Preparation and characterization of monoliths HKUST-1 MOF via straightforward 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 a straightforward 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 the removal of eosin yellow (EY) and malachite green (MG) dyes from a binary aqueous solution. A central composite design as one type of experimental design method was used to investigate the main effect and interaction effect of experimental variables such as the initial dye concentration, monolithic HKUST-1 MOF mass, pH and sonication time while the dye removal percentage (R%) was considered as the response. A maximum removal efficiency of 83.4 and 94.9% for MG and EY pollutants, respectively, was obtained at the optimized settings at: 8.0 mg L⁻¹ of EY, 8.0 mg L⁻¹ of MG, 0.015 g of monolithic HKUST-1 MOF mass, 3.0 min sonication time and pH 6.0. A flexible mathematic relationship between the operational factor and responses was modelled by an artificial neural network (ANN). The model predicted results that show a good agreement with the 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 1.75 × 10⁻⁵ and 7.43 × 10⁻⁵ were obtained for the MG and EY models, respectively. Isotherm and kinetic investigations revealed that a pseudo second order and Langmuir isotherm model have the best behavior for both dye adsorptions onto monolithic HKUST-1 MOF.