Hybrid Mamdani–ANFIS Data-Driven Control on an Industrial Heating Furnace

Abstract

The research presented provides an overview of the latest progress in data-driven control methods used for industrial heating furnaces. Although the data-driven methodologies reviewed provide good performance metrics compared to conventional control strategies, they lack the integration of energy efficiency considerations into the controller design process. This research presents a comprehensive control design framework for a novel energy-efficient data-driven controller applied to an industrial heating furnace. It proposes a novel Hybrid Mamdani–ANFIS controller developed using real-time data from an industrial heating furnace. A novel ANFIS-based energy model is also presented in this work to evaluate the energy efficiency of the presented controller models. The results demonstrated that the proposed novel Hybrid Mamdani–ANFIS controller outperforms both the Fuzzy PID and conventional Fuzzy controller in terms of energy efficiency, achieving approximately 30% energy savings and exhibiting a faster disturbance response time. This study makes a considerable contribution to the field of control theory by synthesizing existing knowledge, addressing identified research gaps, and introducing a novel control design framework that enhances energy efficiency, robustness, and adaptability across a wide spectrum of control applications in industrial heating furnace systems.