DESIGN AND PERFORMANCE EVALUATION OF ADVANCED CONTROL STRATEGIES FOR BIOLOGICAL WASTEWATER TREATMENT PLANTS

Abstract

The utilization of biological processes to treat polluted wastewater has become prevalent, encompassing both conventional domestic and industrial wastewater. This approach aims to eliminate nutrients, specifically carbon, nitrogen, and phosphorus, while adhering to regulatory guidelines for reducing nutrient discharge into surface water, as mandated by municipal water directives. There is a growing interest for enhancing the effluent quality (EQ) of sewage wastewater treatment facilities. Wastewater treatment plants are complex, nonlinear, and slow processes. The lack of adequate instrumentation, stringent environmental regulations, and the need for cost-effective plants have highlighted the significance of automating wastewater treatment processes. However, the process's complexity makes it difficult to successfully implement control systems. The main challenge is developing a control strategy that reduces operational costs (OC) while also improving EQ. This study looks into the development of various control strategies to meet these challenges. The Benchmark Simulation Model No. 1-P (BSM1-P) and Sequencing Batch Reactor (SBR) serve as test platforms for these control strategies. The primary goal is to prevent violations in effluent ammonia, total nitrogen, and total phosphorus levels while reducing operational costs and improving effluent quality. The proposed control strategies use proportional integral (PI), fractional PI (FPI), fuzzy logic controller (FLC), and model predictive control (MPC). To meet strict environmental laws, wastewater treatment plants (WWTPs) must balance efficiency and cost-effectiveness in their extremely non-linear operations. The ASM3bioP framework inside a BSM1-P is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers – a classical PI and a non-integer (fractional) order PI – with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. Control theory has actively explored fractional calculus and its applications in recent years. This work regulates DO and NO concentrations in aerobic and anoxic reactors using IMC-based fractional filters cascaded with PI and FPI controls. Based on integer and non vi | P a g e integer commands, these controllers optimize plant efficiency, longevity, production costs, and effluent nutrient content. IMC fractional PI controllers prioritize maximal sensitivity (Ms) within gain margin (GM) and phase margin (PM) as limitations. Fractional-order calculus advances highlight the dynamic character of real-time complicated processes, reducing complexity while retaining complex system dynamics. The fractional-order PID (PIλDμ) controller, an improved variant of the integer-order PID, adds integration (λ) and differentiation (μ) orders, improving closed-loop response stability with parameter alterations. The lower level Fractional controller with a fractional order model improves both the effluent quality index (EQI) and operational cost index (OCI) significantly. For such biological WWTP, a hierarchical Fuzzy logic controller or a MPC are designed to adjust the dissolved oxygen in the seventh reactor (DO7) to control ammonia. The implemented supervisory layer control strategy improves EQI while increasing OCI marginally. The treatment of wastewater is highly challenging due to large fluctuations in flow rates, pollutants, and variable influent water compositions. A sequencing batch reactor (SBR), and Modified SBR Cycle-Step-Feed Process (SSBR) configuration are studied in this work to effectively treat municipal wastewater while simultaneously removing nitrogen and phosphorus. To control the amount of Dissolved Oxygen in a SBR, three axiomatic control strategy (PI, FPI), and Fuzzy logic controllers (FUZZY)) is presented. A biological process and relevant control algorithm has been designed using real-time plant data with the models of biological processes (SBR, and SSBR), and aeration system. ASM2d mathematical modelling framework is considered for development of control relevant simulations. The use of the intricate ASM2d model, as well as the application of a control strategy to a batch process, makes the work significant. A comparison of plant performance concerning PI, FPI and FUZZY control framework is included. A comparison of FPI with the other two control strategies showed a significant reduction in nutrient levels and added an improvement in effluent quality. The SSBR, which is improved by precisely optimizing nutrient supply and aeration, establishes a delicate equilibrium. This refined method reduces oxygen requirements while reliably sustaining important biological functions. This thesis also proposes a novel supervisory control scheme for SBR based WWTP. It integrates hierarchical fuzzy control, based on ammonia and nitrate observations, in the presence of lower-level PI and FPI controllers, with the dual goal of aeration cost reduction and effluent quality enhancement. In the hierarchical control system, variable DO trajectories are generated by the supervisory fuzzy logic controller and passed to the lower level controller, vii | P a g e according to ammonia and nitrate profiles within SBR. It is crucial to adjust this element properly in order to maximize wastewater treatment efficiency and reduce plant costs, especially for the aeration system. A notable aspect of nitrate based hierarchical control scheme is to curtail the fresh oxygen use since nitrate (SNO), a product of nitrification, is utilized for limiting aeration costs. Six distinct control techniques are implemented of which PI and FPI controllers for control of DO at the lower level. Four types of hierarchical ammonia and nitrate based controllers employing intelligent Fuzzy control are deployed. Addition of Fuzzy controller contributes to an airflow reduction of 40.08% for ammonia control and 31.58% for nitrate control strategies. This study highlights the superiority of the ammonia-based control strategy, particularly coupled with lower level FPI controller, based on its ability to minimize airflow without affecting effluent quality. These findings offer helpful insights for advancing the field of wastewater treatment, improving efficiency, and promoting cost-effective and sustainable practices in SBR. Another study which aims to investigate the effect of different seasons where the temperature would be different on the performance (phosphorous, nitrogen, and organic matter removal) of SBR based wastewater treatment plants. The modified ASM2d module, including the microbial kinetics is used to simulate the EBPR-based SBR process and the temperature is chosen between 10 to 33°C. Influent data from distinct wastewater treatment plants located in India and Europe are considered. The investigation of the kinetic variables is performed over a wide temperature range, and significant increases are seen as the temperature rises. The effluent parameters are within the government guidelines. It is clear that an increase in temperature results in better effluent quality with reduced COD, BOD, NH, and TN values and a slight increase in TP and TSS. In conclusion, this study highlights the importance of considering the effect of temperature on the performance of SBR-based wastewater treatment plants in different climatic conditions.