Ensemble self-organizing recursive neural network for modeling furnace temperature in municipal solid waste incineration

The modeling of furnace temperature (FT) is the foundation of optimizing and controlling in municipal solid waste incineration (MSWI) process. However, owing to the high nonlinearity and dynamicity, complex reaction mechanisms, and strong coupling phenomena of MSWI process, accurately modeling the FT remains a significant challenge. In this paper, an ensemble self-organizing recursive neural network with information fusion gain algorithm (ESORNN-IFG) is proposed for FT modeling in MSWI process. First, the AdaBoost algorithm is introduced to combine multiple base learners by weighting them to enhance the accuracy and robustness of the strong learner. Second, an IFG index is introduced to appraise the contribution of hidden neurons and their interrelationships. Third, a self-organizing strategy combined with the IFG index is designed for adjusting the structure of the base learners during model training. Finally, the merits and effectiveness of the proposed ESORNN-IFG is confirmed by comparison with other existing approaches after testing the experimental results on several benchmark problems and the practical application of FT modeling in MSWI process.