Semi-mechanistic modeling of chemical processes with neural networks

Abstract

One of the major drawbacks of nonlinear black-box models is the scaling problem. A black-box model derived for a certain process scale or pilot plant cannot be used for other process scales. To avoid this problem, the combination of white-box and black-box modeling techniques is worth investigating. In this paper an approach based on a combination of white-box and black-box techniques based on neural networks is described. All the known parts of the process are based on first principles, and the remaining, unknown parts are modeled by black-box models consisting of a neural network. The black-box model is incorporated in the white-box model. Both modeling techniques are compared in an example. This comparison shows that for that particular example the semi-mechanistic modeling technique outperforms the straightforward nonlinear black-box model. The application of neural networks to the black-box modeling of nonlinear processes always depends heavily on the availability of enough informative data. If the process operation does not allow for the measuring of many states and outputs under varying operational conditions, then the application of neural networks is not admissable. The application of semi-mechanistic models is then preferable.