{"type":"ScholarlyArticle","title":"MORL-CA: Dynamic Multi-Objective Reinforcement Learning for Chlor-Alkali Process Optimization Under Time-Varying Conditions","authors":[{"name":"Derun Gan","slug":"derun-gan","given":"Derun","family":"Gan","affiliation":""},{"name":"Renhao Yin","slug":"renhao-yin","given":"Renhao","family":"Yin","affiliation":""},{"name":"Guangzhi Qu","slug":"guangzhi-qu","given":"Guangzhi","family":"Qu","affiliation":""},{"name":"Feng Zhang","slug":"feng-zhang","given":"Feng","family":"Zhang","affiliation":""}],"abstract":"Chlor-alkali production is a large-scale industrial process whose operating conditions and equipment states evolve over time. Its process optimization requires ongoing trade-offs among conflicting objectives such as product yield, energy consumption, and equipment life. Existing optimization approaches are typically static and must be re-optimized after environmental changes, limiting their real-world applicability. In this work, we model the problem as a dynamic multi-objective sequential decision-making problem that continuously tracks a time-varying Pareto set under changing conditions. We propose MORL-CA, a multi-objective reinforcement learning framework that integrates offline pretraining on historical data with constrained online policy refinement. MORL-CA introduces a state-aware adaptive objective weighting mechanism within a multi-critic actor-critic architecture, enabling localized Pareto-improving policy updates while satisfying operational and safety constraints. Extensive experiments in an environment conducted from real chlor-alkali data demonstrate that MORL-CA achieves superior Pareto solution quality and smoother adaptation to dynamics compared with state-of-the-art multi-objective optimizers and MORL baselines.","doi":"10.24963/ijcai.2026/716","url":"https://ijcai.org/proceedings/2026/716","pdf":"https://ijcai.org/proceedings/2026/0716.pdf","markdown":"https://ijcai.org/proceedings/2026/716.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/716","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":716,"pages":{"first":6436,"last":6444},"keywords":["Domain-specific AI4Tech: Other AI4Tech applications","Domain-specific AI4Tech: AI4Manufacturing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}