Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass. (September 2022)

• Record Type: Journal Article
• Authors: Li, Yize; Gupta, Rohit; You, Siming
• Is Part Of: Bioresource technology. Volume 359(2022)
• Publication Date: 2022-09
• Subjects: Neural Networks -- Fuzzy Inference System -- Organic Waste -- Bioenergy -- Sustainable Development; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127511
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Machine learning predict biochar production from pyrolysis of organic waste. Biochar yield, proximate and ultimate compositions are predicted simultaneously. Statistical analysis of various feedstock and biochar properties are performed. Neural network outperforms fuzzy inference system for prediction purposes. Sensitivity analysis reveals influence of optimal model parameters. Abstract: Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.