An Industrial Scale Simulation Method for Predicting the Contamination Evolution in Long-Distance Multiproduct Pipelines

Summary Petroleum products are frequently transported successively through the same multiproduct pipeline. Due to turbulent and convective diffusion mass transfer, two adjacent oils will mix with each other, forming a mixed-oil segment. Accurate and rapid prediction of mixed-oil concentration is crucial for the precise management of mixed-oil segments. Conventional 1D modeling methods exhibit shortcomings in accurately representing the asymmetric distribution characteristics of mixed-oil concentration curves, and high-dimensional models are not practically applied due to their prohibitive computational time costs. Building on the 1D model framework, this paper proposes a “1+” mechanism model by considering the convective mass transfer behavior between the turbulent core region and the laminar boundary layer, and new governing equations and corresponding numerical solution methods are also introduced. Simulation experiments affirm the ability of the new model to characterize the asymmetric distribution features of mixed-oil concentration curves, along with its high computational efficiency in engineering applications. This is demonstrated by the computational time of approximately 30 seconds for simulating a pipeline of 300 km in length (Δx = 10 m, Δt = 1 second, CPU: i5-12500H, RAM: 16 GB). When applied to pipelines in industrial scenarios, the new model is shown to accurately predict the distribution of mixed-oil concentration curves. The research findings are significantly beneficial in assisting field personnel to gain advanced insights into the mixed-oil concentration distribution at the station, enabling timely and well-informed strategies for handling mixed-oil segment, thereby enhancing the operational efficiency of multiproduct pipelines.

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An Industrial Scale Simulation Method for Predicting the Contamination Evolution in Long-Distance Multiproduct Pipelines

Cited by 3 publications

References 43 publications

Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length

Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length

Numerical Investigation of Contaminant Oil in the Batch Transportation of Crude Oil and Diesel Oil with Different Out-Station Temperatures

A 1+ Mechanism Model for Predicting the Mixed-Oil Concentration in Multiproduct Pipelines

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