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Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation

Using computational models, engineers and researchers are able to observe the results of their research beyond the range of their tested experimental parameters. Process modelling resembles the approximation behaviour. This paper reports the development of a mathematical model in predicting the wate...

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Main Authors: Idris, Iylia, Ahmad, Zainal, Othman, Mohd. Roslee, Rohman, Fakhrony Sholahudin, Rushdan, Ahmad Ilyas, Azmi, Ashraf
Format: Conference or Workshop Item
Published: 2022
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/101359/
http://dx.doi.org/10.1016/j.matpr.2022.04.084
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spelling my.utm.1013592023-06-08T09:51:44Z http://eprints.utm.my/id/eprint/101359/ Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation Idris, Iylia Ahmad, Zainal Othman, Mohd. Roslee Rohman, Fakhrony Sholahudin Rushdan, Ahmad Ilyas Azmi, Ashraf Q Science (General) Using computational models, engineers and researchers are able to observe the results of their research beyond the range of their tested experimental parameters. Process modelling resembles the approximation behaviour. This paper reports the development of a mathematical model in predicting the water flux from Pre-Treated Palm Oil Mill Effluent (POME) using Direct Contact Membrane Distillation (DCMD). This model allows the identification for the range of feed temperature and feed velocity, in which optimal operating conditions (maximum production of water flux) can be obtained. An artificial neural network (ANN) was selected to predict the production of flux at various temperatures due to its capability to predict any nonlinear system. A multilayer Feedforward Artificial Neural Network (FANN) model consisting of 1 hidden layer with 13 hidden neurons, was developed with three input variables (feed temperature, feed velocity and membrane) and one output (permeate water flux) with 18 experimental data points. The lowest mean square error (MSE) obtained was 0.0034, while the regression coefficient (R) values for training, validation, and testing were 0.9859, 0.9986, and 0.9984, respectively. The most sensitive parameter was the feed temperature compared to feed velocity. The usage of this model may lead to the development of efficient and economical designs for separation processes. 2022 Conference or Workshop Item PeerReviewed Idris, Iylia and Ahmad, Zainal and Othman, Mohd. Roslee and Rohman, Fakhrony Sholahudin and Rushdan, Ahmad Ilyas and Azmi, Ashraf (2022) Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation. In: 2nd International Conference on Chemical Engineering and Applied Sciences (ICChEAS 2021), 3 November 2021 - 4 November 2021, Semarang, Indonesia. http://dx.doi.org/10.1016/j.matpr.2022.04.084
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/
topic Q Science (General)
spellingShingle Q Science (General)
Idris, Iylia
Ahmad, Zainal
Othman, Mohd. Roslee
Rohman, Fakhrony Sholahudin
Rushdan, Ahmad Ilyas
Azmi, Ashraf
Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
description Using computational models, engineers and researchers are able to observe the results of their research beyond the range of their tested experimental parameters. Process modelling resembles the approximation behaviour. This paper reports the development of a mathematical model in predicting the water flux from Pre-Treated Palm Oil Mill Effluent (POME) using Direct Contact Membrane Distillation (DCMD). This model allows the identification for the range of feed temperature and feed velocity, in which optimal operating conditions (maximum production of water flux) can be obtained. An artificial neural network (ANN) was selected to predict the production of flux at various temperatures due to its capability to predict any nonlinear system. A multilayer Feedforward Artificial Neural Network (FANN) model consisting of 1 hidden layer with 13 hidden neurons, was developed with three input variables (feed temperature, feed velocity and membrane) and one output (permeate water flux) with 18 experimental data points. The lowest mean square error (MSE) obtained was 0.0034, while the regression coefficient (R) values for training, validation, and testing were 0.9859, 0.9986, and 0.9984, respectively. The most sensitive parameter was the feed temperature compared to feed velocity. The usage of this model may lead to the development of efficient and economical designs for separation processes.
format Conference or Workshop Item
author Idris, Iylia
Ahmad, Zainal
Othman, Mohd. Roslee
Rohman, Fakhrony Sholahudin
Rushdan, Ahmad Ilyas
Azmi, Ashraf
author_facet Idris, Iylia
Ahmad, Zainal
Othman, Mohd. Roslee
Rohman, Fakhrony Sholahudin
Rushdan, Ahmad Ilyas
Azmi, Ashraf
author_sort Idris, Iylia
title Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
title_short Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
title_full Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
title_fullStr Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
title_full_unstemmed Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
title_sort application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation
publishDate 2022
url http://eprints.utm.my/id/eprint/101359/
http://dx.doi.org/10.1016/j.matpr.2022.04.084
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score 13.211869

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