AN INTELLIGENT DECISION SUPPORT SYSTEM FOR HAZARD IDENTIFICATION AND RISK ASSESSMENT IN FERTILIZER INDUSTRIES

This paper proposed an Intelligent Decision Support System for hazard identification and risk assessment in fertilizer industries. The proposed framework integrates conventional safety-analysis techniques, domain knowledge, machine learning, explainable AI, and decision-support functionality. The system is designed to collect heterogeneous industrial safety information, identify potential hazards, estimate risk, prioritize hazardous conditions, explain the factors contributing to the prediction, and provide safety recommendations. The hybrid architecture addresses an important limitation of purely manual or purely data-driven approaches by combining expert knowledge with analytical intelligence. For an M.Tech research project, the proposed framework provides opportunities for significant experimental work in machine learning, explainable AI, database management, risk analytics, and industrial safety. Future implementation and validation using real or appropriately constructed industrial datasets can establish the effectiveness, generalizability, and practical value of the proposed approach.

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