Anaerobic Co-digestion Process Research Articles

Study on the Environmental Impact and Benefits of Incorporating Humus Composites in Anaerobic Co-Digestion Treatment.

This study evaluated the environmental impact and overall benefits of incorporating humus composites in the anaerobic co-digestion of kitchen waste and residual sludge. The life cycle assessment method was used to quantitatively analyze the environmental impact of the entire anaerobic co-digestion treatment process of waste, including garbage collection, transportation, and final product utilization. Moreover, the comprehensive assessment of the environmental impact, energy-saving and emission-reduction abilities, and economic cost of using humus composites in the anaerobic co-digestion treatment process was conducted using a benefit analysis method. The results showed that the anaerobic co-digestion of kitchen waste and residual sludge significantly contributed to the mitigation of global warming potential (GWP), reaching -19.76 kgCO2-eq, but had the least impact on the mitigation of acidification potential (AP), reaching -0.10 kgSO2-eq. In addition, the addition of humus composites significantly increased the production of biogas. At a concentration of 5 g/L, the biogas yield of the anaerobic co-digestion process was 70.76 m3, which increased by 50.62% compared with the blank group. This amount of biogas replaces ~50.52 kg of standard coal, reducing CO2 emissions by 13.74 kg compared with burning the same amount of standard coal. Therefore, the anaerobic co-digestion treatment of kitchen waste and residual sludge brings considerable environmental benefits.

Efficient reuse of Sargassum spp. biomass and organic fraction of municipal solid waste by anaerobic co-digestion in the Dominican Republic: Evaluation of biochemical methanogenic potential and reaction rates

Anaerobic digestion (AD) is a potential solution to valorize invasive pelagic Sargassum spp. Sargassum spp. (SP) biomass is characterized by a low carbon/nitrogen (C/N) ratio, which, in addition to the presence of indigestible fiber, sulfide, salt, ash, and polyphenol content, are inhibitors to the AD process. Furthermore, its chemical composition depends on the season and region of harvesting. To increase biogas yields, biomass must be subjected to pre-treatment or an anaerobic co-digestion process with other waste biomass. In this paper results of co-digestion of Sargassum spp. and municipal solid waste (OFMSW) batches with different weight ratios are reported and compared with the mono-digestion of the two organic matrices. The objective is to provide an optimized SP to OFMSW ratio for the sustainable production of biogas in the Dominican Republic. Mono-digestion of Sargassum spp. showed the longest reaction time and the lowest biomethane yield as it lasted 30 days and provided a cumulative volume of biomethane equal to 79.68 NmLg−1VS. The addition of OFMSW led to the shortening of the reaction time to 10 days and to the increase of the yield and cumulative volume of biomethane. It can be attributed to the more favorable C/N ratio, to the presence of more readily digestible compounds and lower ash content of those batches. The reaction kinetics of all the investigated batches is properly fitted by the Modified Gompertz model. The system with a Sargassum spp.-OFMSW weight ratio of 33:67 allows to obtain a notable bio-methane volume of 327.27 ± 15.93 NmLg−1VS, ten times higher than from Sargassum spp. alone.

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Many operational parameters, either discretely or collectively, can influence the biodegradation performance towards enhancing biogas yield and quality. Among the operating parameters, organic loading rate (OLR), inoculum-substrate ratio, and carbon-nitrogen ratio (C/N) are the most critical parameters in the optimization and enhancement of biogas yield. Optimization of the biogas production processes depends on the ability of anaerobic microorganisms to respond to variations in operational parameters such as pH, redox potential, and intermediate products to enhance the biogas yield. This review article focuses on the role of process parameters, kinetic models, artificial intelligence, Aspen Plus (AP), and anaerobic digestion model no. 1 (ADM1) in optimizing biogas yield via an anaerobic codigestion (AcoD) process. The review showed that biomaterials codigestion upgraded biogas yield to the extent of 400%, and organic removal efficiency reached up to 90% compared to a single substrate. In addition, the current work has verified that the kinetic model is the most effective tool for signifying that the hydrolysis phase is the rate-limiting step, whereas AP is the most effective tool in the design and optimization of the AcoD process parameters. The reviewed kinetic and AI models show strong correlation values ranging from 0.931 to 0.9991 and 0.8700 to 0.9998, respectively. The AcoD system involves complex chemical reactions, but AP might have limitations in representing such complex chemical processes with nonideal behavior and complicated reaction mechanisms. The design and optimization of AcoD with reliable input parameters are highly limited or nonexistent. The AcoD process design with AP opens fresh research opportunities, including improved efficiency, finding appropriate retention time, and saving time, as well as finding the optimum biogas yield. This review article gives an insightful understanding of AcoD process parameter optimization and valuable strategies for policy development enhancing sustainability in the biogas sector.

