Modeling of Removal of Heavy Metals from Industrial Wastewater Using Ash and Rice Husk with Fuzzy Logic

The expansion of industries in the world, the limitation of resources and the increasing consumption of water have led researchers to pay more attention to wastewater treatment than in the past. Wastewater treatment is done in order to stabilize the produced organic matter, reuse water and solid materials resulting from wastewater treatment, and also to be able to discharge the waste water into the environment and protect the environment.

This research has been investigated with the method of biological treatment information modeling using fuzzy logic. One of the cost-effective methods for purifying the sanitary wastewater of the refinery is modeling using the fuzzy logic method. Fuzzy inference systems are a popular computing framework based on the concept of fuzzy sets, if-then rules, and fuzzy reasoning. This category of systems has a successful application in the fields of automatic control, data classification, decision analysis, expert systems, time series prediction, robotics and pattern recognition. In this research, MATLAB R2012 software has been used for fuzzy logic modeling by Mamdani method. Information obtained from the tables in the article "H.A. Hegazi. 2013".

By examining all the diagrams and models, we found that the modeling done is reliable and can be used to obtain the results of other experiments without conducting experiments. Also, the best operating conditions can be called rice husk adsorbent concentrations of 50, 60, 60, 60, 60, for the removal of Fe, Pb, Cd, Cu and Ni metals. For the ash absorbent, the absorbent concentrations of 60, 60, 60, 50, and 60, respectively, were called optimal absorbent concentrations for metals. Also, among the two absorbents, rice husk is a better absorbent. The accuracy of the model was reached around 95% and proved the reliability of the model. It can also be concluded that ash and rice husk worked very well as natural absorbents and the removal efficiency was up to 90%.