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dc.contributor.authorLim, Chuan Chuan
dc.contributorFaculty of Engineering Technologyen_US
dc.date2023-12
dc.date.accessioned2025-05-09T00:59:50Z
dc.date.available2025-05-09T00:59:50Z
dc.date.issued2018-12
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/83753
dc.descriptionAccess is limited to UniMAP community.en_US
dc.description.abstractIn this study, magnetic multi-walled carbon nanotubes (MMWCNTs) was used to study the effect of adsorption parameters, predict optimization of adsorption condition and deduce the kinetic and isotherm on the adsorption of methylene blue (MB). Temperature, MB concentration and MMWCNTs dosage were chosen as the important parameters to the MB removal efficiency and the optimum adsorption condition was predicted using the response surface methodology (RSM) coupled with central composite design (CCD). The adsorption kinetic and isotherm were also deduced. All of these studies are preferred to further understand the process of MB adsorption on MMWCNTs and also to solve the ordinary wastewater treatment method especially in the dye removal by using MMWCNTs. Using the DOE software, important analysis including ANOVA, regression model development, graphical diagnostics, contour plots (2D) and 3D surface graph were carried out in order to study the interaction between the three factors. After the removal of insignificant term, the final proposed response model was a quadratic equation with MB removal efficiency (%), (R1) = 93.07 + 2.81A – 10.21B + 10.52C + 3.05AB + 7.65BC – 3.30B2 – 3.97C2. From the contour plots and 3D graph, MMWCNTs dosage is the most significant factor followed by the MB concentration and temperature.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.subject.otherMagnetic Multi-Walled Carbon Nanotubes (MMWCNTs)en_US
dc.subject.otherMethylene Blue (MB)en_US
dc.subject.otherAdsorptionen_US
dc.titleRemoval of Methylene Blue using magnetic multi-walled carbon nanotubes (MMWCNTs): optimization and kinetic studyen_US
dc.typeLearning Objecten_US
dc.contributor.advisorHoo Peng Yong, Dr.


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