ADM1-Based Modeling of Biohydrogen Production through Anaerobic Co-Digestion of Agro-Industrial Wastes in a Continuous-Flow Stirred-Tank Reactor System

Biological treatment is a promising alternative for waste management considering the environmentally sustainable concept that the European Union demands. In this direction, anaerobic digestion comprises a viable waste treatment process, producing high energy-carrier gases such as biomethane and biohydrogen under certain operating conditions. The mathematical modeling of this bioprocess can be used as a valuable tool for process scale-up with cost-effective implications. The scope of this work was the evaluation of the well-established Anaerobic Digestion Model 1 (ADM1) for use in two-stage anaerobic digestion of agro-industrial waste. Certain equations for the description of the metabolic pathways for lactate and bioethanol accumulation were implemented in the existing mechanistic model in order to enhance the model’s accuracy. The model presents a high estimation ability regarding the final product (H2 and biogas) reaching the same maximum value for the theoretical as the experimental data of these products (0.0012 and 0.0036 m3/d, respectively). The adapted ADM1 emerges as a useful instrument for designing anaerobic co-digestion processes with the goal of achieving high yields in fermentative hydrogen production, considering mixed biomass growth mechanisms.

Synergistic effect of hydrothermal sludge and food waste in the anaerobic co-digestion process: microbial shift and dewaterability.

While thermal hydrolysis technology is commonly employed for sewage sludge treatment in extensive wastewater treatment facilities, persistent challenges remain, including issues such as ammonia-induced digestive inhibition and reduced productivity stemming from nutrient deficiency within the hydrothermal sludge. In this study, the effects of hydrothermal sludge-to-food waste mixing ratios and fermentation temperatures on anaerobic co-digestion were systematically investigated through a semi-continuous experiment lasting approximately 100days. The results indicated that anaerobic co-digestion of hydrothermal sludge and food waste proceeded synergistically at any mixing ratio, and the synergistic effect is mainly attributed to the improvement of carbohydrate removal and digestive system stability. However, thermophilic digestion did not improve the anaerobic performance and methane yield. On the contrary, mesophilic digestion performed better in terms of organic matter removal, especially in the utilization of soluble carbohydrates, soluble proteins, and VFAs. Microbial community analysis revealed that the transition from mesophilic to thermophilic anaerobic co-digestion prompts changes in the methane-producing pathways. Specifically, the transition entails a gradual shift from pathways involving acetoclastic and hydrogenotrophic methanogenesis to a singular hydrogenotrophic methanogenesis pathway. This shift is driven by thermodynamic tendencies, as reflected in Gibbs free energy, as well as environmental factors like ammonia nitrogen and volatile fatty acids. Lastly, it is worth noting that the introduction of food waste did lead to a reduction in cake solids following dewatering. Nevertheless, it was observed that thermophilic digestion had a positive impact on dewatering performance.

Enhanced Biogas Production from Rice Husks and Okra Stalks by Co-digestion with Ostrich Dung and Cow Manure

