[HTML][HTML] The role of deep learning in urban water management: A critical review

G Fu, Y Jin, S Sun, Z Yuan, D Butler - Water Research, 2022 - Elsevier
Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

Smart water resource management using Artificial Intelligence—A review

SR Krishnan, MK Nallakaruppan, R Chengoden… - Sustainability, 2022 - mdpi.com
Water management is one of the crucial topics discussed in most of the international forums.
Water harvesting and recycling are the major requirements to meet the global upcoming …

Advances in application of machine learning to life cycle assessment: a literature review

A Ghoroghi, Y Rezgui, I Petri, T Beach - The International Journal of Life …, 2022 - Springer
Abstract Purpose Life Cycle Assessment (LCA) is the process of systematically assessing
impacts when there is an interaction between the environment and human activity. Machine …

Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review

H Lu, H Wang, Q Wu, H Luo, Q Zhao, B Liu, Q Si… - Science of the Total …, 2023 - Elsevier
In order to promote low-carbon sustainable operational management of the wastewater
treatment plants (WWTPs), automatic control and optimal operation technologies, which …

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 …

Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent …

Z Zhang, W Tian, Z Liao - Water Research, 2023 - Elsevier
The real-time control (RTC) of urban drainage systems can make full use of the capabilities
of existing infrastructures to mitigate combined sewer overflow (CSO) and urban flooding …

Where reinforcement learning meets process control: Review and guidelines

RR Faria, BDO Capron, AR Secchi, MB de Souza Jr - Processes, 2022 - mdpi.com
This paper presents a literature review of reinforcement learning (RL) and its applications to
process control and optimization. These applications were evaluated from a new …

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

EG Al-Sakkari, A Ragab, H Dagdougui… - Science of The Total …, 2024 - Elsevier
Carbon capture, utilization, and sequestration (CCUS) is a promising solution to
decarbonize the energy and industrial sector to mitigate climate change. An integrated …

[HTML][HTML] Robust control for anaerobic digestion systems of Tequila vinasses under uncertainty: A Deep Deterministic Policy Gradient Algorithm

TA Mendiola-Rodriguez… - Digital Chemical …, 2022 - Elsevier
The disposal of high concentrated Tequila vinasses is an environmental threat. An
alternative to solve this problem is through anaerobic digestion processes to reduce organic …

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 …