Abstract
A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
Highlights
In the process industry, many heat exchangers-condensers are used for refrigeration
The results showed that artificial neural networks (ANN) with a configuration of 7 input neurons, 7 hidden neurons, and 1 output neuron presented an excellent agreement, with the root mean squared error root mean square error (RMSE) = 3.6588 ∙ 10−7, average absolute percentage error mean absolute percentage error (MAPE) = 0.1295 %, and high determination coefficient of R2 = 0.99996
The Levenberg–Marquardt algorithm was considered as the learning algorithm because it might have a smaller maximum relative absolute error (RAEmax) compared to the other learning algorithms
Summary
Many heat exchangers-condensers are used for refrigeration. The accumulation of unwanted deposits on the heat exchanger surfaces is generally called fouling. Fouling is a major problem in condensers and heat exchangers. The existence of these deposits resists the transfer of heat, and decreases its effectiveness. All industrial circuits cooled with water are affected by the phenomenon of biological fouling consisting of the growth of biofilms and the settlement of several types of living organisms. Eguía et al.[1] attempted to show the growth of biological fouling inside the cooling tubes of an exchanger-condenser, keeping the temperature of the wall constant and using seawater. Casanueva et al.[3] studied the development, growth, and control of marine biological fouling in condenser heat exchangers in order to assess the impact of new regulations on the particular plant and site. Qureshi et al.[4] studied the effect of fouling on the thermo-hydrau-
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