Objective Oil mixing control is identified as one of the urgent challenges to be addressed for the batching transport pipelines of product oils. Accurate oil mixing information provides essential data for optimizing the operational efficiency of these pipelines. However, monitoring results obtained from on-site sensors often fall short of meeting the field need for access to oil mixing information in advance, primarily due to deviations. Predictions made using soft-sensing methods based on purely data-driven models tend to exhibit low accuracy, as they do not consider the complexities of the pipeline transportation process and the characteristics of instrument measurements.
Methods This paper presents a variational Bayesian Gaussian mixture regression model incorporating physical cognition, developed from an analysis of the pipeline transportation process and the monitoring process of measurement instruments along batching transport pipelines. Additionally, it proposes a soft-sensing method for oil mixing information of these pipelines, by introducing a novel one-dimensional oil mixing concentration evolution model. Based on the operational parameters of pipeline transportation acquired by physical sensors mounted at stations and yards, high-precision “soft” sensors are developed to characterize the variation patterns of oil mixing information in batch pipelining. They are designed primarily to position oil mixing interfaces, predict the density measurements of trailing oils, and predict the density distributions at oil mixing interfaces, ultimately enabling the accurate prediction of oil mixing information.
Results The proposed soft-sensing technique demonstrated improved applicability. The predicted arrival times of oil mixing interfaces at stations exhibited an error reduction of 76.2％, compared with the method based on sensor measurements. The predicted density measurements for trailing oils showed a 41.4％ fall in Root Mean Square Error (RMSE) compared with the purely data-driven soft-sensing method. The curves for predicated density distributions at oil mixing interfaces remained below 0.9 kg/m³. Furthermore, the computation time for these predictions was less than 30 s.
Conclusion The soft-sensing method for oil mixing information in batch transport pipelines facilitates efficient predictions by integrating physical cognition with data. This approach provides on-site operators with more accurate information to identify the batch status in pipeline transportation and optimize the batch management of oils. Consequently, this technique serves as a technical support system for smart logistics in batch pipelining.
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