GEP modeling of oxygen transfer efficiency prediction in aeration cascades
作者： Ahmet Baylar , Mehmet Unsal , Fahri Ozkan
DOI: 10.1007/S12205-011-1282-X
关键词: Oxygen transfer 、 Genetic expression programming 、 Flow (mathematics) 、 Square error 、 Mathematical optimization 、 Engineering 、 Genetic programming 、 Artificial intelligence 、 Applications of artificial intelligence 、 Machine learning
摘要: Artificial intelligence is the area of computer science focusing on creating machines that can engage behaviors humans consider intelligent. In past few years, applications artificial methods have attracted attention many investigators. Many been applied in various areas civil and environmental engineering. The aim this study to develop models estimate oxygen transfer efficiency nappe, transition skimming flow regimes over stepped cascades. For aim, genetic expression programming, a new member computing techniques, used. It similar, but not equivalent algorithms, nor programming. regimes, three are constructed using experimental data. test results indicate for model equations obtained, correlation coefficients very high minimum square error values less than 0.0033. So, programming approach be successfully used cascades predict efficiency.
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