Bolstering Electric Furnace Temperature Control: A Comparative Study of PID-Based Metaheuristic Algorithms

Abstract

The management of electric furnace temperatures stands as a critical concern across various industrial sectors. Traditional controllers, like the PID controller, struggle to effectively handle variations in parameters and sudden disturbances. This research aims to tackle this challenge by examining the effectiveness of a PID controller enhanced by cutting-edge Metaheuristic Algorithms (MAs) for Electric Furnace Temperature Control (EFTC). Five emerging MAs were utilized to fine-tune the PID controller for the EFTC system, with their effectiveness assessed across five different performance criteria. Five recent and novel MAs were employed to optimize the PID controller for the EFTC system, with the controllers' performances evaluated under five distinct performance metrics. The results indicated that the PID-based NGO emerged as the most efficient optimization strategy, showcasing the shortest rise time (1.419 s), settling time (10.8155 s), peak time (5.1552), and objective value (4.071). These findings imply that the NGO algorithm holds significant potential as an optimization approach for enhancing the performance of EFTC systems in industrial applications.