In this study, two sides were studied. First one included single and combined pretreatment which were performed to treat milled rice husks compared to increase the production of biogas untreated rice husks. The other side included investigate the effect of two types of mixing: mixing rice husks and okra stalks using three mixing ratios inoculated with ostrich dung compared to rice husks and okra stalks inoculated with ostrich dung separately and mixing ostrich dung and cow manure using three mixing ratios with rice husks compared to using rice husks inoculated with ostrich dung and cow manure separately. The ten reactors, which were carried out in this study, were in batch mode. Single pretreatment included hydrothermal and oxidative pretreatment with 50% (v/v) H2O2, while combined pretreatment consisted of both hydrothermal and H2O2 pretreatment. The cow manure was used as inoculum in this anaerobic co-digestion process. The results clarified that the increase of biogas productions were by 5.42%, 48.05%, and 59.07% for hydrothermal, H2O2, and combined pretreatment of hydrothermal and H2O2, respectively. For first mixing, 25:75 ratio (rice husks: okra stalks) was better than other ratios in the production of biogas (48.77 ml/g VS). In the second case of mixing, 75:25 ratio (ostrich dung: cow manure) was better than other ratios in the production of biogas (21.85 ml/g VS). Kinetic study was applied using modified Gompertz model, and there was well agreement between the predicted and measured values of the all pretreatments with correlation coefficient > 0.95. The pretreatment samples of rice husks and the mixing of materials improve the production of biogas and methane.

Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes

Accurate prediction of biogas yield is crucial for optimizing waste-to-energy conversion systems in anaerobic co-digestion processes. In this study, a double input and single output (DISO) fuzzy mamdani model (FMM) was developed for the prediction of biogas yield in a pilot scale of 105-L mesophilic anaerobic sludge bio-digester. The input variables considered are the combination of cow dung and pig waste and the retention time (RT), while the output variable is the experimental biogas yield. Triangular Fuzzy Membership Functions (TFMF) were utilized to define the input and output datasets, and rules were derived from de-fuzzification. Comparative analysis between the FMM's predicted results and experimental values showcased its effectiveness in forecasting biogas yield during the anaerobic co-digestion of the hybrid wastes. Significantly, the FMM consistently produced results with low error values for the sample dataset, underscoring its accuracy even under stochastic conditions. This study emphasizes the FMM's ability to generate predictions with minimal deviations, offering superior results. As a prospect for future research, the implementation of hybrid algorithms may further enhance biogas yield prediction accuracy within waste-to-energy systems.

Response Surface Methodology Application for the Optimization of Biogas Yield from an Anaerobic Co-Digestion Process

Response Surface Methodology Application for the Optimization of Biogas Yield from an Anaerobic Co-Digestion Process

Examining the synergistic effects through machine learning prediction and optimisation in the anaerobic Co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) with economic analysis

The anaerobic co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) is gaining considerable attention as it helps to overcome insufficient feedstock issues in anaerobic digesters during low crop seasons. However, the ACoD process involves complex non-linear relationships between input parameters and process outcomes, posing challenges in accurately evaluating the synergistic effects and optimising biogas production on an industrial scale using traditional modelling approaches. In this study, three supervised machine learning algorithms, i.e., Random Forest (RF), Artificial Neural Network (ANN) and Elastic Net (EN) are applied to model the biogas production and COD removal from a biogas plant. These algorithms are used to learn and uncover the intricate interdependencies between input variables (e.g., temperature, organic loading rate, dilution ratio) and resulting synergistic effects on output variables (biogas production and COD removal) through their non-linear modelling capabilities. Results demonstrate that the Random Forest (RF) model provides the most accurate predictions for biogas production and COD removal, with a high coefficient of determination (R2) of 0.9961 and the lowest root mean square error (RMSE) of 0.110. Optimal conditions for the anaerobic digester, including a temperature of 41.4 °C, an organic loading rate (OLR) of 0.87 kg COD/m3day, and a dilution ratio of 1:10, led to a 14.9% improvement in biogas production (corresponding methane yield of 0.3 Nm3 CH4/kgCODremoved) and a 17.5% increment in COD removal efficiency (88.2%). Feature importance analysis revealed that the dilution ratio significantly influenced biogas production, while OLR played a dominant role in COD removal. Future research efforts will focus on implementing pre-treatment methods for DC in the ACoD system to further enhance biogas production.

