A comparative study of machine learning methods for bio-oil yield prediction – A genetic algorithm-based features selection. (September 2021) - Ullah, Zahid; khan, Muzammil; Raza Naqvi, Salman; Farooq, Wasif; Yang, Haiping; Wang, Shurong; Vo, Dai-Viet N.
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.