Principal component analysis-artificial neural network and genetic algorithm optimization for removal of reactive orange 12 by copper sulfide nanoparticles-activated carbon

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)

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