# Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling

## TL;DR

This study uses modeling and optimization techniques to improve the removal of a pesticide using porous pumice.

## Contribution

The novel use of ANN-GA and RSM for optimizing pesticide adsorption by pumice is presented.

## Key findings

- Optimal adsorption occurs at pH 3, 4 g/L adsorbent dosage, 30 min contact time, and 6.2 mg/L pesticide concentration.
- Langmuir and Temkin isotherms best describe equilibrium adsorption data.
- Pseudo-first and second-order models fit the kinetic data well.

## Abstract

In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted to isotherm, kinetic and thermodynamic models. The quantification of adsorbent elements was determined using EDX. XRD analysis was utilized to study the crystalline properties of adsorbent. The FT-IR spectra were taken from adsorbent before and after adsorption to study the presence and changes in functional groups. The constituted composition of the adsorbent was determined by XRF. Also, the ionic strength and adsorbent reusability were explored. The influences of operational parameters like pH, initial pesticide concentration, adsorbent dosage and contact time were investigated systematically. ANN-GA and RSM techniques were used to identify the optimal process variables that result in the highest removal. Based on the RSM approach, the optimization conditions for maximum removal efficiency is obtained at pH = 3, adsorbent dosage = 4 g/L, contact time = 30 min, and initial pesticide concentration = 6.2 mg/L. To accurately identify the parameters of nonlinear isotherm and kinetic models, a hybrid evolutionary differential evolution optimization (DEO) is applied. Results indicated that the equilibrium adsorption data were best fitted with Langmuir and Temkin isotherms and kinetic data were well described by pseudo-first and second-order kinetic models. The thermodynamic parameters such as entropy, enthalpy and Gibbs energy were evaluated to study the effect of temperature on pesticide adsorption.