Abstract
This article presents the results of the study of the heat transfer in a heat ex-changer where the working fluid is the crude oil prepared for desalination, and the thermic agent is the re-circulating heavy gasoline fraction. Firstly, the Reynolds numbers have been computed using the temperatures and flow rates of the fluids as input variables. Then, general regression neural network and multi-layer perceptron were used for the outlet temperatures estimation using the inlet temperatures and the Reynolds numbers as input variables. The best models on the training dataset were obtained utilizing a multilayer perceptron with one hidden layer, while the best performance on the validation dataset was obtained using a multilayer perceptron network with two hidden layers.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
