Parameter identification based on resistance furnace model

Mengli Sun

doi:10.1117/12.2686712

10 August 2023 Parameter identification based on resistance furnace model

Mengli Sun

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127591D (2023) https://doi.org/10.1117/12.2686712
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

With the popularity of heat treatment technology, the accuracy of temperature control and the correctness of control method selection are crucial. More and more researchers combine genetic algorithms, model predictive control algorithms, and particle swarm algorithms for the parameter identification of resistance furnace models have disadvantages such as local optimum and slow convergence speed. Therefore, the paper establishes an approach to the parameter identification of resistance furnace model based on the Improved Moth-flame optimization algorithm (IMFO) algorithm, selects the Particle Swarm Optimization (PSO) and Moth-flame optimization algorithm (MFO) as comparison algorithms, and obtains the parameter identification results of the above three intelligent optimization algorithms through MATLAB simulation tests and compares them with the actual resistance furnace output. It is found that the IMFO can identify the resistance furnace temperature sintering process more accurately, and the parameter identification results of the resistance furnace model using the IMFO are better fitted with the actual resistance furnace parameter variation curve. Compared with Moth-flame optimization algorithm (MFO) and Particle Swarm Optimization (PSO), the Improved Moth-flame optimization algorithm (IMFO) has the advantages of fast convergence, solving the local optimum problem, and improving the speed of finding the optimum, etc. It is very effective in the field of parameter identification of furnace temperature sintering process and better meets the needs of general industrial control.

Citation Download Citation

Mengli Sun "Parameter identification based on resistance furnace model", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127591D (10 August 2023); https://doi.org/10.1117/12.2686712

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KEYWORDS

Resistance

Flame

Mathematical optimization

Particle swarm optimization

Fire

Mathematical modeling

Matrices

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