Predicting %0, in the flue gas during furnace operations is crucial for ensuring complete fuel
combustion, which is vital for maintaining the integrity of the furnace and preventing internal
deterioration. Incomplete combustion not only degrades mechanical components but also leads to inefficiencies and increased maintenance costs. One significant challenge is consistently achieving complete combustion under varying operating conditions.
A viable solution involves predicting the oxygen content (%0,) in the flue gas stream. By monitoring %0, in real time, operators can gain insights into combustion efficiency and furnace performance.

Implementing machine learning models allows for accurate prediction of %0,levels, enabling the operations team to make timely and informed process corrections. This proactive approach ensures consistent performance, optimizes combustion, and reduces the risk of damage to furnace components. The ability to predict %02 not only enhances operational efficiency but also contributes to the longevity and reliability of furnace operations.