Theoretical study of Graphene nanoparticles surface effects on Removal of Pharmaceuticals Contaminants from water by neural network computational method

Abstract

One of the major crises we face is water and wastewater pollution, as the population increases, industries develop and pollution increases. The pharmaceutical industries produce a large amount of polluted wastewater containing the drug. The drugs, due to their antimicrobial properties, inhibit the activity of bacteria in the biological treatment system and disrupt the purification process without being eliminated. Therefore, common methods used in wastewater treatment in the pharmaceutical industries such as biological treatment are not generally effective. Nanotechnology has provided opportunities to develop low-cost, high-cost, environmentally friendly water and wastewater treatment processes. Using Nanotechnology, more studies can be carried out on many water-resistant pollutants, such as water-based pharmaceutical pollutants and pollutants that we cannot disinfect using conventional methods. Also using this technology will have great results. This study was computational and we used Nano-graphene in this study to investigate the Removal of 4 drug contaminants (diazepam - Lindal - phenobarbital - phenytoin - theophylline - diuron) using the smart neural network system, Fuzzy inference system, based on matching and application software (ChemOffice software, Open Babel software, Gaussian & GaussView software, Multiwfn software, and MATLAB software). Molecular parameters of materials (surface, volume, main dipole and molecular orbitals) were calculated by modeling and analyzing compounds. The test error value for the parameter of available polar level in five Tables and the test error value for the size parameter in three Tables is lower than the other parameters. This means that the contamination of the compounds studied by these parameters is more important than other molecular parameters. Considering different conditions, the volume and main dipole parameters have a greater effect on water contamination than the other parameters studied in the presence and absence of carbon Nano-graphene.