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Ali Daneshfar

Academic rank: Professor
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Education: PhD.
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Faculty: Basic Science
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Research

Title
Principal component analysis-artificial neural network and genetic algorithm optimization for removal of reactive orange 12 by copper sulfide nanoparticles-activated carbon
Type
JournalPaper
Keywords
Adsorption Copper sulfide nanoparticles Activated carbon Modeling, Artificial neural network Reactive orange 12
Year
2013
Journal JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY
DOI
Researchers M.Ghaedi ، A.M.Ghaedi ، F.Abdi ، M.Roosta ، Reza Sahraei ، Ali Daneshfar

Abstract

In this study a green approach described for the synthesis of copper sulfide nanoparticles loaded on activated carbon (CuS-NP-AC) and usability of it for the removal of reactive orange 12 (RO-12). This material was characterized using instruments such as scanning electron microscopy (SEM) and X-ray diffraction (XRD). The effects of variables were optimized using Principal component analysis-artificial neural network (PCA-ANN). Fitting the experimental equilibrium data shows the suitability of the Langmuir isotherm. The small amount of proposed adsorbent (0.017 g) is applicable for successful removal of RO-12 (RE > 95%) in short time (31.09 min) with high adsorption capacity (96.9 mg g−1)