Bioenergy
J. Rezaeifar; A. Rohani; M. A. Ebrahimi-Nik
Abstract
In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations ...
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In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations of Fe and Fe3O4 (10, 20, and 30 mg L-1) and DWTS (6, 12, and 18 mg L-1). Leveraging an extensive library of non-linear regression (NLR) models, 26 candidates were scrutinized and eight emerged as robust predictors for the entire methane production process. The Michaelis-Menten model stood out as the superior choice, unraveling the kinetics of dairy manure AD with the specified additives. Fascinatingly, the findings revealed that different levels of DWTS showcased the highest methane production, while Fe3O420 and Fe3O430 recorded the lowest levels. Notably, DWTS6 demonstrated approximately 34% and 42% higher methane production compared to Fe20 and Fe3O430, respectively, establishing it as the most effective treatment. Additionally, DWTS12 exhibited the highest rate of methane production, reaching an impressive 147.6 cc on the 6th day. Emphasizing the practical implications, this research underscores the applicability of the proposed model for analyzing other parameters and optimizing AD performance. By delving into the potential of iron-based additives and DWTS, this study opens doors to revolutionizing methane production from dairy manure and advancing sustainable waste management practices.
Bioenergy
D. Baveli Bahmaei; Y. Ajabshirchy; Sh. Abdollahpour; S. Abdanan Mehdizadeh
Abstract
This research aims to optimize the mixing process in gas-lift anaerobic digesters of municipal sewage sludge since mixing and maintaining uniform contact between methanogenic bacteria and nutrients is essential. Wastewater municipal sludge sampling was performed at the Ahvaz West treatment plant (Chonibeh, ...
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This research aims to optimize the mixing process in gas-lift anaerobic digesters of municipal sewage sludge since mixing and maintaining uniform contact between methanogenic bacteria and nutrients is essential. Wastewater municipal sludge sampling was performed at the Ahvaz West treatment plant (Chonibeh, Iran) during the summer of 2022. A Computational Fluid Dynamics (CFD) model was implemented to simulate, optimize, and confirm the simulation process using ANSYS Fluent software 19.0. The velocity of the inlet-gas into the digester was determined and a draft tube and a conical hanging baffle were added to the digester design. Different inlet-gas velocities were investigated to optimize the mixing in the digester. Furthermore, turbulence kinetic energy and other evaluation indexes related to the sludge particles such as their velocity, velocity gradient, and eddy viscosity were studied. The optimal inlet-gas velocity was determined to be 0.3 ms-1. The simulation results were validated using the Particle Image Velocimetry (PIV) method and the correlation between CFD and PIV contours was statistically sufficient (98.8% at the bottom corner of the digester’s wall). The results showed that the model used for simulating, optimizing, and verifying the simulation process is valid. It can be recommended for gas-lift anaerobic digesters with the following specifications: cylindrical tank with a height-to-diameter ratio of 1.5, draft tube-to-digester diameter ratio of 0.2, draft tube-to-fluid height ratio of 0.75, the conical hanging baffle distance from the fluid level equal to 0.125 of the fluid height, and its outer diameter-to-digester diameter of 2/3.
Bioenergy
M. Nowroozipour; R. Tabatabaei koloor; A. Motevali
Abstract
IntroductionThe world’s growing population has led to an inevitable increase in energy demand, and this, in addition to the depletion of non-renewable energy sources, can lead to several environmental issues. Wind power has proven to be a reliable and sustainable source of electricity, particularly ...
