Abstract
The zero-pollution ambition of the European Union requires improvements in wastewater treatment to meet increasingly stringent regulations at achievable cost. One promising approach consists in model-based optimal control. However, wastewater treatment plants involve highly nonlinear and time-varying processes, making existing mechanistic models such as the Benchmark Simulation Model no.1 (BSM1) challenging for direct use in online control. Therefore, this study explores a hybrid modeling approach using the Hammerstein-Wiener (HW) structure. The proposed model combines a mechanistic steady-state model, derived from BSM1, with a data-driven approximation of the system dynamics, incorporating low-order linear dynamic models. In this work, the HW model was used as a surrogate for BSM1. The HW surrogate model attained coefficients of determination (R2) often exceeding 0.95 across key water quality indicators, such as total nitrogen and ammonium concentration. This accuracy was found to be substantially better than that of a First-Order-Plus Dead-Time model as conventional low-order linear model, demonstrating the usefulness of the HW structure in capturing key nonlinearities.