文章基本信息

标题：Data-driven Linear Predictor based on Maximum Likelihood Nonnegative Matrix Decomposition for Batch Cultures of Hybridoma Cells
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作者：Guilherme A. Pimentel ; Laurent Dewasme ; Alain Vande Wouwer 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：7
页码：903-908
DOI：10.1016/j.ifacol.2022.07.559
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents an original design of low-rank linear predictors of nonlinear process state variables based on nonnegative matrix decomposition (NMD). Therefore, this predictor is data-driven and does not require an accurate model description of the process. In addition, measurement errors are considered, conferring maximum likelihood (ML) properties to the estimator and resulting in a maximum likelihood nonnegative matrix decomposition (MLNMD) formulation. The latter is validated in simulation with a model developed by the authors, describing monoclonal antibody (MAb) production from sequential batch hybridoma cell cultures that are further validated with real-life experimental data. To this end, two available experimental data sets are used for direct and cross-validation, highlighting the good predictive properties of the method.
关键词：Keywordsnonnegative matrix decompositionmaximum likelihoodmeasurements errorshybridoma cells cultures