Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes
This study introduces a novel method for identifying dynamic systems aimed at deriving reduced-fractional-order models. Applicable to processes exhibiting an S-shaped step response, the method effectively characterizes fractional behavior within the range of fractional orders (α∈[0.5,1.0]). The uniq...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024000100 |
| _version_ | 1797247736876105728 |
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| author | Juan J. Gude Pablo García Bringas Marco Herrera Luis Rincón Antonio Di Teodoro Oscar Camacho |
| author_facet | Juan J. Gude Pablo García Bringas Marco Herrera Luis Rincón Antonio Di Teodoro Oscar Camacho |
| author_sort | Juan J. Gude |
| collection | DOAJ |
| description | This study introduces a novel method for identifying dynamic systems aimed at deriving reduced-fractional-order models. Applicable to processes exhibiting an S-shaped step response, the method effectively characterizes fractional behavior within the range of fractional orders (α∈[0.5,1.0]). The uniqueness of this approach lies in its hybrid nature, combining one-variable optimization techniques for estimating the model fractional order α with analytical expressions to estimate parameters T and L. This hybrid approach leverages information from the reaction curve obtained through an open-loop step-test experiment. The proposed method demonstrates its efficacy and simplicity through several illustrative examples, showcasing its advantages over established analytical and optimization-based techniques. Notably, the hybrid approach proves particularly advantageous compared to methods relying on the process reaction curve. To highlight its practical applicability, the identification algorithm based on this hybrid approach is implemented on hardware using a microprocessor. The experimental prototype successfully identifies the First-Order Plus Dead Time (FFOPDT) model of a thermal-based process, validating the proposed method's real-world utility. |
| first_indexed | 2024-03-08T14:21:05Z |
| format | Article |
| id | doaj.art-5c83167265ab4d51a6861758f7dd7e47 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T20:03:26Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-5c83167265ab4d51a6861758f7dd7e472024-03-24T07:00:33ZengElsevierResults in Engineering2590-12302024-03-0121101757Fractional-order model identification based on the process reaction curve: A unified framework for chemical processesJuan J. Gude0Pablo García Bringas1Marco Herrera2Luis Rincón3Antonio Di Teodoro4Oscar Camacho5Department of Computing, Electronics and Communication Technologies, Faculty of Engineering, University of Deusto, 48007, Bilbao, Spain; Corresponding author.Department of Mechanics, Design and Industrial Management, Faculty of Engineering, University of Deusto, 48007, Bilbao, SpainColegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito 170157, EcuadorColegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito 170157, EcuadorColegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito 170157, EcuadorColegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito 170157, EcuadorThis study introduces a novel method for identifying dynamic systems aimed at deriving reduced-fractional-order models. Applicable to processes exhibiting an S-shaped step response, the method effectively characterizes fractional behavior within the range of fractional orders (α∈[0.5,1.0]). The uniqueness of this approach lies in its hybrid nature, combining one-variable optimization techniques for estimating the model fractional order α with analytical expressions to estimate parameters T and L. This hybrid approach leverages information from the reaction curve obtained through an open-loop step-test experiment. The proposed method demonstrates its efficacy and simplicity through several illustrative examples, showcasing its advantages over established analytical and optimization-based techniques. Notably, the hybrid approach proves particularly advantageous compared to methods relying on the process reaction curve. To highlight its practical applicability, the identification algorithm based on this hybrid approach is implemented on hardware using a microprocessor. The experimental prototype successfully identifies the First-Order Plus Dead Time (FFOPDT) model of a thermal-based process, validating the proposed method's real-world utility.http://www.sciencedirect.com/science/article/pii/S2590123024000100OptimizationFractional first-order plus dead-time modelFractional-order systemsProcess identification |
| spellingShingle | Juan J. Gude Pablo García Bringas Marco Herrera Luis Rincón Antonio Di Teodoro Oscar Camacho Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes Results in Engineering Optimization Fractional first-order plus dead-time model Fractional-order systems Process identification |
| title | Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes |
| title_full | Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes |
| title_fullStr | Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes |
| title_full_unstemmed | Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes |
| title_short | Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes |
| title_sort | fractional order model identification based on the process reaction curve a unified framework for chemical processes |
| topic | Optimization Fractional first-order plus dead-time model Fractional-order systems Process identification |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024000100 |
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