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

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dc.contributor.authorPoopathi, Veshmen
dc.date.accessioned2022-09-13T02:18:11Z
dc.date.available2022-09-13T02:18:11Z
dc.date.issued2021-07-01
dc.description.abstractThe 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/16044
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.titleForecasting The Adsorption Capacity Of Organic Dye By Using Zirconium-Based Metal-Organic Framework (MOF): Comparison Studies Between Response Surface And Neural Network Modelsen_US
dc.typeOtheren_US
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