文献类型：期刊文献

英文题名：Model-based process design of a ternary protein separation using multi-step gradient ion-exchange SMB chromatography

作者：He, Qiao-Le[1,2]; Zhao, Liming[1,2]

机构：[1] State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] R&D Center of Separation and Extraction Technology in Fermentation Industry, East China University of Science and Technology, Shanghai, 200237, China

年份：2019

外文期刊名：arXiv

收录：EI(收录号：20200626805)

语种：英文

外文关键词：Bayesian networks - Chromatographic analysis - Inference engines - Ion chromatography - Markov processes - Optimization - Uncertainty analysis

摘要：Prominent features of simulated moving bed (SMB) chromatography processes in the downstream processing is based on the determination of operating conditions. However, effects of different types of uncertainties have to be studied and analysed whenever the triangle theory or numerical optimization approaches are applied. In this study, a Bayesian inference based method is introduced to consider the uncertainty of operating conditions on the performance assessment, of a glucose-fructose SMB unit under linear condition. A multiple chain Markov Chain Monte Carlo (MCMC) algorithm (i.e., Metropolis algorithm with delayed rejection and adjusted Metropolis) is applied to generate samples. The proposed method renders versatile information by constructing from the MCMC samples, e.g., posterior distributions, uncertainties, credible intervals of the operating conditions, and posterior predictive check, and Pareto fronts between each pair of the performance indicators. Additionally, the MCMC samples can be mapped onto the (mII,mIII) and (mIV,mI) planes to show the actually complete separation region under uncertainties. The proposed method is a convenient tool to find both optimal values and uncertainties of the operating conditions. Moreover, it is not limited to SMB processes under the linear isotherm; and it should be more powerful in the nonlinear scenarios. ? 2019, CC BY-NC-SA.