A hybrid stochastic–deterministic optimization approach for integrated solvent and process design

The best solution to computer-aided solvent and process design problems can be only achieved by the simultaneous optimization of solvent molecules and process operating conditions. In this contribution, a hybrid stochastic-deterministic optimization approach is proposed for integrated solvent and process design. It is a combination of a genetic algorithm (GA) that optimizes the discrete molecular variables and a gradient-based deterministic algorithm that solves the continuous nonlinear optimization problem of the process at fixed molecular variables as proposed by the GA. The method is demonstrated on a coupled absorption-desorption process where solvent molecular structures as well as the operating conditions of the absorption and desorption columns are optimized simultaneously. While deterministic mixed-integer nonlinear programming (MINLP) algorithms rely on well-selected initial estimates, the proposed hybrid approach can reliably and steadily solve the problem under random initializations. The combination of the advantages of stochastic and deterministic algorithms makes the approach a promising alternative to conventional MINLP algorithms for solving integrated solvent and process design problems.