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Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system

Just-suspension speed (N-js) is one of the important criteria for the design of agitators for solid-liquid mixing processes. In this manuscript a novel approach on using Artificial Neural Network (ANN) modeling for of just-suspension speed prediction is developed based previous published work that c...

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Main Authors: Choong, Choe Earn, Ibrahim, Shaliza, El-Shafie, Ahmed
Format: Article
Published: Flow Measurement and Instrumentation 2020
Subjects:
QA Mathematics
Online Access:http://eprints.um.edu.my/36840/
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spelling my.um.eprints.368402023-11-14T03:17:58Z http://eprints.um.edu.my/36840/ Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system Choong, Choe Earn Ibrahim, Shaliza El-Shafie, Ahmed QA Mathematics Just-suspension speed (N-js) is one of the important criteria for the design of agitators for solid-liquid mixing processes. In this manuscript a novel approach on using Artificial Neural Network (ANN) modeling for of just-suspension speed prediction is developed based previous published work that contains 950 datasets including various solid loading, solid density, solid diameter, tank diameter, solution density, impeller diameter, number of impeller blade, blade hub angle, blade tip angle, the width of blade and the ratio of clearance between an impeller and the bottom of the tank with the tank diameter whereas the corresponding to just-suspension speed as an output. Multilayer perceptron type of feed-forward back-propagation neural network was employed for building the ANN model. It found that the configuration of 8 neurons in 1 hidden layer using tangent sigmoid as transfer function presented as the optimum ANN model (11-8-1). Results show the proposed ANN model could provide the desired accuracy on predicting just-suspension speed by achieves 0.96 of correlation coefficient and 0.0059 of mean square error. In addition, the results showed that the integrated Genetic Algorithm-Artificial Neural Network (GA-ANN) model enhanced the accuracy for predicting the just-suspension speed compare ANN model. This novel approach showed the high potential to be applied in chemical process industrial design system. Flow Measurement and Instrumentation 2020-03 Article PeerReviewed Choong, Choe Earn and Ibrahim, Shaliza and El-Shafie, Ahmed (2020) Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system. Flow Measurement and Instrumentation, 71. ISSN 0263-2241, DOI https://doi.org/10.1016/j.flowmeasinst.2019.101689 <https://doi.org/10.1016/j.flowmeasinst.2019.101689>. 10.1016/j.flowmeasinst.2019.101689
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA Mathematics
spellingShingle QA Mathematics
Choong, Choe Earn
Ibrahim, Shaliza
El-Shafie, Ahmed
Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system
description Just-suspension speed (N-js) is one of the important criteria for the design of agitators for solid-liquid mixing processes. In this manuscript a novel approach on using Artificial Neural Network (ANN) modeling for of just-suspension speed prediction is developed based previous published work that contains 950 datasets including various solid loading, solid density, solid diameter, tank diameter, solution density, impeller diameter, number of impeller blade, blade hub angle, blade tip angle, the width of blade and the ratio of clearance between an impeller and the bottom of the tank with the tank diameter whereas the corresponding to just-suspension speed as an output. Multilayer perceptron type of feed-forward back-propagation neural network was employed for building the ANN model. It found that the configuration of 8 neurons in 1 hidden layer using tangent sigmoid as transfer function presented as the optimum ANN model (11-8-1). Results show the proposed ANN model could provide the desired accuracy on predicting just-suspension speed by achieves 0.96 of correlation coefficient and 0.0059 of mean square error. In addition, the results showed that the integrated Genetic Algorithm-Artificial Neural Network (GA-ANN) model enhanced the accuracy for predicting the just-suspension speed compare ANN model. This novel approach showed the high potential to be applied in chemical process industrial design system.
format Article
author Choong, Choe Earn
Ibrahim, Shaliza
El-Shafie, Ahmed
author_facet Choong, Choe Earn
Ibrahim, Shaliza
El-Shafie, Ahmed
author_sort Choong, Choe Earn
title Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system
title_short Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system
title_full Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system
title_fullStr Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system
title_full_unstemmed Artificial Neural Network (ANN) model development for predicting just suspension speed in solid-liquid mixing system
title_sort artificial neural network (ann) model development for predicting just suspension speed in solid-liquid mixing system
publisher Flow Measurement and Instrumentation
publishDate 2020
url http://eprints.um.edu.my/36840/
_version_ 1783876658878480384
score 13.211869

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