Mixed integer linear programming(MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material,energy,and other balance constrains.But the efficiency will decrease significantly when this method is applied in a large-scale problem because there are too many binary variables involved.In this article,an improved method is proposed in order to generate gross error candidates with reliability factors before data rectification.Candidates are used in the MILP objective function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates.Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.
材料流动的管理和控制形成在石油化学工业生产执行系统(MES ) 的核心。在 MES 的申请的瓶颈是与生产过程匹配材料流动模型的能力。一个动态材料流动模型在石油化学工业在生产过程的材料流动特征的分析以后在这篇论文被建议。主要材料流动事件被描述，包括运动，存储，变，再循环，并且材料的消除。材料流动事件的空间、时间的人物被描述，并且材料流动模型被构造。此处介绍的动态材料流动模型是在 MES 的另外的分系统的基础。另外，它是有在 MES 的最少的规模的分系统。材料流动的动态建模的方法在 SinoMES 模型的发展被使用了。从测量，存储，运动，和材料的留下的平衡的方面管理与坦克和设备有关的全部流动信息帮助石油化学工厂。作为结果，它由错误消除和数据和解匹配生产进程。另外，它便于申请模块的集成进 MES 并且在未来应用保证 SinoMES 的潜在的发展。
