Journal
NEURAL COMPUTING & APPLICATIONS
Volume 19, Issue 7, Pages 979-986Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-010-0437-x
Keywords
ANN; PCA; Modeling; Glucose isomerization; Pre-processing; Industrial isomerization process
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available