NARX-based BPSO modelling for time-varying steam temperature of steam distillation pilot plant

Abstract

This paper focuses on a nonlinear modelling for a time-varying process of steam temperature by employing a polynomial Nonlinear Auto-Regressive with Exogenous Input (NARX) structure based on Binary Particle Swarm Optimization (BPSO) algorithm. The system identification time-varying steam temperature data was collected from Steam Distillation Pilot Plant. Three models’ criterion were implemented: Akaike Information Criterion, Model Descriptor Length (MDL) and Final Prediction Error (FPE) for optimization process of NARX-based BPSO modelling. The results demonstrated that the FPE criterion model was presented a slightly better model with lowest CRV from the testing set, small fitness value and a  minimum number of parameter in the output model. The accuracy was evaluated by the high R-squared, small MSE value and passed all the correlation and histogram tests.

Keywords: identification; distillation column; temperature; NARX; particle swarm optimization.