Predictive model of mixed oil length for sequential transportation of multi-product pipeline by combining mechanism and Gaussian mixture regression algorithm
The oil mixing phenomenon occurs during the sequential transportation of the multi-product pipeline, and the accurate prediction of the length of the mixed oil is of great significance for the cutting batch segment. The mechanism model is faced with problems such as low accuracy and complex numerical simulation. In the current global predictive models derived from machine learning algorithms, the multi-mode characteristics of actual operating conditions are ignored, thus the predictive accuracy is limited. The Gaussian mixture regression algorithm cannot accurately characterize the complex nonlinear relationship among variables if it is directly introduced to identify the data mode. Based on the existing mechanism equation and the Gaussian mixture regression algorithm, we develop a local modeling algorithm that integrates the mechanism knowledge. Based on the real product oil pipeline sequential transportation mixed oil length data set, a comparison experiment among different models was carried out, and the results show that the mechanism and local modeling algorithm can effectively characterize the functional relationship of variables, and the predictive accuracy of the new model has obvious advantages.