Chance-Constrained Model Predictive Control A reformulated approach suitable for sewer networks

TL;DR

This paper introduces a revised Chance-Constrained Model Predictive Control formulation tailored for sewer networks, demonstrating its mathematical basis and comparing its performance with deterministic MPC through simulations on a benchmark sewer model.

Contribution

The paper presents a new formulation of CC-MPC specifically adapted for sewer systems, addressing overflow control and computational efficiency.

Findings

Similar overflow avoidance performance between CC-MPC and deterministic MPC

Slight increase in computation time for CC-MPC

Operational behaviors become more limited with CC-MPC

Abstract

In this work, a revised formulation of Chance-Constrained (CC) Model Predictive Control (MPC) is presented. The focus of this work is on the mathematical formulation of the revised CC-MPC, and the reason behind the need for its revision. The revised formulation is given in the context of sewer systems, and their weir overflow structures. A linear sewer model of the Astlingen Benchmark sewer model is utilized to illustrate the application of the formulation, both mathematically and performance-wise through simulations. Based on the simulations, a comparison of performance is done between the revised CC-MPC and a comparable deterministic MPC, with a focus on overflow avoidance, computation time, and operational behavior. The simulations show similar performance for overflow avoidance for both types of MPC, while the computation time increases slightly for the CC-MPC, together with operational behaviors getting limited