Computer-aided solvent selection for increased reaction rate
Sustainable process-product development & green chemistry
Green Organic Synthesis Routes (T1-2)
Keywords: solvent design, reactions, stochastic optimization, sensitivity analysis
Solvents are widely used as reaction media in the fine chemicals and other industries where they serve to bring solid reactants together by dissolving them, to control temperature, and to enhance reaction rate. Reichardt (1988) reports that the solvolysis of 2-chloro-2-methylpropane is 335,000 times faster in water than in ethanol. In spite of the importance of solvent choice on productivity, there has been little work on systematic approaches for the selection of solvents for reactions.
Therefore, we propose an iterative approach that combines experimental work with computations for the design of solvents for a reaction. Such a tool can be adapted to plant-wide process integration and enable balancing reaction rate against other processing requirements. A key issue when tackling this problem is to identify a relationship that links certain specific solvent properties to the reaction rate in a way that can quantify solvent effects on a particular reaction. We propose the use of the multi-parameter solvatochromic equation (Abraham et al., 1987), simple and easily applicable, which correlates solvent properties (empirical solvatochromic parameters and cohesive energy density parameter) with the logarithm of the reaction rate constant. We obtain the values of the solvent parameters used in this equation by group-contribution prediction techniques using first-order UNIFAC groups which make further integration with previous solvent design approaches easier.
The proposed methodology consists of three steps. The first step is concerned with the development of a model of solvent effects on the reaction. It involves gathering the necessary data for eight predetermined solvents and generating the solvatochromic equation by fitting the coefficients by linear regression. The solvents should be chosen in such a manner as to cover a wide range of polarity and classes of chemicals. The second step is an optimization step in which the design problem is formulated as an MILP and solved using standard techniques. The first objective we consider is to identify a solvent in which the reaction rate constant under given conditions is maximized. Implementation of integer cuts enables generation of successive candidate molecules.
Since we build our model based on kinetic data in a few solvents only, and then use it for further extrapolation, we believe there is uncertainty associated with the model coefficients. This is confirmed by wide 95% confidence intervals obtained for each coefficient. We investigate the impact of uncertainty through multi-parametric (mp) programming. Solving the mp-MILP (Dua and Pistikopoulos, 2000) provides a complete map of optimal solutions in the uncertainty space. The optimal solutions are given as a function of the varying parameters, and the most robust solution is the one found to be optimal for the widest range of parameters. Comparing that solution against the one arrived at by deterministic optimization proves the robustness of the MILP solution.
The methodology is illustrated through application to a solvolysis reaction.
References:
1. Abraham, M.H., Doherty, R.M., Kamlet, M. J., Harris, J.M. and Taft, R.W. (1987). Journal of the Chemical Society – Perkin Transactions 2, 913-920.
2. Reichardt, C. (1988). Solvents and Solvent Effects in Organic Chemistry. VCM Publishers (UK).
3. Dua, V., Pistikopoulos, E.N. (2000). Annals of Operations Research 99,123-139.
Presented Tuesday 18, 16:00 to 16:20, in session Green Organic Synthesis Routes (T1-2).
