Assessing Emptying Operations with Admitted Air in Single Pipelines Employing Machine Learning Models

Water utilities frequently perform pipeline-emptying operations for maintenance, repair, and operational management. This process involves transient flow conditions with entrapped air. It must be carefully controlled, as the expansion of air pockets can generate sub-atmospheric pressures that may lead to pipeline collapse. The mathematical modelling of emptying processes with air valves has been extensively studied in recent years; however, such approaches typically rely on complex algebraic–differential equation systems. This study advances understanding of this phenomenon by proposing a novel procedure that uses a machine learning model to approximate system behaviour while avoiding fully coupled hydraulic formulations. An experimental facility consisting of a pipeline with an internal diameter of 0.042 m and a total length of 4.6 m was used, in conjunction with a complete regulation valve manoeuvre. The system was first calibrated using experimental data and subsequently employed in Monte Carlo simulations to generate a dataset for training the machine learning model. The results demonstrate that a Rational Quadratic Gaussian Process Regression model can accurately predict the minimum sub-atmospheric pressure, achieving a coefficient of determination greater than 0.999 during validation and testing. The proposed framework is presented as a proof-of-concept and has been validated only for the specific case study analysed. While the results highlight its potential to support planning for emptying operations under varying air-admission conditions and air-pocket sizes, further validation is required before generalising to real-world water distribution systems. For practical implementation, the model must be appropriately trained for each specific installation.