Fractional-order model identification based on the process reaction curve: a unified framework for chemical processes

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dc.contributor.authorGude, Juan José
dc.contributor.authorGarcía Bringas, Pablo
dc.contributor.authorHerrera, Marco
dc.contributor.authorRincón, Luis
dc.contributor.authorTeodoro, Antonio di
dc.contributor.authorCamacho, Oscar
dc.date.accessioned2024-11-15T10:09:33Z
dc.date.available2024-11-15T10:09:33Z
dc.date.issued2024-03
dc.date.updated2024-11-15T10:09:33Z
dc.description.abstractThis 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.en
dc.description.sponsorshipA. Di Teodoro and O. Camacho thank the Universidad San Francisco de Quito for supporting this work through the Poli-Grants Program under Grant 17965. Juan J. Gude and Pablo García Bringas thank the Basque Government for its funding support through the BEREZ-IA Elkartek project (ref. KK-2023/00012)en
dc.identifier.citationGude, J. J., García Bringas, P., Herrera, M., Rincón, L., Di Teodoro, A., & Camacho, O. (2024). Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes. Results in Engineering, 21. https://doi.org/10.1016/J.RINENG.2024.101757
dc.identifier.doi10.1016/J.RINENG.2024.101757
dc.identifier.issn2590-1230
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1898
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2024 The Author(s)
dc.subject.otherFractional first-order plus dead-time model
dc.subject.otherFractional-order systems
dc.subject.otherOptimization
dc.subject.otherProcess identification
dc.titleFractional-order model identification based on the process reaction curve: a unified framework for chemical processesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleResults in Engineering
oaire.citation.volume21
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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