(untitled) (Eureka Journal of Artificial Intelligence and Data Innovation, 2026)
In modern industrial environments, ensuring the operational safety of manipulators used in gas-fired furnaces is a critical challenge due to complex thermal and mechanical interactions. This study presents an ontology-based HAZOP (Hazard and Operability) approach for systematic analysis and identification of potential risks in manipulator control systems operating under high-temperature conditions. The proposed method integrates semantic modeling with traditional HAZOP analysis to enhance decision-making and automate hazard detection. As a result, the ontology framework provides a unified knowledge base that improves safety assessment accuracy, reduces human error, and supports intelligent control of industrial furnace manipulators. This article proposes the integration of an ontology-based HAZOP (Hazard and Operability Analysis) approach to ensure the safe operation of controlled manipulators. The HAZOP method is widely used to identify hazards and malfunctions in technological systems, but its dependence on human factors makes the results ambiguous. The study recommends the development of an automated hazard analysis system based on an ontological knowledge base, causation models, and logical inference algorithms. The proposed system is justified to integrate with a neural network-controlled manipulator model in the MATLAB/Simulink environment, enabling the detection and evaluation of hazardous situations in real time.