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.