In the domain of fired heater operations, accurate monitoring and prediction of oxygen levels in flue gases play a critical role in ensuring operational efficiency and safety. This project focuses on conducting correlation and trend analysis to identify key process parameters (KPIs) that influence oxygen concentrations in flue gases. By examining factors such as fuel composition, combustion temperature, and airflow rates, this analysis aims to uncover significant correlations and trends. Leveraging these insights, predictive models can be developed to forecast the percentage of oxygen in flue gases under varying operating conditions. Such predictive capabilities enable proactive adjustments to combustion parameters, optimizing fuel efficiency and minimizing emissions. Ultimately, this approach enhances the reliability, performance, and environmental sustainability of fired heater systems.
