GEP modeling of oxygen transfer efficiency prediction in aeration cascades

Abstract

Artificial intelligence is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. In the past few years, the applications of artificial intelligence methods have attracted the attention of many investigators. Many artificial intelligence methods have been applied in various areas of civil and environmental engineering. The aim of this study is to develop models to estimate oxygen transfer efficiency in nappe, transition and skimming flow regimes over stepped cascades. For this aim, genetic expression programming, a new member of genetic computing techniques, is used. It is similar, but not equivalent to genetic algorithms, nor genetic programming. For nappe, transition and skimming flow regimes, three models are constructed using the experimental data. The test results indicate that for the model equations obtained, the correlation coefficients are very high and the minimum square error values are less than 0.0033. So, genetic expression programming approach can be successfully used in stepped cascades to predict the oxygen transfer efficiency.