The design of solvents for optimal reaction rates

Folić, Milica; Adjiman, Claire S.; Pistikopoulos, Efstratios N.

doi:10.1016/s1570-7946(04)80095-6

Cited by 13 publications

(12 citation statements)

References 8 publications

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“…169 • Product and process design. [170][171][172][173][174] These problems are numerically difficult due to including many nonlinear and integer variables, many components, and complex property estimation. Approaches to overcome the complexity include (1) surrogates 172 and clustering and averaging techniques; 175 (2) a hierarchical approach [175][176][177] to screen, using the most relevant material properties and processes, and determine the optimal molecule using more rigorous models; and (3) an iterative scheme by formulating a single optimization 170 or two optimizations 171 where the "master problem" selects the optimal path and a detailed nonlinear optimizer determines the optimal process conditions.…”

Section: Integrated Frameworkmentioning

confidence: 99%

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

et al. 2022

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Section: Integrated Frameworkmentioning

confidence: 99%

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

et al. 2022

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“…Based on these considerations, our goal is to develop a systematic approach to solvent design for reactions. The methodology presented here is an extension of our previous work 12, 13. The basic premise is that since there is a lack of generic predictive models of solvent effects on reactions, an iterative strategy based on targeted experiments, model development, candidate generation by CAMD, and experimental verification must be adopted.…”

Section: Introductionmentioning

confidence: 99%

Design of solvents for optimal reaction rate constants

2007

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in Wiley InterScience (www.interscience.wiley.com).A hybrid experimental/computer-aided methodology for the design of solvents for reactions is presented. It is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design problem (CAMD) in which the reaction rate under given conditions is maximized. In order to verify the suitability of the solvent candidates identified in the CAMD step, and to assess the reliability of the model used, feedback can be introduced. When the reliability of the model is found to be insufficient, experimental rate data for the candidate solvents are obtained and added to the original data set to create an updated reaction model, which can be used to find new candidate solvents. Since very few measurements are used to build the reaction model, we perform a sensitivity analysis on the model to assess the impact of uncertainty. Using this information to generate scenarios, we then solve a stochastic optimization problem, which aims to determine the solvents that give the best average performance. The final output consists of a list of candidate solvents which can be targeted for experimentation. This methodology is illustrated, step by step, through application to a solvolysis reaction.

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“…18−21 The correlations based on these parameters became known as solvatochromic equations and have been used explicitly for solvent design. 22 New research directions are constantly applied to solvent selection and design. Struebing et al 23,24 combined quantum mechanical calculations with solvatochromic equations to predict rate constants for solvent design.…”

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confidence: 99%

“…Many quantitative structure−property relationships (QSPR) have been proposed to correlate the effect of solvents, for example, on reaction rates. 22,30 Of particular interest are relationships based on one of the solvatochromic parameter sets and the Hansen solubility parameters.…”

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Solvent Selection Methods and Tool

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Baumeister

Salvesen

et al. 2019

Org. Process Res. Dev.

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An interactive tool with a browser-type interface has been developed for solvent selection using the software R. Two main classes of considerations can be taken into account: technical suitability for the intended duties, and practical considerations including costs and health, safety environment (HSE) impact. The tool builds on quantitative analyses of properties selected by the user for the application at hand. The underlying philosophy is to assist the thought processes of the tool's users, rather than to prescribe set answers. The tool is a stepping stone toward design-of-experiment in chemical process development, enabling parameter space exploration without specialized software licenses, and grouping properties to assist the users. Six examples of use are given to illustrate various methodologies. In building the tool, scientific software development was found to be more intrinsically iterative than originally expected, with mock-ups and sharing of user stories more agile than lengthy user requirement specifications. Technical improvements for the future were identified, such as the automation of regressions and Hansen parameter calculations, a more extensive chemical knowledge formalism, and the addition of electron descriptions.

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The design of solvents for optimal reaction rates

Cited by 13 publications

References 8 publications

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

Design of solvents for optimal reaction rate constants

Solvent Selection Methods and Tool

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