Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes

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Autor(es): dc.creatorAlexander, Ronald-
Autor(es): dc.creatorCampani, Gilson-
Autor(es): dc.creatorDinh, San-
Autor(es): dc.creatorLima, Fernando V.-
Data de aceite: dc.date.accessioned2026-02-09T12:47:15Z-
Data de disponibilização: dc.date.available2026-02-09T12:47:15Z-
Data de envio: dc.date.issued2021-08-20-
Data de envio: dc.date.issued2021-08-20-
Data de envio: dc.date.issued2020-10-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/46907-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1168275-
Descrição: dc.descriptionThis paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the future including the incorporation of more efficient optimization techniques and development of heuristics to streamline the further adoption of MHE.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMultidisciplinary Digital Publishing Institute - MDPI-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
???dc.source???: dc.sourceProcesses-
Palavras-chave: dc.subjectState estimation-
Palavras-chave: dc.subjectNonlinear system-
Palavras-chave: dc.subjectExtended Kalman filter-
Palavras-chave: dc.subjectMoving horizon estimation-
Palavras-chave: dc.subjectEstimação de estado-
Palavras-chave: dc.subjectSistema não linear-
Palavras-chave: dc.subjectFiltro de Kalman estendido-
Palavras-chave: dc.subjectEstimador de horizonte móvel-
Título: dc.titleChallenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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