Time Accounting Artificial Neural Networks for Biochemical Process Models
This paper is focused on developing more efficient computational schemes for modeling in biochemical processes. A theoretical framework for estimation of process kinetic rates based on different temporal (time accounting) artificial neural network (ANN) architectures is introduced. Three ANNs that explicitly consider temporal aspects of modeling are exemplified: (i) Recurrent Neural Network (RNN) with global feedback (from the network output to the network input); (ii) time-lagged feedforward neural network (TLFN), and (iii) reservoir computing network (RCN). Crystallization growth rate estimation is the benchmark for testing the methodology. The proposed hybrid (dynamical ANN and analytical submodel) schemes are promising modeling framework when the process is strongly nonlinear and particularly when input--output data is the only information available.
KeywordsArtificial Neural Network Recurrent Neural Network Distillation Column Observation Error Crystal Size Distribution
This work was financed by the Portuguese Foundation for Science and Technology within the activity of the Research Unit IEETA-Aveiro, which is gratefully acknowledged.
- 2.Bastin, G., Dochain, D.: On-line Estimation and Adaptive Control of Bioreactors. Elsevier Science Publishers, Amsterdam (1990)Google Scholar
- 3.Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
- 6.Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing, Boston, MA (1996)Google Scholar
- 8.Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Center for Information Technology (2001)Google Scholar
- 10.Mandic D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability (Adaptive & Learning Systems for Signal Processing, Communications & Control). Wiley (2001)Google Scholar
- 11.Noykove, N., Muller, T.G., Gylenberg, M., Timmer J.: Quantitative analysis of anaerobic wastewater treatment processes: identifiably and parameter estimation. Biotechnol. Bioeng. 78(1), 91–103 (2002)Google Scholar
- 12.Oliveira, C., Georgieva, P., Rocha, F., Feyo de Azevedo, S.: Artificial Neural Networks for Modeling in Reaction Process Systems, Neural Computing & Applications. Springer 18, 15–24 (2009)Google Scholar
- 13.Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and Adaptive Systems: Fundamentals Through Simulations. Wiley, New York (2000)Google Scholar
- 14.Steil, J.J.:. Backpropagation-Decorrelation: Online recurrent learning with O(N) complexity. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), vol. 1, pp. 843–848Google Scholar