Abstract—Industrial wastewater is a major source of environment pollution and synthetic dyes are the most undesirable pollutant of the water. Wastewater treatment processes have been widely discussed in literature to find economic and safe solutions for water quality problem. In this paper, soft computing methodologies are used to model and optimize the dye removal process. Combination of Artificial Neural Networks and Genetics Algorithm results in a hybrid approach that contains advantages of each method. An Artificial Neural Network is trained using an experimental data set to approximate the relation between initial dye concentration, adsorbent, pH, and contact time as inputs and dye removal percentage as output. Genetic algorithm approach is employed to suggest the best combination of input elements to maximizing dye removal for each initial dye concentration produced by factory. This combination decreases the costs and time of the process and has economical profits for large factories.
Index Terms—Dye removal process, Artificial Neural Networks, Genetic Algorithms, optimization, wastewater treatment.
S. Attarzadeh is with the Department of Computer, Meymeh Baranch, Islamic Azad University, Meymeh, Iran (e-mail: sh_attarzadeh@iaumeymeh.ac.ir).
F. Jalalinia is with the Department of Computer, Meymeh Baranch, Islamic Azad University, Meymeh, Iran (e-mail: farnoosh.jalalinia@iaumeymeh.ac.ir).
Cite: Shahrzad Attarzadeh and Farnoosh Jalalinia, "Improving the Efficiency of Wastewater Treatment Process by Soft Computational Methods," International Journal of Modeling and Optimization vol. 1, no. 3, pp. 180-184, 2011.
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