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

• URL: http://arxiv.org/abs/2403.15091v1
• Date: Fri, 22 Mar 2024 10:20:09 GMT
• Title: Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning
• Authors: Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Mikkel Stokholm-Bjerregaard, Aviaja Anna Hansen, Petar Durdevic,
• Abstract summary: We implement two methods to improve the trained models for wastewater treatment data. The experimental results showed that implementing these methods can improve the behavior of simulators in terms of Dynamic Time Warping throughout a year.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge [0.0]
  We propose a hybrid modeling workflow for process-based hydrologic models and machine learning surrogate models. As a case study, we apply this workflow to simulations of variably saturated groundwater flow at a prospective managed aquifer recharge site. Our results demonstrate that ML surrogate models can achieve under 10% mean absolute percentage error and yield order-of-magnitude runtime savings.
  arXiv  Detail & Related papers  (2024-07-30T15:24:27Z)
• Deep Learning Based Simulators for the Phosphorus Removal Process Control in Wastewater Treatment via Deep Reinforcement Learning Algorithms [0.0]
  Phosphorus removal is vital in wastewater treatment to reduce reliance on limited resources. Applying deep reinforcement learning to chemical and biological processes is challenging due to the need for accurate simulators. This study trained six models to identify the phosphorus removal process and used them to create a simulator for the DRL environment.
  arXiv  Detail & Related papers  (2024-01-23T14:55:46Z)
• Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning [0.8250374560598492]
  We propose a deep learning method for predicting displacement fields of soft tissues with viselastic properties. The proposed method achieves a better accuracy over the conventional CNN models. It is hoped that the present investigation will help in filling the gap in applying deep learning in virtual reality.
  arXiv  Detail & Related papers  (2023-01-11T18:17:10Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning [0.8164433158925593]
  Data-Driven Simulation (DDS) methods learn process simulation models from event logs. Deep Learning (DL) models are able to accurately capture such temporal dynamics. This paper presents a hybrid approach to learn process simulation models from event logs.
  arXiv  Detail & Related papers  (2021-03-22T15:34:57Z)
• Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models [33.935755695805724]
  Learn-by-Calibrating (LbC) is a novel deep learning approach for designing emulators in scientific applications. We show that LbC provides significant improvements in generalization error over widely-adopted loss function choices. LbC achieves high-quality emulators even in small data regimes and more importantly, recovers the inherent noise structure without any explicit priors.
  arXiv  Detail & Related papers  (2020-05-05T16:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.