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVolatile matter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eX-ray diffraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYHB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHB yield in terms of dry weight of HH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNived S Menon:\u003c/strong\u003e Investigation, Data analysis; Formal analysis; Writing-Original draft preparation; \u003cstrong\u003eRejeti Venkat Srinadh\u003c/strong\u003e: Data curation, Methodology, Data analysis and validation, Writing-Original draft preparation; \u003cstrong\u003eNeelancherry Remya\u003c/strong\u003e: Supervision, Conceptualization, Funding acquisition, Writing-review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Indian Institute of Technology Bhubaneswar, India\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used during the study appear in the presented article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have nothing to declare.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCisse I, Hernandez-Charpak YD, Diaz CA, Trabold TA (2022) Biochar Derived from Pyrolysis of Common Agricultural Waste Feedstocks and Co-pyrolysis with Low-Density Polyethylene Mulch Film. 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Waste Manag 173:51\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.WASMAN.2023.11.006\u003c/span\u003e\u003cspan address=\"10.1016/J.WASMAN.2023.11.006\" 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":true,"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":"Hemp hurds, Microwave-assisted pyrolysis, Negative emission technology, Fuel replacement, Biochar optimization, Life-cycle assessment","lastPublishedDoi":"10.21203/rs.3.rs-8918479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8918479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndustrial hemp hurds (HH) are the biomass residues following bast separation from inner core of hemp plant for fibre extraction. Current research emphasized on microwave-assisted pyrolysis (MAP) of HH for the optimization of hemp biochar (HB) production, its characterization and cradle to gate life cycle analysis (LCA) to explore its application as fossil fuels alternate. Utilizing central composite design (CCD), MAP was optimized for two operational parameters: microwave power (P) and pyrolysis time (T) with HB yield and higher heating value (HHV) as output parameters. The highest HB yield of 75.3% was obtained at P and T of 600 W and 10 min respectively with model Eq.\u0026nbsp;325.1 -0.37P -12.17T\u0026thinsp;+\u0026thinsp;0.004PT\u0026thinsp;+\u0026thinsp;1.3\u0026times;10-4P\u003csup\u003e2\u003c/sup\u003e +0.18T\u003csup\u003e2\u003c/sup\u003e, validated with an average error of 4.8%. The maximum HHV of 29.5 MJ/kg prevailed at 1000 W and 25 min with model Eq.\u0026nbsp;71.91 -0.18P -0.11T\u0026thinsp;+\u0026thinsp;1.3\u0026times;10-4P\u003csup\u003e2\u003c/sup\u003e and an average error of 5.2%. The negative greenhouse gas (GHG) emission of -10.06 t CO\u003csub\u003e2\u003c/sub\u003e e/yr suggested the sustainability of MAP for HB production through carbon capture. The life cycle assessment (LCA) using ReCiPe Midpoint (H) indicated that the HB production process significantly impacts several environmental categories, with the highest impact being on global warming potential, at 167.3 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;It is followed by human non-carcinogenic toxicity, at 3199.9 kg 1, 4-DCB, and terrestrial ecotoxicity, at 2380.1 kg 1, 4-DCB. The sensitivity analysis evaluated the environmental impacts derived from four distinct impact assessment methodologies, facilitating the identification of overarching trends in environmental impacts.\u003c/p\u003e \u003cp\u003eNovelty statement\u003c/p\u003e \u003cp\u003eThe current study employed microwave-assisted pyrolysis (MAP) technique for biochar production from industrial hemp hurds. Biochar production was optimized using response surface methodology for maximizing heating value and yield emphasizing its applicability as a potential and sustainable fuel alternative. Detailed characterization studies like scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, Energy-dispersive X-ray spectroscopy, BET surface area analysis, etc., were performed to examine the quality of the obtained biochar. Apart from biochar production through advanced technology like MAP, this study also focused on the environmental impact of such process through a thorough investigation on negative emission technology potential and life-cycle assessment using cradle-to-gate approach. This research highlighted the negative greenhouse gas (GHG) emission of -10.06 t CO\u003csub\u003e2\u003c/sub\u003e e/yr suggesting the sustainability of MAP for biochar production through carbon capture.\u003c/p\u003e \u003cp\u003eTherefore, this research presents novel contributions to the field of waste valorization and renewable and clean energy production through biofuel production from MAP of waste biomass.\u003c/p\u003e","manuscriptTitle":"Optimization and life-cycle assessment of biochar production through microwave–assisted pyrolysis of industrial hemp hurd","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 13:02:28","doi":"10.21203/rs.3.rs-8918479/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":"d77c6a8b-eb01-4aa7-84c4-0883b3a8dfa3","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T05:23:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 13:02:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8918479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8918479","identity":"rs-8918479","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}