Decision-making is an essential task in process engineering; it drives process design, operations, control, and logistics. Systematic approaches for decision-making require accurate models that simulate the real-world consequences of decisions and optimization strategies that use these models to navigate the decision tree. The quality of these decisions is strongly influenced by the efficiency of optimization strategies as well as their integration with simulation models.
“In engineering practice, most optimization strategies are still viewed as glorified case study approaches, where a search technique is wrapped around a simulation model and is directed to provide simulation solutions until the ‘best’ answer is found,” Lorenz Biegler of Carnegie Mellon Univ. writes in the April AIChE Journal Perspective, “New Nonlinear Programming Paradigms for the Future of Process Optimization.”
“No doubt such tools are effective and lead to significant and consistent improvements over the current state, even when used by less experienced engineers,” he continues. “On the other hand, such approaches can be very expensive to execute, and often it is not clear whether all options have been explored within the computational budget allocated for...
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