Anaerobic Digestion Of Biomass Research Articles

Assessment of the Potential of Sunflower Grown in Metal-Contaminated Soils for Production of Biofuels

Environmental biotechnology needs solutions that are associated with a low budget and cleaner remediation, and which are connected to resources and energetic valorization, to be able to encourage a circular bioeconomy. A prospective resolution for heavy-metal-contaminated soils is the application of phytoremediation approaches merged with bioenergy generation using the resulting biomass. Sunflower (Helianthus annuus) has been studied as a feedstock for biodiesel generation, and appears to be very attractive for biogas and bioethanol production. The current study reports an innovative energetic valorization approach of H. annuus biomass derived from the application of a phytoremediation strategy devised to remove Zn and Cd from an industrially contaminated soil (599 mg Zn kg−1 and 1.2 mg Cd kg−1)—and its comparison to the analysis of the same energetic valorization pathway for sunflower plants growing in an agricultural non-contaminated soil. After plant harvesting, bioethanol was produced from the aboveground tissues, and applied in the transesterification of the oil obtained through seed extraction for the generation of biodiesel. Also, biogas production was assessed through the root’s biomass anaerobic digestion. Similar yields of oil extraction—0.32 and 0.28 mL g−1 DW—were obtained when using seeds from H. annuus cultured in contaminated and non-contaminated soils, respectively. The production yield of bioethanol was superior using biomass from the agricultural non-contaminated soil (0.29 mL g−1 DW) when compared to the industrial metal-contaminated soil (0.20 mL g−1 DW). Zinc was measured in minor levels in bioethanol and oil (ca. 1.1 and 1.8 mg mL−1, correspondingly) resulting from the biomass cultivated in the industrialized soil, whereas Cd was not detected. The production yield of biogas was superior when using root biomass from H. annuus cultivated in agricultural non-contaminated soil (VS max. ca. 104 mL g−1) when compared to the one deriving from the industrial contaminated soil (VS max ca. 85 mL g−1). Generally, results demonstrate that substantial production yields of the tested biofuels were attained from biomass resulting from phytoremediation, corroborating this integrated original approach as a valuable alternative for the phytoremediation of HM-polluted soils and as an important strategy for plant biomass valorization.

A Case Study on Small- and Centralized Biogas Plants and Energy Production Capacities in South Gyeongsang Province

Objectives : The primary goals of this research were to assess the viability and practicality of small-scale village facilities as well as central commercial biogas plants. Additionally, the study aimed to create predictive models by exploring various codigestion scenarios.Methods : The study conducted a comprehensive analysis of available biomass and its maximum energy potential through anaerobic digestion in every city, county, and village within South Gyeongsang province. Five distinct codigestion scenarios were explored, encompassing assessments of processing capacity, energy production potential, and the necessary digester capacity for anaerobic digestion. At the village level, the scenarios comprised: C1, which involved sole digestion of manure; C2, codigestion of manure and food waste in a 7:3 ratio; C3, codigestion of pig slurry and slaughterhouse waste in a 9:1 ratio; C4, multiple codigestion with PS:FW:SW=6.5:2.8:0.8; and finally, C5, involving the addition of sewage and sewage sludge to the codigestion process of C4's biomass.Results and Discussion : The biomass generated in South Gyeongsang province was 9430 tons/day, with a methane production potential of 167 million cubic meters/year. This biomass had an energy production potential of 156,000 TOE/year and a potential electricity generation of 732.7 GW/year, based on the annual petroleum conversion ton. Codigestion (C5) enabled up to 720% more electricity generation compared to sole digestion of manure (C1). Mixing pig slurry and food waste in a 7:3 ratio resulted in approximately 18% more electricity production compared to the case where manure was mixed with slaughterhouse by-products in a 9:1 ratio.Conclusion : Biomass imbalance was significant in most regions, particularly due to high variations in food waste generation between regions. Obtaining alternative resources and integrating various biomass for anaerobic digestion, especially in rural areas, is crucial for achieving stable anaerobic digestion and high methane production. Regions with high biomass density are predicted to support large-scale biogas facilities following European standards, while 25 villages showed the potential for small-scale biogas facilities.