Industrial-scale anaerobic Co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) for maximizing methane yield: An integrated machine learning and simulation-based economic analysis approach

This study employs machine learning (ML) algorithms, including multiple linear regression (MLR), decision tree (DT) and support vector regression (SVR) to predict the methane yield from the anaerobic co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) in an industry-scale anaerobic covered lagoon. Results showed that the DT model outperformed the other models with a high R2 value of 0.9763 and the lowest MAE, MSE, and RMSE values. It exhibited over 95 % similarity to the actual results, validating its effectiveness in capturing real-world scenarios. Optimisation was conducted using response surface methodology (RSM) to achieve maximum biogas production (14,245 m3/day) and methane yield (0.285 Nm3 CH4/kg CODremoved), where the optimal range of pH, organic loading rate (OLR) and dilution ratio of DC were found to be 6.83–6.94, 0.82–0.83 kg COD/m3. day and 0.09–0.11 respectively. The ACoD process was simulated, and an economic analysis was performed using SuperPro Designer v10.6. ACoD of POME and DC was more economically viable than mono-digestion of POME, with a 43.16 % improvement in return on investment (ROI), considering the trade-off between the additional cost of pre-treating DC and the additional revenue from improved biogas production using ACoD.

Response surface methodology and artificial neural network-genetic algorithm for modeling and optimization of bioenergy production from biochar-improved anaerobic digestion

Biochar can be used to improve the anaerobic digestion (AD) of agricultural wastes for higher methane production. However, the interaction of biochar addition with other factors of the anaerobic co-digestion (Co-AD) process has rarely been investigated. In this study, process models based on response surface methodology (RSM) and artificial neural network (ANN) were compared in modeling the methane yield (MY, mL CH4/g VS added) from the Co-AD of poultry litter and wheat straw with biochar addition. Box-Behnken design was applied, with the controlling parameters being carbon to nitrogen ratio (C/N), total solids (TS, %), and biochar addition (Biochar, % TS). Numerical optimization and genetic algorithm (GA) were used as optimization tools for RSM and ANN, respectively. A significant second-order quadratic model was built by RSM (R2 = 0.9981 and RMSE = 0.91), which demonstrated significant interactions between C/N and TS (p < 0.0001), and between C/N and Biochar (p < 0.05). The trained ANN (3−3−1) was less accurate (R2 = 0.9926, RMSE = 1.80) compared to RSM. The optimization results by RSM and ANN coupled with GA (ANN-GA) were both validated with prediction errors <0.5%. The optimization results by ANN-GA should be used since it generated a higher maximum MY of 290.7 ± 0.2 mL CH4/g VS added, under the optimal conditions of C/N ratio 24.46, TS 5.03%, and Biochar 8.73% TS, showing an improvement of 20.6% (compared to the control) through process optimization. The methods can also be applied in other scenarios for process modeling and optimization. The optimized results could support real applications of using additives including biochar, active carbon, nanoparticles, etc., to promote the bioenergy production from AD of agricultural wastes.

Enhanced Biogas Production from Rice Husks and Okra Stalks by Co-digestion with Ostrich Dung and Cow Manure

In this study, two sides were studied. First one included single and combined pretreatment which were performed to treat milled rice husks compared to increase the production of biogas untreated rice husks. The other side included investigate the effect of two types of mixing: mixing rice husks and okra stalks using three mixing ratios inoculated with ostrich dung compared to rice husks and okra stalks inoculated with ostrich dung separately and mixing ostrich dung and cow manure using three mixing ratios with rice husks compared to using rice husks inoculated with ostrich dung and cow manure separately. The ten reactors, which were carried out in this study, were in batch mode. Single pretreatment included hydrothermal and oxidative pretreatment with 50% (v/v) H2O2, while combined pretreatment consisted of both hydrothermal and H2O2 pretreatment. The cow manure was used as inoculum in this anaerobic co-digestion process. The results clarified that the increase of biogas productions were by 5.42%, 48.05%, and 59.07% for hydrothermal, H2O2, and combined pretreatment of hydrothermal and H2O2, respectively. For first mixing, 25:75 ratio (rice husks: okra stalks) was better than other ratios in the production of biogas (48.77 ml/g VS). In the second case of mixing, 75:25 ratio (ostrich dung: cow manure) was better than other ratios in the production of biogas (21.85 ml/g VS). Kinetic study was applied using modified Gompertz model, and there was well agreement between the predicted and measured values of the all pretreatments with correlation coefficient &gt; 0.95. The pretreatment samples of rice husks and the mixing of materials improve the production of biogas and methane.

