Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning

TL;DR

This paper enhances LSTM-based simulators for wastewater treatment using deep reinforcement learning by reducing prediction errors through novel training methods, enabling more accurate long-term process modeling solely from time series data.

Contribution

It introduces two methods to improve LSTM models for wastewater treatment simulation, addressing long-term accuracy and compounding errors, advancing data-driven process modeling.

Findings

Achieved up to 98% improvement in Dynamic Time Warping accuracy.

Enhanced long-term stability of wastewater treatment simulators.

Demonstrated effectiveness without prior process knowledge.

Abstract

Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industrial processes like wastewater treatment. One of the challenges is the lack of a simulation environment that will represent the actual plant as accurately as possible to train DRL policies. Stochasticity and non-linearity of wastewater treatment data lead to unstable and incorrect predictions of models over long time horizons. One possible reason for the models' incorrect simulation behavior can be related to the issue of compounding error, which is the accumulation of errors throughout the simulation. The compounding error occurs because the model utilizes its predictions as inputs at each time step. The error between the actual data and the prediction accumulates as the simulation continues. We implemented two methods to improve the trained models for wastewater treatment data, which resulted in more accurate simulators: 1- Using the model's prediction data as input in the training step as a tool of correction, and 2- Change in the loss function to consider the long-term predicted shape (dynamics). The experimental results showed that implementing these methods can improve the behavior of simulators in terms of Dynamic Time Warping throughout a year up to 98% compared to the base model. These improvements demonstrate significant promise in creating simulators for biological processes that do not need pre-existing knowledge of the process but instead depend exclusively on time series data obtained from the system.