Advanced Temperature-Integrated Backpropagation Neural Network for Enhanced Prediction of Syngas Composition in Complex Organic Waste Gasification.

Accurate prediction of syngas compositions in multicomponent organic waste gasification is challenging because of its intricate composition and abundant volatile matter, which contrasts with traditional coal gasification influenced mainly by oxygen-coal ratio. Through process analysis, we identified the furnace temperature as a crucial factor directly impacting gasification reactions. Herein, we developed a hybrid backpropagation neural network (BPNN) model integrating furnace temperature data obtained from a temperature soft-sensing model and utilizing principal component analysis (PCA) for dimensionality reduction. The resulting T-PCA-BPNN model demonstrated outstanding predictive performance, achieving values of 0.95, 0.97, and 0.94 for CO, CO, and H, respectively. Compared to the base BPNN model, the total mean square error (MSE) and mean absolute error (MAE) decreased by 49.4% and 13.3%, respectively. Furthermore, the percentage of predictive errors within 1% (QR) surpassed 90%, underscoring the model's practical applicability. Leveraging PCA and SHapley Additive exPlanations (SHAP) analysis, we established a syngas regulation strategy that controls critical parameters to identify postdimensionality reduction through practical operational adjustments. This data-driven model enhances syngas prediction, thereby facilitating improved process control and optimization in complex organic waste gasification.