Anaerobic Co-digestion Of Organic Wastes Research Articles

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

The biogas has the potential to serve as a substitute as fossil derived fuel. The present study investigates anaerobic co-digestion of organic waste for predictive modelling. Several co-digestion studies were performed with different pH, solid concentration, temperature, and co-digestion ratios. A water displacement apparatus was used to test biogas yield, and data was collected meticulously. Linear regression (LR) and Random Forest (RF) based models were built with Python-based tools and tested using statistical measures for the prediction of biogas yield. The LR showed a strong linear association, with R and R2 values of 0.9892 and 0.9785, respectively. However, RF surpassed LR, with higher R and R2 values of 0.9919 and 0.9826, respectively. Furthermore, RF had lower MSE and MAE values, indicating higher prediction accuracy and precision. RF consistently scored well in tests, demonstrating its ability to capture complicated relationships while minimizing prediction mistakes. Taylor's diagrams further demonstrated RF's excellent performance during both the training and testing periods. Overall, RF emerges as the optimum model for reliably estimating biogas output in anaerobic co-digestion systems, with important implications for waste-to-energy processes.

Understanding Anaerobic Co-digestion of Organic Wastes through Meta-Analysis

Understanding Anaerobic Co-digestion of Organic Wastes through Meta-Analysis

A comprehensive machine learning-coupled response surface methodology approach for predictive modeling and optimization of biogas potential in anaerobic Co-digestion of organic waste

The increasing demand for renewable energy sources has driven the research and development of biogas production as a sustainable and efficient solution. Biogas production depends on various process parameters, which are complicated and highly non-linear processes that need to be controlled at optimal levels to ensure high productivity. The current study seeks to predict and optimize the biogas production yield in an anaerobic digester using co-digestion. Using the central composite design (CCD), L30 orthogonal arrays were developed for experimentation at four factor five levels of parameters: solid concentrations (5–25 %), pH levels (4–8), temperatures (30–50 °C), and co-digestions (0–40 %). Furthermore, three different gradient boosting algorithms (AdaBoost, Light Gradient Boosting Machine, and Extreme Gradient Boosting) were used to develop an ML-based prognostic model using experimental data. The developed prognostic model was used to predict the biogas yield. Three performance indicators (R2, RMSE, and MAE) were used to evaluate the robustness of the algorithms. Based on the coefficient of determination (R2 = 0.999), RMSE (0.6265), and MAE (0.4669), the XGB model has the most accurate predictions, followed by LGBM (R2 = 0.996, RMSE = 1.488, MAE = 1.1316), and then AdaBoost (R2 = 0.988, RMSE = 8.8943, MAE = 7.265). To optimize the parameters of biogas production, the Response Surface Methodology (RSM)-based desirability approach was employed. The finding showed that all of the considered parameters significantly affected the biogas yield. The optimal combination of the findings included a solid concentration of 11.44 %, a pH of 6.96, a temperature of 38.94 °C, and a co-digestion rate of 39.0 %, which led to a biogas yield of 6029.28 ml. The RSM finding was confirmed by an experimental investigation, and the error rate was within an acceptable limit. It was observed that RSM coupled with machine learning is an effective hybrid technique for modeling, predicting, and optimizing biogas production yield.

Mesophilic anaerobic co-digestion of two-phase olive-mill waste and cattle manure: Optimization of semi-continuous process

Anaerobic co-digestion of organic wastes is an effective technology for the management of two or more substrates with different characteristics. In this context, the main objective of this work was the optimization of biogas production for the treatment of a mixture of two-phase olive-mill waste (2POMW) and cattle manure (CM) (60:40 w/w) at mesophilic temperature range (35 °C). The effect of hydraulic retention time (HRT) on the performance and stability of the digestion process was studied. A decreasing series of HRTs in the range of 40–12 days was analyzed. The corresponding organic loading rates (OLR) were in the range 2.01–6.07 gVS/LR·d. Pseudo steady-state operation of the reactor was established for HRTs between 40 and 15 days. For 15-days HRT, the maximum values of methane productivity (0.94 LCH4/LR·d) and specific methane yield (0.52 LCH4/gVSremoved) were obtained while total acidity (measured as acetic acid) in the effluent were<150 mg/L, verifying process stability. In addition, the removal efficiencies of volatile solids (VS) and dissolved organic carbon (DOC) were 38 and 67 %, respectively. At 12-days HRT, decreases in methane production and organic matter removal efficiencies were observed, with values of 27 and 47 % for VS and DOC removal, respectively. Therefore, 12-days HRT was considered as inadequate for the anaerobic co-digestion of 2POMW and CM because a clear increase in volatile fatty acids was observed at the end of this period, leading to process destabilization and a decrease in biogas production.

