New directions for nonlinear process optimization

Abstract

The last three decades have seen tremendous advances in nonlinear programming (NLP) algorithms and software for process optimization. Moreover, powerful optimization modeling environments enable the formulation and solution of large-scale optimization applications. In fact, the combination of modern NLP algorithms and optimization platforms leads to fast solution strategies that now routinely solve problems with 104–106 variables, with major impacts in process design, operations and control. This is illustrated here with two dynamic optimization case studies to emphasize these characteristics. Moreover, these powerful optimization strategies integrate to accessible optimization modeling platforms that can be incorporated within a broad spectrum of engineering tasks.