A comparative study of machine learning methods for bio-oil yield prediction – A genetic algorithm-based features selection

• Published in: Bioresource technology Vol. 335; p. 125292
• Main Authors: Ullah, Zahid, khan, Muzammil, Raza Naqvi, Salman, Farooq, Wasif, Yang, Haiping, Wang, Shurong, Vo, Dai-Viet N.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2021
• Subjects: Bio-oil yield; biofuels; biomass; Biomass to energy; comparative study; computer software; Genetic algorithm; Machine learning; prediction; Pyrolysis; regression analysis; yield forecasting; Biomass to energy; Pyrolysis; Genetic algorithm; Bio-oil yield; Machine learning
• ISSN: 0960-8524; 1873-2976; 1873-2976
• DOI: 10.1016/j.biortech.2021.125292

[Display omitted] •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. 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.