MODELING CHEMICAL PROCESSES USING PRIOR KNOWLEDGE AND NEURAL NETWORKS

We present a method for synthesizing chemical process models that comb ines prior knowledge and artificial neural networks. The inclusion of prior knowledge is investigated as a means of improving the neural net work predictions when trained on sparse and noisy process data. Prior knowledge enters the hybrid model as a simple process model and first principle equations. The simple model controls the extrapolation of th e hybrid in the regions of input space that lack training data. The fi rst principle equations, such as mass and component balances, enforce equality constraints. The neural network compensates for inaccuracy in the prior model. In addition, in equality constraints are imposed dur ing parameter estimation. For illustration, the approach is applied in predicting cell biomass and secondary metabolite in a fed-batch penic illin fermentation. Our results show that prior knowledge enhances the generalization capabilities of a pure neural network model. The appro ach is shown to require less data for parameter estimation, produce mo re accurate and consistent predictions, and provide more reliable extr apolation.