Hybrid modelling of fermentation processes using artificial neural networks: a study on identification and stability

When processes are complex and poorly understood in a mechanistic sense, hybrid modelling through knowledge integration can be employed with advantage because the model accuracy can be increased by the incorporation of alternative and complementary sources of knowledge. In this work a bioreactor hybrid model structure is studied that combines first principles modelling with artificial neural networks: the bioreactor system is described by a set of mass balance equations, and the cell population system is represented by an adjustable mixture of neural network and mechanistic representations. Two strategies for the identification of embedded neural networks are compared. The sensitivities equations are derived enabling the analytical calculation of the Jacobian Matrix. The application of the theory is illustrated with a simulation case study.