A comparative study of machine learning methods for bio-oil yield prediction – A genetic algorithm-based features selection. (September 2021)

• Record Type: Journal Article
• Authors: Ullah, Zahid; khan, Muzammil; Raza Naqvi, Salman; Farooq, Wasif; Yang, Haiping; Wang, Shurong; Vo, Dai-Viet N.
• Is Part Of: Bioresource technology. Volume 335(2021)
• Publication Date: 2021-09
• Subjects: Pyrolysis -- Bio-oil yield -- Machine learning -- Genetic algorithm -- Biomass to energy; 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.2021.125292
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: A genetic algorithm-based approach was used for feature selection. Random forest outperformed all other ML models in predicting bio-oil yield. Analysis of Partial Dependence Plot showed inside details for pyrolysis process. A Graphical User Interface for predicting bio-oil yield was developed. Abstract: A novel genetic algorithm-based feature selection approach is incorporated and based on these features, four different ML methods were investigated. According to the findings, ML models could reliably predict bio-oil yield. The results showed that Random forest (RF) is preferred for bio-oil yield prediction (R2 ~ 0.98) and highly recommended when dealing with the complex correlation between variables and target. Multi-Linear regression model showed relatively poor generalization performance (R2 ~ 0.75). The partial dependence analysis was done for ML models to show the influence of each input variable on the target variable. Lastly, an easy-to-use software package was developed based on the RF model for the prediction of bio-oil yield. The current study offered new insights into the pyrolysis process of biomass and to improve bio-oil yield. It is an attempt to reduce the time-consuming and expensive experimental work for estimating the bio-oil yield of biomass during pyrolysis.