A Biomimetic Gazelle Optimization Approach for Enhanced Temperature Regulation in Electric Furnaces.

Area of Science:

• Industrial Automation
• Control Systems Engineering
• Metaheuristic Optimization

Background:

• Accurate temperature control is critical for industrial electric furnaces, but nonlinear dynamics and thermal inertia pose significant challenges.
• Existing control methods struggle to balance steady-state accuracy with transient response performance.

Purpose of the Study:

• To develop an advanced optimization-based control framework for enhancing temperature regulation in electric furnace systems.
• To integrate the Gazelle Optimization Algorithm (GOA) for automatic tuning of Proportional-Integral-Derivative (PID) controller parameters.

Main Methods:

• A mathematical model of the electric furnace was developed to represent its dynamic behavior.
• A Proportional-Integral-Derivative (PID) controller was implemented and its parameters were optimized using the Gazelle Optimization Algorithm (GOA).
• A composite objective function combining integral absolute error, overshoot, and settling time was used for optimization.

Main Results:

• The GOA-PID controller demonstrated stable convergence, achieving an average objective value of 2.4674 over 25 runs.
• The optimized system exhibited improved dynamic characteristics: 1.8509s rise time, 3.6834s settling time, and 1.5104% overshoot.
• Comparative analysis showed the GOA-PID strategy outperformed other tuning methods in response speed, stability, and accuracy.

Conclusions:

• The proposed GOA-based PID tuning framework effectively enhances electric furnace temperature control performance.
• This approach offers a superior balance of response speed, stability, and tracking accuracy compared to conventional methods.
• The study highlights the potential of metaheuristic algorithms like GOA in industrial process control optimization.