Sustainable methane energy from bagasse treated via bokashi technology: comparative between neural network and mathematical modeling

Bagasse is the major leftover material from the sugarcane industry, and it has significant untapped energy. Biogas production from bagasse is employed as eco-friendly energy but its intricate composition makes it resistant to degradation. This study endeavors to explore the impact of bokashi technology, a technique that applies effective microorganisms on the potential methane production from bagasse. According to findings, bagasse had the ability to produce biogas but applying bokashi technology to bagasse led to getting more methane production. The methane production from treated bagasse for one month via bokashi bran was 243.80 LCH4/kgVS compared to 106.84 LCH4/kgVS only from fresh bagasse which is often attributed to improved fibrous carbohydrates degradation by the pre-treatment process. The reduction of total solids and chemical oxygen demand were more with treated bagasse. Two-dimensional mathematical modeling (TDMM) and artificial neural network (ANN) were utilized to forecast the production of methane through the anaerobic co-digestion process. The main advantage of ANN model is its ability to be constructed and trained for any experiment, regardless of the availability of a pre-existing study or understanding of the underlying phenomena. On the other hand, existence of a mathematical model that accurately describes the behavior of the current experiment is a fundamental requirement for constructing the TDMM model. The TDMM model remains stable in each run, as it relies on the established mathematical equations. On the other hand, ANN model may exhibit variations in each run due to the random initialization of weights.Graphical abstract

Modelisation of the Biomethane Accumulation in Anaerobic Co-Digestion of Whey and Sugarcane Molasse Mixtures

The biomethane accumulation of several combinations of whey and sugarcane molasses, inoculated with sludge from a treatment facility of one of the dairy enterprises of the Imbabura province in Ecuador, was assessed in the current experiment at a constant COD0/VSin ratio of 0.5. The whey/molasses (W:M) ratios for each treatment were (in % (m/m)) 0:100, 25:75, 50:50, 75:25, and 100:0, with a constant temperature of 37 °C and an initial pH adjustment of 7.5. Half a litre of total mixes was used for each treatment in duplicate. Six kinetic models were evaluated to account biomethane accumulation in anaerobic co-digestion processes in batch of whey and sugarcane molasses. Five of these have been tested by other researchers, and one was developed by modifying a first-order model to consider changes in the biomethane accumulation profile. This proposed model, along with the modified two-phase Gompertz model, resulted in the ones that were best able to adjust the experimental data, obtaining in all cases an R2 ≥ 0.949, indicating the accuracy of both models. In addition, the proposed here model has five parameters, one less than the modified two-phase Gompertz model, making it more straightforward and robust.

Anaerobic mono and co-digestion of agro-industrial waste and municipal sewage sludge: Biogas production potential, kinetic modelling, and digestate characteristics

Lignocellulosic substrates, including wastes generated from agro-industrial activities, are bio-residues evaluated to put an end to the energy needs and sustainable waste disposal problems that the world is trying to deal with today. However, the slow rate of hydrolysis of such lignocellulosic wastes limits the biogas production efficiency with the mono-anaerobic digestion (AD) process. Therefore, anaerobic co-digestion (AnCoD) of such wastes with secondary raw materials rich in microorganisms and various nutrients, such as sewage sludge, is an economical and useful method for both enhancing biogas production and disposal of more waste. In this study, AD and AnCoD processes for biogas and methane production were investigated on okra waste (OW) and municipal sewage sludge (MSS) by using cattle manure as inoculation, which constitutes only 8% of the total working volume. Five different substrate mixing ratios (100%OW, 75%OW-25%MSS, 50%OW-50%MSS, 25%OW-75%MSS, and 100MSS%) were prepared to observe the effect of C/N ratio, maximum biogas production potential, methane content, and organic matter removal efficiency on AD and AnCoD processes. All bioreactors were operated in a batch process with 8% TS in mesophilic temperature for 25 days. The amount of biogas produced in the 100%OW experimental set for the AD process was 44% more than 100%MSS. However, the AnCoD process was more efficient than AD, and biogas and methane production increased by up to 66% and 112%, respectively, compared to the mono-substrate experimental set. The highest biogas production value was 1060 mL (375 mL/gVSremoved) with 60% methane content in the bioreactor consisting of 25%OW-75%MSS. As a result of AD and AnCoD process, soluble carbohydrate, protein and COD removal for all bioreactors were between 62% and 79%, 28%-68% and 54%-70%, respectively. The FT-IR peaks seen in digestates were consistent with the presence of functional groups formed as a result of AnCoD. Monod, Cone, Transference function, and Logistic function kinetic models were used for the estimation of cumulative biogas production efficiency. In addition, the energy potential of the methane produced as a result of the AD and AnCoD processes and the financial gain that can be obtained from its direct sale were evaluated.

