Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization

Area of Science:

• Industrial Engineering
• Control Systems Engineering
• Process Optimization

Background:

• Precise temperature control is critical in industrial processes for efficiency and product quality.
• Traditional proportional-integral-derivative (PID) controllers are widely used but have limitations in complex systems.
• Novel control strategies are needed to enhance temperature stability and system responsiveness.

Purpose of the Study:

• To introduce and evaluate a novel real proportional-integral-derivative plus second-order derivative (RPIDD²) controller for industrial electric furnace temperature control.
• To optimize the RPIDD² controller parameters using various metaheuristic algorithms.
• To compare the performance of the proposed QIO-RPIDD² controller against other optimized RPIDD² variants.

Main Methods:

• Development of the real PID plus second-order derivative (RPIDD²) control algorithm.
• Optimization of RPIDD² controller parameters using Flood Optimization Algorithm (FLA), Reptile Search Algorithm (RSA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Quadratic Interpolation Optimization (QIO).
• Comparative performance analysis based on transient and frequency response metrics for different controller configurations.

Main Results:

• The Quadratic Interpolation Optimization (QIO) algorithm combined with the RPIDD² controller (QIO-RPIDD²) demonstrated superior performance compared to FLA, RSA, PSO, and DE optimized controllers.
• The QIO-RPIDD² controller exhibited fast adaptation to varying reference temperatures and excellent performance on key indicators.
• Comparative analyses confirmed the enhanced transient and frequency response of the QIO-RPIDD² controller.

Conclusions:

• The proposed QIO-RPIDD² controller is a highly effective and promising solution for precise industrial temperature control applications.
• This novel approach contributes to the development of more efficient and adaptive optimization techniques in process control.
• The study highlights the potential of QIO for optimizing advanced control strategies in demanding industrial environments.