Design of control strategies for nutrient removal in a biological wastewater treatment process

Hybrid supervised hierarchical control of a biological wastewater treatment plant

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

Modeling the Operating Performance of a Drinking Water Biological Aerated Filter and the Formation of Organic Nitrogen

In this study, simultaneous storage and growth mechanism, as well as the formation processes of organic nitrogen (ON), were both introduced into activated sludge model 3 (ASM3), and ASM3-ON was formed to predict the operation of biofilm treatment processes and the formation of dissolved organic nitrogen (DON). ASM3-ON was applied to a lab-scale biological aerated filter (BAF) for water supply. During the simulation, the sensitivities of chemical oxygen demand (COD), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO_x^--N), and DON to the stoichiometric and kinetic coefficients in the model were analyzed first by the Sobol method. Then, the model prediction results were compared with experimental values to calibrate ASM3-ON. In the validation process, ASM3-ON was applied to predict the variations of COD, NH₄⁺-N, NO₂^--N, and NO₃^--N in BAF under different aeration ratios (0, 0.5:1, 2:1, and 10:1) and different filtration velocities (0.5, 2, and 4 m/h). The comparison with the experimental results showed that ASM3-ON could accurately predict the variation characteristics of COD, NH₄⁺-N, NO_x^--N, and DON in BAF. This study provided a practical model approach to optimize the operating performance of BAF and reduce the formation of ON through nonexperimental methods.

Fractional-order models identification and control within a supervisory control framework for efficient nutrients removal in biological wastewater treatment plants

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework 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. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO₇) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally.

Developing a practical model for the optimal operation of wastewater treatment plant considering influent characteristics

Wastewater treatment plants (WWTPs) play an important role in protecting the quality of water sources. The optimum operation of WWTPs in response to continuous changes in the characteristics of the influent of the WWTP is very important, and it can improve the quality of the effluent of the WWTP. In this study, an approach for optimal operation of the WWTP has been presented considering the quantitative and qualitative variables of influent. In the proposed method, first, the simulation model of WWTP is developed and calibrated using the recorded data of its influent and effluent characteristics as well as operation conditions. Then, the influent is classified into clusters quantitatively and qualitatively k-means clustering method. In the final step, after determining the effective operation parameters, the AMOEA-MAP optimization algorithm is used to determine the optimal values of operation parameters for each cluster of influents based on its quantitative and qualitative characteristics including flow rate, COD, ammonium, and temperature. The proposed approach was implemented on a WWTP in the South of Tehran, the capital of Iran. Dissolved oxygen (DO) in the aeration tank, waste-activated sludge flow rate (Q_WAS) and the ratio of the supernatant flow rate of the sludge dewatering unit to the effluent flow rate (Q_d/Q_e) were considered as operation parameters affecting the performance of the system in removing pollutants and their optimal values were obtained as DO, 0.25-1.7 mg/l, Q_WAS, 875-2000 m³/day, and Q_d/Q_e, 10-14%. Using this method, i.e., changing system operation conditions based on influent characteristics, has improved the performance of a system in reducing COD, ammonium, and nitrate in the effluent by 11-41, 17-20 and 15-34, respectively.

An effective dynamic immune optimization control for the wastewater treatment process

To resolve the conflict between multiple performance indicators in the complicated wastewater treatment process (WWTP), an effective optimization control scheme based on a dynamic multi-objective immune system (DMOIA-OC) is designed. A dynamic optimization control scheme is first developed in which the control process is divided into a dynamic layer and a tracking control layer. Based on the analysis of the WWTP performance, the energy consumption and effluent quality models are next established adaptively in response to the environment by an optimization layer. An adaptive dynamic immune optimization algorithm is then proposed to optimize the complex and conflicting performance indicators. In addition, a suitable preferred solution is selected from the numerous Pareto solutions to obtain the best set of values for the dissolved oxygen and nitrate nitrogen. Finally, the solution is evaluated on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem for multiple performance indicators in WWTPs and has a competitive advantage in its control effect.

