Principal component analysis-artificial neural network and genetic algorithm optimization for removal of reactive orange 12 by copper sulfide nanoparticles-activated carbon
摘要：
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 (0.017g) is applicable for successful removal of RO-12 (RE>95%) in short time (31.09min) with high adsorption capacity (96.9mgg1)
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关键词：
Adsorption Copper sulfide nanoparticles Activated carbon Modeling, Artificial neural network Reactive orange 12
DOI：
10.1016/j.jiec.2013.06.008
被引量：
年份：
2014
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