Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Red algae species, Euchema Spinosum (ES) in Malaysia possesses excellent biosorbent properties in removing dyes from aqueous solutions. In the present study, the experimental design for the biosorption process was carried out via response surface methodology (RSMCCD). A total of 20 runs were carried...

Full description

Bibliographic Details
Main Authors: Nadiah, Mokhtar, Edriyana, Abd Aziz, Azmi, Aris, W. F. W., Ishak, Anwar, P. P. Abdul Majeed, Syazwan, N. Moni, Siti Kamariah, Md Sa’at
Format: Article
Language:English
Published: Science Publishing Corporation 2018
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/23625/
http://umpir.ump.edu.my/id/eprint/23625/
http://umpir.ump.edu.my/id/eprint/23625/1/IJET-22908.pdf
id ump-23625
recordtype eprints
spelling ump-236252019-01-08T08:07:46Z http://umpir.ump.edu.my/id/eprint/23625/ Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Nadiah, Mokhtar Edriyana, Abd Aziz Azmi, Aris W. F. W., Ishak Anwar, P. P. Abdul Majeed Syazwan, N. Moni Siti Kamariah, Md Sa’at TA Engineering (General). Civil engineering (General) Red algae species, Euchema Spinosum (ES) in Malaysia possesses excellent biosorbent properties in removing dyes from aqueous solutions. In the present study, the experimental design for the biosorption process was carried out via response surface methodology (RSMCCD). A total of 20 runs were carried out to generate a quadratic model and further analysed for optimisation. Prior to the evaluation, the characterisation study of the ES was performed. It was observed that the maximum uptake capacity of 399 mg/g (>95%) is obtained at equilibrium time of 60 min, pH solution of 6.9-7.1, dosage of 0.72 g/L and initial dye concentration of 300 g/L through statistical optimisation (CCD-RSM) based on the desirability function. It is demonstrated in the present study that the ANN model (R2=0.9994, adjR2=0.9916, MSE=0.19, RMSE=0.4391, MAPE=0.087 and AARE=0.001) is able to provide a slightly better prediction in comparison to the RSM model (R2= 0.9992, adj-R2= 0.9841, MSE=1.95, RMSE=1.395, MAPE=0.08 and AARE=0.001). Moreover, the SEM-EDX analysis indicates the development of a considerable number of pore size ranging between 132 to 175 m. From the experimental observations, it is evident that the ES can achieve high removal rate (>95%), indeed become a promising eco-friendly biosorptive material for MB dye removal. Science Publishing Corporation 2018-12-03 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/23625/1/IJET-22908.pdf Nadiah, Mokhtar and Edriyana, Abd Aziz and Azmi, Aris and W. F. W., Ishak and Anwar, P. P. Abdul Majeed and Syazwan, N. Moni and Siti Kamariah, Md Sa’at (2018) Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN). International Journal of Engineering & Technology, 7 (4.35). pp. 551-559. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/22908
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Nadiah, Mokhtar
Edriyana, Abd Aziz
Azmi, Aris
W. F. W., Ishak
Anwar, P. P. Abdul Majeed
Syazwan, N. Moni
Siti Kamariah, Md Sa’at
Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
description Red algae species, Euchema Spinosum (ES) in Malaysia possesses excellent biosorbent properties in removing dyes from aqueous solutions. In the present study, the experimental design for the biosorption process was carried out via response surface methodology (RSMCCD). A total of 20 runs were carried out to generate a quadratic model and further analysed for optimisation. Prior to the evaluation, the characterisation study of the ES was performed. It was observed that the maximum uptake capacity of 399 mg/g (>95%) is obtained at equilibrium time of 60 min, pH solution of 6.9-7.1, dosage of 0.72 g/L and initial dye concentration of 300 g/L through statistical optimisation (CCD-RSM) based on the desirability function. It is demonstrated in the present study that the ANN model (R2=0.9994, adjR2=0.9916, MSE=0.19, RMSE=0.4391, MAPE=0.087 and AARE=0.001) is able to provide a slightly better prediction in comparison to the RSM model (R2= 0.9992, adj-R2= 0.9841, MSE=1.95, RMSE=1.395, MAPE=0.08 and AARE=0.001). Moreover, the SEM-EDX analysis indicates the development of a considerable number of pore size ranging between 132 to 175 m. From the experimental observations, it is evident that the ES can achieve high removal rate (>95%), indeed become a promising eco-friendly biosorptive material for MB dye removal.
format Article
author Nadiah, Mokhtar
Edriyana, Abd Aziz
Azmi, Aris
W. F. W., Ishak
Anwar, P. P. Abdul Majeed
Syazwan, N. Moni
Siti Kamariah, Md Sa’at
author_facet Nadiah, Mokhtar
Edriyana, Abd Aziz
Azmi, Aris
W. F. W., Ishak
Anwar, P. P. Abdul Majeed
Syazwan, N. Moni
Siti Kamariah, Md Sa’at
author_sort Nadiah, Mokhtar
title Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
title_short Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
title_full Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
title_fullStr Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
title_full_unstemmed Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
title_sort comparative prediction of red alga biosorbent performance in dye removal using multivariate models of response surface methodology (rsm) and artificial neural network (ann)
publisher Science Publishing Corporation
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/23625/
http://umpir.ump.edu.my/id/eprint/23625/
http://umpir.ump.edu.my/id/eprint/23625/1/IJET-22908.pdf
first_indexed 2023-09-18T22:35:28Z
last_indexed 2023-09-18T22:35:28Z
_version_ 1777416565883404288

Similar Items