Methane production enhancement from Tetraselmis biomass co-digestion using frying oil residue as co-substrate and ultrasonication as pretreatment

Tetraselmis sp. microalgae biomass co-digestion with frying oil residue was investigated without or with an ultrasonication pretreatment to enhance methane production. Interestingly, compared to untreated Tetraselmis biomass, sonication pretreatment using specific energy of 1.6 kJ/g VS significantly improved specific methane production by 60.5%. However, increasing specific energy sonication to 3.2 kJ/g VS decreased the positive energy balance of whole process from 8.6 to 5.5 ± 0.3 kJ/g VS. Furthermore, it was demonstrated that the correction of the C/N ratio with frying oil waste coupled to the sonication of Tetraselmis biomass resulted in the highest methane yield of 443.7 ± 6.4 ml CH4/g VS and highest energy gains of 14.1 ± 0.2 kJ/g VS. This study proved the presence of a complementary effect between the co-digestion of Tetraselmis biomass with frying oil and the sonication pretreatment on specific methane production and energy recovery. Low ultrasonication specific energy is recommended as pretreatment process before Tetraselmis biomass anaerobic digestion.

ANALISA UNJUK KERJA GENERATOR SET BERBAHAN BAKAR BIOGAS DARI LIMBAH RUMAH MAKAN

Anaerobic digestion of biomass is a beneficial strategy for energy production as well as waste management system. Through the fermentation process, the sources of biomass such as domestic organic and restaurant wastes can be utilized to generate renewable energy. In this study, the biogas was produced from food waste to generate electricity. The performance of biogas-fueled generator sets was evaluated and compared with gasoline (pertalite) fuel at various electric loads. There are seven different load variations: idle, 100, 300, 500, and 700 Watts. Data was collected and analyzed using the combustion engine gas cycle theory. The biogas-fueled engine test results indicated that the brake power on average was 1.9 kW, or 65.6% of the maximum brake power specified by the engine manufactured. The average torque was 7.02 Nm, or 68.14% of the maximum torque that specified on the name plate. The average brake specific fuel consumption (BSFC) of biogas was 0.41 kg/kWh, while the BSFC of pertalite fuel was 0.168 kg/kWh. Compared to biogas fuel, the BSFC of pertalite is less with a difference of 0.242 kg/kWh. Overall, biogas produced from restaurant waste is technically feasible to be used as an alternative fuel for electric generator engine.

Sustainable enhancement of biogas and methane yield of macroalgae biomass using different pretreatment techniques: A mini-review

Macroalgae can be grown without the use of fertilizer, fresh water, or arable land. These qualities support its use for biofuel production because it frees up land for other traditional energy sources and food crops. It has been investigated as biogas feedstock to substitute for fossil fuels burning with attendant effects on the ecosystem. The microstructural arrangement of macroalgae biomass is restricting their conversion to biogas. Therefore, application of pretreatment before anaerobic digestion is needed to enhance their availability to microbial degradation and subsequent increase in biogas yield. Pretreatment application for substrate catalysis is vital to recovering eco-friendly and economical energy from macroalgae. This study summarizes the state of the art of various pretreatment methods employed to enhance macroalgae biomass's anaerobic digestion process. These methods were categorized as thermal, biological, chemical, nanoparticle additives, mechanical, and combined. Merits and challenges associated with each of these methods were also considered. The study shows that all the pretreatment methods considered can improve the biogas yield if the appropriate method is selected based on the type of macroalgae species. Pilot-scale studies that will assist in assessing their feasibility on the full-scale implementation are still missing.

