Enhancing and upgrading biogas and biomethane production in anaerobic digestion: a comprehensive review

Aworanti, O. A.; Ajani, Ayobami O.; Agbede, Oluseye Omotoso; Agarry, S. E.; Ogunkunle, Oyetola; Laseinde, Opeyeolu Timothy; Kalam, Md. Abul; Fattah, I.M. Rizwanul

doi:10.3389/fenrg.2023.1170133

Cited by 16 publications

(2 citation statements)

References 304 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But despite the obvious advantages, several barriers must be overcome before waste-energy biomethane technology is widely adopted technical barriers, such as efficiency and scalability, associated economic considerations investment and energy pricing, regulatory framework for waste management and renewable energy. Public acceptance and perception, are some of the factors contributing to these challenges [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Modelling of Biomethane Production from Waste: Application of Extreme Gradient Boosting

Pham,

Nguyen

2024

COMIEN

View full text Add to dashboard Cite

The escalating fossil fuel prices and greenhouse gases need urgent attention for a sustainable solution. The present study explores as modern machine learning approaches can be employed to prognosticate the complex biomethane generation process from organic wastes, like biowaste or food waste. The research investigates the use of organic sludges and how intelligent approaches can be employed to comprehend the complex nonlinear processes involved in biomethane production. Linear regression and Extreme gradient boosting (XGBoost) based prediction-models were developed and assessed employing a diverse set of statistical parameters, including R, R2, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Kling-Gupta Efficiency (KGE). The results show that the XGBoost model beat the classical Linear Regression (LR) model in both the training and testing phases. During training, the XGBoost had an impressive R2 value of 0.99994, indicating a perfect fit to the data. In contrast, LR achieved an R2 value of 0.65464. Similarly, during the test period, XGBoost outperformed LR with R2 values of 0.9553 to 0.9902. Furthermore, XGBoost reduced prediction errors, with significantly lower MSE and MAE values than LR. Taylor’s graph better illustrates the excellent performance of the XGBoost over LR in both training and testing. These data demonstrate the ability of XGBoost to predict biomethane production, as well as its ability to improve the biomethane production process.

show abstract

Section: Introductionmentioning

confidence: 99%

Prognostic Modelling of Biomethane Production from Waste: Application of Extreme Gradient Boosting

Pham,

Nguyen

2024

COMIEN

View full text Add to dashboard Cite

show abstract

“…The majority of the recent research that has been conducted on renewable energy sources has focused on a variety of waste products, including food waste, animal manure, organic waste, and municipal solid waste [2], [3]. Through the process of transforming these waste products into fuel, it is possible to lessen the many adverse impacts that these waste materials have on the environment and on living species, including people [4].…”

Section: Introductionmentioning

confidence: 99%

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

Le,

Dong

2024

COMIEN

View full text Add to dashboard Cite

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.

show abstract

Enhanced anaerobic digestion of brewers’ spent grain: effect of inoculum, poultry manure application and iron (iii) chloride supplementation on biogas production and its kinetics

Edunjobi,

Agbede,

Aworanti

et al. 2023

Biomass Conv. Bioref.

View full text Add to dashboard Cite

The brewery industry generates a huge quantity of brewers’ spent grain (BSG) which can pose waste disposal and pollution problems. Anaerobic digestion of BSG, a recalcitrant lignocellulosic waste, is slow but can be enhanced by bioaugmentation, biostimulation and co-digestion to obtain a higher biogas yield. Hence, the effect of inoculum from brewery wastewater sludge (BWWS), iron (III) chloride (FeCl3) and co-digestion with poultry manure (PM) on the production of biogas from BSG was investigated. Cumulative biogas and biomethane yields of 588.19 NL/kgVS and 400.34 NLCH4/kgVS, respectively, were obtained from a slurry consisting of a blend of 60% BSG and 40% PM plus 15 mg L-1 FeCl3 in BWWS, after 30 day retention time at 37 oC. However, mono-digestion of BSG in only water yielded 402.17 NLbiogas/kgVS and 262.86 NLCH4/kgVS. The synergistic effects of inoculum from BWWS, FeCl3 and poultry manure on anaerobic digestion of BSG resulted in 46% and 52% increases in biogas and methane yields, respectively, compared with BSG mono-digestion. The biogas and biomethane production kinetics were well described by the dual pooled first order, logistic and modified Gompertz models.

show abstract

Enhancing and upgrading biogas and biomethane production in anaerobic digestion: a comprehensive review

Cited by 16 publications

References 304 publications

Prognostic Modelling of Biomethane Production from Waste: Application of Extreme Gradient Boosting

Prognostic Modelling of Biomethane Production from Waste: Application of Extreme Gradient Boosting

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

Enhanced anaerobic digestion of brewers’ spent grain: effect of inoculum, poultry manure application and iron (iii) chloride supplementation on biogas production and its kinetics

Contact Info

Product

Resources

About