Physics-Informed LSTM Network for Flexibility Identification in Evaporative Cooling System

In energy-intensive industrial systems, an evaporative cooling process may introduce operational flexibility. Such flexibility refers to a system's ability to deviate from its scheduled energy consumption. Identifying the flexibility, and therefore, designing control that ensures efficient and reliable operation presents a great challenge due to the inherently complex dynamics of industrial systems. Recently, machine learning (ML) models have attracted attention for identifying flexibility, due to their ability to model complex nonlinear behavior. This article presents ML-based methods that integrate system dynamics into the ML models (e.g., neural networks) for better adherence to physical constraints. We define and evaluate physics-informed long-short term memory networks (PhyLSTMs) and physics-informed neural networks (PhyNN) for the identification of flexibility in the evaporative cooling process. These physics-informed networks approximate the time-dependent relationship between control input and system response while enforcing the dynamics of the process in the neural network architecture. Our proposed PhyLSTM provides less than 2% system response estimation error, converges in less than half iterations compared to a baseline NN, and accurately estimates the defined flexibility metrics. We include a detailed analysis of the impact of training data size on the performance and optimization of our proposed models.

Automated summary

This article discusses the importance of identifying and designing control for flexibility in the evaporative cooling process using machine learning methods. The integration of system dynamics into ML models such as PhyLSTMs and PhyNNs allows for better modeling of complex nonlinear behavior. The performance and optimization of these methods are analyzed in relation to training data size.