Research on low-energy consumption automatic real-time regulation of cascade gates and pumps in open-canal based on reinforcement learning

T Gan, Y Jiang, H Zhao, J He… - Journal of Hydroinformatics, 2024 - iwaponline.com
Cascade gates and pumps are common hydraulic structures in the open-canal section of
water transfer projects, characterized by high energy consumption and substantial costs …

Predictive control for the operation of cascade pumping stations in water supply canal systems considering energy consumption and costs

L Kong, Y Li, H Tang, S Yuan, Q Yang, Q Ji, Z Li… - Applied Energy, 2023 - Elsevier
Water supply canal systems (WSCSs) have a significant environmental and energetic impact
due to the large amount of energy consumed in water pumping and water losses. The safe …

Online Control of the Raw Water System of a High-Sediment River Based on Deep Reinforcement Learning

Z Li, L Bai, W Tian, H Yan, W Hu, K Xin, T Tao - Water, 2023 - mdpi.com
Water supply systems that use rivers with high sedimentation levels may experience issues
such as reservoir siltation. The suspended sediment concentration (SSC) of rivers …

An application of multi-objective reinforcement learning for efficient model-free control of canals deployed with IoT networks

T Ren, J Niu, J Cui, Z Ouyang, X Liu - Journal of Network and Computer …, 2021 - Elsevier
Canals have been widely constructed to deliver water from rich areas to poor areas to ease
water shortages. Efficient controlling of canals is essential for high-performance water …

An efficient model-free approach for controlling large-scale canals via hierarchical reinforcement learning

T Ren, J Niu, X Liu, J Wu, X Lei… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Large-scale canals with cascaded pools are constructed wordwide to divert water from rich
to arid areas to mitigate water shortages. Efficient control of canals is essential to improve …

State selection and cost estimation for deep reinforcement learning-based real-time control of urban drainage system

W Tian, K Xin, Z Zhang, Z Liao, F Li - Water, 2023 - mdpi.com
In recent years, a real-time control method based on deep reinforcement learning (DRL) has
been developed for urban combined sewer overflow (CSO) and flooding mitigation and is …

An Optimal Model and Application of Hydraulic Structure Regulation to Improve Water Quality in Plain River Networks

F Huang, H Zhang, Q Wu, S Chi, M Yang - Water, 2023 - mdpi.com
The proper dispatching of hydraulic structures in water diversion projects is a desirable way
to maximize project benefits. This study aims to provide a reliable, optimal scheduling model …

Study on auxiliary operation control of machine learning in multiobjective complex drainage system

P Li, S Zhou, J Cao, W Xu, J Zhou - Water Science and Technology, 2022 - iwaponline.com
Recently, urban waterlogging prevention and treatment of black–odorous rivers have
become a social concern and the upgradation of drainage system and the development of …

Deep reinforcement learning for real-time optimization of pumps in water distribution systems

G Hajgató, G Paál, B Gyires-Tóth - Journal of Water Resources …, 2020 - ascelibrary.org
Real-time control of pumps can be an infeasible task in water distribution systems (WDSs)
because the calculation to find the optimal pump speeds is resource intensive. The …

Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks

H Patel, Y Zhou, AP Lamb, S Wang, J Luo - arXiv preprint arXiv …, 2023 - arxiv.org
This article addresses the pump-scheduling optimization problem to enhance real-time
control of real-world water distribution networks (WDNs). Our primary objectives are to …