Efficient Economic Model Predictive Control of Water Treatment Process with Learning-based Koopman Operator

TL;DR

This paper introduces a data-driven, learning-based Koopman operator approach for economic model predictive control of water treatment processes, improving efficiency and operational costs.

Contribution

It develops a deep learning-enabled input-output Koopman modeling framework and a convex predictive control scheme, enabling efficient optimization for water treatment operations.

Findings

Significant reduction in operational costs.

Enhanced computational efficiency over benchmark methods.

Improved economic performance of water treatment process.

Abstract

Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework. First, we propose a deep learning-enabled input-output Koopman modeling approach, which predicts the overall economic operational cost of the wastewater treatment process based on input data and available output measurements that are directly linked to the operational costs. Subsequently, by leveraging this learned input-output Koopman model, a convex economic predictive control scheme is developed. The resulting predictive control problem can be efficiently solved by leveraging quadratic programming solvers, and complex non-convex optimization problems are bypassed. The proposed method is applied to a benchmark wastewater treatment process. The proposed method significantly improves the overall economic operational performance of the water treatment process. Additionally, the computational efficiency of the proposed method is significantly enhanced as compared to benchmark control solutions.