A model-based framework assisting the design of vapor-liquid equilibrium experimental plans. (February 2021)

• Record Type: Journal Article
• Authors: Duarte, Belmiro P.M.; Atkinson, Anthony C.; Granjo, José F.O.; Oliveira, Nuno M.C.
• Is Part Of: Computers & chemical engineering. Volume 145(2021)
• Publication Date: 2021-02
• Subjects: Sequential optimal design of experiments -- Vapor-liquid equilibrium -- Semidefinite programming -- NRTL Model -- Nonlinear programming; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107168
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: In this paper we propose a framework for Model-based Sequential Optimal Design of Experiments to assist experimenters involved in Vapor-Liquid equilibrium characterization studies to systematically construct thermodynamically consistent models. The approach uses an initial continuous optimal design obtained via semidefinite programming, and then iterates between two stages (i) model fitting using the information available; and (ii) identification of the next experiment, so that the information content in data is maximized. The procedure stops when the number of experiments reaches the maximum for the experimental program or the dissimilarity between the parameter estimates during two consecutive iterations is below a given threshold. This methodology is exemplified with the D-optimal design of isobaric experiments, for characterizing binary mixtures using the NRTL and UNIQUAC thermodynamic models for liquid phase. Significant reductions of the confidence regions for the parameters are achieved compared with experimental plans where the observations are uniformly distributed over the domain.