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Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation. (2022)
Conference Proceeding
ADEGBOYE, M.A., KARNIK, A., FUNG, W.-K. and PRABHU, R. 2022. Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation. In Proceedings of the 9th International conference on soft computing and machine intelligence 2022 (ISCMI 2022), 26-27 November 2022, Toronto, Candada. Piscataway: IEEE [online], pages 129-134. Available from: https://doi.org/10.1109/iscmi56532.2022.10068436

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely p... Read More about Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation..

An improved computational fluid dynamics (CFD) model for predicting hydrate deposition rate and wall shear stress in offshore gas-dominated pipeline. (2022)
Journal Article
UMUTEME, O.M., ISLAM, S.Z., HOSSAIN, M. and KARNIK, A. 2022. An improved computational fluid dynamics (CFD) model for predicting hydrate deposition rate and wall shear stress in offshore gas-dominated pipeline. Journal of natural gas science and engineering [online], 107, article 104800. Available from: https://doi.org/10.1016/j.jngse.2022.104800

Gas hydrates in pipelines is still a flow assurance problem in the oil and gas industry, and requires a proactive hydrate plugging risk predicting model. As an active area of research, this work has developed a 3D 10m length by 0.0204m diameter horiz... Read More about An improved computational fluid dynamics (CFD) model for predicting hydrate deposition rate and wall shear stress in offshore gas-dominated pipeline..