Abstract
A comprehensive computer-aided mixture/blend design methodology for formulating a general mixture design problem where the number, identity and composition of mixture constituents are optimized simultaneously is presented in this work. Within this approach, Generalized Disjunctive Programming (GDP) is employed to model the discrete decisions (number and identities of mixture ingredients) in the problems. The identities of the components are determined by designing molecules from UNIFAC groups. The sequential design of pure compounds and blends, and the arbitrary pre-selection of possible mixture ingredients can thus be avoided, making it possible to consider large design spaces with a broad variety of molecules and mixtures. The proposed methodology is first applied to the design of solvents and solvent mixtures for maximising the solubility of ibuprofen, often sought in crystallization processes; next, antisolvents and antisolvent mixtures are generated for minimising the solubility of the drug in drowning out crystallization; and finally, solvent and solvent mixtures are designed for liquid–liquid extraction. The GDP problems are converted into mixed-integer form using the big-M approach. Integer cuts are included in the general models leading to lists of optimal solutions which often contain a combination of pure and mixed solvents.
Highlights
The importance of mixture and product design has long been established across the field of chemical engineering (Gani and Ng, 2015)
A full description of the problem statement was given in previous work (Jonuzaj et al, 2016), where solvent molecules were selected from a small list, and here we present a brief summary of the main features of the case study
The best solution obtained in restricted problem E1, where the addition of water yields the lowest solubility of ibuprofen, is found in the generalized problem E3 where the number of antisolvents in the mixture is unknown
Summary
The importance of mixture and product design has long been established across the field of chemical engineering (Gani and Ng, 2015). The design of candidate chemicals, be it a single molecule, a mixture or formulation, at minimum cost and time, is essential in improving process and product performance, but is challenging because it requires finding the optimal number, identities and compositions of mixture components and using nonlinear property models. The use of trial-and-error methods to find the most appropriate combination of chemicals and processing technology that will lead to the desired product/process attributes only allows a limited exploration of the design space due to the effort, cost, and time required. The development of systematic methodologies, tools, and strategies that combine predictive property models within a computer-assisted search for mixture and product design is crucial to sustainability in a highly competitive environment. Jonuzaj et al / Computers and Chemical Engineering 116 (2018) 401–421
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