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Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor

In a fermentation process, dissolved oxygen (DO) concentration is mostly affected by aeration rate, and agitation speed and temperature. Thus it is beneficial to model the relationship of DO concentration with these variables based on real process data for further use in controller design. Formulati...

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Main Authors: Mamat, Nor Hana, Mohd Noor, Samsul Bahari, Che Soh, Azura, Taip, Farah Saleena, Ab Rashid, Ahmad Hazri, Jufika Ahmad, Nur Liyana, Mohd Yusuff, Ishak
Format: Conference or Workshop Item
Language:English
Published: 2017
Online Access:http://psasir.upm.edu.my/id/eprint/64397/1/ENG%20Poster%20111117%204.pdf
http://psasir.upm.edu.my/id/eprint/64397/
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spelling my.upm.eprints.643972018-07-05T09:25:11Z http://psasir.upm.edu.my/id/eprint/64397/ Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Taip, Farah Saleena Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak In a fermentation process, dissolved oxygen (DO) concentration is mostly affected by aeration rate, and agitation speed and temperature. Thus it is beneficial to model the relationship of DO concentration with these variables based on real process data for further use in controller design. Formulation of bioprocess model using process data or data driven technique is able to describe the true process conditions better than a model driven technique that focused on ideal steady state condition of process map the relationship of DO concentration with other physical and chemical process variable that has influence on the process. Artificial neural network (ANN) is a reliable and popular tool for approximation of nonlinear relationship between input and output data with little knowledge and no assumption of the process, also when dealing with problems involving prediction of variables. The structure of a neural network model namely input layer, hidden layer and output layers has significant effect on predicted results. While the number of neurons in input and output layers are determined based on the number of respective input and output parameters, there is no straightforward method to determine the optimal number of neurons in hidden layer. In order to select the appropriate structure, trial and error method or repeated runs are usually used to find the number of hidden neurons that gives smallest value of error and highest value of correlation coefficient. In this paper, a ranking system based on repeated runs of neural network model is used to determine the architecture with optimal number of hidden neurons for three different division of data for training and testing. The ranks are applied together for both training and testing datasets. The backpropagation neural network model with Lavenberg Marquardt learning algorithm was developed using 1476 samples real process dataset obtained from a fermentation process in a 200L bioreactor. The ranking system applied to simulation results shows that the best prediction of dissolved oxygen level was obtained for 80%/20% data division with 6 hidden neurons. 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64397/1/ENG%20Poster%20111117%204.pdf Mamat, Nor Hana and Mohd Noor, Samsul Bahari and Che Soh, Azura and Taip, Farah Saleena and Ab Rashid, Ahmad Hazri and Jufika Ahmad, Nur Liyana and Mohd Yusuff, Ishak (2017) Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor. In: 5th International Symposium on Applied Engineering and Sciences (SAES2017), 14-15 Nov. 2017, Universiti Putra Malaysia. (p. 4).
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In a fermentation process, dissolved oxygen (DO) concentration is mostly affected by aeration rate, and agitation speed and temperature. Thus it is beneficial to model the relationship of DO concentration with these variables based on real process data for further use in controller design. Formulation of bioprocess model using process data or data driven technique is able to describe the true process conditions better than a model driven technique that focused on ideal steady state condition of process map the relationship of DO concentration with other physical and chemical process variable that has influence on the process. Artificial neural network (ANN) is a reliable and popular tool for approximation of nonlinear relationship between input and output data with little knowledge and no assumption of the process, also when dealing with problems involving prediction of variables. The structure of a neural network model namely input layer, hidden layer and output layers has significant effect on predicted results. While the number of neurons in input and output layers are determined based on the number of respective input and output parameters, there is no straightforward method to determine the optimal number of neurons in hidden layer. In order to select the appropriate structure, trial and error method or repeated runs are usually used to find the number of hidden neurons that gives smallest value of error and highest value of correlation coefficient. In this paper, a ranking system based on repeated runs of neural network model is used to determine the architecture with optimal number of hidden neurons for three different division of data for training and testing. The ranks are applied together for both training and testing datasets. The backpropagation neural network model with Lavenberg Marquardt learning algorithm was developed using 1476 samples real process dataset obtained from a fermentation process in a 200L bioreactor. The ranking system applied to simulation results shows that the best prediction of dissolved oxygen level was obtained for 80%/20% data division with 6 hidden neurons.
format Conference or Workshop Item
author Mamat, Nor Hana
Mohd Noor, Samsul Bahari
Che Soh, Azura
Taip, Farah Saleena
Ab Rashid, Ahmad Hazri
Jufika Ahmad, Nur Liyana
Mohd Yusuff, Ishak
spellingShingle Mamat, Nor Hana
Mohd Noor, Samsul Bahari
Che Soh, Azura
Taip, Farah Saleena
Ab Rashid, Ahmad Hazri
Jufika Ahmad, Nur Liyana
Mohd Yusuff, Ishak
Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
author_facet Mamat, Nor Hana
Mohd Noor, Samsul Bahari
Che Soh, Azura
Taip, Farah Saleena
Ab Rashid, Ahmad Hazri
Jufika Ahmad, Nur Liyana
Mohd Yusuff, Ishak
author_sort Mamat, Nor Hana
title Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
title_short Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
title_full Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
title_fullStr Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
title_full_unstemmed Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
title_sort rank-based optimal neural network architecture for dissolved oxygen prediction in a 200l bioreactor
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/64397/1/ENG%20Poster%20111117%204.pdf
http://psasir.upm.edu.my/id/eprint/64397/
_version_ 1643838011138375680
score 13.211869

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