Semantic Cognitive Refineries: Integration of Industrial IoT, Knowledge Graphs, and Large Language Models for Intelligent Real Time Decision Making

The convergence of Industrial Internet of Things (IIoT), semantic knowledge representation, and large language models (LLMs) has opened a transformative frontier in industrial automation and decision support. This paper introduces the concept of the Semantic Cognitive Refinery (SCR) a layered computational architecture that ingests raw sensor streams from industrial environments, enriches them through ontology-driven knowledge graphs, and exposes actionable intelligence via natural language interfaces powered by LLMs. Unlike conventional supervisory control and data acquisition (SCADA) systems or isolated machine learning pipelines, the SCR framework treats industrial data not merely as numerical telemetry but as a rich semantic substrate from which causal, contextual, and predictive reasoning can emerge in real time. We formalize the SCR architecture across four strata: the sensing and ingestion layer, the semantic enrichment layer, the graph-based reasoning layer, and the language-mediated decision layer. We demonstrate the framework's applicability across three industrial domains predictive maintenance in petroleum refining, anomaly detection in smart power grids, and quality assurance in pharmaceutical manufacturing. Our empirical evaluation, conducted on hybrid real-world and synthetic datasets, shows that SCR-enabled systems reduce mean time to decision (MTTD) by up to ۴۳% compared to baseline expert systems, improve fault classification accuracy by ۱۷.۶ percentage points, and enable non-expert operators to query and act on complex system states through natural language interfaces with an intent accuracy exceeding ۹۱%.

کلیدواژه ها:

Industrial IoT ، Knowledge Graphs ، Large Language Models ، Semantic Reasoning ، Predictive Maintenance ، Real-Time Decision Support ، Ontology Engineering ، Cognitive Architecture