Toward Quantum Enabled Digital Process Engineering: Autonomous Cognitive Refineries Through Hybrid Scientific AI, Digital Twins, and Carbon Aware Process Intelligence

The convergence of quantum computing, hybrid scientific artificial intelligence, high-fidelity digital twins, and carbon-aware process intelligence represents a foundational shift in how complex industrial systems are conceived, designed, and operated. This article presents a unified theoretical and architectural framework for the emerging paradigm of Quantum-Enabled Digital Process Engineering (QDPE), centered on the concept of the Autonomous Cognitive Refinery a self-optimizing, sustainability-aware industrial facility governed by integrated quantum-classical intelligence. We survey the current state of quantum algorithms applicable to process simulation and optimization, examine the maturation of digital twin architectures from descriptive to prescriptive modalities, and articulate how hybrid scientific AI combining physics-informed neural networks, symbolic reasoning, and probabilistic machine learning closes the gap between theoretical quantum advantage and industrial deployment. We further propose a carbon-aware intelligence layer that embeds lifecycle emissions accounting directly into the optimization objective, enabling real-time decarbonization trade-offs at the process level. Case studies in petroleum refining, pharmaceutical manufacturing, and green hydrogen production illustrate the transformative potential of this framework. We conclude with an honest assessment of near-term barriers qubit coherence limitations, data ontology fragmentation, and regulatory uncertainty and a technology readiness roadmap extending to ۲۰۳۵.

کلیدواژه ها:

quantum computing ، digital twins ، process engineering ، hybrid AI ، carbon intelligence ، autonomous systems ، Industry ۵.۰ ، decarbonization ، scientific machine learning