Biogas optimisation processes and effluent quality: A review

This study evaluates the effects of the varying substrate to inoculum ratios (S:I) of 0.5, 1, 2, 3, 4, 5, and 6 (volatile solids/VS basis) on the kinetics of biogas production during batch mesophilic (35 ± 1 °C) anaerobic digestion (AD) of simulated food waste (FW), using anaerobic digestate as the inoculum. Kinetic parameters during biogas production (scrubbed with NaOH solution) are predicted by the first-order and the modified Gompertz model. The observed average specific biogas yields are in descending order corresponding to the S:I ratios 1, 2, 4, 6, 3, 5, and 0.5, respectively, and the significant effect of the S:I ratio was observed. The tests with the S:I of 1 have the maximum average biogas production rates of 88.56 NmL/gVS.d, whereas tests with the S:I of 6 exhibited the lowest production rates (24.61 NmL/gVS.d). The maximum biogas yields, predicted by the first order and the modified Gompertz model, are 668.65 NmL/gVS (experimental 674.40 ± 29.10 NmL/gVS) and 653.17 NmL/gVS, respectively. The modified Gompertz model has been proven to be suitable in predicting biogas production from FW. VS removal efficiency is greater in higher S:I ratios, with a maximum of 78.80 % at the S:I ratio of 6, supported by the longer incubation time. Moreover, a significant effect of the S:I ratio is seen on kinetics and energy recovery from the AD of FW.

Indonesia has great potential in producing large quantities of renewable energy sources, such as biomass. Biogas is a renewable energy source produced from biomass. It is can be developed in agricultural countries producing rice and coffee, where a large amount of waste is produced in the form of rice husks and coffee grounds. This study examined the effect of physiochemical pretreatment and the C/N ratio on biogas production using coffee grounds and rice husk mixtures. Physical pretreatment was conducted by grinding the mixture up to 50 mesh size, followed by chemical pretreatment by soaking the mixture in 3% KOH; moreover, the variation in the C/N ratio was set at 25 and 30. Anaerobic bacteria were acquired from rumen fluid. The ratio of the coffee ground material, rice husks, and rumen fluid was 1:1:1. This research was conducted in duplicate under batch conditions at ambient temperature (25–35 oC) with a digester volume of 1.5 L. Biogas productivity was measured every 2 d for 60 d. The experimental results indicated that biogas production with a C/N ratio of 30 was 13.3–66.5% higher than that with a C/N ratio of 25. The inclusion of physical pretreatment at a C/N ratio of 30 increased biogas production by up to 31.3%. Moreover, the inclusion of a chemical pretreatment at a C/N ratio of 30 resulted in 30.3% higher biogas production. The kinetics model of biogas production showed that a C/N ratio of 30 with physical and alkaline pretreatment can produce maximum biogas yields of 6,619 mL and 6,570 mL, respectively. Overall, both pretreatments sequentially increased the biogas production significantly.

Potentiality of municipal sludge for biological gas production at Soba Station South of Khartoum (Sudan)

Effects of salt on anaerobic digestion are dosage-dependent. As salt is a widely used condiment in food processing, effects of salt are bound to be considered when food waste is digested. In this study, salt addition effects (0, 2, 4, 6, 9, 12 g∙L−1) on biogas and methane yields and kinetics of biogas production were researched. Meanwhile, component characteristics (food waste featured in carbohydrate, protein and fat, respectively) and fermentation concentrations (5 and 8 gVS∙L−1) were also taken into consideration. Results showed that 2–4 g∙L−1 salt addition was the optimal addition dosage for AD systems as they not only have the maximum biogas and methane yields, but also the maximum vs. removal in most cases. Also, according to the results of a modified Gompertz model, which is used to predict biogas and methane production rates, suitable salt addition can accelerate biogas production, improving the maximum biogas production rate (Rmax). Factorial design (2 × 2) proved that interaction of salt and fermentation concentrations was significant for food waste featured with carbohydrate and with protein (p < 0.05). High salt addition and fermentation concentration can break the AD system when the feeding material was food waste featured with carbohydrate, but for food waste featured with protein, interaction of fermentation concentrations and salt addition can alleviate inhibition degrees.

