Enhanced neural network modelling for a real multivariable chemical process

Application of several Neural Network (NN) modelling techniques to model a Multi-Input Multi-Output (MIMO) nonlinear chemical process is investigated. The process is a laboratory scale chemical reactor with three inputs and three outputs. It typically represents industrial processes due to its nonlinearity, coupling effects and lack of a mathematical model. Different techniques have been used in collecting training data from the reactor. A novel method was used to select the model order and time-delay to determine the NN model input. A Radial Basis Function Network (RBFN) model was then developed. A Recursive Orthogonal Least Squares (ROLS) algorithm was applied as a numerically robust method to update the RBFN weight matrix. In this way, degradation of the modelling error due to ill-conditioning in the training data is avoided. Real data experiments show that the RBFN model developed has high accuracy.