Feed Forward Neural Network Model for Isopropyl Myristate Production in Industrial-scale Semi-batch Reactive Distillation Columns
The application of the artificial neural network (ANN) model in chemical industries has grown due to its ability to solve complex model and online application problems. Typically, the ANN model is good at predicting data within the training range but is limited when predicting extrapolated data....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor's University
2015
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| Subjects: | |
| Online Access: | http://eprints.usm.my/42785/1/JES_Vol._11_2015_-_Art._6%2859-65%29.pdf http://eprints.usm.my/42785/ http://web.usm.my/jes/11_2015/JES%20Vol.%2011%202015%20-%20Art.%206(59-65).pdf |
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| Institution: | Universiti Sains Malaysia |
| Language: | English |
| Summary: | The application of the artificial neural network (ANN) model in chemical
industries has grown due to its ability to solve complex model and online application
problems. Typically, the ANN model is good at predicting data within the training range
but is limited when predicting extrapolated data. Thus, in this paper, selected optimum
multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) models
are used to predict the bottom (xb) compositions of extrapolated data. The MIMO and
MISO models both managed to predict the extrapolated data with MSE values of 0.0078
and 0.0063 and with R2 values of 0.9986 and 0.9975, respectively. |
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