Machine learning-enabled analysis of product distribution and composition in biomass-coal co-pyrolysis

Co-pyrolysis of biomass and coal presents a promising opportunity for large-scale biomass utilization while reducing fossil fuel consumption. However, this process is highly complex and influenced by various factors, including the physicochemical properties of biomass and coal, their blending ratio, and operating parameters. To effectively comprehend and optimize the biomass-coal co-pyrolysis process, many experiments are required to achieve the desired product quantity and quality. In addressing this challenge, machine learning technology emerges as a valuable solution, as it can learn the relationships between input and output variables from available examples without explicit knowledge of the underlying mechanisms. This study conducts a comprehensive literature review to establish an extensive database encompassing biomass-coal compositions and pyrolysis reaction conditions. Using the collected data, robust statistical analyses are applied to understand better the underlying mechanisms governing the biomass-coal co-pyrolysis process. Four machine learning methods, namely support vector regression, artificial neural network, random forest regression, and gradient boosting regression, are employed to model the process. The best-performing model is subjected to an extensive feature importance analysis to identify the essential input features associated with each output response. The gradient boosting regression technique demonstrates superior performance with excellent results, characterized by higher coefficients of determination (R² > 0.96) and lower errors (RMSE < 3.01 and MAE < 2.27). The temperature range of 450 to 550 °C, biomass blending ratios between 30 and 60%, and heating rates of 30 to 50 °C/min were identified as the conditions that maximize pyrolysis oil yield. Furthermore, the feature importance analysis reveals that the operating temperature and the biomass blending ratio are the most significant descriptors in the biomass-coal co-pyrolysis process.