Integrated supervisory and override control strategies for effective biological phosphorus removal and reduced operational costs in wastewater treatment processes

A novel control strategy is developed for a municipal wastewater treatment plant (WWTP) consisting of anaerobic-anoxic-aerobic reactors. The idea is to generate more organic matter with a reduction of nitrate concentration in the anoxic section so that more biological phosphorus (P) removal happens. For this, the Supervisory and Override Control Approach (SOPCA) is designed based on the benchmark simulation model (BSM1-P) and is evaluated by considering dynamic influent. In the supervisory layer, proportional integral (PI) and fuzzy controllers are designed. Additionally, dissolved oxygen (S_o) control loops in the aerobic reactors are designed. PI controller is designed for control of nitrate levels in the anoxic reactors and is integrated with override control and supervisory layer. It is found that the novel SOPCA approach gave better nutrient removal with slightly higher operating costs when S_o control is not put in place. With three S_o control loops in place, the WWTP showed better effluent quality and lower cost. Here, the improved removal efficiency of 28.5% and 20.5% are obtained when Fuzzy and PI control schemes respectively are used in the supervisory layer. Therefore, the application of SOPCA is recommended for a better P removal rate.

Design of Feedback Control Strategies in a Plant-Wide Wastewater Treatment Plant for Simultaneous Evaluation of Economics, Energy Usage, and Removal of Nutrients

Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass transfer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality.

Supervisory control configurations design for nitrogen and phosphorus removal in wastewater treatment plants

Model predictive control (MPC) and Fuzzy controllers are designed in a two-level hierarchical supervisory control framework for control of activated sludge-based wastewater treatment plants (WWTP) in order to efficiently remove nitrogen and phosphorus. Benchmark simulation model No.3 with a bio-phosphorus (ASM3bioP) module is used as a working platform. The hierarchical control framework is used to alter the dissolved oxygen in the seventh reactor (DO₇ ) to control ammonia. Lower-level PI, MPC, and Fuzzy are used to control the nitrate levels in the fourth reactor (S_NO4 ) by manipulating internal recycle (Q_intr ) and DO₇ in the seventh tank by manipulating mass transfer coefficient (K_L a₇ ). MPC and Fuzzy are designed in the supervisory layer to alter the DO₇ set-point based on the ammonia composition in the seventh reactor (NH₇ ). From the analysis, it is observed that the effluent quality is improved with a decrease in ammonia, TN, and TP. Though a little difference was observed in the cost for all the control strategies, a trade-off is maintained between cost and percentage improvement of effluent quality. MPC-MPC combination showed significant removal in ammonia and better effluent quality when compared to other control strategies. PRACTITIONER POINTS: Developed novel strategies in hierarchical configurations for better nutrient removal with optimal costs in an A² O process. Lower level control strategies deals with dissolved oxygen in last aeration tank and nitrate in fourth anoxic tank (PI/MPC) Higher level control strategy deals with ammonia in the last aeration tank (MPC/Fuzzy). Average and violations of nutrient removal, economy and overall effluent quality for three weather conditions (Dry, Rain and Strom) are studied. A trade-off is observed between EQI and OCI.

Nested control loop configuration for a three stage biological wastewater treatment process

In every urban infrastructure, Wastewater Treatment Plant (WWTP) requires special attention because of its adverse effects on the environment and also for resource recovery. Therefore, there arises a need to treat the wastewater in order to meet the effluent norms prior to discharge. Different control strategies and various scenarios of plant layout can be tested and evaluated through modelling and simulation studies on the benchmark layouts. In this paper, a feedforward nested loop control structure based on ammonia concentration is implemented on Benchmark Simulation Model (BSM1-P) developed based on Activated Sludge Model No. 3 bioP (ASM3bioP) for controlling the dissolved oxygen in aerobic zones and nitrate level in anoxic zones and nutrient removal by adding two anaerobic zones. By using this control strategy, pumping energy, percentage violations of ammonia and nitrogen concentrations in the effluent, and effluent quality are reduced effectively.