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

ABSTRACT: 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 R ² values of 0.95, 0.97, and 0.94 for CO₂, CO, and H

SUBMITTER: Yan M 

PROVIDER: S-EPMC11873848 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature