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.
Highlights
In the management of a wastewater treatment plant (WWTP) operation, one of the most important factors that determines economics is the energy used to provide oxygen to the aerobic processes, and the pumping energy for the recycles of the plant
The considered economic model predictive control strategies are presented. These strategies are followed by the process description, which presents the wastewater treatment plant layout and process model, as well as the WWTP control problem and performance indices, in order to evaluate the performance of the plant operation
The comparison between the standard non-linear model predictive controller (NMPC), which is focused on the set-point tracking, and the HEMPC (HEMPC-overall cost index (OCI): w1 = 1, w2 = 0, HEMPC-NH: w1 = 0, w2 = 1)
Summary
In the management of a wastewater treatment plant (WWTP) operation, one of the most important factors that determines economics is the energy used to provide oxygen to the aerobic processes (aeration energy), and the pumping energy for the recycles of the plant. The influent of the wastewater treatment plants exhibits an oscillating behavior, with daily and seasonal patterns associated with the human activities during the day and the seasonal rainfall. Weather conditions, such as rain and storms, produce significant changes in the influent flowrate and load [4]. Due to the variable influent behavior, the pollution load to be treated is continuously changing, and so are the energy and chemical requirements for the treatment In such a scenario, conservative operation regulating the critical variables around the nominal working point might ignore the disturbances introduced by the influent. It is forecasted that significant energy savings could be achieved with an operation based on dynamic optimization that accounts for the influent conditions
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