Hybrid Neural Network and Genetic Algorithm method to Optimize Wastewater Treatment Process
Soft computing methodologies can handle the complexity of modeling and optimizing natural processes. Combination of these methods results in hybrid approaches that contain advantages of each one. In this paper, artificial neural network and genetic algorithm approaches are used to model and optimize the process of dye removal, which is an important part of waste water treatment. An experimental data set is used to approximate the relation between initial dye concentration, adsorbent, pH, and contact time as inputs and dye removal percentage as output through artificial neural network. 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. Results show that proposed input combination leads to 92％ average dye removal with less economic cost
Artificial Neural Networks Genetic Algorithms optimization dye removal process wastewater treatment
Shahrzad Attarzadeh Farnoosh Jalalinia
Department of Computer Engineering Islamic Azad University of Meymeh Meymeh, Iran
国际会议
重庆
英文
142-145
2011-01-21（万方平台首次上网日期，不代表论文的发表时间）