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Preparation and characterization of monoliths HKUST-1 MOF via straightway conversion of Cu(OH)2-based monoliths and its application for wastewater treatment: Artificial neural network and central composite design modeling

Abstract

Highly crystalline water stable monolithic HKUST-1 MOF by straightway conversion of Cu(OH)2-based monoliths was prepared and characterized via FE-SEM, XRD and EDS analysis. The prepared water stable monolithic HKUST-1 MOF as a new adsorbent was applied for removal of eosin yellow (EY) and malachite green (MG) dyes from binary aqueous solution. Central composite design as one type of design of experiment method was used to investigation of main effect and interaction effect of experimental variables such as initial dyes concentration, monolithic HKUST-1 MOF mass, pH and sonication time while the dyes removal percentage (R%) was considered as response. Maximum removal efficiency of 83.4 and 94.9 % for MG and EY pollutants, respectively was obtained at optimize set as at: 8.0 mg L-1 of EY, 8.0 mg L-1 of MG, 0.015 g monolithic HKUST-1 MOF mass, 3.0 min sonication time and pH 6.0. Flexible mathematical relationship between operational factor and responses were modeled by artificial neural network (ANN). The model predicated results show a good agreement with experimental data. Absolute average deviations (AADs) of 1.07% and 0.49%, R2 values of 0.9974 and 0.9963 and mean square error (MSE) of 1.75E-05 and 7.43E-05 were obtained for MG and EY model, respectively. Isotherm and kinetic investigations revealed that pseudo second order and Langmuir isotherm model have the best behaviour for both dyes adsorption onto monolithic HKUST-1 MOF.

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Publication details

The article was received on 07 Mar 2018, accepted on 09 May 2018 and first published on 09 May 2018


Article type: Paper
DOI: 10.1039/C8NJ01067F
Citation: New J. Chem., 2018, Accepted Manuscript
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    Preparation and characterization of monoliths HKUST-1 MOF via straightway conversion of Cu(OH)2-based monoliths and its application for wastewater treatment: Artificial neural network and central composite design modeling

    K. Dashtian, F. Yousefi, F. borousan, N. Parsazadeh and M. Ghaedi, New J. Chem., 2018, Accepted Manuscript , DOI: 10.1039/C8NJ01067F

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