Ka. High et Rd. Laroche, PARALLEL NONLINEAR OPTIMIZATION TECHNIQUES FOR CHEMICAL PROCESS DESIGN-PROBLEMS, Computers & chemical engineering, 19(6-7), 1995, pp. 807-825
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.