Optimizing anaerobic co-digestion of Xyris capensis and duck waste using neuro-fuzzy model and response surface methodology

Optimization of biomethane yield from the anaerobic co-digestion process has taken a new dimension from the application of common statistical techniques to the use of intelligence models in the Artificial Intelligence (AI) field. In this study, the performance of fuzzy c-means (FCM)-clustered adaptive neuro-fuzzy inference system (ANFIS) was compared with the Response Surface Methodology (RSM) in investigating the impact of critical process parameters, namely temperature, retention time, and mixing ratio on the methane yield of anaerobic co-digestion of duck waste and Xyris capensis. Relevant statistical metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), mean absolute error (MAE) and mean absolute deviation (MAD) was used to evaluate the performance of the FCM-ANFIS model developed. The developed RSM and FCM-ANFIS models were compared based on the predicted yields and optimum conditions. It was observed from the results that the process parameters have a significant influence on the methane yield of anaerobic co-digestion of duck waste and Xyris capensis. The cumulative yield of 478.42, 478.43, and 436.20 mL CH4/g VSadded was observed for experimental, RSM, and ANFIS-FCM5clusters models, respectively. The optimum daily methane yield was observed when the mixing ratio was 75% duck waste and 25% Xyris capensis for the experimental and ANFIS-FCM5 clusters model. In contrast, the RSM model observed it at 100% duck waste. The RMSE, MAD, MAE, and MAPE values of the optimal neuro-fuzzy model (ANFIS-FCM5clusters) test indicated that the neuro-fuzzy model developed performs better than the RSM model with minimal error and higher accuracy. RSM and ANFIS models were noticed to be practical models for methane yield optimization and prediction from anaerobic co-digestion of livestock waste and lignocellulose feedstocks in a multiple-input variable without undertaking an experiment within a short time and error. These models can be used to optimize the anaerobic digestion process of lignocellulose materials and livestock waste at the industrial scale.

Revealing sustainable energy opportunities through the integrated use of Canna indica biomass and buffalo manure for biogas generation

Mature Canna indica L., contains significant percentages of hemicellulose (21.6±0.22%) and lignin (20.14±0.13%), showing its high potential as a biogas source. This study explores the potential of using C. indica biomass harvested from waterlogged clay areas for biogas production. The research focuses on optimizing the anaerobic co-digestion process with swine dung through varying calcium oxide (CaO) pretreatment concentrations during a 45-day experiment. CaO pretreatment significantly enhances biogas yield, with 2% CaO yielding the highest biogas production at 8024.10 mL. Methane concentration analysis reveals that higher CaO concentrations, notably 2% and 3%, accelerate methane production, indicating an optimal CaO concentration of around 2% for maximizing methane yield. This study outperforms others in anaerobic co-digestion, achieving a methane concentration of 64.93%. Data on C. indica at different CaO concentrations as a substrate underscores the need for precise CaO tuning for optimal methane production. The findings open avenues for sustainable waste management and renewable energy production, hinting at promising developments in energy solutions through optimized anaerobic co-digestion processes using C. indica and buffalo dung.