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IntroductionThe world’s growing population has led to an inevitable increase in energy demand, and this, in addition to the depletion of non-renewable energy sources, can lead to several environmental issues. Wind power has proven to be a reliable and sustainable source of electricity, particularly in light of the pressing need to mitigate environmental impact and promote the use of renewable energy. The purpose of this research is to investigate and compare the environmental effects of electricity production from two wind power plants, Aqkand and Kahak, using wind turbines with a capacity of 2.5 megawatts for a period of three different lifetimes (20, 25, and 30 years).Materials and MethodsThe present study investigates the environmental effects of electricity generation during the life cycle of wind farms (Kahak and Aqkand) during the construction and operation of these power plants and the cumulative exergy demand index. The specifications of the wind turbines used in the current research are: turbine capacity of 2.5 MW, rotor diameter of 103 meters, rotor weight of 56 tonnes, three blades, each blade is 50.3 meters long and weighs 34.8 tonnes. The turbines are manufactured by Mapna and used in dry conditions. A functional unit of one kilowatt of electricity was selected and the data were analyzed in SIMAPRO software using IMPACT2002+ method with 15 midpoint indicators and four final indicators.Results and DiscussionThe results showed that the stage of raw materials and production has the highest impact on the creation of midpoint indicators, which is due to extraction, manufacturing, and production of parts such as steel casting using non-renewable energy and activities such as high-temperature welding. The total environmental index of Aqkand and Kahak wind power plants for 1 kWh of generated electricity was 5.84 and 4.45 μPt respectively, more than half of which belongs to the damage to human health category. The investigation of the ionizing radiation index showed that the use of diesel fuel in the installation phase resulted in the highest amount of emissions in both of the power plants, so the share of pollutant emissions in the raw materials and production phase is more than 40%, and in the installation phase due to diesel fuel consumption was more than 48%. The investigation of the eutrophication index showed that the raw materials and production stage accounted for more than 95% of the damage to the ecosystem quality category, and in the meantime, copper and electrical components had the highest amount of contribution to the raw materials and production stage. Additionally, diesel fuel accounted for the largest part of the result in the installation stage, and the transportation and maintenance stage included less than 1% of this result. The investigation of the renewable energy consumption index showed that the stage of raw materials and turbine production in the Aqkand power plant with a share of 68% and the Kahak power plant with a share of 70% had the greatest effect on the category of resource damage. Also, the installation and commissioning phase was the second most effective factor in the category of resource damage due to the use of diesel fuel. The study of the cumulative exergy demand index showed that non-renewable-fossil resources had the largest share in exergy demand (0.15 MJ) to produce one kilowatt of electricity generated from power plants.ConclusionIn this study, the results showed that in both plants, about 70% of various respiratory effects, 60% of human health issues, and 25% of acidification and global warming are caused in the raw materials and manufacturing phase. Furthermore, the installation phase is responsible for 17% and 16% of climate change in the Aqkand and Kahak power plants respectively, and between 14% and 26% of other environmental factors.
Bioenergy
M. Zarei; M. R. Bayati; M. A. Ebrahimi-Nik; B. Hejazi; A. Rohani
Abstract
IntroductionAnaerobic bacteria break down organic materials like animal manure, household trash, plant wastes, and sewage sludge during the anaerobic digestion process of biological materials and produce biogas. One of the main issues in using biogas is hydrogen sulfide (H2S), which can corrode pipelines ...
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IntroductionAnaerobic bacteria break down organic materials like animal manure, household trash, plant wastes, and sewage sludge during the anaerobic digestion process of biological materials and produce biogas. One of the main issues in using biogas is hydrogen sulfide (H2S), which can corrode pipelines and engines in concentrations between 50 and 10,000 ppm. One method for removing H2S from biogas with minimal investment and operation costs is biofiltration. Whether organic or inorganic, the biofilter's bed filling materials must adhere to certain standards including high contact surface area, high permeability, and high absorption. In this study, biochar and compost were used as bed particles in the biofilter to study the removal of H2S from the biogas flow in the lab. Afterward, kinetic modeling was used to describe the removal process numerically.Material and MethodsTo remove H2S from the biogas, a lab-sized biofilter was constructed. Biochar and compost were employed separately as the material for the biofilter bed. Because of its high absorption capacity and porosity, biochar is a good choice for substrate and packed beds in biofilters. The biochar pieces used were broken into 10 mm long cylindrical pieces with a diameter of 5 mm. Compost was used as substrate particles because it contains nutrients for microorganisms. Compost granules with an average length of 7.5 mm and 3 mm in diameter were used in this study. For