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Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction. Processes (Basel) 2020. [DOI: 10.3390/pr8020190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Considering the competitive and strongly regulated pharmaceutical industry, mathematical modeling and process systems engineering might be useful tools for implementing quality by design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However, a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification of model candidates from a set of various model hypotheses. To identify the best experimental design suitable for a reliable model selection and system identification is challenging for nonlinear (bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness for model selection problems under uncertainty, and thus translates the model selection problem to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal controls for improved model selection trajectories are expressed analytically with low computational costs. We further demonstrate the impact of parameter uncertainties on the differential flatness-based method and provide an effective robustification strategy with the point estimate method for uncertainty quantification. In a simulation study, we consider a biocatalytic reaction step simulating the carboligation of aldehydes, where we successfully derive optimal controls for improved model selection trajectories under uncertainty.
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Quaglio M, Waldron C, Pankajakshan A, Cao E, Gavriilidis A, Fraga ES, Galvanin F. An online reparametrisation approach for robust parameter estimation in automated model identification platforms. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.01.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature: From in silico to in vivo. Food Res Int 2016; 89:689-700. [DOI: 10.1016/j.foodres.2016.08.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 07/25/2016] [Accepted: 08/06/2016] [Indexed: 11/22/2022]
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Li M, Ma M, Zhu KX, Guo XN, Zhou HM. Critical conditions accelerating the deterioration of fresh noodles: A study on temperature, pH, water content, and water activity. J FOOD PROCESS PRES 2016. [DOI: 10.1111/jfpp.13173] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Man Li
- School of Food Science and Engineering; Qingdao Agricultural University; Qingdao Shandong Province 266109 People's Republic of China
| | - Meng Ma
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology; Jiangnan University; Wuxi Jiangsu Province 214122 People's Republic of China
| | - Ke-Xue Zhu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology; Jiangnan University; Wuxi Jiangsu Province 214122 People's Republic of China
| | - Xiao-Na Guo
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology; Jiangnan University; Wuxi Jiangsu Province 214122 People's Republic of China
| | - Hui-Ming Zhou
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology; Jiangnan University; Wuxi Jiangsu Province 214122 People's Republic of China
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