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New directions for nonlinear process optimization

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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… Click to show full 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 10 4 –10 6 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.

Keywords: new directions; process; nonlinear process; optimization; directions nonlinear; process optimization

Journal Title: Current opinion in chemical engineering
Year Published: 2018

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