Data-driven modeling approaches for pressure drop prediction in a multi-phase flow system
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Abstract
Accurate prediction of pressure drops in multi-phase flow systems is essential for optimizing processes in industries such as oil and gas, where operational efficiency and safety depend on reliable modeling. Traditional models often need help with the complexities of multi-phase flow dynamics, resulting in high relative errors, particularly under varying flow regimes. In this study, we simulate a comprehensive multiphase flow experimental data collected from the lab. This study presents innovative methods for accurately modeling pressure drops in multi-phase flow systems. It also studies the complicated dynamics of multi-phase flows, which are flows with more than one phase at the same time. It does this by using two different data-driven models, nonlinear ARX and Hammerstein-Wiener, instead of neural networks (NNs), so that the models don’t get too good at fitting environments with lots of changes and little data. Our research applies system identification approaches to the intricacies of this domain, providing new insights into choosing the best appropriate modeling strategy for multi phase flow systems, taking into account their distinct properties. The experimental results show that the nonlinear Hammerstein-Wiener and ARX models were much better than other methods, with fitting accuracy rates of 81.12% for the Hammerstein-Wiener model and 86.52% for the ARX model. This study helps the creation of more advanced control algorithms by providing a reliable way to guess when the pressure drops and showing how to choose a model that fits the properties of the multi-phase flow. These findings contribute to enhanced pressure management and optimization strategies, setting a foundation for future studies on real-time flow control and broader industrial applications.
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