Toward Fully Autonomous Refineries: Hierarchical Multi-Agent Reinforcement Learning Integrated with Cognitive Digital Twins for Self-Optimizing Process Operations

The concept of fully autonomous refineries represents a paradigm shift in industrial process engineering, where traditional rule-based automation is replaced by adaptive, learning-driven systems capable of continuous self-optimization. This study explores the integration of Hierarchical Multi-Agent Reinforcement Learning (HMARL) with Cognitive Digital Twins (CDTs) to enable intelligent, decentralized control of complex refinery operations. In this framework, multiple reinforcement learning agents operate at different hierarchical levels, coordinating local and global decision-making across interconnected process units such as distillation columns, catalytic crackers, and heat exchanger networks. These agents learn optimal control policies through reward-driven interactions within a high-fidelity digital twin environment that mirrors real-time refinery behavior. Cognitive digital twins serve as the foundational simulation layer, continuously synchronized with live industrial data streams from sensors and control systems. Unlike conventional digital twins, they incorporate machine learning and predictive analytics to support reasoning, forecasting, and scenario evaluation. This allows reinforcement learning agents to safely explore operational strategies in a virtual environment before deployment in the physical system, significantly reducing risk and improving learning efficiency. The integration of HMARL and CDTs enables a closed-loop intelligence system in which perception, prediction, and control are unified. Lower-level agents manage real-time equipment adjustments, while higher-level agents optimize process-wide objectives such as energy efficiency, throughput, emissions reduction, and operational safety. Coordination mechanisms ensure alignment between local and global objectives, preventing suboptimal trade-offs. This autonomous architecture enhances adaptability in the face of fluctuating feedstock quality, dynamic market demand, and equipment degradation. It also introduces significant improvements in sustainability by continuously optimizing energy consumption and minimizing waste. Overall, the proposed framework demonstrates a scalable pathway toward next-generation autonomous industrial systems, transforming refineries into self-learning, self-optimizing ecosystems capable of intelligent decision-making with minimal human intervention.

کلیدواژه ها:

Hierarchical Multi-Agent Reinforcement Learning ، Cognitive Digital Twins ، Autonomous Refineries ، Process Optimization ، Industrial Artificial Intelligence