融合机制与高斯混合回归算法的成品油管道顺序输送混油长度预测模型

Predictive model of mixed oil length for sequential transportation of multi-product pipeline by combining mechanism and Gaussian mixture regression algorithm

成品油管道顺序输送过程中会出现混油现象,精确预测混油长度对油品批次切割具有重要意义,混油长度机制模型存在精度不高,数值计算量庞杂等问题。当前基于机器学习算法构建的全局预测模型未考虑实际工况多模态特性,预测精度受限;直接引入高斯混合回归算法辨识数据模态难以准确表征变量间复杂非线性关系。采用现有机制计算公式与高斯混合回归算法构建融合机制认知的局部建模算法,基于真实成品油管道顺序输送混油长度数据集进行不同模型预测结果对比试验。结果表明,融合机制认知与局部建模算法能有效表征变量间函数关系,新模型预测精度有明显优势。

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.