Improving Industrial Boiler Efficiency With Quantitative Feedback Theory-Based Controllers

The aim of this study is to improve industrial boiler efficiency with QFT-based controllers. The operational parameters of industrial boilers are frequently highly uncertain, which can have an impact on their performance and stability. PID controllers and other conventional control techniques have not been able to effectively manage these uncertainties. The proposed methodology involves identifying and modeling the dynamics of the industrial boiler system, accounting for parametric uncertainties like fuel flow, air flow, and pressure changes. A QFT controller is designed using frequency-domain techniques, incorporating phase and magnitude information to ensure robust stability and performance under uncertain conditions. The designed controller is validated through simulations and real-time testing to demonstrate its effectiveness in improving boiler efficiency and reducing fuel consumption. According to the simulation results, the QFT-based controller outperform conventional controllers in terms of disturbance rejection, settling times, and smoother responses. The controller is then synthesized to satisfy these bounds, ensuring that the system remains stable and performs satisfactorily under all specified uncertainties. The system increases overall system stability, decreases fuel consumption, and improves fuel efficiency by the use of QFT. The controller successfully mitigates the impact of uncertainties, ensuring that key performance indicators such as response time, overshoot, and disturbance rejection, remain within acceptable limits. The results shows that implementing QFT-based controllers significantly improves industrial boiler efficiency. By addressing the challenges of high parametric uncertainty, the QFT controller achieves robust stability, enhanced performance, and better handling of dynamic variables such as fuel flow and boiler pressure. This leads to smoother system responses, reduced overshoot, faster settling time, and ultimately contributes to reduced fuel consumption and increased overall operational efficiency.