Boosting manure biogas production with the application of pretreatments: A meta-analysis

Anaerobic digestion (AD) is the biological waste treatment method for the organic fraction of municipal solid waste (OFMSW). AD is notable for its ability to reduce volume and produce biogas from waste. However, the conventional AD of OFMSW has a low degradation rate. In recent years, some treatment method has been used to promote the biogas and methane production of AD. One of these methods is hydrothermal carbonization (HTC). This study aimed to evaluate the effect of hydrothermal carbonization (HTC) temperature and hydrochar: OFMSW ratio as factors on biogas production, methane production, and methane content of anaerobic digestion (AD) as responses was investigated. This study determined the biomethane potential of raw and pretreated OFMSW (hydrochars) in 118 ml serum glass bottles. Based on the Hansen method, all tests were conducted at mesophilic temperature (37 ± 1 °C) in an incubator for 45 days. The response surface method and central composite model were used for designing experimental conditions. Quadratic models were used to estimate the correlation between factors and responses. Also, the optimal conditions for maximizing responses were determined. Biogas production of mixing hydrochar and OFMSW was 41% more than control groups which contained OFMSW and inoculum. The optimal operating conditions to maximize all responses were applied in HTC temperature and hydrochar: OFMSW ratio of 179.366 °C and 2.406, respectively. In this condition, the maximum biogas production, methane production, and methane content were 394 mL/g VS, 284.351 mL/g VS, and 73.176%, respectively. As an OFMSW HTC pretreatment for AD, hydrochar additive has a significantly positive and negative effect on biogas production, methane production, and methane content of biogas depending on operating conditions. Therefore. It is necessary to consider the individual and interaction effects of the temperature and hydrochar: OFMSW ratio, obtain the optimal conditions and determine responses.

Co-digestion of microbial biomass with animal manure in three-stage anaerobic digestion

Estimates of the quantity of biogas and methane produced by a dairy manure-based anaerobic digester are an important design parameter; they are used to size collection, transport, and biogas clean-up and utilization equipment prior to digester construction. They are also used to estimate potential return on the producer's investment. Current methane production estimation methods include stoichiometric methane production calculations based on manure Chemical Oxygen Demand (COD) content, an estimate of digester COD removal, and data from the past performance of other dairy manure digesters. However, these methods can overestimate the actual biogas and methane production. This paper compares measured anaerobic digester biogas and methane production to estimated production based on laboratory biochemical methane potential (BMP) data developed from manure samples collected at six New York State dairy farms operating anaerobic digesters. Laboratory BMP tests of each digester's influent (manure and food wastes) were compared to on-farm monitored biogas and methane quantities calculated from biogas methane content. These comparisons were used to determine the ability of laboratory BMPs to predict on-farm production from dairy manure digesters. The results suggest that BMP assays could provide useful information to estimate methane production for dairy manure anaerobic systems. The results showed that using BMPs to estimate biogas production may not be accurate, but that predicting methane production with BMPs may be feasible. The linear regression results did not show a relationship that could be used for predicting biogas production from BMPs. However, a relationship and statistical similarities were found for predicting methane production from BMPs.

Anaerobic digestion, which is the process by which bacteria breakdown organic matter to produce biogas (renewable energy source) and digestate (biofertiliser) in the absence of oxygen, proves to be the ideal concept not only for sustainable energy provision but also for effective organic waste management. However, the production amount of biogas to keep up with the global demand is limited by the underperformance in the system implementing the AD process. This underperformance is due to the difficulty in obtaining and maintaining the optimal operating parameters/states for anaerobic bacteria to thrive with regards to attaining a specific critical population number, which results in maximising the biogas production. This problem continues to exist as a result of insufficient knowledge of the interactions between the operating parameters and bacterial community. In addition, the lack of sufficient knowledge of the composition of bacterial groups that varies with changes in the operating parameters such as temperature, substrate and retention time. Without sufficient knowledge of the overall impact of the physico-environmental operating parameters on anaerobic bacterial growth and composition, significant improvement of biogas production may be difficult to attain. In order to mitigate this problem, this study has presented a nonlinear multi-parameter system modelling of mesophilic AD. It utilised raw data sets generated from laboratory experimentation of the influence of four operating parameters, temperature, pH, mixing speed and pressure on biogas and methane production, signifying that this is a multiple input single output (MISO) system. Due to the nonlinear characteristics of the data, the nonlinear black-box modelling technique is applied. The modelling is performed in MATLAB through System Identification approach. Two nonlinear model structures, autoregressive with exogenous input (NARX) and Hammerstein-Wiener (NLHW) with different nonlinearity estimators and model orders are chosen by trial and error and utilised to estimate the models. The performance of the models is determined by comparing the simulated outputs of the estimated models and the output in the validation data. The approach is used to validate the estimated models by checking how well the simulated output of the models fits the measured output. The best models for biogas and methane production are chosen by comparing the outputs of the best NARX and NLHW models (each for biogas and methane production), and the validation data, as well as utilising the Akaike information criterion to measure the quality of each model relative to each of the other models. The NLHW models mhw2 and mhws2 are chosen for biogas and methane production, respectively. The identified NLHW models mhw2 and mhws2 represent the behaviour of the production of biogas and methane, respectively, from mesophilic AD. Among all the candidate models studied, the nonlinear models provide a superior reproduction of the experimental data over the whole analysed period. Furthermore,…

