Spatiotemporal Topology-Informed Multiagent Reinforcement Learning Framework for Structured Multiprocess...

Area of Science:

• Industrial Process Optimization
• Artificial Intelligence
• Chemical Engineering

Background:

• Industrial processes involve complex spatiotemporal dependencies.
• Traditional methods often overlook fine-grained interdependencies between operational variables across subprocesses.
• Existing reinforcement learning and optimization techniques may treat subprocesses as independent entities.

Purpose of the Study:

• To introduce a novel framework for multiprocess collaborative optimization.
• To address the limitations of traditional methods in capturing interdependencies at the operational variable level.
• To develop a more effective optimization strategy for complex industrial systems.

Main Methods:

• Developed a spatiotemporal topology-informed multiprocess collaborative optimization (STI-MCO) framework.
• Pioneered action-level interdependency modeling using a spatiotemporal graph architecture.
• Employed a hierarchical two-stage decision framework operating at the operational variable level.

Main Results:

• STI-MCO demonstrated superior performance compared to baseline methods in benchmark environments.
• Achieved up to 38.9% improvement over centralized methods and 171.9% over multiagent strategies.
• Showcased enhanced convergence efficiency and practical applicability in a real-world chemical process.

Conclusions:

• STI-MCO offers a paradigm shift from subprocess-level to variable-level collaboration.
• The framework enables more precise coordination, temporal consistency, and scalability.
• Establishes a new approach for optimizing complex industrial processes with strong interunit coupling.