Development of a Universal Pressure Drop Model in Pipelines Using Group Method of Data Handling-Type Neural Networks Model

This paper presents a universal pressure drop model in pipelines using the group method of data handling (GMDH)-type neural networks technique. The model has been generated and validated under three phase flow conditions. As it is quite known in production engineering that estimating pressure drop under different angles of inclination is of a massive value for design purposes. The new correlation was made simple for the purpose of eliminating the tedious and yet the inaccurate and cumbersome conventional methods such as empirical correlations and mechanistic methods. In this paper, GMDH-type neural networks technique has been utilized as a powerful modeling tool to establish the complex relationship between the most relevant input parameters and the pressure drop in pipeline systems under wide range of angles of inclination. The performance of the model has been evaluated against the best commonly available empirical correlations and mechanistic models in the literature. Statistical and graphical tools were also utilized to show the significance of the generated model. The new developed model reduced the curse of dimensionality in terms of the low number of input parameters that have been utilized as compared to the existing models.