Advanced soft-sensing techniques for predicting furnace temperature in industrial organic waste gasification

Accurately real-time monitoring furnace temperature in multi-component industrial organic waste gasification presents considerable challenges due to the process's inherent complexity and variability. Herein, we have developed a sophisticated soft-sensing model that utilizes data-driven methods for precise temperature prediction, using input variables measured in industrial process. The data, primarily centered on cooling water, were derived from an in-depth analysis of heat transfer within the gasifier. Three distinct machine learning models were constructed and demonstrated enhanced performance compared to conventional heat transfer model. The Back Propagation Neural Network (BPNN) model, exhibiting a remarkable R2 2 value of 0.94, emerged as the most effective. Employing SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP), we further revealed the critical role of cooling water temperature in furnace temperature prediction. Overall, the application of data-driven models into the furnace temperature prediction offers a significant advancement in handling the complexities inherent in industrial processes.