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High accuracy models can be obtained by using different types of surrogate models that accurately approximate equipment phenomenological models and can be used in synthesis problems, leading to faster and more precise solutions. Two types of surrogate models are used to approximate equipment phenomenological models: polynomial and neural network-based. In some cases, these surrogate models are not able to represent more complex equipment. An original methodology to reformulate these models using equations from shortcut equipment design is proposed. A medium-size case study involving fifteen units is presented. The synthesis problem is solved in a short computational time, leading many local solutions. Since several local optima objective function values are very close to each other, the choice of the best configuration among those found should be done qualitatively, because the differences among the objective function values are not significant if compared to the accuracy of equipment cost correlations in the literature. |
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