Temperature Control of Electric Furnace Using Adaptive Lag Compensator Based on Improved Gorilla Troops Optimization: Towards Energy Efficiency

Abstract

A lot of study has been done in recent years to increase the energy efficiency of engineering systems. It is essential to create effective temperature control systems since electric furnaces (EF) account for a significant portion of energy usage. The majority of established techniques need accurate system parameter knowledge/sufficient data. Nevertheless, in the case of dynamic parameter variation, these methods might not operate as well. In many industrial applications, controlling the temperature of EFs is regarded as one of the key problems. In this paper, an EF temperature system with an adaptive lag compensator is proposed. Application of artificial gorilla troops optimization (GTO) supported by the balloon effect (BE) (GTO+BE) identifier estimates the integral coefficient of the adaptive lag compensator for temperature control purposes. Due to the low efficiency of the objective functions employed in ordinary optimization, the BE identifier is used to raise the optimization technique's objective function's efficiency and the controller's ability to handle system problems, both of which rise as a result. The issue of parameter fluctuations and step disruption is intractable for conventional controls like PID controllers. The proposed technique adaptive lag compensator based on GTO+BE is compared with the modified flower pollination algorithm (MFPA)-based PIDA, and MFPA-based PID controllers. From the results, the proposed adaptive lag compensator with GTO+BE gives the best dynamic performance of an EF temperature system with the minimum overshoot, rise time, and settling time.