Theoretical Study of Relation among Structural Parameter and Water Decontamination Behaviors of some Drugs in Presence of Carbon Nanotube

Authors

Department of Civil Engineering, Mashhad branch, Islamic Azad University, Mashhad Iran

Abstract

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.

Keywords

References

  1. Ihsanullah A.A., Adnan M. Al-Amer, Tahar L., Mohammed J. Al-Marri, Mustafa S. Nasser, Majeda Kh., Muataz Ali Atieh., 2016. Heavy metal removal from aqueous solution by advanced carbon nanotubes: Critical review of adsorption applications. Separation and purification Technology. 157, 141-161
  2. Sayadi Anari A.R., Asadpur M., Sheybani Z., sayadi Anari M.H., 2012. Pharmaceutical Pollution of the eco-system and Its Detrimental Effects on Public Health. JRUMS. 11(3), 269-284
  3. Daniel R. Dietrich, Simon F. Webb, Thomas Petry, 2002. ââ‚‌œHot spot pollutants: pharmaceuticals in the environment. Toxicol Lett. 131(1-2), 1-3.
  4. Sayadi M.H., Trivedy R.K., Pathak R.K., 2010. Pollution of pharmaceuticals in environment Jr. of Industrial Pollution Control. 26 (1), 89-94
  5. Christian G. Daughton, 2014. ââ‚‌œEco-directed sustainable prescribing: feasibility for reducing water contamination by drugsââ‚‌ Science of The Total Environment. 493, 392-404
  6. Fattahi Mehr V., 2017. M.Sc Thesis, "Theoretical study of relation among structure and water decontamination behaviors of some drugs in presence of carbon nanotube", Islamic Azad University of Mashhad, Mashhad, Iran.
  7. Dehdashti B. , Pourzamani H. R., Amin M. M., Mokhtari M., Babaei F., 2017. A Review of the Performance of Carbon Nanotubes in Reducing Environmental Pollution. J Environ Health Sustain Dev. 2(2), 300-11.
  8. Farkhondeh S., 2010. Application of fuzzy logic and neural networks in prediction of clay soil density indexââ‚‌ Thesis M.Sc., Ferdowsi University, Mashhad, Iran.
  9. Menhaj M.B., 2008. Basics of Neural Networks of Computational Intelligenceââ‚‌ Vol. 1, 5 ed, Amir Kabir University of Industrial, Tehran, Iran.
  10. Hushmand M.M., 2011. Estimation of maximum bridge scour depth with the help of Fuzzy-Neural Adaptive Inference Systemââ‚‌ Thesis M.Sc., Sistan and Bluchestan University, zahedan, Iran
  11. Lin C.T., Lee C.S.G., 1996. Neural Fuzzy Systems: A Neuro-fuzzy synergism to intelligent systems, Prentice-Hall Inc., Upper Saddle River, N.J.ââ‚‌ ed (May 1, 1996)
  12. Jeon J., 2007. Fuzzy and Neural Network Models for Analyses of Piles, In partial fulfillment of the Requirements . PhD Thesis of Philosophy in Civil Engineering, North Carolina.
  13. Kia M., "Neural Networks in MATLAB," 3rd ed , Kian Ryan Green Publishing, Tehran 2010.
Journal of Chemical Health Risks

Volume 8, Issue 1
Spring 2018

Files

History

  • Receive Date: 28 November 2017
  • Revise Date: 04 April 2020
  • Accept Date: 29 October 2018

Share

How to cite

Statistics