Parallel nonlinear optimization techniques for chemical process design problems

Abstract

Parallel processing methods with constrained nonlinear optimization were developed to show the potential to efficiently perform chemical process design calculations. A sequential optimization subroutine was developed that used a SQP algorithm and the BFGS inverse Hessian update. This subroutine had parallelism introduced at various points. Most of these methods examined ways to perform and use the parallel simultaneous function and gradient evaluations. Function evaluations are generally expensive calculations in chemical process optimization. Algorithms using a parallel finite difference Hessian (PH), Stracter's parallel variable metric (PVM) update, and Freeman's projected parallel variable metric (PPVM) update were investigated. Other algorithms investigated included Schnabel's parallel partial speculative gradient evaluation and parallel line searching. The success of a global optimization algorithm using simultaneous minimization shows potential for robust and reliable global minimization of complex multiextremal problems. The results of this work show the potential of decreasing large scale problem optimization time with parallel processing.