Optimal-Setpoint-Based Control Strategy of a Wastewater Treatment Process

Abstract

This paper presents an optimal-setpoint-based control strategy of a wastewater treatment process (WWTP). The treatment plant serves the city of Galati, located in Eastern Romania, a city with a population of 250,000 inhabitants. As the treatment plant includes several control loops (based upon PI controllers), an efficient operation means the establishing of an optimal operating point regardless of the pluviometric regime (DRY, RAIN and STORM) or transitions between regimes. This optimal operating point is given by the optimal setpoint set (setpoints of the dissolved oxygen concentration in the aerated tanks, setpoint of the nitrate concentration, external recirculation flow, sludge flow extracted from the primary clarifier and excess sludge flow from the secondary clarifier) of the treatment plant control loops. The control algorithm has two distinct parts: the first part consists of computing the optimal aforementioned setpoints, based on the mathematical model of the treatment plant developed in SIMBA. For optimization (performed with genetic algorithms) an aggregate performance criterion that takes into consideration the quality of the effluent, the cost of the wastewater treatment as well as the percentage exceeding of the main parameters of the treated water was used; the second part consists of computing the optimal setpoint set which will be further applied directly in the process based on the membership to the current operating regime. The computation of the membership degrees to the current operating regime was performed with a fuzzification block, based on the information about the inflow rate in the biological treatment plant. For simulations, three data files of the influent were created, aiming at determining the optimal setpoints in each operating regime, and a fourth one containing an influent scenario able to globally test the system operation. The obtained results showed the efficiency of the biological treatment, the effluent quality index being about ten times lower than that of the influent. Furthermore, the genetic algorithm used in optimization determines accurately enough the minimum value of the performance criterion in the case of each pluviometric regime, the lowest value of the performance criterion being obtained in DRY operating regime and the highest values in RAIN and STORM regimes. This is mainly due to the increase of the treatment cost and to small exceeding of the limits of several quality parameters such as chemical oxygen demand and ammonium concentration in the two regimes mentioned above. The fuzzification block aims to achieve a smooth transition from one operating regime to another, thus determining easier operating regimes of the treatment plant actuators and contributing to the increase of their life cycle.