Forecasting Wastewater Treatment Results with an ANFIS Intelligent System

Authors

  • M. Mahshidnia Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
  • A. Jafarian Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
Volume: 6 | Issue: 5 | Pages: 1175-1181 | October 2016 | https://doi.org/10.48084/etasr.745

Abstract

Wastewaters caused by industrial and manufacturing production containing pollutants which beside degradation and depletion of natural resources, impose excessive pressure on the Earth's ecosystems and exacerbate water shortages. One of the pollutants is a toxic substance named Malachite Green (MG). Wastewater treatment means to obtain usable water by separating contaminants of contaminated water. One of its main purposes is the recovery and re-use of wastewater for a variety of uses including agriculture and aquaculture, especially in arid and semi-arid countries, as well as providing environmental protection. The main purpose of the present study was to investigate MG separation efficiency by nano composite materials. Poly-aniline was covered on Wheat Husk Ash in order to prepare this type of nano composite. The material was analyzed by X-ray radiation and scanned by an electron microscope. The level of separation depends on the initial value of wheat husk ash and poly-aniline and the initial concentration of MG and the intensity of ultraviolet radiation and radiation time. The effect of these parameters was investigated and optimum operating conditions were obtained. An adaptive neural fuzzy intelligent system was used to forecast the results of the MG separation process. The comparison between the results forecasted by the designed model and experimental data strengthens the validity of the process.

Keywords:

Malachite Green (MG), industrial wastewater treatment, adaptive neural fuzzy intelligent system, ANFIS

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How to Cite

[1]
M. Mahshidnia and A. Jafarian, “Forecasting Wastewater Treatment Results with an ANFIS Intelligent System”, Eng. Technol. Appl. Sci. Res., vol. 6, no. 5, pp. 1175–1181, Oct. 2016.

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