the biofilter reactor, each of these substrates was put inside a cylinder with a diameter of 6 cm and a height of 60 cm. The biofilter's bottom is where the biogas enters, and its top is where it exits. During the experiment, biogas flowed at a rate of 72 liters per hour. Mathematical modeling was used to conduct kinetic studies of the process to better comprehend and generalize the results. This method involves feeding the biofilter column with biogas that contains H2S while the biofilm is present on the surface of the biofilter bed particles. The bacteria in the biofilm change the gaseous H2S into the harmless substance sulfur and store it in their cells. The assumptions that form the foundation of the mathematical models are: the H2S concentration is uniform throughout the gas flow, the gas flow is constant, and the column's temperature is constant at a specific height.Results and DiscussionIn the beginning, biochar was used as a substrate in the biofilter to test its effectiveness, and the results obtained for removing H2S from the biogas were acceptable. H2S concentration in biogas was significantly reduced using biochar beds. It dropped from 300 ppm and 200 ppm to 50 ppm where the greatest H2S concentration reduction was achieved. The level of Methane in the biogas was not significantly impacted by the biofilter. This is regarded as a significant outcome when taking into account the goal which is producing biogas with a high concentration of methane. The H2S elimination effectiveness was 94% with the biochar bed and biogas input with 185 ppm H2S concentration. The removal efficiency reached 76% with the compost bed and input concentration of 70 ppm. Using mathematical models, the simulation was carried out by modifying the model's parameters until the predicted results closely matched the experimental data. It may be concluded that the suggested mathematical model is sufficient for the quantitative description of H2S removal from biogas utilizing biofilm in light of how closely the calculation results matched the experimental data. The only model parameter that was changed to make the model results almost identical to the experimental data was the value of the maximum specific growth rate (μmax) which has the greatest influence on the model results. The value of μmax for the biochar bed was calculated as 0.0000650 s-1 and for the compost bed at 70 ppm and 35 ppm concentrations as 0.0000071 s-1 and 0.0000035 s-1, respectively.ConclusionThe primary objective of this study is to examine the removal of H2S from biogas using readily available and natural substrates. According to the findings, at a height of 60 cm, H2S concentration in biochar and compost beds decreased from 185 ppm to 11 ppm (removal efficiency: 94%) and from 70 ppm to 17 ppm (removal efficiency: 76%), respectively. The mathematical models that were created can quantify the H2S elimination process, and the μmax values in biochar and compost were calculated as 0.0000650 s-1 and 0.0000052 s-1, respectively.AcknowledgmentThe authors would also like to thank UNESCO for providing some of the instruments used in this study under grant number No. 18-419 RG, funded by the World Academy of Sciences (TWAS).
Bioenergy
S. R. Mousavi Seyedi; S. M. R. Miri
Abstract
IntroductionIncreasing industrialization, growing energy demand, limited reserves of fossil fuels, and increasing environmental pollution have jointly necessitated for exploration of a substitute for conventional liquid fuels. Vegetable oils can be used as alternatives to petroleum fuels for engine operation. ...
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IntroductionIncreasing industrialization, growing energy demand, limited reserves of fossil fuels, and increasing environmental pollution have jointly necessitated for exploration of a substitute for conventional liquid fuels. Vegetable oils can be used as alternatives to petroleum fuels for engine operation. These oils are mixtures of free-fatty acid molecules to contain carbon, hydrogen, and oxygen atoms. The ability to simulate the process of converting chemical energy to heat, energy users of computational fluid dynamics software in the design, analysis, and optimization of high-tech tools. Also, simulation saves time and reduces costs, workforce, and the space required.Materials and MethodsIn this research, a one-dimensional computational fluid dynamics solution with GT-Power software was used to simulate a four-cylinder, four-stroke, direct injection diesel engine to study the performance and exhaust emissions characteristics with different speeds and blends at full load. The engine speeds were chosen to be 1100 to 1400 rpm at an interval of 100 rpm. Also, fuel blends such as diesel (as a base), B5, and B10 biodiesel were selected for engine testing. To model a engine, we should have the dimensions of the engine, input air collection, output gases collection, the amount of sprinkled fuel, valves properties, combustion, and some of the estimates corresponding to the cylinder’s thermodynamic parameters when opening the output and input gate and to exchange the heat inside the cylinder as the input data. The model mainly consisted of an air cleaner, intake valve, exhaust valve, intake and exhaust port, injection nozzle, engine cylinder, and engine. Engine cylinder’s intake and exhaust ports are modeled geometrically with pipes. Before this investigation was carried out, a validation model for evaluation was done by experimental and simulation data. The validation results showed that the software model error is acceptable.Results and DiscussionThe engine performance and emissions were evaluated in terms of engine torque, specific fuel consumption, NOx, and CO emission at different engine speeds and fuels at full load. The results showed that with increasing the engine speeds, torque increased. On the other hand, the maximum engine torque for the diesel engine is slightly lower than the biodiesel-blended that increased by 4.4% because of the higher density and viscosity of biodiesel than diesel. Specific Fuel Consumption (SFC) is a measure of the fuel efficiency of any prime mover that burns fuel and produces rotation, or shaft, power. The results indicated that by increasing engine speeds, the SFC increased. A fuel with a lower heating value should be injected with more mass into the engine. This will increase the SFC. So, the maximum engine SFC for the diesel engine is more than the biodiesel-blended that decreased by 4.45% because of better fuel combustion and more power generation of biodiesel than diesel. The only nitrogen oxide that can be formed in an engine combustion temperature is nitrogen monoxide (NO). This pollutant factor can be converted to nitrogen dioxide (NO2) over the time of exhaust gas. The results showed that with increasing the engine speeds, the NOX emissions decrease steadily and then increases, which is due to the high temperature in the cylinder. The viscosity and density of fuels have an effect on NOX emission, and because of the larger droplets of the fuel, it released NOX. The highest NOx emissions belong B10 biodiesel in 1400 rpm, due to the high oxygen content of this fuel and the lowest NOx emissions belong B10 biodiesel in 1300 rpm, due to the low density of the fuel compared to diesel. CO is a colorless and odorless gas, whose even very low concentrations are dangerous for humans and animals. The results showed that with increasing the engine speeds, the CO emission decreased and the minimum CO emission for diesel engine is more than the biodiesel-blended that decreased by 37.61% because of excess oxygen availability and complete combustion in biodiesel than diesel.ConclusionThe results of this study showed that the B10 blend in high engine speeds, generally had the best performance and emissions characteristics among the three fuels used in this study. Also, this investigation will assist in the development of WCO biodiesel as a viable sustainable fuel source through the use of a CFD model, optimized engine configuration, and technical report.
Bioenergy
A. Waismoradi; M. E. Khorasani; H. Bahrami; S. M. Safieddin Ardebili; H. Zaki Dizaji
Abstract
IntroductionToday, the number of diesel engines is increasing due to their high efficiency and low greenhouse gases. In the present study, the effect of adding nano cellulose as nanoparticles to diesel fuel on the performance parameters and emissions of diesel engine was investigated. Nano cellulose ...
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IntroductionToday, the number of diesel engines is increasing due to their high efficiency and low greenhouse gases. In the present study, the effect of adding nano cellulose as nanoparticles to diesel fuel on the performance parameters and emissions of diesel engine was investigated. Nano cellulose was provided by the Nano Novin Company in Sari. Nano cellulose values were considered at 3 levels of zero, 25 ppm and 75 ppm. Also, the tests were performed at 3 engine speed of 1600, 2000 and 2400 rpm in full load mode.Materials and MethodsIn this study, nanocellulose was used as nanoparticles to add to diesel and to evaluate the performance and emission parameters of the engine. To prevent the deposition of nano cellulose in diesel fuel, jelly type nano cellulose was used. The samples were named after adding different amounts of nano cellulose, abbreviated D100N0, D100N25 and D100N75. D100 means 100% pure diesel and N means different amounts of nano cellulose with different amounts. Ultrasound was used to obtain homogeneous samples. About 3 liters were prepared from each sample so that it could be used for at least 3 repetitions. The required tests were performed at three different speeds of 1600, 2000 and 2400 rpm in full load mode. The necessary equipment was used to measure the performance parameters and air emissions, including diesel engine connected to the dynamometer, emissions measuring device, fuel system and control room (to apply the load and provide conditions for each treatment and data collection). The air-cooled, four-stroke, compression-ignition single-cylinder engine made by the Italian company Lombardini was used. The D400 eddy current dynamometer made in Germany was used. The ability to measure power by this dynamometer is a maximum of 21 hp, a maximum speed of 10,000 rpm and a maximum torque of 80 N.m. To measure of emissions, the MAHA MGT5 emissions meter was used. This device is able to measure the values of CO, CO2, NOX, O2 and UHC.Results and DiscussionThe results showed that increasing engine speed in all fuel combinations increased engine power, specific fuel consumption, carbon monoxide and unburned hydrocarbons and decreased torque. Also, increasing the amount of nano cellulose per engine speed increased the amount of power and torque, but reduced the specific fuel consumption, carbon monoxide and unburned hydrocarbons. The amount of NOX increased with increasing engine speed, but at each engine speed the addition of 25 ppm nanocellulose to pure diesel significantly increased the amount of NOX. But at low speed, increasing 75 ppm nanocellulose to pure diesel reduced the amount of NOX.ConclusionThe results of this study showed that the addition of nano cellulose as nanoparticles can improve the performance of diesel engines and also reduce the amount of emissions gases emitted from the engine. The results also showed that increasing 25ppm nanocellulose had a greater effect on engine performance. But to reduce the amount of emissions, 75 ppm nanocellulose was better.
