AI-Driven Optimization Of Flare Stack Combustion Processes In Oil Refineries: Integrating Real-Time Data Analytics, Predictive Modeling, And Emission Reduction Algorithms

Flare stacks are essential in managing excess gases and preventing the release of harmful volatile organic compounds (VOCs) and greenhouse gases into the atmosphere in oil refineries. However, flare stack operations often result in inefficiencies and excessive emissions. This paper proposes an AI-driven approach to optimize flare stack combustion processes by integrating real-time data analytics, predictive modelling, and emission reduction algorithms. The framework leverages IoT-based sensor networks, machine learning models, and dynamic control systems to enhance combustion efficiency, minimize emissions, and improve energy recovery. The results show that this approach significantly reduces operational inefficiencies, improves predictive maintenance, and supports sustainable refining practices.

کلیدواژه ها:

Artificial Intelligence ، Flare ، Refinery