The shortage and extremely high utilization of water resources and the necessity of water with optimal quality for drinking and agricultural use and the preservation of the environment, human has led to the purification of industrial waste and wastewater by preventing t
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The shortage and extremely high utilization of water resources and the necessity of water with optimal quality for drinking and agricultural use and the preservation of the environment, human has led to the purification of industrial waste and wastewater by preventing the entrance of pollutants into surface water and underground water and creating a trusted cycle. One of the most dangerous pollutants that threating human health and the environment is Pharmaceutical pollution. Due to the high solubility of drugs, conventional purification cannot completely eliminate these contaminants, and the researchers have noted the use of new techniques such as the use of nanotechnology in water treatment, which entails high initial costs. For this reason, one has to look for a solution to these costs; one of the most commonly used methods of using theoretical methods. In this study, an effective method for treating carbon nanotubes in artificial nanotubes using the artificial neural network (ANN) system and the fuzzy-nerve adaptive inference system (ANFIS) has been used. Some structural descriptors Such as polar surface area, LUMO, HOMO and molecular volume were calculated and studied for more influential performance in experimental experiments. In this regard, the control error and the test error were analyzed and the correlation between the effective parameters was determined. Then, a parameter that has a greater impact on water contamination has been identified in the presence and absence of carbon nanotubes. As a result, the polar surface has the greatest impact.
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