Bioeconomy for sustainable development of biomethane sector: Potential and challenges for agro-industrial by-products

Agro-industrial transformation chains generate huge amounts of by-products which can be used as biomass for anaerobic digestion (AD) process, by avoiding their disposal in landfill and therefore environmental issues. This study has investigated a biomass mix to increase biogas production optimizing AD process parameters with the aim of enhancing AD performance in terms of produced biogas quality. In detail, both Biochemical Methane Potential (BMP) and AD approaches were adopted for investigating the effect of mixing seven Mediterranean feedstocks (i.e., olive pomace, citrus pulp, poultry and cattle manure, whey, tomato peels, and cereal straw) on methane production for bioenergy generation. The BMP test demonstrated that the tested mix had potential to be used for biogas production with 60.9% of methane content in the produced biogas. In detail, the specific production was equal to 343.1 Nm3CH4/tVS, confirmed by the results obtained during the AD test, about 333 Nm3CH4/tVS. As result, the tested mix demonstrated good net energy outputs and provide a flexible and useful solution to generate bioenergy from by-products. These findings confirm the possibility of using these by-products as energy sources and initiating virtuous bioeconomy processes for the sustainable utilization of renewable natural resources, transforming waste-to-resources.

Purification of biogas for methane enrichment using biomass-based adsorbents: A review

Among the others, CO2 and H2S are the major impurities present in biogas which typically hampers its direct application as biofuel. Therefore, an upgradation for the purity of biogas is a high research endeavour. In the recent past, several attempts are made to improve the quality of biogas with a focus to reduce the impact of global warming due to the excessive release of CO2. This is interesting to indicate that most of the biogas cleaning methods are expensive and very rare documents detailed the usage of sustainable materials for the purification of biogas. Given that, developments in the applications of natural materials as low-cost adsorbents used for the purification of biogas are well documented in the present review paper. Globally more than 40% of the countries utilise water scrubbing as the most popular method for the separation of biomethane. This article also reviews the other most accepted technologies such as different types of scrubbing, swing adsorption, and cryogenic separation with biological upgradation. Hybrid biological methods are used in methane upgrading systems to increase production of pure CH4 while reducing the need for energy and chemicals. Moreover, the article also summarized different numerical models, frequently adopted for the prediction of the upgradation of biomass. Finally, the article concludes with major bottlenecks involved in the processes and recommended scientific directives towards successfully transforming bench-scale studies into large-scale implementation. So, this article will ensure the readers gather overall technical information on the upgradation of biogas obtained from the anaerobic digestion of biomass.

Modeling and simulation of biomass anaerobic digestion for high biogas yield and CO2 mineralization

Bioenergy is one of several renewable energy options derived from biomass that can help satisfy our energy needs. Anaerobic digestion is a viable method for producing bioenergy in the form of biogas from biomass. The anaerobic digestion process is challenged with low biogas recovery, and low-quality effluent or CO2 emission, which contribute to environmental pollution and the carbon footprint in the atmosphere. Computational process modelling and simulation can provide realistic information for dealing with the technological challenges involved with anaerobic digestion. In this study, modeling and simulation of the simplified anaerobic digestion process were done using SuperPro Designer software fed with biomass feedstock containing carbohydrates, proteins, and fats, as well as yeast, at 37 °C mesophilic temperature. The anaerobic digestion process yielded 89.655% of CH4 and 10.345% of CO2 and confirmed that the carbohydrate feedstock produces more CH4 composition in the biogas. Mineralization of CO2 using MgO yielded 0.23% MgCO3, consuming > 99% of the CO2 produced during the anaerobic digestion process. Environmental impact assessment of the effluent discharge yielded 0.142 kg Slds/L volatile solid with 6.01% COD reduction per batch of the anaerobic digestion process in an anaerobic digester with 90% (1.925 kg/batch) feedstock dosage. The data indicate that single-batch effluent cannot be discharged into the environment, hence indicating the possible recycling for multiple anaerobic digestion processing. The results are a significant guide for the realistic scalable production of high-quality biogas for bioenergy application, CO2 mineralization, and environmental remediation.

Comparison of the Effect of Solid and Liquid Digestate on the Growth of Lettuce (Lactuca sativa L.) Plants

As a consequence of the Russian invasion of Ukraine, Europe is facing a shortage of chemical fertilizers for agriculture. Therefore, the use of byproducts of biomass anaerobic digestion, e.g., solid (SD) and liquid (LD) digestate, could be a key solution to cope with this problem. In this framework, the present study aimed to evaluate the effect of both SD and LD, derived from the same feedstock, on the biometric, physiological, and biochemical parameters of lettuce (Lactuca sativa L.) plants. Compared to the controls, the plants treated with 3% (w/w) SD showed a reduction in leaf fresh biomass, chlorophyll content, performance index, fractal dimension, and antiradical activity, while vitamin C increased by 18.8%. An opposite response was observed for the lettuce plants treated with 3% (v/w) LD, which showed an increase in all the above-mentioned parameters, except for vitamin C, which decreased by 39.8%. Interestingly, the content of malondialdehyde, which is correlated with cell membrane lipid peroxidation, increased in the SD-treated plants (+39.7%) and decreased (−42.1%) in the LD-treated plants. These results strongly support the use of LD in agriculture as a valuable product to improve the productivity and nutritional quality of crop plants.

Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.

Modification of anaerobic digestion model No.1 with Machine learning models towards applicable and accurate simulation of biomass anaerobic digestion

This work proposed a so-called M−ADM1 model for anaerobic digestion simulation, which uses machine learning model to predict the kinetic parameters in anaerobic digestion model No.1 (ADM1). A total of 75 biomass samples were used to establish the machine learning model. Inputs used to predict the kinetic parameters included C, H, O, N, S contents, and digestion temperature. The sensitivities of 17 kinetic parameters were evaluated, and 7 kinetic parameters with the highest sensitivities were selected as model outputs. After model optimization, the average R2 for predicting the 7 kinetic parameters reached 0.92, and the root mean square error reached 0.167. The accuracy of the overall M−ADM1 expressed by Theil inequality coefficient of municipal solid waste, kitchen waste, and sludge were 0.0163, 0.0327, and 0.0361, respectively. The results validated the hypothesis that accurately predicting some crucial intermediate parameters using machine learning models could enhance the performance of tradition ADM1.

Sensitivity Analysis and Anaerobic Digestion Modeling: A Scoping Review

A growing awareness of global climate change has led to an increased interest in investigating renewable energy sources, such as the anaerobic digestion of biomass. This process utilizes a wide range of microbial communities to degrade biodegradable material in feedstock through a complex series of biochemical interactions. Anaerobic digestion exhibits nonlinear dynamics due to the complex and interacting biochemical processes involved. Due to its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions, anaerobic digestion is highly susceptible to instabilities. Therefore, in order to model and operate a biogas production unit effectively, it is necessary to understand which parameters are most influential on the model outputs. This also reduces the amount of estimation required. Through a scoping review, the present study analyzes the studies on the application of sensitivity analysis in anaerobic digestion modeling. Both local and global sensitivity analysis approaches were carried out using different mathematical models. The results indicate that anaerobic digestion model no.1 (ADM1) was the most commonly used model for analyzing sensitivity. Both local and global sensitivity analyses are widely employed to investigate the influence of key model parameters such as kinetic, stoichiometric, and mass transfer parameters on model outputs such as biogas production, methane concentration, pH, or economic viability of the plant.

Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass

Biogas derived from the anaerobic digestion of biomass can provide a carbon-neutral resource for green energy supply in the future. The biochemical methane potential (BMP) test has been widely applied to assess the characteristics of methane production from anaerobic digestion in batch mode. However, the determination of key parameters in the BMP test, such as specific methane yield (SMY), usually requires long-term experiments, especially for lignocellulosic feedstocks with slow degradation rates. This study aims to propose an appropriate data-driven model for the efficient prediction of the SMY using data from 277 samples of various lignocellulosic biomass materials by evaluating ten different machine learning (ML) methods. The Pearson coefficient matrix indicates that the chemical components are more relevant as attributes for the ML models, compared to element compositions, and the content of lignin has a strong linear correlation with SMY. Classic nonlinear ML methods (R2 ≥ 0.61) perform better than linear methods (R2 ≤ 0.56), and an ensemble learning model (R2 = 0.71) is better than a single learner (R2 ≤ 0.67). A K-nearest neighbor (KNN) model using leave-one-out cross-validation (LOOCV) obtains the best performance (R2 = 0.75, MAE = 30.2 mL/gVS). The generalization performance of the best model is found to have an average relative error of 10.05%.

