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.
|Number of pages||6|
|Journal||IFAC Proceedings Volumes (IFAC-PapersOnline)|
|Publication status||Published - 2004|
|Event||9th IFAC International Symposium on Computer Applications in Biotechnology, CAB 2004 - Nancy, France|
Duration: 28 Mar 2004 → 31 Mar 2004
- Artificial neural networks
- Hybrid modelling