Anaerobic co-digestion of agro-industrial wastes using anaerobic sequencing batch reactor for bio-energy recovery: Focus on process performance and stability of the methanogenic step

Anaerobic co-digestion of tannery wastewater (TWW) with dairy wastewater (DWW) using a two-phase anaerobic sequencing batch reactor (ASBR) was evaluated. For this purpose, laboratory-scale anaerobic digesters, one for acidogenesis and the other for methanogenesis, with 0.4 and 0.6 L of working volume, respectively, operated in semi-continuous mode, were used. The results showed that a methanogenic reactor treating the effluent acidogenic reactor (50 % TWW and 50 % DWW) exhibits the best process performances in terms of daily biogas (64.7 mL/day), methane production (461 mL/day), specific methane yield (892 mL CH4/g COD), methane content (71.2 %), and total chemical oxygen demand (TCOD) removal efficiency (85.6 %) when operated at a hydraulic retention time (HRT) of 15 days. This indicated that the addition of DWW improves the anaerobic co-digestion process, increasing biogas production (64.7 mL/day) by about 41 % as compared with anaerobic co-digestion of 100 % TWW (27.4 mL/day). The process stability of the methanogenic stage of the two-stage anaerobic digestion process was also evaluated, and the obtained result showed suitable pH (6.70), no total volatile fatty acid (TVFA) accumulation (VFA/alkalinity: 0.363), alkalinity (3350 mg CaCO3/L), and ammonia (231 mg/L) within the optimum operating range. Furthermore, high overall performance efficiency in terms of TCOD reduction (88.8 %) in the two-phase (acidogenesis and methanogenesis) anaerobic digester of 50:50 (TWW:DWW) was obtained when operated at a HRT of 18 days (3 days for acidogenesis and 15 days for methanogenesis). Anaerobic co-digestion of TWW with DWW generally resulted in increased process stabilities and performances compared to mono-digestion of TWW.

Optimisation of the anaerobic co-digestion process of Calotropis procera leaves, stems, and cow dung using a mixture design

Batch tests were carried out to evaluate the biogas production potential of the co-digestion of Calotropis Procera and cow manure. An augmented simplex centroid design was used to establish the mixture proportions for the co-digestion process. Cow dung, Calotropis procera leaves, and Calotropis procera stems were the three components that went into forming the mixtures. Before and after the digestion process, each mixture combination was analyzed for Total Solids content, Volatile Solids content, and pH. Cumulative biogas production, cumulative methane yield, and the percentage of volatile solids reduction were the responses utilized to evaluate the performance of the reactors. The Response Surface Methodology was used in order to determine the optimal combination that would result in the maximum amount of biogas and bio-methane generation. Contour, 3D surfaces and residual plots were used to locate and display the regions that showed the best performance in terms of responses. The results showed that the optimal proportions for maximum biogas yield, cumulative methane production, and percentage reduction in volatile solids were 17.17% leaves and 82.83% stems. The study has thus revealed that the co-digestion of Calotropis Procera leaves with stems is more significant than that of the mixtures containing cow dung. The findings of this study suggest that although the co-digestion of Calotropis Procera with cow dung is a viable solution for improving the biogas production, the co-digestion of Calotropis Procera leaves and stems has a higher yield.

A comprehensive review on current trends and development of biomethane production from food waste: Circular economy and techno economic analysis

Biogas production from food waste with anaerobic digestion process and co-digestion process obtained high purity of biogas. Food waste consists of proteins, carbohydrates, inorganic compounds, fibre, sugars, and lipids. Fossil fuels have caused some environmental and human health problems, such as effects of global warming, greenhouse gas emissions. Biogas production is a simple process and is purified into methane and used in many applications, heat, electricity and vehicle fuel. Different types of food waste are collected and processed into biogas, which is essential for human use and as an alternative to fossil fuels. The collection of food waste, pre-treatment process, production of biogas, purification of biogas, upgradation of biomethane, and circular bioeconomy are clearly explained. Anaerobic digestion and co-digestion processes have increased biomethane yield, up gradation and purification of biogas have also been discussed. Five different types of upgradation processes are included, such as physical scrubbing, chemical scrubbing, pressure swing adsorption, membrane separation, and cryogenic separation. The upgradation process and the purity level also mentioned in this review, it represents the biomethane quality. Techno economic effects and circular bioeconomy levels are addressed in this review. Life cycle assessment, life cycle impact analysis and environmental impact level are discussed. However, Challenges and future perspectives of biogas production and to improve the biomethane purity level. 89% of the biogas production is obtained from fruit waste with anaerobic digestion, after the upgradation process, 99% purity of biomethane is separated.