Solvent selection is a crucial decision in many high valueadded chemical manufacturing processes. Computational approaches for solvent selection may substantially reduce the experimental burden during early process development. Furthermore, the selection of optimal operating conditions is closely related to the solvent selection. Computational approaches for simultaneous solvent selection and process design need to balance various trade-offs between solvent-intensive unit operations, which is especially important for continuous processes. This work presents a computational framework involving a generalized thermodynamic framework based on the electrolyte perturbed-chain statistical associating fluid theory (ePC-SAFT) equation of state for the simultaneous selection of solvents and optimization of the operating conditions of continuous processes involving the common sequence of reaction−extraction− crystallization steps with possible recycling of solvents. A predictive activity coefficient model based on group contributions is used for the estimation of the PC-SAFT pure component parameters. The proposed framework is illustrated for the continuous manufacture of dalfampridine. The optimization problem can be solved successfully with a mixed-integer nonlinear programming relaxation strategy, followed by either continuous mapping or a branchand-bound approach for solvent identification. The computational tractability of the proposed computational framework indicates the good potential for applications to industrially relevant cases featuring similar thermodynamic equilibria and complexity.
5-Methyl-2-[(2-nitrophenyl) amino]-3-thiophenecarbonitrile, known as ROY for its red, orange, and yellow crystal colors, has attracted great attention for its rich polymorphism and has been widely adopted as a benchmark compound in crystallization studies. Form Y of ROY is the most stable polymorph at low temperature. Solubility of form Y in six pure solvents (methanol, ethanol, ethyl acetate, acetic acid, acetone, and DMSO) at different temperatures within a range of 292.15 to 334.15 K as well as in four organic (methanol, ethanol, acetone, DMSO) aqueous binary mixtures at different solvent compositions and a temperature of 301.15 K were determined via a UV spectrophotometric method. The experimental solubility data were modeled applying a state-of-the-art semi-predictive model: the PC-SAFT equation-of-state. Both the measured data and the proposed modeling tool could further facilitate studies on solvent screening and polymorphic control for the solution crystallization of ROY.
Solvent selection is a critical part of crystallization process design and is inherently intertwined with optimization of the operating conditions. Computer-aided tools can greatly assist in solving these two problems simultaneously. However, the integration of predictive thermodynamic models and process optimization tools is often complicated, which may hamper industry adoption. This work presents a workflow for simultaneous solvent selection and process optimization for solution crystallization processes based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. The workflow is provided with readily executable computational tools and aims to strike a balance between the resources needed to obtain experimental input data and good prediction performance. The use of the workflow is demonstrated through a case study involving aspirin crystallization, which shows that the workflow can provide suitable solvents and operating conditions for the crystallization process based on either cooling, antisolvent, or evaporative crystallization.
A novel adaptive refined grid search strategy is developed for representative characterization of process feasible region boundaries and accurate estimation of its hypervolume. In particular, a linked list data structure adopted from the field of computer science is used to maintain the grid connectivity information. A uniform perturbation scheme is used to refine the search only near the boundaries. The volumetric flexibility index (FIV) is calculated directly from a summation of feasible hypercubes in the grid, without the need to apply shape reconstruction techniques. The proposed adaptive grid search strategy can capture complex region shapes with reduced sampling costs and without randomness, increasing reproducibility. Operational flexibility is optimized traditionally at a process scale. A case study on refrigerant selection is presented to demonstrate that the developed strategy can be combined within a computer‐aided molecular design framework for operational flexibility optimization at the molecular scale.
A novel adaptive refined grid search strategy is developed for representative characterization of process feasible region boundaries and accurate estimation of its hypervolume. In particular, a linked list data structure adopted from the field of computer science is used to maintain the grid connectivity information. A uniform perturbation scheme is used to refine the search only near boundaries. The volumetric flexibility index FI_V is calculated directly from a summation of feasible hypercubes in the grid, without the need to apply shape reconstruction techniques. The proposed adaptive grid search strategy can capture complex region shapes with reduced sampling costs and without randomness for better reproducibility. Operational flexibility is optimized traditionally at a process scale. A case study on refrigerant selection is presented to demonstrated that the developed strategy could be combined within a computer-aided molecular design framework for operational flexibility optimization in molecular scale.
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