Prediction of Phenol Adsorption by Sawdust from Wastewater Using Intelligent Methods

Subject Areas : environmental management

Mohsen Keshavarz Tork ¹ , Ahad Ghaemi ² , Mansour Shirvani ³

1 - - Msc. in Chemical Engineering Department, University of Science and Technology, Tehran, Iran.
2 - ‌Assistant Professor, Chemical Engineering Department, University of Science and Technology, Tehran, Iran. *(Corresponding Author)
3 - Associate Professor of Environmental Pollutants, University of Science and Technology, Tehran, Iran.

Received: 2015-06-25 Accepted : 2016-07-23 Published : 2019-04-21

Keywords: Sawdust, Absorption of Phenol, Simulation, Intelligent Methods, Optimal Conditions,

Abstract :

  Background and Objective: Phenol presence and its derivatives in water and waste water on human health and the environment is one the major concerns. Because of the toxicity of phenol and also because of the presence of even low concentrations in natural resources, water disinfection and oxidation processes can lead to the formation of additional components. This material is one of the most common organic pollutants in water. In this research, adsorption of phenol from wastewater by sawdust was simulated using intelligent techniques. Method: Intelligent techniques including multi-layer Perceptron, radial basis functions network and support vector regression were used. To design the network structure as well as the training and testing of 125 sets of experimental data is used. Performance evaluation criteria and stop network consists of % AARE and, which is used for all three models. Findings: All models compared results showed that the support vector regression with 0.5132 and 0.979, respectively, for %AARE and  is the best model. All models are better results than the quadratic polynomial model showed. Discussion and Conclusion: Models showed good agreement with experimental data. The optimum conditions for the removal of phenol were 127.6 mg/l of initial phenol concentration, 0.84 g/l of adsorbent dose, natural pH value of 3.62 and 146.9 min of contact time, under these conditions the maximum removal efficiency was 91.23%.

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