Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering

For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurem...

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Autores principales:	Robert Dürr, Stefanie Duvigneau, Carsten Seidel, Achim Kienle, Andreas Bück
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	unscented Kalman filtering Bayesian estimation multisensor data fusion model identification Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/63ba30550b5e4fa09605feaa215ff4de
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spelling	oai:doaj.org-article:63ba30550b5e4fa09605feaa215ff4de2021-11-25T18:51:17ZMulti-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering10.3390/pr91119902227-9717https://doaj.org/article/63ba30550b5e4fa09605feaa215ff4de2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1990https://doaj.org/toc/2227-9717For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.Robert DürrStefanie DuvigneauCarsten SeidelAchim KienleAndreas BückMDPI AGarticleunscented Kalman filteringBayesian estimationmultisensor data fusionmodel identificationChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1990, p 1990 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	unscented Kalman filtering Bayesian estimation multisensor data fusion model identification Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	unscented Kalman filtering Bayesian estimation multisensor data fusion model identification Chemical technology TP1-1185 Chemistry QD1-999 Robert Dürr Stefanie Duvigneau Carsten Seidel Achim Kienle Andreas Bück Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering
description	For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.
format	article
author	Robert Dürr Stefanie Duvigneau Carsten Seidel Achim Kienle Andreas Bück
author_facet	Robert Dürr Stefanie Duvigneau Carsten Seidel Achim Kienle Andreas Bück
author_sort	Robert Dürr
title	Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering
title_short	Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering
title_full	Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering
title_fullStr	Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering
title_full_unstemmed	Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering
title_sort	multi-rate data fusion for state and parameter estimation in (bio-)chemical process engineering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/63ba30550b5e4fa09605feaa215ff4de
work_keys_str_mv	AT robertdurr multiratedatafusionforstateandparameterestimationinbiochemicalprocessengineering AT stefanieduvigneau multiratedatafusionforstateandparameterestimationinbiochemicalprocessengineering AT carstenseidel multiratedatafusionforstateandparameterestimationinbiochemicalprocessengineering AT achimkienle multiratedatafusionforstateandparameterestimationinbiochemicalprocessengineering AT andreasbuck multiratedatafusionforstateandparameterestimationinbiochemicalprocessengineering
_version_	1718410649553338368

Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering

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