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Software sensor for measuring lactic acid concentration: Effect of input number and node number

Artificial Neural Network (ANN) approach was applied in developing software sensor for production of lactic acid using pineapple waste from Lactobacillus delbreuckii. Lactic acid production currently is one of the significant materials in industry and production with renewable source such as pineapp...

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Main Authors: Rashid, Roslina, Esivan, S. M. M., Radzali, S. R., Idris, Ani
Format: Article
Language:English
Published: Asian Network for Scientific Information 2010
Subjects:
TP Chemical technology
Online Access:http://eprints.utm.my/id/eprint/26581/1/AniIdris2010_SoftwareSensorforMeasuringLacticAcid.pdf
http://eprints.utm.my/id/eprint/26581/
http://dx.doi.org/10.3923/jas.2010.2578.2583
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spelling my.utm.265812018-10-31T12:30:36Z http://eprints.utm.my/id/eprint/26581/ Software sensor for measuring lactic acid concentration: Effect of input number and node number Rashid, Roslina Esivan, S. M. M. Radzali, S. R. Idris, Ani TP Chemical technology Artificial Neural Network (ANN) approach was applied in developing software sensor for production of lactic acid using pineapple waste from Lactobacillus delbreuckii. Lactic acid production currently is one of the significant materials in industry and production with renewable source such as pineapple waste made the production of lactic acid faced a lot of disturbances in measuring the quality of lactic acid produced. An artificial neural network (ANN) was developed to predict the concentration of lactic acid, using collected data from an offline analysis. Multi layer perceptron (MLP) was used for mapping between the input and output parameters. Two variables were used as input parameters. MSE was used to evaluate the predictive performance of MLP. Logsig and purelin was used as the activation function and Levenberg-Marquadt was utilized as the training algorithm. The result showed that having 2 inputs is better in predicting the concentration of lactic acid; instead of 1 input. The optimum structure found was 2-5-1. Asian Network for Scientific Information 2010 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/26581/1/AniIdris2010_SoftwareSensorforMeasuringLacticAcid.pdf Rashid, Roslina and Esivan, S. M. M. and Radzali, S. R. and Idris, Ani (2010) Software sensor for measuring lactic acid concentration: Effect of input number and node number. Journal of Applied Sciences, 10 (21). 2578 -2583. ISSN 1812-5654 http://dx.doi.org/10.3923/jas.2010.2578.2583 DOI:10.3923/jas.2010.2578.2583
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
Rashid, Roslina
Esivan, S. M. M.
Radzali, S. R.
Idris, Ani
Software sensor for measuring lactic acid concentration: Effect of input number and node number
description Artificial Neural Network (ANN) approach was applied in developing software sensor for production of lactic acid using pineapple waste from Lactobacillus delbreuckii. Lactic acid production currently is one of the significant materials in industry and production with renewable source such as pineapple waste made the production of lactic acid faced a lot of disturbances in measuring the quality of lactic acid produced. An artificial neural network (ANN) was developed to predict the concentration of lactic acid, using collected data from an offline analysis. Multi layer perceptron (MLP) was used for mapping between the input and output parameters. Two variables were used as input parameters. MSE was used to evaluate the predictive performance of MLP. Logsig and purelin was used as the activation function and Levenberg-Marquadt was utilized as the training algorithm. The result showed that having 2 inputs is better in predicting the concentration of lactic acid; instead of 1 input. The optimum structure found was 2-5-1.
format Article
author Rashid, Roslina
Esivan, S. M. M.
Radzali, S. R.
Idris, Ani
author_facet Rashid, Roslina
Esivan, S. M. M.
Radzali, S. R.
Idris, Ani
author_sort Rashid, Roslina
title Software sensor for measuring lactic acid concentration: Effect of input number and node number
title_short Software sensor for measuring lactic acid concentration: Effect of input number and node number
title_full Software sensor for measuring lactic acid concentration: Effect of input number and node number
title_fullStr Software sensor for measuring lactic acid concentration: Effect of input number and node number
title_full_unstemmed Software sensor for measuring lactic acid concentration: Effect of input number and node number
title_sort software sensor for measuring lactic acid concentration: effect of input number and node number
publisher Asian Network for Scientific Information
publishDate 2010
url http://eprints.utm.my/id/eprint/26581/1/AniIdris2010_SoftwareSensorforMeasuringLacticAcid.pdf
http://eprints.utm.my/id/eprint/26581/
http://dx.doi.org/10.3923/jas.2010.2578.2583
_version_ 1643647800911593472
score 13.211869

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