review · Energy Sources Part A Recovery Utilization and Environmental Effects
Conventional energy sources drive domestic and industrial activity but cause environmental degradation, climate disruption, and health risks. Biogas offers an alternative renewable energy solution. This review evaluates various biomass materials utilised in biogas generation, focusing on how biomass co-digestion and pre-treatment technologies influence methane yields. Without pre-treatment, methane output from various biomaterials ranges from 3.6 to 443.55 normal litres per kilogramme of volatile solids, whereas pre-treated materials yield varying outputs across different experimental configurations. The review highlights a scarcity of data regarding blends of cow dung, mango pulp, and Chromolaena odorata. Furthermore, it examines the application of artificial intelligence tools, including adaptive-neuro-fuzzy inference systems, to model and optimise anaerobic digestion parameters and blend ratios. Machine learning models demonstrated strong predictive accuracy, achieving correlation factors between 0.8700 and 0.9998.
Transitioning away from fossil fuels requires dependable, renewable energy options. Optimising biogas production through the co-digestion of agricultural and plant wastes helps reduce pollution while generating useful fuel. Demonstrating that artificial intelligence can accurately model and optimise digestion parameters allows researchers and plant operators to forecast methane yields and improve process efficiency without relying solely on trial and error.
This research informs biogas plant developers, waste management operators, and process engineers seeking to optimise feedstock blend ratios and digestion settings. The use of artificial intelligence models such as adaptive-neuro-fuzzy inference systems provides a tool to predict yields accurately. While the underlying literature includes practical experimental data, this work remains a secondary review of models and yields, suggesting an early to intermediate stage of application before direct deployment in commercial facilities.
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Energy is an essential bedrock, which plays a high impact role in the running of domestic and industrial activities. Most energy used for these activities is majorly from conventional sources, which after combustion result in ecological imbalance, climatic affray, health hazards, and degradation of natural resources. Therefore, the quest for eco-friendly energy has made researchers to investigate on alternative energy, such as biogas. This review study presents a comprehensive analysis of various biomass used for biogas production considering the effects that co-digestion of these materials has on biogas yield, as well as the technology involved. It further evaluated the applicability of artificial intelligence for modeling and optimization of the anaerobic digestion process including the blend ratios, process parameters and so on. These indices determine the percentage methane yield from biomaterial. The review effort revealed that methane content of biomaterials digested without pre-treatment varies from 3.6 ± 0.7 to 443.55 ± 13.68 NLkg−1VS while the yield from biomaterials pre-treated using various methods varies from 301.38 mL CH4/g VSadded to 0.73–5.87 L/week. Anaerobic digestion of the blends of cow dung, mango pulp, and Chromolaena odorata was reportedly necessary, as information is scantily available on it. The modeling of the resulting experimental data using different machine learning techniques such as an adaptive-neuro-fuzzy inference system and ANFIS for predicting biogas yield is a major information gathered in this study. The AI models reviewed have high correlation factors ranging from 0.8700 to 0.9998. This information gathered in this paper will motivate the production of useful fuel to complement the existing energy sources while offering a near-term and practical means for reduction of environmental pollution.
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DOI: 10.1080/15567036.2022.2085823
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