Neuro-Symbolic Artificial Intelligence Frameworks Integrating Thermodynamic First-Principles, Industrial Knowledge Graphs, and Large Language Models for Autonomous Digital Process Engineering

The convergence of symbolic reasoning, thermodynamic physics, and deep generative language modeling represents a transformative frontier in autonomous process engineering. This article presents a unified neuro-symbolic artificial intelligence (NS-AI) framework that integrates thermodynamic first-principles, industrial knowledge graphs (IKGs), and large language models (LLMs) to enable fully autonomous digital process engineering. Classical process simulation tools rely on deterministic thermodynamic solvers that lack the adaptive reasoning and contextual generalization required for complex, multi-objective industrial workflows. Conversely, purely data-driven LLMs, while powerful in natural language understanding and code generation, suffer from thermodynamic inconsistency and domain hallucination. Our proposed architecture bridges this gap through a three-layer integration: (۱) a thermodynamic reasoning engine grounded in Gibbs free energy minimization and equation-of-state (EoS) formalisms, (۲) an industrial knowledge graph encoding process ontologies, equipment constraints, and regulatory frameworks, and (۳) a fine-tuned LLM acting as a natural-language interface and generative orchestrator. Evaluated across refinery distillation optimization, green hydrogen production scheduling, and pharmaceutical batch synthesis, the NS-AI framework achieves a ۳۴.۷% improvement in convergence speed over conventional process simulators, reduces energy penalty by ۱۸.۲%, and attains near-zero thermodynamic constraint violations (< ۰.۳%). These results demonstrate the feasibility and industrial scalability of physics-informed, knowledge-augmented LLM systems for next-generation autonomous process plants.

کلیدواژه ها:

neuro-symbolic AI ، thermodynamic first-principles ، knowledge graphs ، large language models ، digital process engineering ، autonomous systems ، process optimization ، physics-informed AI