Economic and environmental assessment of organic waste to biomethane conversion

Biomethane and biofertilizer production by anaerobic co-digestion of organic waste serves a promising method for reducing the environmental footprint of organic waste management. This study evaluated the techno-economic feasibility and environmental impacts of organic waste to biomethane development in Glasgow, UK using net present value (NPV) analysis and life cycle assessment. Four different biogas upgrading technologies (pressurized water scrubbing, chemical scrubbing, membrane separation, and pressure swing adsorption) were compared. The membrane separation technology-based biomethane production meets 0.8% of the gas demand for Glasgow households with a conversion efficiency of 83%. The organic waste to biomethane development saved up to 264 kg CO2-eq annually per tonne of waste treated, with an NPV ranged between £-9.0 million and £-12.0 million based on the upgrading technology. High costs for waste collection and transportation are primarily responsible for the negative NPV. Carbon taxes between £31.30 and £58.02 per tonne of CO2 are needed for economically viable biomethane production.

Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models

Interest in anaerobic co-digestion (AcoD) has increased significantly in recent decades owing to enhanced biogas productivity due to the utilization of different organic wastes, such as food waste and sewage sludge. In this study, a robust AcoD model for biogas prediction is developed using deep learning (DL). We propose a hybrid DL architecture, i.e., DA–LSTM–VSN, wherein a dual-stage-attention (DA)-based long short-term memory (LSTM) network is integrated with variable selection networks (VSNs). To enhance the model predictability, we perform hyperparameter optimization. The model accuracy is validated using long-term AcoD monitoring data measured over two years of municipal wastewater treatment plant operation and then compared with those of two other DL-based models (i.e., DA–LSTM and the standard LSTM). In addition, the feature importance (FI) is analyzed to investigate the relative contribution of input variables to biogas production prediction. Finally, we demonstrate the successful application of the validated DL model to the AcoD process optimization. Results show that the model accuracy improved significantly by incorporating DA into LSTM, i.e., the coefficient of determination (R2) increased from 0.38 to 0.68; however, the R2 can be further increased to 0.76 by combining DA–LSTM with a VSN. For the biogas prediction of the AcoD model, the VSN contributes significantly by employing the discontinuous time series of measurement data on biodegradable organic-associated variables during AcoD. In addition, the VSN allows the AcoD model to be interpretable via FI analysis using its weighted input features. The FI results show that the relative importance is vital to variables associated with food waste leachate, whereas it is marginal for those associated with the primary and chemically assisted sedimentation sludges. In conclusion, the AcoD model proposed herein can be utilized in practical applications as a robust tool because it can provide the optimal sludge conditions to improve biogas production. This is because it facilitates the time-series biogas prediction at the full scale using unprocessed datasets with either missing value imputation or outlier removal.

Tree-Based Automated Machine Learning to Predict Biogas Production for Anaerobic Co-digestion of Organic Waste

The dynamics of microbial communities involved in anaerobic digestion of mixed organic waste are notoriously complex and difficult to model, yet successful operation of anaerobic digestion is critical to the goals of diverting high-moisture organic waste from landfills. Machine learning (ML) is ideally suited to capturing complex and nonlinear behavior that cannot be modeled mechanistically. This study uses 8 years of data collected from an industrial-scale anaerobic co-digestion (AcoD) operation at a municipal wastewater treatment plant in Oakland, California, combined with a powerful automated ML method, Tree-based Pipeline Optimization Tool, to develop an improved understanding of how different waste inputs and operating conditions impact biogas yield. The model inputs included daily input volumes of 31 waste streams and 5 operating parameters. Because different wastes are broken down at varying rates, the model explored a range of time lags ascribed to each waste input ranging from 0 to 30 days. The results suggest that the waste types (including rendering waste, lactose, poultry waste, and fats, oils, and greases) differ considerably in their impact on biogas yield on both a per-gallon basis and a mass of volatile solids basis, while operating parameters were not good predictors of yield at this facility.

