No abstract

This paper addresses the implementation of economic-oriented model predictive controllers for the dynamic real-time optimization of the operation of wastewater treatment plants (WWTP). Both the economic-optimizing controller (pure-EMPC) and the economic-oriented tracking controller (Hybrid-EMPC, or HEMPC) formulations are validated in the benchmark simulation model (BSM1) platform that represents the behavior of a characteristic activated sludge process. The objective of the controllers is to ensure the appropriate operation of the plant, while minimizing the energy consumption and the fines for violations of the limits of the ammonia concentration in the effluent along the full operating period. A non-linear reduced model of the activated sludge process is used for predictions to obtain a reasonable computing effort, and techniques to deal with model-plant mismatch are incorporated in the controller algorithm. Different designs and structures are compared in terms of process performance and energy costs, which show that the implementation of the proposed control technique can produce significant economic and environmental benefits, depending on the desired performance criteria.

In this paper, a control approach for improving the overall efficiency of a wastewater treatment plant (WWTP) is presented. It consists of a cascaded control system that uses a global performance indicator as the controlled variable to drive the plant to operating conditions that satisfies trade-offs involved in the WWTP operation, improving the global performance of the plant. The selected global performance indicator is the N/E index that measures the ratio between the amount of nitrogenated compounds eliminated (kgN) and the energy (kWh) required to achieve that goal. This index links the variables of the activated sludge process with the energy consumed in the whole plant, thus the control strategy takes actions based on plantwide considerations. An external Proportional Integral (PI) controller changes the DO set point according to the N/E index and the basic dissolved oxygen (DO) control scheme in the activated sludge process follows this reference changes varying the aeration intensity. An outer loop with an event-based controller is used to compute the index values when the DO concentration is driven to excessively low limits, preventing long operation periods in this undesirable condition. Simple proportional integral controllers (PI) are used to adapt the strategy to the automation systems available in WWTPs. The implementation in the Benchmark Simulation Model 2 (BSM2) demonstrates the potential of the proposed approach. The results show the possibilities of the N/E index to be used as an indicator of global performance of WWTPs. It provides a link between water line objectives and energy consumption in the whole plant that can be exploited to introduce plantwide considerations in alternative control strategies formulated to drive the plant to operating conditions that optimize the overall process efficiency.

Optimization and control strategies are necessary to keep wastewater treatment plants (WWTPs) operating in the best possible conditions, maximizing effluent quality with the minimum consumption of energy. In this work, a benchmarking of different hierarchical control structures for WWTPs that combines static and dynamic Real Time Optimization (RTO) and non linear model predictive control (NMPC) is presented. The objective is to evaluate the enhancement of the operation in terms of economics and effluent quality that can be achieved when introducing NMPC technologies in the distinct levels of the multilayer structure. Three multilayer hierarchical structures are evaluated and compared for the N-Removal process considering the short term and long term operation in a rain weather scenario. A reduction in the operation costs of approximately 20% with a satisfactory compromise to Effluent Quality is achieved with the application of these control scheme.

In this work, a novel methodology for the Integrated Design (ID) of processes with linear Model Predictive Control (MPC) is addressed, providing simultaneously the plant dimensions, the control system parameters and a steady state working point. The MPC chosen operates over infinite horizon in order to guarantee stability and it is implemented with a terminal penalty. The ID methodology considers norm based indexes for controllability, as well as robust performance conditions by using a multi-model approach. Mathematically, the ID is stated as a multiobjective nonlinear constrained optimization problem, tackled in different ways. Particularly, objective functions include investment, operating costs, and dynamical indexes based on the weighted sum of some norms of different closed loop transfer functions of the system. The paper illustrates the application of the proposed methodology with the ID of the activated sludge process of a wastewater treatment plant (WWTP).