Szczegóły publikacji
Opis bibliograficzny
Metaheuristic-based model optimization of a steam-filled chamber / Hubert GUZOWSKI, Roman Senkerik, Maciej SMOŁKA, František Gazdoš, Miroslav Pálka, Libor Pekař, Michal PLUHÁČEK, Adam Viktorin, Tomas Kadavy, Aleksander BYRSKI, Zuzanna Komínková Oplatková, Radek Matušů, Janusz KACPRZYK // IEEE Access [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2169-3536. — 2025 — vol. 13, s. 102144-102158. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 102155-102156, Abstr. — Publikacja dostępna online od: 2025-05-28
Autorzy (13)
- AGHGuzowski Hubert
- Senkerik Roman
- AGHSmołka Maciej
- Gazdos Frantisek
- Pálka Miroslav
- Pekar Libor
- AGHPluháček Michal
- Viktorin Adam
- Kadavy Tomas
- AGHByrski Aleksander
- Kominkova-Oplatkova Zuzana
- Matusu Radek
- AGHKacprzyk Janusz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160600 |
|---|---|
| Data dodania do BaDAP | 2025-07-09 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ACCESS.2025.3574414 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | IEEE Access |
Abstract
Steam-filled chambers are an important part of many technological processes, among others, in tire manufacturing and electricity production from thermal power plants. This work proposes a chamber model as a strongly nonlinear process with time-delays, where parameters depend on operating conditions and may vary in time. To identify system parameters, a parametric optimization task is formulated that minimizes the fit of the model to factory-measured data under varying valve opening conditions. Two significantly different approaches were used to solve this nonlinear optimization task. The first utilized local and semi-local optimization with prior knowledge derived from solving a simplified variant of the task. The second used global optimization methods without any prior knowledge. The obtained parametric models were compared based on the quality of fit and the sensitivity and stability analysis of the obtained solutions. The achieved models reflect real data with high accuracy, with mean squared errors as low as 0.0138 on output values ranging from 0.0 to 20.0, representing less than 0.1% of the output range. The solutions differ significantly in the values of the obtained parameters. The use of multiple methods has thus made it possible to obtain a diverse set of solutions, which is particularly valuable in applications for difficult engineering problems. Results of this work can be further used e.g. for subsequent step - optimal control system design for the given process and operating conditions.
