Prediction of product properties and identification of key influencing parameters in microwave pyrolysis of microalgae using machine learning

Exploring the effects of operating parameters and microalgae composition on product distribution and properties using machine learning is an effective and economical approach. However, there is currently a lack of research utilizing machine learning to comprehensively describe the microwave pyrolysis of microalgae. In this study, machine learning was employed to accurately model microalgae microwave pyrolysis based on microalgae composition, operating parameters, and the yields and properties of three-phase products. Using the collected data set, machine learning models, including Support Vector Regressor, Random Forest Regressor, Gradient Boosting Regressor, and eXtreme Gradient Boosting, were employed to model microalgae microwave pyrolysis. Results showed that the Gradient Boosting Regressor model outperformed the other three models, with average coefficients of determination (R2) 2 ) of 0.998 and 0.762 in the training and testing phases, respectively. SHapley Additive exPlanations analysis revealed that reaction temperature, reaction time, and microwave power significantly influenced the predictions of the yields and properties of three-phase products. The findings of this study can effectively save costs and time in the future study on microwave pyrolysis of microalgae, and serve as an important tool for guiding subsequent experiments and process optimization.