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Artificial neural network models for HFCS isomerization process

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Abstract

This work presents an approach to the modeling of a real industrial isomerization reactor by using artificial neural networks (ANN) pre-processed with principal component analysis (PCA). The initial model considered the output fructose concentration as the output variable, while the flow rate of substrate to the reactor as the principal input variable. Then, the ANN model was restructured and inversely trained by assuming the exit fructose concentration as the input variable and the feed flow rate as the output variable. Results indicate good performance by the application of the developed strategy to an extensive industrial data set. The results are expected to be useful in future, controlling the fructose concentration in the HFCS isomerization reactor.

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Acknowledgments

The author acknowledges the industrial data provided by CARGILL Inc. Orhangazi-Turkey through Yontem Beyazkus and Ercan Erdas.

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  1. Department of Chemical Engineering, Faculty of Engineering, Inonu University, 44280, Malatya, Turkey

    Mehmet Yuceer

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  1. Mehmet Yuceer
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Correspondence to Mehmet Yuceer.

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Yuceer, M. Artificial neural network models for HFCS isomerization process. Neural Comput & Applic 19, 979–986 (2010). https://doi.org/10.1007/s00521-010-0437-x

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  • DOI: https://doi.org/10.1007/s00521-010-0437-x

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