Feasibility of annual wet anaerobic digestion temperature-controlled by solar energy in cold areas

Energy production from piggery waste using anaerobic digestion: Current status and potential in Cyprus

Anaerobic digestion was the best way to treat organic waste biologically. It uses microorganisms to biodegrade biosolids without oxygen, producing biogas and compost. The study involved conducting batch experiments to investigate biogas and fertilizer production. Three digestive systems (A, B, and C) with three different mixing ratios were operated simultaneously and had a retention time of thirty-five days. The organic fraction of municipal solid waste, sewage sludge, and bacteria were employed in the experiments. The Al-Rustamiyah wastewater treatment plant was the source of the sewage sludge. The organic municipal solid waste fraction was utilized as materials, including fruit peels, rotten fruits, vegetable peels, eggshells, rotten vegetables, and garden leaves. The bacterial strain used in this research was E. coli, isolated directly from the local sewage sample and selected as an eco-friendly bacteria after conducting many tests. The digester's initial temperature was 30 °C, and it reached a high of 42 °C on the 16th day. Three digesters had initial pH values (6.7, 6.3, 6) and final values (7.3, 7.1, 7). The results showed good anaerobic digestion performance. The digester (C) achieved the highest biogas production, with a maximum cumulative biogas production value of 0.0549 m3. It also demonstrated removal efficiencies of 78.30% for TS, 89.13% for VS, 86.36% for BOD5, and 80.5% for COD. Additionally, the moisture content was measured at 43.30%. The anaerobic digestion’s final product was used as a high-quality fertilizer.

Enhancement of anaerobic sludge digestion by high-pressure homogenization

Biogas production from solid wastes removed from fish farm effluents

Anaerobic digestion is an effective technology for converting organic waste into biogas while reducing environmental pollution. This study investigates the impact of co-digesting waste-activated sludge (WAS) with wheat straw, rice straw, and bokashi on biogas production. Nine anaerobic batch reactors were operated under mesophilic conditions (35 °C), incorporating different proportions of bokashi (1% and 2%) along with rice and wheat straw (4%). The results revealed that reactors supplemented with wheat and rice straw exhibited higher biogas production than the control reactor (sludge only). Wheat straw outperformed rice straw in improving biogas yield, total solids (TS) reduction, total volatile solids (TVS) degradation, and chemical oxygen demand (COD) removal. The addition of bokashi enhanced biogas production, confirming its role in accelerating organic matter breakdown. The maximum biogas yield was observed in the reactor containing sludge co-digested with wheat straw and 2% bokashi, which generated three times more biogas than the control. This reactor also exhibited the highest degradation rates of TS (57.83%), TVS (66.37%), and COD (71.53%). Furthermore, pH remained stable within the optimal range across all reactors, ensuring a balanced digestion process. Statistical analysis revealed significant correlations between organic matter degradation (COD, TS, TVS reduction) and biogas production, demonstrating that effective substrate decomposition improves biogas yield. The recurrent neural network (RNN) model was applied to experimental data to predict biogas production. With an exceptionally low root mean square error (RMSE) of 0.0041, R2 close to 1, and MAE 0.0117, the model exhibited excellent accuracy and reliability in generating precise predictions.