Combustion Control of Ship's Oil-Fired Boilers Based on Prediction of Flame Images

This study proposes and validates a novel combustion control system for oil-fired boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs and applies internal model control (IMC)-based proportional-integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC filter constant tuning through experimentation is essential for achieving effective control performance. Experimental results showed that optimal control performance was achieved when the filter constant lambda was set to 1.5. In this scenario, the peak overshoot Mp was reduced to 0.19245, and the Integral of Squared Error (ISE) was minimized to 10.1159, ensuring a stable response with minimal oscillation and maintaining a fast response speed. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.