Addressing the synergy determination in anaerobic co-digestion and the inoculum activity impact on BMP test

Anaerobic mono-digestion and co-digestion are nowadays widely used in wastewater treatment plants (WWTP). However, the data processing of the conventional biochemical potential test (BMP) carried out to assess potential substrates should be enhanced to reduce the uncertainty of the results. In this study, two methodologies aiming to improve the data processing in anaerobic digestion studies were proposed. The methodologies aimed at the estimation of synergy in anaerobic co-digestion of organic waste and the standardization of the BMP test results by considering the activity of the inoculums under mono-digestion conditions. Both methodologies comprise the application of the Gompertz equation. For the first methodology, four cosubstrates and two types of substrates were used. Regarding synergy estimation, the cosubstrates dairy whey and grease sludge had an impact on the degradation kinetic. In regard to the second methodology, the results indicate that the activity of the inoculums exerts an influence on the BMP analysis, and it should be considered. This can be meaningful when comparing results among studies when different inoculums are used or even for studies where the same inoculum is used but it is taken at different reactor operational moments.

Fermentative hydrogen and methane co-production from anaerobic co-digestion of organic wastes at high loading rate coupling continuously and sequencing batch digesters.

The anaerobic co-digestion of the most abundant organic wastes was investigated for enhancing biogas production rate and quality. The used feedstock was composed of fruit and vegetable waste (FVW), waste-activated sludge (WAS), olive mill wastewater (OMW) and cattle manure (CM). A considerable methane yield of 340L/kg volatile solid (VS) inlet was obtained using single-stage anaerobic sequencing batch reactors (ASBRs). However, VS biodegradation becomes difficult at high organic loading rate (OLR). Therefore, a continuously stirred tank reactor (CSTR) was integrated to the ASBR for waste pre-digestion. The dark fermentation leads to the improvement of organic matter solubilisation and bio-hydrogen productivity, reaching 0.73L/L/day (H2 content of 49.8%) when pH decreased to 5.8. Therefore, methane productivity increased from 0.6 to 1.86L/L/day in the methanogenic reactor with a better VS biodegradation (91.1%) at high OLR. Furthermore, the bio-hythane production was performed through a controlled biogas recirculation from the dark fermentation stage into the methaniser to reach 842.4L/kg VS inlet. The produced biogas was composed of 8% H2, 28.5% CO2 and 63.5% CH4. Therefore, two-stage anaerobic co-digestion with coupled CH4 and H2 recuperation may be an important contribution for pollution control and high-rate bioenergy recovery (21.1kJ/g VS inlet) from organic wastes.

Anaerobic co-digestion of catering waste with partially pretreated lignocellulosic crop residues

Cellulosic crop residues are produced in large quantities in Pakistan and are considered resistant to biological degradation. Partial pretreatment of these crop residues and optimization of the feed rate could have a synergistic effect on the anaerobic co-digestion of organic waste. The aim of this study was to examine the possibility of enhancing biodegradation and biogas production from the anaerobic co-digestion of catering waste and cellulosic crop residues. Post-harvest crop residues (wheat straw and sugar cane bagasse) that are rich in lignocellulose were partially degraded using a combined microwave (800 J/g/min) and steam (121 °C, 40 min) pretreament. These pretreated wastes were co-digested with catering waste at different feeding rates (20–50% based on total solid, w/w) in batch conditions. High organic-matter removal efficiency was obtained from the feed composition of 20% pretreated wheat crop residues. Similarly, high levels of soluble chemical oxygen demand were observed (up to 85% conversion of insoluble chemical-oxygen demand) in the reactor containing the catering waste and pretreated crop residues. The accumulative biogas produced in both treatments was more than double the biogas observed in catering waste alone. The increased biogas production was due to the presence of more biodegradable carbon in the form of soluble chemical-oxygen demand. The results suggest that the use of a low-cost method (microwave and steam) for the partial pretreatment of lignocellulosic crop residues could increase rate of co-digestion and biogas production. This study provides a novel take on anaerobic co-digestion for the degradation of recalcitrant lignocellulosic material, and the development of an energy-efficient strategy for waste management.

Identification of synergistic impacts during anaerobic co-digestion of organic wastes

Anaerobic co-digestion has been widely investigated, but there is limited analysis of interaction between substrates. The objective of this work was to assess the role of carbohydrates, protein and lipids in co-digestion behaviour separately, and together. Two sets of batch tests were done, each set consisting of the mono-digestion of three substrates, and the co-digestion of seven mixtures. The first was done with pure substrates – cellulose, casein and olive oil – while in the second slaughterhouse waste – paunch, blood and fat – were used as carbohydrate, protein and lipid sources, respectively. Synergistic effects were mainly improvement of process kinetics without a significant change in biodegradability. Kinetics improvement was linked to the mitigation of inhibitory compounds, particularly fats dilution. The exception was co-digestion of paunch with lipids, which resulted in an improved final yield with model based analysis indicating the presence of paunch improved degradability of the fatty feed.

