Computer-Aided Solvent Design for Reactions: Maximizing Product Formation

Abstract

A hybrid experimental/computer-aided methodology for the design of solvents for reactions, recently proposed by the authors [Folić et al., AIChE J. 2007, 53, 1240–1256], is extended. The methodology 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 (CAMD) problem. The treatment of complex reaction systems, such as competing or consecutive reactions, is considered through the incorporation of a simple reactor model in the problem formulation. This approach is applied to two model reaction schemes, and it is shown that, in principle, it is possible to identify solvents that maximize product formation by enhancing the main reaction and suppressing byproduct formation. Since very few measurements are used to build the reaction model, the effect of uncertainty is tackled explicitly in a stochastic formulation of the CAMD problem. An approach to sensitivity analysis for the identification of the key model parameters is discussed. Using this information to generate scenarios, a stochastic optimization problem (whose objective is to determine the solvents with the best expected performance) is then solved. The final output consists of a list of candidate solvents that can be targeted for experimentation. The methodology is demonstrated on a Menschutkin reaction, which is a representative SN2 reaction. This shows that the uncertainty in the reaction model has little impact on the types of solvent molecules that have the best performance. Dinitrates are found to be a promising class of solvents, with regard to maximizing the reaction rate constant.

Computer-aided molecular design of solvents for accelerated reaction kinetics

“…It relies on the integration of quantum mechanical (QM) rate-constant calculations into a CAMD framework. 10,11 The approach allows the rapid exploration of a solvent design space consisting of thousands of 3 potential molecules and leads to a shortlist of promising solvents that can then be assessed experimentally.Initial attempts to develop systematic approaches for the identification of the best reaction solvents were based on chemometrics, 12 and multivariate analysis. [13][14][15][16][17][18] The use of these techniques is however limited by the need for large amounts of data to ensure statistical significance, and the fact that the information obtained for one reaction cannot be transferred reliably to another reaction, even within the same class.…”

“…It relies on the integration of quantum mechanical (QM) rate-constant calculations into a CAMD framework. 10,11 The approach allows the rapid exploration of a solvent design space consisting of thousands of potential molecules and leads to a shortlist of promising solvents that can then be assessed experimentally.…”

In silico design of solvents for carbon capture with simultaneous optimisation of operating conditions

“…This problem has traditionally been circumvented by decoupling the solvent and process optimisation problems and by optimising the solvent to specific desired property targets. Prominent works in this field include the works of Gani and co-workers (Achenie et al, 2003;Gani, 2004;Gani et al, 2000Gani et al, , 1991, Joback (1989), Odele and Macchietto (1993), Kier and Hall (Hall et al, 1993;Kier et al, 1993), Sahinidis and co-workers (Rios and Sahinidis, 2013;Samudra and Sahinidis, 2013) and Folic and co-workers (Folić et al, 2007(Folić et al, , 2008Struebing et al, 2013). These past works show that optimising solvents to desired property targets has been limited in finding an optimal solvent due to interdependent property targets.…”

Toward Sustainable Solvent-Based Postcombustion CO2 Capture

“…Computer-aided molecular design (CAMD) methods (Giovanoglou et al, 2003;Papadopoulos and Linke, 2006a) enable the fast investigation of a vast number of molecular solvent structures that can significantly increase important separation drivers (e.g., selectivity) compared to the use of conventional solvents. The CAMD framework can be used to identify solvents that present beneficial properties, such as reduced toxicity (Papadopoulos and Linke, 2006b), increased safety (Papadopoulos et al, 2010), inertness to reactions (Papadopoulos and Linke, 2009), and enhancement of reaction rates, (Folic et al, 2007(Folic et al, , 2008Struebing et al, 2013), to name a few. The combined utilization of novel solvents with systematically identified modifications in their host separation systems (Kenig and Seferlis, 2009) results in significant reductions in the associated costs, compared to solvente process systems used in industry.…”