Wastewater treatment plants (WWTPs) are large-scale and nonlinear processes with tightly integrated operating units. The application of online optimization-based control strategies, such as model predictive control (MPC), to WWTPs generally faces high computational complexity. This paper proposes an event-triggered approach to address this issue. The model predictive controller updates information and solves the optimization problem only when the corresponding triggered logic is satisfied. The triggered logic sets the maximum allowable deviation for the tracking variables. Moreover, to ensure system performance, the design of the event-triggered logic incorporates the effluent quality. By obtaining the optimal sequence for the effluent quality within the receding horizon of the MPC, the cumulative deviation between the predicted and desired effluent quality is analyzed to evaluate the performance within that horizon. Based on these two conditions, the need for adjusting control actions is determined. Even if the maximum allowable range for the tracking variables in the triggered logic design is set unreasonably, the consideration of effluent quality factors in the triggered conditions ensures good performance. Simulation results demonstrate an average reduction in computational effort of 25.49% under different weather conditions while simultaneously ensuring minimal impact on the effluent quality and total cost index and compliance with effluent discharge regulations. Furthermore, this method can be combined with other approaches to guarantee effluent quality while further reducing computation time and complexity.

To address the challenge of low computational efficiency in nonlinear Economic Model Predictive Control (EMPC) for large-scale systems such as wastewater treatment plants (WWTPs), this paper proposes a Trajectory Piecewise Linearization (TPWL)-based EMPC framework integrated with global maximum error control (GMEC) and Proper Orthogonal Decomposition (POD). The TPWL method constructs a reduced-order model framework, while GMEC iteratively refines the linearization point selection process. A two-stage strategy is employed: first, coarse selection of candidate linearization points along the original nonlinear model’s state trajectory based on Euclidean distance, followed by refinement to determine optimal points that minimize global approximation errors. Simulation results demonstrate that the proposed method reduces computational time by at least 65% under identical weather conditions while maintaining effluent quality and total cost indices within acceptable thresholds. Compared with conventional TPWL-POD approaches, this framework achieves higher model accuracy and superior EMPC control performance. These advancements underscore the method’s potential for real-time implementation in complex industrial systems, balancing computational efficiency with control precision. Additionally, the framework’s modular design enables integration with existing optimization techniques to further reduce computational complexity without compromising effluent quality compliance.

In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.