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dc.contributor.authorSchenk, C. 
dc.contributor.authorBiegler, L.T.
dc.contributor.authorHan, L.
dc.contributor.authorMustakis, J.
dc.date.accessioned2020-10-07T12:57:43Z
dc.date.available2020-10-07T12:57:43Z
dc.date.issued2020-08-07
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1157
dc.description.abstractLaboratory and process measurements from spectroscopic instruments are ubiquitous in pharma processes, and directly using the data can pose a number of challenges for kinetic model building. Moreover, scaling up from laboratory to industrial level requires predictive models with accurate parameter values. This means that process identification implies not only kinetic parameter estimation but also the identification of the absorbing species and estimation of variances for both the data and parameters. The recently developed, open-source toolkit KIPET (Short, M.; Schenk, C.; Thierry, D.; Rodriguez, J. S.; Biegler, L. T.; Garcı́a-Muñoz, S. Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, 2019, 47, 299; Schenk, C.; Short, M.; Thierry, D.; Rodriguez, J. S.; Biegler, L. T.; Garcı́a-Muñoz, S.; Chen, W. Comput. Chem. Eng.2020, 134, 106716) addresses these topics and provides an alternative to standard parameter estimation packages, in particular for spectroscopic data problems. Moreover, batch processes commonly used in the chemical and pharmaceutical industries involve multiple stages to carry out synthesis operations in a step-by-step manner, often dealing with heterogeneous mixtures, wide operating temperatures, and constant additions and removals of product and waste. For such cases novel modeling approaches are required, as the structure of the kinetic model may vary with time, with model switches that are state dependent. This study presents a new modeling approach and methodology that deals with these practical issues. In developing kinetic models, it approximates the solid dissolution process and deals with multiple stages with different reactor temperatures. Moreover, variances, parameters, concentration, and absorbance profiles are estimated for the process stages using the approach presented by Chen et al. (Chen, W.; Biegler, L. T.; Garcı́a Muñoz, S. J. Chemom.2016, 30, 506). The application of these developed concepts results in realistic profiles as well as reliable kinetic parameter values. The outcomes of this work show that KIPET is a useful toolkit for dealing with pharmaceutical processes with capabilities for dealing with challenging kinetic modeling problems.en_US
dc.description.sponsorshipFunded by Pfizer Inc.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectkinetic parameter estimationen_US
dc.subjectdifferential algebraic equationsen_US
dc.subjectspectroscopic dataen_US
dc.subjectpharmaceutical processesen_US
dc.subjectsolid-liquiden_US
dc.subjectdissolutionen_US
dc.titleKinetic Parameter Estimation from Spectroscopic Data for a Multi-Stage Solid-Liquid Pharmaceutical Processen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1021/acs.oprd.0c00277
dc.relation.publisherversionhttps://pubs.acs.org/doi/pdf/10.1021/acs.oprd.0c00277en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen_US
dc.journal.titleOrganic Process Research & Developmenten_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España