Dynamic Water-Energy-Carbon Trade-Off Optimization for Heavy Industry Decarbonization via Deep Reinforcement Learning: A UK Case Study

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🔬研究者:Provides a novel deep reinforcement learning approach for multi-objective optimization in carbon capture systems, useful for those modeling industrial decarbonization with resource constraints.

🏢実務担当者:Offers a data-driven methodology for cement plants to optimize water and energy use while deploying CCUS, reducing operational costs and environmental impact.

🏛政策担当者:Highlights the need to consider water-energy-carbon nexus in industrial decarbonization policies and can inform integrated resource planning for net-zero clusters.

In recent years, the industrial decarbonization in the cement sector has introduced secondary environmental impact due to an increase in power and water demand. Deploying carbon capture, utilization, and distributed storage requires an uninterrupted supply of power and water to achieve net-zero targets. However, the traditional static optimization algorithms seem insufficient in addressing the high-frequency and dynamic renewable networks. To overcome these issues, this work develops a dynamic water-energy-carbon trade-off optimization model for industrial decarbonization, with the deployment of Carbon Capture, Utilization, and Storage system in the cement sector within a United Kingdom industrial cluster. The key objective is to quantify and control the secondary burden that low-carbon interventions can impose on electricity systems and local water resources. Firstly, the Water-Energy-Carbon problem is treated as a tri-lemma, which is formulated as a continuous Markov Decision Process. Then the optimization problem is solved via a Soft Actor-Critic Deep Reinforcement Learning algorithm under coupled and resource-constrained abstraction inputs. This work further introduces the Water-Carbon Mitigation Penalty Index as a diagnostic metric for measuring the marginal increase in water burden associated with carbon mitigation. The results show that unmanaged distributed carbon-mitigation pathways increase local hydrological stress by 2.15–5.17% relative to baseline operating conditions. Although the proposed algorithm successfully reduces the nexus cost by up to 70.5% and achieves 13.83% carbon reduction by shifting from freshwater abstraction to reclaimed municipal wastewater and by coordinating operation with low-carbon hydropower availability. These results show that dynamic AI-based scheduling can support net-zero transitions while reducing pressure on regional hydro-ecological systems.

• crossref https://doi.org/10.3390/w18091112first seen 2026-05-14 22:23:40