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A new control scheme for temperature adjustment of electric furnaces using a novel modified electric eel foraging optimizer

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Název: A new control scheme for temperature adjustment of electric furnaces using a novel modified electric eel foraging optimizer
Autoři: Sarah A. Alzakari, Davut İzci, Serdar Ekinci, Amel Ali Alhussan, Fatma A. Hashim
Zdroj: AIMS Mathematics, Vol 9, Iss 5, Pp 13410-13438 (2024)
Informace o vydavateli: American Institute of Mathematical Sciences (AIMS), 2024.
Rok vydání: 2024
Témata: Artificial intelligence, FOS: Mechanical engineering, Control (management), 02 engineering and technology, Mathematical analysis, 7. Clean energy, Environmental science, Control Systems and Network Applications, electric furnace, electric eel foraging optimization, Engineering, pid-f controller design, QA1-939, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Foraging, Biology, temperature control, Scheme (mathematics), metaheuristics, Temperature control, Ecology, Mechanical Engineering, Control engineering, Induction Heating Technology and Applications, Computer science, Mechanical engineering, non-linear analysis, Model Predictive Control in Industrial Processes, Control and Systems Engineering, 13. Climate action, FOS: Biological sciences, Physical Sciences, Electric heating, Mathematics
Popis: In this study, we present a comprehensive framework for enhancing the temperature control of electric furnaces, integrating three novel components: a proportional-integral-derivative controller with a filter (PID-F), a customized objective function, and a modified electric eel foraging optimization (mEEFO) algorithm. The PID-F controller, introduced for the first time in the literature for temperature control of electric furnaces, leverages a filter coefficient to effectively mitigate the kick effect, improving transient and frequency responses. To further optimize the PID-F controller, we employed the mEEFO, a recently proposed metaheuristic inspired by the social predation behaviors of electric eels, with tailored modifications for electric furnace temperature control. The study also introduces a new objective function, based on the modification of the integral of absolute error (IAE) performance index. The proposed framework was evaluated through extensive comparisons with established metaheuristic algorithms, including statistical analysis, Wilcoxon signed-rank test, and time and frequency domain analyses. Comparative assessments with reported methods, such as genetic algorithms and Ziegler–Nichols-based PID controllers, validated the efficacy of the proposed approach, highlighting its transformative impact on electric furnace temperature regulation. The non-ideal conditions such as measurement noise, external disturbance, and saturation at the output of the controller were also evaluated in order to demonstrate the superior performance of the proposed approach from a wider perspective. Furthermore, the robustness of the proposed approach against variations in system parameters was also demonstrated.
Druh dokumentu: Article
Other literature type
ISSN: 2473-6988
DOI: 10.3934/math.2024654
DOI: 10.60692/dte95-xy558
DOI: 10.60692/7d1xy-34b19
Přístupová URL adresa: https://doaj.org/article/da59c9b6534e4919a98ed855d1328729
Přístupové číslo: edsair.doi.dedup.....0545e921d045ae063de5126091ebf6c7
Databáze: OpenAIRE
Popis
Abstrakt:In this study, we present a comprehensive framework for enhancing the temperature control of electric furnaces, integrating three novel components: a proportional-integral-derivative controller with a filter (PID-F), a customized objective function, and a modified electric eel foraging optimization (mEEFO) algorithm. The PID-F controller, introduced for the first time in the literature for temperature control of electric furnaces, leverages a filter coefficient to effectively mitigate the kick effect, improving transient and frequency responses. To further optimize the PID-F controller, we employed the mEEFO, a recently proposed metaheuristic inspired by the social predation behaviors of electric eels, with tailored modifications for electric furnace temperature control. The study also introduces a new objective function, based on the modification of the integral of absolute error (IAE) performance index. The proposed framework was evaluated through extensive comparisons with established metaheuristic algorithms, including statistical analysis, Wilcoxon signed-rank test, and time and frequency domain analyses. Comparative assessments with reported methods, such as genetic algorithms and Ziegler–Nichols-based PID controllers, validated the efficacy of the proposed approach, highlighting its transformative impact on electric furnace temperature regulation. The non-ideal conditions such as measurement noise, external disturbance, and saturation at the output of the controller were also evaluated in order to demonstrate the superior performance of the proposed approach from a wider perspective. Furthermore, the robustness of the proposed approach against variations in system parameters was also demonstrated.
ISSN:24736988
DOI:10.3934/math.2024654