Reinforcement learning applied to wastewater treatment process control optimization: Approaches, challenges, and path forward

HC Croll, K Ikuma, SK Ong, S Sarkar - Critical Reviews in …, 2023 - Taylor & Francis
Wastewater treatment process control optimization is a complex task in a highly nonlinear
environment. Reinforcement learning (RL) is a machine learning technique that stands out …

Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm

W Valladares, M Galindo, J Gutiérrez, WC Wu… - Building and …, 2019 - Elsevier
The aim of this work is to propose an artificial intelligence algorithm that maintains thermal
comfort and air quality within optimal levels while consuming the least amount of energy …

Systematic performance evaluation of reinforcement learning algorithms applied to wastewater treatment control optimization

HC Croll, K Ikuma, SK Ong… - Environmental Science & …, 2023 - ACS Publications
Treatment of wastewater using activated sludge relies on several complex, nonlinear
processes. While activated sludge systems can provide high levels of treatment, including …

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

F Guo, S woo Ham, D Kim, HJ Moon - Applied Energy, 2025 - Elsevier
With the recent demand for decarbonization and energy efficiency, advanced HVAC control
using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible …

Residual physics and post-posed shielding for safe deep reinforcement learning method

Q Zhang, MHB Mahbod, CB Chng… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) has been researched for computer room air conditioning
unit control problems in data centers (DCs). However, two main issues limit the deployment …

Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control

G Demirezen, AS Fung… - International Journal of …, 2020 - Wiley Online Library
This research accounts for the outcome of a major cloud‐based smart dual fuel switching
system (SDFSS) project, which is a dual‐fuel integrated hybrid heating, ventilation, and air …

HVAC system modeling and control methods: a review and case study

SM Dawood, A Hatami, RZ Homod - Journal of Energy Management and …, 2022 - jemat.org
Improvement of air quality and provision of the residents' comfort in different buildings are
the main tasks of HVAC (heating, ventilating, and air conditioning) systems. A large number …

Methodology for interpretable reinforcement learning model for HVAC energy control

O Kotevska, J Munk, K Kurte, Y Du… - … Conference on Big …, 2020 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) approaches have been used in various application
areas to improve efficiency, optimization, or automation. However, very little is known about …

[PDF][PDF] Deep reinforcement learning on HVAC control

I Namatēvs - Information Technology and Management …, 2018 - pdfs.semanticscholar.org
Due to an increase in computing power and innovative approaches of an end-to-end
reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now …

The potential of control models based on reinforcement learning in the operating of solar thermal cooling systems

JJ Diaz, JA Fernández - Processes, 2022 - mdpi.com
The objective of this research work was to investigate the potential of control models based
on reinforcement learning in the optimization of solar thermal cooling systems (STCS) …