Application of nanomaterials in anaerobic digestion processes: A new strategy towards sustainable methane production

Anaerobic digestion is an established biomass conversion technology that is renewable, environmentally friendly, and technologically robust. However, to turn it into a more profitable and sustainable business model, stringent research into process optimization strategies is required. As a result, there is a continuous search for strategies and pathways to improve process stability as well as product quality and yield and to decrease the production of inhibitory compounds. More recently, nanoparticles have been utilized as catalysts to enhance the performance of anaerobic digestion technologies. The current paper provides a state-of-the-art review on utilizing nano-catalysts in the biomass anaerobic digestion process. The potential of several kinds of nano-catalyst has been discussed to enhance biogas production with a cost-effective approach. The results of this research suggest that while nanoparticle supplementation is a promising strategy, further research is required to address the energetic, technological, and economical utilization of nanoparticles in anaerobic digestion processes.

Microalgae and Cyanobacteria Biomass Pretreatment Methods: A Comparative Analysis of Chemical and Thermochemical Pretreatment Methods Aimed at Methane Production

Anaerobic digestion of microalgae and cyanobacteria was first proposed as a destination for algal biomass accumulated on stabilization ponds since it could not be disposed of directly in the environment. Now, the versatility of algal biomass makes them a suitable candidate to produce biofuels and other biomolecules in biorefineries. Anaerobic digestion of biomass is advantageous because it does not require the extraction of specific cellular constituents or drying of the biomass. Nevertheless, challenges remain regarding biomass concentration and their resistant cell walls, which are factors that could hamper methane yield. Many pretreatment methods, including chemical and thermochemical, have been proposed to break down the complex polymers present on the cell wall into smaller molecules. Unfortunately, the relationship between biomass solubilization and methane yield is not well defined. This article intends to review the anaerobic digestion of algal biomass and the role of chemical and thermochemical pretreatments in enhancing methane production. Several pretreatment conditions selected from the scientific literature were compared to verify which conditions actually improve methane yield. The severity of the selected pretreatments was also assessed using the combined severity factor. Results suggest that thermochemical pretreatment in less severe conditions is the most efficient, leading to a greater increase in methane yield. Only enzymatic pretreatments and some thermal pretreatments result in a positive energy balance. The large-scale implementation of pretreatment methods requires technological innovations to reduce energy consumption and its integration with other processes in wastewater treatment plants.

METHANATION TECHNOLOGIES FOR PRODUCING SYNTHETIC RENEWABLE METHANE

Methanation, or the generation of synthetic methane through the combination of carbon dioxide and hydrogen, has been attracting more and more attention of researchers and energy scientists in recent years due to the fact that the development of an effective and economically feasible technology for the implementation of this process will allow solving a number of energy and environmental problems. First, it is the accumulation of excess renewable electricity from solar and wind power plants by using it in the creation of another energy-intensive product, namely synthetic natural gas, which removes the problem of coordinating unstable sources of electricity with energy networks. Secondly, methanation becomes another technology for enriching biogas and turning it into biomethane, which will allow it to be used through existing gas networks and contribute to solving the problem of natural gas shortage.
 The development and improvement of methanation technologies are engaged in many organizations of the world - Germany, Denmark, France, the USA, Japan and others. Research is conducted in two main directions: catalytic methanation and biological methanation. In the first direction, methanation is carried out through the Sabatier reaction using catalysts. The problems of such methanation are: the development of catalysts with high activity, selectivity and resistance to the heat of reaction, the provision of optimal reaction modes, in particular temperature and pressure, through the use of various methods of reactor cooling, control of the reaction mechanism, the use of three-phase reactors, changing their structure, and so on. Biological methanation is carried out using of biological methanogens - so-called archaea, which act as a kind of catalyst. The methanation is carried out either directly in the biomass anaerobic digestion reactor (in-situ methanation) or in a separate reactor into which biogas and hydrogen are fed separately (ex-situ methanation). One of the main problems of in-situ methanation is the simultaneous provision of optimal conditions for both acetoclastic and hydrogenotrophic methanogens. This problem is solved by ex-situ methanation, in which the optimal conditions for anaerobic digestion and methanation processes are provided separately. It is clear that optimal conditions are also provided for biomethanation of pure CO2 and H2, when the «broth» for archaea is created separately. A comparison of catalytic and biological methanation technologies shows that catalytic methanation provides higher energy efficiency and requires much smaller reactor sizes than biological methanation for the same methane yield. However, the latter has a higher resistance to harmful impurities than the catalytic one.

