ELM Based Temperature Prediction Control for MISO Hammerstein-Wiener Decomposition Furnace under Collaborative Garbage Disposal

To address the limitation that traditional linear models are insufficient to describe the complex system of the decomposition furnace, in collaborative garbage disposal， we propose a multiple-input single-output (MISO) Hammerstein-Wiener model for decomposition furnace temperature modeling and predictive control based on an extreme learning machine (ELM). This approach aims to achieve stable temperaturecontrol of the decomposition furnace. The model uses coal feeding rate and refuse-derived fuel (RDF) as inputs， decomposition furnace temperature as the output， and applies ELM to capturing nonlinear relationships while employing an autoregressive moving average with exogenous input (ARMAX) model to describe dynamic linear relationships. We identify the mixed parameters of the model using the recursive least squares method and estimate the model parameters through singular value decomposition. The control method for the decomposition furnace follows a two-step predictive control approach. First， we establish a nonlinear inverse model. Then， we map these intermediate variables through a nonlinear process to determine the control variables of the model. Simulation experiments demonstrate that incorporating ELM improves the fitting accuracy of the model. The proposed approach exhibits greater stability and improved tracking performance compared to traditional predictive control methods.