Bioenergy
M. Eshaghi Pireh; M. Gholami Par-Shokohi; D. Mohammad Zamani
Abstract
IntroductionBiodiesel is an eco-friendly renewable alternate fuel and is made from transesterification of vegetable oils and animal fat. The use of biodiesel fuel as a strategy to conserve energy and reduce emissions is becoming increasingly important in engines. Biodiesel fuels increase NOx emissions ...
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IntroductionBiodiesel is an eco-friendly renewable alternate fuel and is made from transesterification of vegetable oils and animal fat. The use of biodiesel fuel as a strategy to conserve energy and reduce emissions is becoming increasingly important in engines. Biodiesel fuels increase NOx emissions in the engines. Compensate for the negative effect, the use of particles additive can be a reliable solution. In this study, the state of heat balance in a single-cylinder, four-stroke diesel engine with different fuel combinations with DXBYGZ formula (X % diesel fuel, Y % biodiesel mass, and Z ppm graphene oxide nanoparticles), has been studied experimentally.Materials and MethodsGraphene nanoparticles in three levels of 30, 60, and 90 ppm were mixed with biodiesel produced from cooking waste oil by transesterification method with volume percentages of 5 and 20% and pure diesel was used. The test engine was a diesel engine, single-cylinder, four-stroke, compression ignition, and water cooling, in the laboratory of renewable energies of agricultural faculty, Moghadas Ardabili University. The engine is connected to a dynamometer and data were obtained after reaching steady state conditions. In thermal balance study, the combustion process merely as a process intended to free up energy fuel, and the first law of thermodynamics is used. The energy contained in the fuel is converted to useful and losses energies by combustion. Useful energy measured by dynamometer as brake power and losses energy including exhaust emission and cooling system losses. Variance analysis of all engine energy balance was done by split-plot design based on a completely randomized design and the means were compared with each other using the Duncan test at 5% probability.Results and DiscussionThe results showed that by adding 60 ppm of graphene oxide and 20% biodiesel to diesel fuel, the useful output power is reduced to a minimum and is reduced by about 5.52%. The results of the model evaluation of useful power, exhaust emissions, and thermal losses in the cooling system showed that the exponential model had a better fit. By adding biodiesel and graphene oxide nanoparticles to diesel fuel, the useful power was reduced. In order to achieve the maximum useful output power and with the priority of adding biodiesel to a high amount, the fuel composition of D80B20G90 had relatively better conditions. By adding 30 ppm of graphene to pure diesel fuel, the equivalent power of exhaust fumes was reduced to a minimum of about 18.5%. In general, heat loss through the cooling system in pure diesel fuel (D100) was lower than other fuel compounds. Pure diesel fuel was recognized as the best fuel mixture due to having the highest useful power, and lowest energy losses in the form of exhaust fumes and through cooling.ConclusionBy adding graphene oxide to pure diesel fuel, the useful output power was reduced to a minimum. With the increase of biodiesel to diesel fuel, the amount of power of the cooling system also increased. By adding graphene oxide to pure diesel fuel, the equivalent power of the exhaust fumes was reduced. Heat loss through the cooling system increased with the increase of nano-graphene and biodiesel.
Bioenergy
M. Kamali; R. Abdi; A. Rohani; Sh. Abdollahpour; S. Ebrahimi
Abstract
IntroductionSince anaerobic digestion leads to the recovery of energy and nutrients from waste, it is considered the most sustainable method for treating the organic fraction of municipal solid wastes.However, due to the long solid retention time in the anaerobic digestion process, the low performance ...
