Usage of artificial intelligence methods in free flowing gated closed conduits for estimation of oxygen transfer efficiency
Oxygen transfer is the process which oxygen is transferred from the gaseous to the liquid phase. The oxygen transfer efficiency depends almost entirely on the amount of surface contact between the air and water. This surface contact can be increased by conduit flow that involves airwater mixture flow. In reality, the physical structure of the airwater interface is complex and still awaits clarification. In the past few years, many artificial intelligence methods have been successfully applied to the solution of complex problems. In this study, models based on Adaptive Network based Fuzzy Inference Systems and Least Squares Support Vector Machines methods were developed to predict oxygen transfer efficiency in free flowing gated closed conduits. Experimental results were compared with the results of these artificial intelligence methods. The best performance was obtained with the Least Squares Support Vector Machine model. Average correlation coefficient (R2) and average root mean square error (RMSE) in the Least Squares Support Vector Machine model were achieved equal to 0.9927 and 0.0073, respectively. Extremely good agreement between the predicted and measured values proves the validity of the Least Squares Support Vector Machine model.
gate conduit oxygen transfer aeration anfis ls-svm