Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models

he factors affecting the adsorption capacity of Zirconium Metal Organic Framework were analyzed which includes the pH, contact time, amount of adsorbent and initial dye concentration. The experiment was run based on central composite design (CCD) in response surface methodology (RSM). The experiment...

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Main Author: Poopathi, Veshmen
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2021
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Online Access:http://eprints.usm.my/54564/1/Forecasting%20The%20Adsorption%20Capacity%20Of%20Organic%20Dye%20By%20Using%20Zirconium-Based%20Metal-Organic%20Framework%20%28MOF%29%20Comparison%20Studies%20Between%20Response%20Surface%20And%20Neural%20Network%20Models_Veshmen%20Poopathi_K4_2021_ESAR.pdf
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spelling my.usm.eprints.54564 http://eprints.usm.my/54564/ Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models Poopathi, Veshmen T Technology TP Chemical Technology he factors affecting the adsorption capacity of Zirconium Metal Organic Framework were analyzed which includes the pH, contact time, amount of adsorbent and initial dye concentration. The experiment was run based on central composite design (CCD) in response surface methodology (RSM). The experimental results were used to investigate the effect of input factors on the adsorption capacity of Zirconium MOF and to develop a model to predict system performance. According to the response surface plot, higher adsorption capacity of Zirconium MOF can be achieved with less adsorbent and a higher dye concentration. RSM was used to create a mathematical model, and the model's performance was evaluated using analysis of variance (ANOVA). Another neural network model was created using MATLAB’s neural network toolbox and Mathematica's net operation and predictor function. The adsorption capacity of Zirconium MOF was predicted using a mathematical and neural network model. Due to a shortage of experimental data for neural network training, the mathematical model generated in RSM had a higher accuracy in predicting the output response, with an R2 of 0.97 and an RMSE of 2.87. RSM performed numerical optimization for the adsorption capacity of Zirconium MOF to determine the best operating conditions. The maximum adsorption capacity of Zirconium MOF (46.75 mg/g) was found to be at pH 7, contact time of 70 min, adsorbent amount of 10 mg, and initial dye concentration of 44.99 mg. Universiti Sains Malaysia 2021-07-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/54564/1/Forecasting%20The%20Adsorption%20Capacity%20Of%20Organic%20Dye%20By%20Using%20Zirconium-Based%20Metal-Organic%20Framework%20%28MOF%29%20Comparison%20Studies%20Between%20Response%20Surface%20And%20Neural%20Network%20Models_Veshmen%20Poopathi_K4_2021_ESAR.pdf Poopathi, Veshmen (2021) Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Kimia. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TP Chemical Technology
spellingShingle T Technology
TP Chemical Technology
Poopathi, Veshmen
Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models
description he factors affecting the adsorption capacity of Zirconium Metal Organic Framework were analyzed which includes the pH, contact time, amount of adsorbent and initial dye concentration. The experiment was run based on central composite design (CCD) in response surface methodology (RSM). The experimental results were used to investigate the effect of input factors on the adsorption capacity of Zirconium MOF and to develop a model to predict system performance. According to the response surface plot, higher adsorption capacity of Zirconium MOF can be achieved with less adsorbent and a higher dye concentration. RSM was used to create a mathematical model, and the model's performance was evaluated using analysis of variance (ANOVA). Another neural network model was created using MATLAB’s neural network toolbox and Mathematica's net operation and predictor function. The adsorption capacity of Zirconium MOF was predicted using a mathematical and neural network model. Due to a shortage of experimental data for neural network training, the mathematical model generated in RSM had a higher accuracy in predicting the output response, with an R2 of 0.97 and an RMSE of 2.87. RSM performed numerical optimization for the adsorption capacity of Zirconium MOF to determine the best operating conditions. The maximum adsorption capacity of Zirconium MOF (46.75 mg/g) was found to be at pH 7, contact time of 70 min, adsorbent amount of 10 mg, and initial dye concentration of 44.99 mg.
format Monograph
author Poopathi, Veshmen
author_facet Poopathi, Veshmen
author_sort Poopathi, Veshmen
title Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models
title_short Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models
title_full Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models
title_fullStr Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models
title_full_unstemmed Forecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF) Comparison Studies Between Response Surface And Neural Network Models
title_sort forecasting the adsorption capacity of organic dye by using zirconium-based metal-organic framework (mof) comparison studies between response surface and neural network models
publisher Universiti Sains Malaysia
publishDate 2021
url http://eprints.usm.my/54564/1/Forecasting%20The%20Adsorption%20Capacity%20Of%20Organic%20Dye%20By%20Using%20Zirconium-Based%20Metal-Organic%20Framework%20%28MOF%29%20Comparison%20Studies%20Between%20Response%20Surface%20And%20Neural%20Network%20Models_Veshmen%20Poopathi_K4_2021_ESAR.pdf
http://eprints.usm.my/54564/
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