Abstract
This article presents the procedure undertaken to discover the most suitable model for predicting the outlet pressure of a horizontally oriented pipe with high-viscosity two-phase flow. In order to achieve this, a collection of artificial neural networks (ANN) with diverse architectures was developed, utilizing experimental data for training and validation purposes. The data used in this study were obtained from experiments carried out in a 54-meter-long pipeline, where a mixture of air and glycerin was flowing. The various ANN were constructed based on three different combinations of input variables, namely, the air flow rate (Qa), glycerin flow rate (Qg), and pressure measurements at specific spatial points. Subsequently, a statistical analysis was performed, followed by a comparison of the models' performance across different scenarios.
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