Artificial intelligence for the removal of benzene, toluene, ethyl benzene and xylene (BTEX) from aqueous solutions using iron nanoparticles

Abstract

Magnetic nanosorbents proved to be highly effective in inorganic and organic contaminants removal from aqueous solutions, especially nano zero valent iron (nZVI). The main purpose of this study is to investigate the effect of using nZVI in removing benzene, toluene, ethyl benzene and xylene (BTEX) contaminants from aqueous solutions. The nZVI and the standard BTEX solution were prepared in the laboratory. X-ray diffraction (XRD), UV spectrophotometry, and scanning electron microscopy (SEM) analysis were used for nZVI characterization. The effects of contact time, initial BTEX mixture concentration, adsorbent dose, temperature, and pH on the amount of BTEX absorbed were investigated. The highest removal efficiency of 97% for the BTEX mixture was achieved at a stirring rate of 100 rpm, temperature of 60°C, and pH 7. The minimum effective time for efficient removal was 30 min, while the effective dose for BTEX compounds removal was 0.22 g/L. The Freundlich model was the best fit of experimental data. An artificial neural network (ANN) was used to predict the BTEX removal efficiency. Modeling results showed that ANN with average absolute error of 0.6272% is reliable in describing the adsorption of BTEX onto the iron nanoparticles. It is estimated that the cost of BTEX removal by nZVI under the optimal conditions will be about 3.5 USD per cubic meter.

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