A COSMO-based approach to computer-aided mixture design

Austin, Nick; Sahinidis, Nikolaos V.; Trahan, Daniel W.

doi:10.1016/j.ces.2016.05.025

Cited by 55 publications

(57 citation statements)

References 44 publications

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“…Unlike other approaches, these COSMO-based models use electronic surface charge distributions that are calculated at the quantum chemistry level. These methods are now more amenable to CAMD due to the development of various COSMO-based group contribution methods [119,120,3].…”

Section: Common Design Features In the Form Of Constraintsmentioning

confidence: 99%

“…Zhou et al [171] used COSMO-RS [95] thermodynamics in conjunction with CAMD techniques to design solvents to maximize reaction selectivity [171]. Austin et al [3] designed solvents to maximize a reaction rate using COSMO-RS thermodynamics and projecting the original problem onto a lower-dimensional space. Ionic liquid design is another application area for this subset of CAMD problems.…”

Section: Single Molecule Designmentioning

confidence: 99%

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Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

Austin

Sahinidis

Trahan

2016

Chemical Engineering Research and Design

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170

110

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This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure-property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.

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Section: Common Design Features In the Form Of Constraintsmentioning

confidence: 99%

Section: Single Molecule Designmentioning

confidence: 99%

Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

Austin

Sahinidis

Trahan

2016

Chemical Engineering Research and Design

Self Cite

170

110

View full text Add to dashboard Cite

show abstract

“…We note that these is the same set of moments considered for all subsequent case studies.

f_{M} (n^{*})

is a previously developed group contribution method for σ moments based on the Virginia Tech sigma profile database . This is a group contribution method in the standard form, where each σ moment M k

(k \in {0, \dots, 3, don, acc})

is estimated by the number of occurrences n g of various groups g and their respective coefficients, or contributions, c :

M_{k} = f_{M} (n) = \sum_{g \in G_{M}} c_{g}^{k} n_{g}

…”

Section: The Cosmo‐based Solvent Design Problemmentioning

confidence: 99%

“…The purpose of this work is to build upon previously developed COSMO‐based molecular/mixture design methodology and investigate the utility of COSMO‐based CAMD for reaction solvent design. In particular, we aim to make our design methodology amenable to design pure and mixed solvents for several industrially relevant reactive systems.…”

Section: Introductionmentioning

confidence: 99%

COSMO‐based computer‐aided molecular/mixture design: A focus on reaction solvents

et al. 2017

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In this article, we investigate reaction solvent design using COSMO-RS thermodynamics in conjunction with computeraided molecular design (CAMD) techniques. CAMD using COSMO-RS has the distinct advantage of being a method based in quantum chemistry, which allows for the incorporation of quantum-level information about transition states, reactive intermediates, and other important species directly into CAMD problems. This work encompasses three main additions to our previous framework for solvent design (Austin et al., Chem Eng Sci. 2017;159:93-105): (1) altering the group contribution method to estimate hydrogen-bonding and non-hydrogen-bonding r-profiles; (2) ab initio modeling of strong solute/solvent interactions such as H-bonding or coordinate bonding; and (3) solving mixture design problems limited to common laboratory and industrial solvents. We apply this methodology to three diverse case studies: accelerating the reaction rate of a Menschutkin reaction, controlling the chemoselectivity of a lithiation reaction, and controlling the chemoselectivity of a nucleophilic aromatic substitution reaction. We report improved solvents/mixtures in all cases.

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“…Interestingly, these methods only require the quantum‐chemically derived surface charge density of the involved components . For this reason, many researchers have employed COSMO methods for a priori selection of IL solvents in various separation problems . Nevertheless, there is still large room for improvement regarding the quantitative prediction accuracy of the COSMO models and the development of suitable COSMO databases.…”

Section: Introductionmentioning

confidence: 99%

Computer‐aided design of ionic liquids as solvents for extractive desulfurization

et al. 2017

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Although ionic liquids (ILs) have been widely explored as solvents for extractive desulfurization (EDS) of fuel oils, systematic studying of the optimal design of ILs for this process is still scarce. The UNIFAC‐IL model is extended first to describe the EDS system based on exhaustive experimental data. Then, based on the obtained UNIFAC‐IL model and group contribution models for predicting the melting point and viscosity of ILs, a mixed‐integer nonlinear programming (MINLP) problem is formulated for the purpose of computer‐aided ionic liquid design (CAILD). The MINLP problem is solved to optimize the liquid‐liquid extraction performance of ILs in a given multicomponent model EDS system, under consideration of constraints regarding the IL structure, thermodynamic and physical properties. The top five IL candidates preidentified from CAILD are further evaluated by means of process simulation using ASPEN Plus. Thereby, [C5MPy][C(CN)3] is identified as the most suitable solvent for EDS. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1013–1025, 2018

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A COSMO-based approach to computer-aided mixture design

Cited by 55 publications

References 44 publications

Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

COSMO‐based computer‐aided molecular/mixture design: A focus on reaction solvents

Computer‐aided design of ionic liquids as solvents for extractive desulfurization

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