PARALLEL NONLINEAR OPTIMIZATION TECHNIQUES FOR CHEMICAL PROCESS DESIGN-PROBLEMS

Parallel processing methods with constrained nonlinear optimization we re developed to show the potential to efficiently perform chemical pro cess design calculations. A sequential optimization subroutine was dev eloped that used a SQP algorithm and the BFGS inverse Hessian update. This subroutine had parallelism introduced at various paints. Most of these methods examined ways to perform and use the parallel simultaneo us function and gradient evaluations. Function evaluations are general ly expensive calculations in chemical process optimization. Algorithms using a parallel finite difference Hessian (PH), Straeter's parallel variable metric (PVM) update, and Freeman's projected parallel variabl e metric (PPVM) update were investigated. Other algorithms investigate d included Schnabel's parallel partial speculative gradient evaluation and parallel line searching. The success of a global optimization alg orithm using simultaneous minimization shows potential for robust and reliable global minimization of complex multiextremal problems. The re sults of this work show the potential of decreasing large scale proble m optimization time with parallel processing.