Abstract
The availability of estimators in the algal cultivation processes can lead to improving prediction and optimization. In this study a new simulation method is introduced into the field of algal cultivation. Two network models that describe the cultivation process are developed. The models are based on neural network in a Chlorella protothecoides cultivation process with highly nonlinear characteristics. Two types of feed-forward networks, neural network trained with Levenberg-Marquardt algorithms (LMNN) and radial basis function neural network (RBFNN), are considered in this paper. Modelling effort was focused on selection of the network structures, verification and prediction of the models used. Data sets of input-output patterns were obtained from a thesis and by computer simulation. Neural networks were tested for their predictive abilities, and an agreement between predicted values and that of the test data set was shown. Follow-up studies indicate that these networks can be used as a basis for the establishment of neural network controllers. Possible developments of neural networks in the context of cultivation process modelling are also discussed.
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Zhang, G.Y., Guo, S.Y., Li, L., Zhou, W.B., Cai, M.Y. (2001). Neural Networks for Modelling and Predicting the Chlorella Protothecoides Cultivation Processes. In: Chen, F., Jiang, Y. (eds) Algae and their Biotechnological Potential. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9835-4_5
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DOI: https://doi.org/10.1007/978-94-015-9835-4_5
Publisher Name: Springer, Dordrecht
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