Analysis of diclofenac removal by metal-organic framework MIL-100(Fe) using multi-parameter experiments and artificial neural network modeling

Abstract

The aim of study was to analyze diclofenac (DCF) removal from aqueous solutions by metal-organic framework MIL-100(Fe) using multi-parameter batch experiments and artificial neural network (ANN) modeling. First, single-parameter experiments were performed in terms of initial solution pH, MIL-100(Fe) dosage, initial DCF concentration, and temperature. The DCF removal decreased with an increase in pH from 5 to 10 and became negligible at pH 12. The kinetic and equilibrium data showed that DCF removal reached an equilibrium at 12 h, with a maximum capacity of 414.6 mg/g from the Langmuir isotherm model. The DCF removal was enhanced with increasing temperature. Multi-parameter experiments (n = 56) conducted under 28 duplicate experimental conditions showed DCF removal rates between 70.8 – 90.8% with a final pH range of 4.5 – 5.4 for most of the experimental conditions. The ANN model was developed based on the multi-parameter experimental data. The optimal topology for the ANN model was determined to be 4:7:6:2 (4 input variables, 7 neurons in the first hidden layer, 6 neurons in the second hidden layer, and 2 output variables). Among the four input variables, temperature was the most important variable affecting DCF removal rate under the given experimental ranges.