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Application of surrogate models for the optimization and design of chemical processes

In the computer-aided design of chemical processes the quality of a process design is highly dependent on the reliability of the process models. Models for the prediction of physical properties of mixtures of substances play a crucial role here. In recent years, models with better prediction accuracy have been developed, but these models contain implicit equations which slow down the evaluation of the plant models considerably. In this work it is demonstrated that the application of neural networks as surrogate models of thermodynamic data can make the use of advanced thermodynamic models within algorithmic design approaches possible.