Nonlinear System Identification for Aqueous PVA Degradation in a Continuous UV/H2O2 Tubular Photoreactor

Abstract

In this study, the performance of three black-box identification techniques using linear autoregressive with exogenous input (ARX), nonlinear ARX (NARX), and Hammerstein–Wiener (HW) algorithm to model the dynamics of UV/H₂O₂ continuous tubular photochemical reactor for the treatment of poly(vinyl alcohol) (PVA) based on experimental data is investigated. In addition, the inherent nonlinearity of the reaction process is assessed. The reactor dynamics in the NARX model is estimated by wavelet, sigmoid, and tree partition networks along with the assessment of the performance of each model. Although a sigmoid network describes the nature of chemical processes better, the results show that tree partition network-based NARX is the most suitable estimator for the studied process as represented by its highest quality of fit (91.59% for training data set and 88.17% for validation of data set), lowest loss function (mean-squared error, MSE) (0.0004279), model realizability, open-loop stability, model whiteness, and model independence.