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IntroductionSince anaerobic digestion leads to the recovery of energy and nutrients from waste, it is considered the most sustainable method for treating the organic fraction of municipal solid wastes.However, due to the long solid retention time in the anaerobic digestion process, the low performance of the process in biogas production as well as the uncertainty related to the safety of digested materials for utilizing in agriculture, applying different pretreatments is recommended.Thermal pretreatment is one of the most common pretreatment methods and has been used successfully on an industrial scale. Very little research, nevertheless, has been done on the effects of different temperatures and durations of thermal pretreatment on the enhancement of anaerobic digestion of the organic fraction of municipal solid wastes (OFMSW). The main effect of thermal pretreatment is the rapturing cell membrane and dissolving organic components. Thermal pretreatment at temperatures above 170 °C may result in the formation of chemical bonds that lead to particle agglomeration and can cause the loss of volatile organic components and thus reduce the potential for methane production from highly biodegradable organic waste. Therefore, since thermal pretreatment at temperatures above 100 °C and high pressure requires more energy and more sophisticated equipment, thermal pretreatment of organic materials at low temperatures has recently attracted more attention. According to the researchers, thermal pretreatment at temperatures below 100 °C did not lead to the decomposition of complex molecules but the destruction of large molecule clots.The main purpose of this study was to find the optimal levels of pretreatment temperature and time and the most appropriate concentration of digestible materials to achieve maximum biogas production using a combination of the Box Behnken Response Surface Method to find the objective function followed by optimizing these variables by Genetic Algorithm.Materials and MethodsIn this study, the synthetic organic fraction of municipal solid waste was prepared similar to the organic waste composition of Karaj compost plant. The digestate from the anaerobic digester available in the Material and Energy Research Institute was used as an inoculum for the digestion process. Some characteristics of the raw materials that are effective in anaerobic digestion including the moisture content, total solids, volatile solids of organic waste, and the inoculum were measured. Experimental digesters were set up according to the model used by MC Leod. After size reduction and homogenization, the synthetic organic wastes were subjected to thermal pretreatment (70, 90, 110 °C) at specific times (30, 90, 150 min).The Response Surface methodology has been used in the design of experiments and process optimization. In this study, three operational parameters including pretreatment temperature, pretreatment time, and concentration of organic material (8, 12, and 16%) were analyzed. After extracting the model for biogas efficiency based on the relevant variables, the levels of these variables that maximize biogas production were determined using a Genetic Algorithm.Results and DiscussionThe Reduced Quadratic model, was used to predict the amount of biogas production. The value of the correlation coefficient between the two sets of real and predicted data was more than 0.95. The results suggested that pretreatment time followed by the pretreatment temperature had the greatest contribution (50.86% and 44.81%, respectively) to biogas production. Changes in the organic matter concentration, on the other hand, did not have a significant effect (p ˂ 0.01) on digestion enhancement (1.63%) but were statistically significant at p ˂ 0.10.The response surface diagram showed that the increase in pretreatment time first led to a rise and then a fall in biogas production. The decline in biogas production seemed set to continue with pretreatment time. Meanwhile, the increase in pretreatment temperature from 70 °C to 110 °C first contributed to higher biogas production and then the decrease in gas production occurred. The reason for this fall was probably the browning and Maillard reaction.The regression model was applied as the objective function for variables optimization using the Genetic Algorithm method. Based on the results of this algorithm, the optimal thermal pretreatment for biogas production was determined at 95 °C for 104 minutes and at the concentration of 12%. The expected amount of biogas production by applying the optimal pretreatment conditions was 445 mL-g-1 VS.ConclusionIn this study, the variables including thermal treatment temperature and time as well as the concentration of organic waste to be anaerobically digested were optimized to achieve the highest biogas production from anaerobic digestion.Statistical analysis of the results revealed that the application of thermal pretreatment increased biogas production considerably. According to the regression model, the contribution of pretreatment time and temperature to biogas production was significant (50.86% and 44.81% respectively). In stark contrast, varying substrate concentrations in the range of 8 to 16% had a smaller effect (1.63%) on biogas production. The results of this study also showed that the best pretreatment temperature and time were 95 °C and 104 minutes, respectively, at a concentration of 12% by generating 445 mL-g-1 VS biogas which is 31.17% higher than the biogas yield from anaerobic digestion of untreated organic wastes at this concentration.
