Modeling of PVA Degradation in a Continuous Photochemical Reactor using Experimental Step Testing and Process Identification

In AOP processes, the flow of oxidant must be controlled to minimize the oxidant residuals in a post biological treatment and to maximize the total organic carbon (TOC) removal and degradation. Designing a controller to regulate the hydrogen peroxide (H2O2) begins with a dynamic model determination of a chemical process. Therefore, a step testing technique is employed to construct a dynamic model of the UV/H2O2 degradation of polyvinyl alcohol (PVA) process based on pH and TOC responses to H2O2 step change. The experimental design consists of three different initial PVA concentrations, of 60.0, 280.0, and 500.0 mg PVA/L. Eight experimental tests were conducted for different hydrogen peroxide mass flowrates ranging from 0.336 to 125 mg H2O2/min. For every test, a transfer function was experimentally determined to describe the dynamics of the UV/H2O2 photochemical reactor for the degradation of PVA. System identification toolbox in Matlab software was used to determine first order plus time delay (FOPTD), second order plus time delay (SOPTD) and ARX polynomial models. The transfer functions and ARX models are a good model representation of the pH response data of a specific step change of H2O2 concentration. For example, the standard deviation of the process gain of test # 1 and its replicate was calculated to be 1.18 and standard deviation of the time constant was calculated to be 1.27. The pH response of the first test was fitted with a FOPTD model with a data fitting score of 88.8%. Test # 2 pH response data was fitted with a SOPTD transfer function with data fitting score of 83.6%. Tests # 6 and 7’s pH response was fitted with a FOPTD model with a data fitting score of 94.3 and 87.7 % respectively. The different transfer functions obtained for the low, average, and high PVA concentrations indicate the nonlinearity aspect of polymer systems. All quality models are quite reliable estimations of the pH and TOC response data, since they were developed from experimental tests and parameter estimation techniques based on nonlinear regression approach.