Data-Driven Modeling Using Improved Multi-Objective Optimization Based Neural Network for Coke Furnace System

Abstract

The chamber pressure modeling of the industrial coke furnace is difficult due to the flame instability in the fuel burner and various disturbances. To deal with this issue, a new optimization method using radial basis function (RBF) neural network is proposed to improve the modeling accuracy and simplify the modeling structure. An improved multi-objective evolutionary algorithm (MOEA) is proposed to optimize the input layer, the hidden layer, and the parameters of the basis functions of the RBF neural network. The structure/parameter encoding and local search, prolong and pruning operators are designed to make MOEA suitable for optimization of the RBF neural network. Once a group of Pareto optimal solutions is derived, the RBF neural network with good generalization capability can be chosen succinctly in terms of root-mean-square error of a selected unused dataset. It shows that only a little prior knowledge of the plant is required and the approach has efficiently compromised between the generalization capability, approximation performance, and structure simplification of the RBF neural network when tested on a nonlinear dynamic function and the industrial chamber pressure.