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

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. (C) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

The study investigated diclofenac (DCF) removal from aqueous solutions using metal-organic framework MIL-100(Fe) through multi-parameter batch experiments and artificial neural network (ANN) modeling. Temperature was identified as the most important factor affecting DCF removal rate under the given experimental ranges.