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.

No abstract

No abstract