A hybrid stochastic–deterministic optimization approach for integrated solvent and process design - Zhou, Teng; Zhou, Yageng; Sundmacher, Kai (Chemical engineering science, 2017)
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. •A novel optimization method is proposed for integrated solvent and process design.•The method combines the advantages of stochastic and deterministic algorithms.•The method is demonstrated on a coupled absorption–desorption process.•The method can solve the design problem with high reliability and robustness.•The method can be recommended for use in large-scale MINLP optimizations.