Summary
Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work, a set of computational tools are proposed, allowing researchers in Biotechnology to use ANNs for the modelling of fed-batch fermentation processes. The main task is to predict the values of kinetics parameters from the values of a set of state variables. The tools were validated with two case studies, showing the main functionalities of the application.
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References
Ascher, S., Ruuth.: Implicit-explicit runge-kutta methods for time-dependent partial differential equations. Applied Numerical Mathematics 25, 151–167 (1997)
Coulman, G.A., Stieber, R.W., Gerhardt, P.: Dialysis Continuous Process for Ammonium-Lactate Fermentation of Whey: Mathematical Model and Computer Simulation. American Society for Microbiology (1977)
Haykin, S.: Neural Networks - A Compreensive Foundation, 2nd edn. Prentice-Hall, New Jersey (1999)
Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial Neural Networks: A Tutorial. IEE (1996)
Lednick, P., Mészà ros, A.: Neural Network Modeling in Optimization of Continuous Fermentation Process. Bioprocess Engineering 18, 427–432 (1998)
Lee, D.S., Park, J.M.: Neural Network Modeling for On-line Estimation of Nutrient Dynamics in a Sequentially-operated Batch Reactor. Journal of Biotechnology 75, 229–239 (1999)
Levisauskas, D., Tekorius, T.: Model-Based Optimization of Fed-Batch Fermentation Processes Using Predetermined Type Feed-Rate Time Profiles. A Comparative Study. In: ITC (2005)
Mendes, R., Rocha, M., Rocha, I., Ferreira, E.C.: A Comparison of Algorithms for the Optimization of Fermentation Processes. In: Proceedings of the 2006 IEEE Conference on Evolutionary Computation, pp. 7371–7378. IEEE Computer Society Press, Los Alamitos (2006)
Oliveira, R.: Combining First Principles Modelling and Artificial Neural Networks: A General Framework. Computers and Chemical Engineering 28, 755–766 (2004)
Park, S., Ramirez, W.F.: Optimal Production of Secreted Protein in Fed-batch Reactors. AIChE J. 34(9), 1550–1558 (1988)
Peres, J., Oliveira, R., Azevedo, S.F.: Knowledge Based Modular Networks for Process Modelling and Control. Computers and Chemical Engineering 25, 783–791 (2001)
Rocha, I.: Model-based strategies for computer-aided operation of recombinant E. coli fermentation. PhD thesis, Universidade do Minho (2003)
Stanbury, P.F., Whitaker, A.: Principles of Fermentation Technology. Pergamon Press, Oxford (1984)
Taylor, B.J.: Methods and Procedures for the Verification and Validation of Artificial Neural Networks. Springer, Heidelberg (2006)
Veloso, A.C., Rocha, I., Ferreira, E.C.: On-Line Estimation of Biomass in an E. Coli Fed-Batch Fermentation. In: Enpromer (2005)
Zheng, Y., Gu, T.: Analytical Solutions to a Model for the Startup Period of Fixed-Bed Reactors. Elsevier Science (1996)
Zuo, K., Wu, W.T.: Semi-realtime Optimization and Control of a Fed-batch Fermentation System. Computers and Chemical Engineering 24, 1105–1109 (2000)
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Valente, E., Rocha, I., Rocha, M. (2009). Modelling Fed-Batch Fermentation Processes: An Approach Based on Artificial Neural Networks. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_4
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DOI: https://doi.org/10.1007/978-3-540-85861-4_4
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