Evaluation of Energy Alternatives through FAHP for the Energization of Colombian Insular Areas

In this study, the methodology of the Analytical Hierarchy Process with fuzzy logic (FAHP) is implemented to provide decision criteria in the selection, planning and development of electric power generation projects from renewable energy sources in the insular regions of Colombia. In this study, six renewable energy sources were considered, biomass combustion, anaerobic digestion of biomass, biogas landfills, waste incineration, photovoltaic energy and hydrogen-generated energy, due to their energy potential in insular areas and not interconnected with the national electricity system. To determine the order of priority in the development of energy conversion technologies, a questionnaire was drawn up and sent to a group of experts. Given the need to generate electricity in a sustainable way, the information was analyzed under four main criteria: technical, environmental, social and economic. Sixteen additional subcriteria were selected based on a literature review. In general, the economic criterion is the most relevant in the area due to the high investment and operating costs of electricity generation. The social criterion highlights the opportunity to create new jobs very important for the study area, while the environmental criterion highlights the component of renewable energy substitution and environmental care, a key aspect in the diversification of the energy matrix, which is part of the country's political agenda. Regarding the technological component, self-consumption photovoltaic energy seems the most favorable due to its low environmental impact and the considerable price reduction experienced by the solar panel market in recent years.

Implementation of the Hierarchical Analytical Process in the Selection of the Best Source of Renewable Energy in the Colombian Caribbean Region

In this study, the Analytic Hierarchy Process (AHP) methodology is implemented to provide decision criteria in the selection, planning, and development of electric power generation projects from renewable energy sources in the Caribbean region of Colombia. Six sources of renewable energy; biomass combustion; anaerobic digestion of biomass; biogas landfills; waste incineration; Photovoltaic energy and solar thermal radiation were considered in this study due to their energy potential in rural areas and areas not interconnected to the national electricity system. To determine the order of priority in the development of energy conversion technologies, a questionnaire was developed and sent to a group of experts. Given the need to generate electricity sustainably, the information was analyzed under four main criteria: technical, environmental, social, and economic. Sixteen additional sub-criteria were selected based on a literature review. In general, the economic criterion is the most relevant in the area due to the high investment and operating costs of electricity generation. The social criterion highlights the opportunity to create new jobs, while the environmental criterion highlights the component of substitution of renewable energy, a key aspect in the diversification of the energy matrix, which is part of the country's political agenda. Regarding the technological component, photovoltaic energy seems the most favorable due to its low environmental impact and the considerable reduction in prices experienced by the solar panel market in recent years.

Numerical analysis of bio-digester substrate heating methods

Biogas is a renewable fuel produced by anaerobic digestion of biomass. The performance of anaerobic digestion depends on many factors in which temperature is one of the important parameter. The biogas production rate and yield varies according to the temperature of the substrate. The present work focuses on the importance of temperature in biogas generation from anaerobic digestion and also to compare different in-direct heating methods using ANSYS Fluent K-Epsilon model. The different substrate heating methods are Floor heating, In-vessel heating, and On-vessel heating which can be incorporated into conventional domestic digesters to heat the substrate to the desired optimum temperature for enhanced microbial activity and thereby increased biogas production. Different heating sources are used to heat the slurry like electric coil to heat the circulating fluid and in the case of domestic scale bio digester, solar energy can be utilised. Solar water heater incorporated with a storage system would be appropriate. From the numerical analysis, Floor heating method gave about 5 °C rise in temperature of the slurry and On-vessel showed 5–6 °C rise in temperature and In-vessel showed 6–7 °C rise in temperature of the substrate and was found to be more efficient than other heating methods because it can distribute heat more uniformly to the substrate.

Modification of Anaerobic Digestion Model No.1 with Machine Learning Models Towards Applicable and Accurate Simulation of Biomass Anaerobic Digestion

Modification of Anaerobic Digestion Model No.1 with Machine Learning Models Towards Applicable and Accurate Simulation of Biomass Anaerobic Digestion