基于MEA-BP神经网络热油管道预测模型研究

Research on Prediction Model of Hot Oil Pipeline Based on MEA-BP Neural Network

长输热油管道运行过程中,油温的准确预测是管道安全优化生产的前提。针对以往油温预测方法的误差大,推广应用难等问题,提出利用BackPropagation(BP)神经网络和思维进化算法(MindEvolutionary Algorithm,MEA)优化算法,建立MEA-BP油温预测模型。利用相关性算法获得模型输入参数,下载处理SCADA系统实际生产数据,对模型进行训练。将MEA-BP预测模型应用于实际生产,油温预测误差为0.49℃,相比理论公式及其它预测模型,具有泛化性好、预测准确性高等特点。通过研究获得基于大数据分析方法可有效实现长输管道业务需要,为管道大数据平台分析应用,未来智能化控制奠定基础。

During the operation of long-distance hot oil pipelines,the accurate prediction of oil temperature is the prerequisite for safe and optimized production of pipelines.Aiming at the large error of the oil temperature prediction method and the difficulty of popularization and application,the Back Propagation(BP)neural network and the Mind Evolutionary Algorithm(MEA)optimization algorithm was used to establish the MEA-BP oil temperature prediction model.The correlation algorithm was used to obtain the model input parameters,and the actual production data of the SCADA system were downloaded and processed,and the model was trained.The MEA-BP prediction model was applied to actual production,and the oil temperature prediction error was 0.49℃.Compared with the theoretical formula and other prediction models,it has the characteristics of good generalization and high prediction accuracy.The research based on the big data analysis method can effectively meet the long-distance pipeline business needs,and lay the foundation for the analysis and application of the pipeline big data platform and the future intelligent control.