Chemical process optimization using Newton-like methods

Abstract

Various interrelated issues that effect the reliability and efficiency of Newton-like methods for chemical process optimization are studied. An algorithm for solving large, sparse quadratic programming (QP) problems that is based on an active set strategy and a symmetric, indefinite factorization is presented. The QP algorithm is fast and reliable. A simple asymmetric trust region method is proposed for improving the reliability of successive QP methods. Ill-defined QP subproblems are avoided by adjusting the size of the trust region in an automatic way. Finally, it is shown that reliable initial values of the unknown variables and multipliers can be generated automatically using generic problem information, short-cut techniques and simulation tools. Many relevant numerical results and illustrations are presented.