Agricultural Reuse of the Digestate from Anaerobic Co-Digestion of Organic Waste: Microbiological Contamination, Metal Hazards and Fertilizing Performance

The aim of this study was to evaluate the agricultural reuse of the digestate products (DPs) obtained from mesophilic anaerobic co-digestion of different organic wastes (sludge, cattle slurries and organic fraction of municipal solid wastes). At this scope, the content of faecal indicators and pathogens as well as the heavy metal concentration of DPs was monitored. The fertilizing performance of the DPs was also investigated. Co-digestion trials were performed using laboratory-scale (LRs) and pilot-scale reactors (PRs). The microbiological analysis of DPs showed the common presence of Salmonella and an inadequate reduction of indicator organisms during the digestion process, both in the LRs and the PRs. Moreover, the presence of pathogens (e.g. Listeria monocytogenes) in some DP samples highlighted the importance of the microbiological quality evaluation of the DPs to study the possible health risks for consumer. In several samples of DPs, the Cu, Ni and Zn contents exceeded the maximum admissible concentration for fertilizer, as specified by Italian law, suggesting possible environmental contamination if the DPs are used for agricultural purposes. Considering the fertilizing performance, significant differences of growth parameters were observed only for the DPs that were produced by LRs. In conclusion, this work can be considered as a preliminary study to evaluate the possible agricultural reuse of the digestate obtained from different organic wastes.

Assessing Archeal Indicators of Performance by RT-qPCR Methods During Anaerobic Co-digestion of Organic Wastes

Biogas is a renewable energy resource produced during the anaerobic digestion of various organic substrates. A wide community of microorganisms is involved, including methanogens. These Archaea are the biologic key to the process because they accomplish the methane-forming reaction. Despite its crucial role, the microbiome inside the digester is poorly understood. The aim of this work is to develop bioindicators of efficiency for the anaerobic process through the quantification and characterisation of the methanogens and sulphate-reducing bacteria. From a full-scale digester fed with organic wastes, 31 samples were collected. Temperature, pH, acidity, alkalinity and biogas quantity and quality were monitored over time. The methanogens were detected from the samples both in total and as belonging to different taxa units. These evaluations, by real-time quantitative PCR (RT-qPCR) methods, produced valuable results for Methanosarcina, Methanosaeta, Methanocorpusculaceae and sulphate-reducing bacteria. Methanosarcina was the most abundant family, followed by Methanocorpusculaceae and then Methanosaeta. The methanogen taxa are significantly and directly correlated with each other (p < 0.05). Methanosaeta and Methanocorpusculaceae are present in significantly different amounts at different temperatures. While Methanosaeta levels also change when the organic load increases (t test, p < 0.05), Methanosarcina is more tolerant, and its levels are quite constant. Methanosarcina and Methanosaeta are proposed to be bioindicators of the stability of the process (the first) and of susceptibility (the second) to detect early sufferance conditions in the digester. These methods will be useful in the control and optimisation of an eco-friendly waste-to-energy system.

Anaerobic co-digestion of organic wastes

Over the last years anaerobic digestion has been successfully established as technology to treat organic wastes. The perspective of turning, through a low-cost process, organic wastes into biogas, a source of renewable energy and profit, has certainly increased the interest around this technology and has required several studies aimed to develop methods that could improve the performance as well as the efficiency of this process. The present work reviews the most interesting results achieved through such studies, mainly focusing on the following three aspects: (1) the analysis of the organic substrates typically co-digested to exploit their complementary characteristics; (2) the need of pre-treating the substrates before their digestion in order to change their physical and/or chemical characteristics; (3) the usefulness of mathematical models simulating the anaerobic co-digestion process. In particular these studies have demonstrated that combining different organic wastes results in a substrate better balanced and assorted in terms of nutrients, pre-treatments make organic solids more accessible and degradable to microorganisms, whereas mathematical models are extremely useful to predict the co-digestion process performance and therefore can be successfully used to choose the best substrates to mix as well as the most suitable pre-treatments to be applied.

