Digitalization of phosphorous removal process in biological wastewater treatment systems: Challenges, and way forward.

Phosphorus in wastewater poses a significant environmental threat, leading to water pollution and eutrophication. However, it plays a crucial role in the water-energy-resource recovery-environment (WERE) nexus. Recovering Phosphorus from wastewater can close the phosphorus loop, supporting circular economy principles by reusing it as fertilizer or in industrial applications. Despite the recognized importance of phosphorus recovery, there is a lack of analysis of the cyber-physical framework concerning the WERE nexus. Advanced methods like automatic control, optimal process technologies, artificial intelligence (AI), and life cycle assessment (LCA) have emerged to enhance wastewater treatment plants (WWTPs) operations focusing on improving effluent quality, energy efficiency, resource recovery, and reducing greenhouse gas (GHG) emissions. Providing insights into implementing modeling and simulation platforms, control, and optimization systems for Phosphorus recovery in WERE (P-WERE) in WWTPs is extremely important in WWTPs. This review highlights the valuable applications of AI algorithms, such as machine learning, deep learning, and explainable AI, for predicting phosphorus (P) dynamics in WWTPs. It emphasizes the importance of using AI to analyze microbial communities and optimize WWTPs for different various objectives. Additionally, it discusses the benefits of integrating mechanistic and data-driven models into plant-wide frameworks, which can enhance GHG simulation and enable simultaneous nitrogen (N) and Phosphorus (P) removal. The review underscores the significance of prioritizing recovery actions to redirect Phosphorus from effluent to reusable products for future considerations.

Fractional order-based hierarchical controller design and evaluation with Bürger-Diehl settler model in a total nitrogen removal wastewater treatment process.

The primary objective of this study was to establish two-level structured control techniques based on the globally known benchmark simulation model no. 1 (BSM1) and the Bürger-Diehl settler model in order to improve effluent quality. The latter was based on the activated sludge model no. 1 (ASM1), while the classic Takacs model was superseded by the more recent Bürger-Diehl settler model with enhanced predictive potential. A two-level hierarchical control structure was considered to maintain the dissolved oxygen concentration basing on the ammonia levels and also a nitrate controller was considered to improve nitrogen removal efficiency. Fractional order PI controllers were considered at the secondary level and advanced control techniques, namely, MPC and fuzzy, were implemented at the primary level. Two advance control schemes, being, FPI-MPC and FPI-fuzzy were designed in the present work. The controllers were designed based on the plant model which was identified using prediction-error minimization method. It was observed that the implemented control strategies in consideration with the plant modifications showed a profound impact in improving the plant performance in terms of the effluent quality. FPI-fuzzy resulted in noticeable 60% and 53% reduction in total nitrogen violations for dry and storm climatic conditions, respectively.

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.

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 (So) 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 So control is not put in place. With three So 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.

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 (SNO4 ) by manipulating internal recycle (Qintr ) and DO7 in the seventh tank by manipulating mass transfer coefficient (KL 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.