Optimal design of experiments for liquid–liquid equilibria characterization via semidefinite programming

Duarte, Belmiro P.M., Atkinson, Anthony C., Granjo, Jose F.O and Oliveira, Nuno M.C (2019) Optimal design of experiments for liquid–liquid equilibria characterization via semidefinite programming. Processes, 7 (11). ISSN 2227-9717

Text (Optimal design of experiments for liquid-liquid equilibria characterization via semidefinite programming) - Published Version Available under License Creative Commons Attribution. Download (377kB)

• Author

Abstract

Liquid–liquid equilibria (LLE) characterization is a task requiring considerable work and appreciable financial resources. Notable savings in time and effort can be achieved when the experimental plans use the methods of the optimal design of experiments that maximize the information obtained. To achieve this goal, a systematic optimization formulation based on Semidefinite Programming is proposed for finding optimal experimental designs for LLE studies carried out at constant pressure and temperature. The non-random two-liquid (NRTL) model is employed to represent species equilibria in both phases. This model, combined with mass balance relationships, provides a means of computing the sensitivities of the measurements to the parameters. To design the experiment, these sensitivities are calculated for a grid of candidate experiments in which initial mixture compositions are varied. The optimal design is found by maximizing criteria based on the Fisher Information Matrix (FIM). Three optimality criteria (D-, A- and E-optimal) are exemplified. The approach is demonstrated for two ternary systems where different sets of parameters are to be estimated.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/processes
Additional Information:	© 2019 The authors.
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics H Social Sciences > HA Statistics
Date Deposited:	12 Nov 2019 12:48
Last Modified:	08 Jan 2024 19:18
URI:	http://eprints.lse.ac.uk/id/eprint/102500

Actions (login required)

View Item