The role of different methanogen groups evaluated by Real-Time qPCR as high-efficiency bioindicators of wet anaerobic co-digestion of organic waste

Methanogen populations and their domains are poorly understood; however, in recent years, research on this topic has emerged. The relevance of this field has also been enhanced by the growing economic interest in methanogen skills, particularly the production of methane from organic substrates. Management attention turned to anaerobic wastes digestion because the volume and environmental impact reductions. Methanogenesis is the biochemically limiting step of the process and the industrially interesting phase because it connects to the amount of biogas production. For this reason, several studies have evaluated the structure of methanogen communities during this process. Currently, it is clear that the methanogen load and diversity depend on the feeding characteristics and the process conditions, but not much data is available. In this study, we apply a Real-Time Polymerase Chain Reaction (RT-PCR) method based on mcrA target to evaluate, by specific probes, some subgroups of methanogens during the mesophilic anaerobic digestion process fed wastewater sludge and organic fraction of the municipal solid waste with two different pre-treatments. The obtained data showed the prevalence of Methanomicrobiales and significantly positive correlation between Methanosarcina and Methanosaetae and the biogas production rate (0.744 p < 0.01 and 0.641 p < 0.05). Methanosarcina detected levels are different during the process after the two pre-treatment of the input materials (T-test p < 0.05). Moreover, a role as diagnostic tool could be suggested in digestion optimisation.

Application of the IWA ADM1 model to simulate anaerobic co-digestion of organic waste with waste activated sludge in mesophilic condition

Anaerobic digestion model no. 1 model of international water association was applied to a full scale anaerobic co-digestion process for the treatment of the organic fraction of municipal solid wastes along with activated sludge wastes originating from a municipal wastewater treatment plant. This operation was carried out in a digester of 2000 m 3 in volume. It is operates at an average hydraulic retention time of 26.9 days with an average organic loading rate of 1.01 kg TVS/m 3 day, at a temperature of 37 °C with an average gas production rate of 0.296 m 3/m 3 day. The aim of the present study is to compare the results obtained from the simulation with the experimental values. The simulated results showed a good fit for pH, methane and carbon dioxide percentages, biogas volume, chemical oxygen demand, total volatile fatty acids, inorganic nitrogen and inorganic carbon.

Full-scale anaerobic co-digestion of organic waste and municipal sludge

A full-scale experiment on the anaerobic co-digestion of organic waste from domestic refuse (swill) and municipal sludge is described. In a wastewater treatment plant of 50,000 population equivalents, two conventional mesophilic digesters with a combined volume of 2000 m3 and 20 days hydraulic retention time were used. The digesters’ usual influent is waste sludge from wastewater treatment plants (a mixture of primary sludge and waste activated sludge) with an average organic loading rate of 0.8 kg m−3 d−1 of volatile suspended solids. In the experiment, organic waste was added to the digester influent to increase the organic loading rate by 25% to 1.0 kg m3 d−1 of volatile suspended solids. Biogas quantity increased by 80% and specific biogas production increased from 0.39 m3 kg−1 volatile suspended solids inserted prior to the experiment to over 0.60 m3 kg−1 volatile suspended solids’ inserted, peaking at 0.89 m3 kg−1 volatile suspended solids inserted. The excess biogas was used in a boiler and a 50 kW combined heat and power engine. Electrical energy production increased by 130% and heat production increased by 55%. Volatile suspended solids degradation efficiency increased from 71% to 81% with no increase of volatile suspended solids in the digester effluent. Virtually all of the organic waste was degraded.

Optimised anaerobic treatment of house-sorted biodegradable waste and slaughterhouse waste in a high loaded half technical scale digester

Anaerobic co-digestion of organic wastes from households, slaughterhouses and meat processing industries was optimised in a half technical scale plant. The plant was operated for 130 days using two different substrates under organic loading rates of 10 and 12 kgCOD.m(-3).d(-1). Since the substrates were rich in fat and protein components (TKN: 12 g.kg(-1) the treatment was challenging. The process was monitored on-line and in the laboratory. It was demonstrated that an intensive and stable co-digestion of partly hydrolysed organic waste and protein rich slaughterhouse waste can be achieved in the balance of inconsistent pH and buffering NH4-N. In the first experimental period the reduction of the substrate COD was almost complete in an overall stable process (COD reduction >82%). In the second period methane productivity increased, but certain intermediate products accumulated constantly. Process design options for a second digestion phase for advanced degradation were investigated. Potential causes for slow and reduced propionic and valeric acid degradation were assessed. Recommendations for full-scale process implementation can be made from the experimental results reported. The highly loaded and stable codigestion of these substrates may be a good technical and economic treatment alternative.