Optimization based comparative study of machine learning methods for the prediction of bio-oil produced from microalgae via pyrolysis

Prediction of bio-oil yield using machine learning methods is an effective and economical approach. To examine the correlation between pyrolysis conditions, ultimate, and proximate analysis with bio-oil production is intricate and challenging task for the experimental techniques. Therefore, an efficient and well-organized model must be created to reliably predict the effect of input parameters on the bio-oil yield. Multiple ML models are integrated with PSO and GA for selection of features and hyperparameters optimization. Here, GPR-GA model performed better with R² = 0.997 and RMSE = 0.0185 as compared to GPR-PSO model with R² = 0.994 and RMSE = 0.0120. The values of R² for DT, ANN, ET, SVM integrated with PSO are respectively 0.91, 0.92, 0.83 and 0.43 and for GA based algorithms the values of R² are 0.62, 0.93, 0.94, and 0.55 respectively. The significance of input factors on bio-oil yield was thoroughly examined using partial dependence plots and Shapley method. Moreover, an interface was developed by GPR-GA model to predict bio-oil yield. Yield comparison predicted by GPR-GA model and experimental study shows remarkable synchronization with maximum error of 1.48. It offers technologically advanced process in the pyrolysis of microalgae to enhance production of bio-oil.