They must be treated differently in order to be deleted as many as possible to ensure the reconciliation precision . mges that can be deleted are selected one by one according to their redundacy degrees 根据尽量删除带有显著误差的变量，同时确保协调精度的原则，对所有检测出来的带有显著误差的变量，通过计算其不同阶次的冗余度，按照冗余度的大小逐个选择可以删除的变量。
The data reconciliation procedure based on the improved model tends to make the measurements having gross errors get more modification than the others . therefore , the new data reconciliation model is much more robust than the traditional 改进后的数据协调模型只会对含有显著误差的测量值给予较大的协调量，而使得显著误差对其他测量值协调结果的影响较小，具有较高的鲁棒性。
Data rectification is a modern technique to improve the quality of measurement data , and its main purpose is to eliminate the random errors and gross errors included in original data by making use of applied statistics , identification , optimization and other techniques 数据校正的目的就是综合应用统计、辨识和优化技术，对实测数据进行调整，消除数据中包含的随机误差和显著误差，去掉明显错误的或不可靠的测量数据，从而提高测量数据的质量。
Application results show the proposed method can deal with mges effectively and provide useful suggestion on instruments checking . thus the number of instruments that must be checked is minimized , the maintenance effort and expense are reduced . 4 叙述了显著误差选择删除算法在炼油工业的实际应用情况，说明了该算法对带有显著误差变量的处理的有效性，并为检修工作提供了合理建议，使得必须检修的传感器数目最少，减小了检修的工作量和检修费用。
Data reconciliation is one of the key elements of cims ( computer integrated manufacture system ) in process industry . by using the redundancy in measurements and process models , it can eliminate mesaurements with gross error ( mges ) . reduce random measurment errors and estimate unmeasured variables by using statistical methods 数据协调技术是流程工业cims的关键，它利用信息的冗余性，结合各种统计分析方法和生产过程机理，剔除原始数据中的显著误差，降低随机误差的影响，并设法估计出未测变量，保证了cims信息源的真实性。
For the methods based on constraint residual , they can be used to only tell which node is imbalance but cannot identify where the gross error is . in order to avoid these problems , this thesis proposed a new test method . the new method combines an f - statistic with constraint residual statistic to detect gross errors in steady state processes 这种方法即避免了基于测量残差的检测方法会将显著误差分散到各个测量值中去的缺陷；又避免了基于约束残差的检测方法只能对节点的平衡性进行判断，而无法确定显著误差的具体发生位置的缺陷。
Base on the existing techniques of data reconciliation and gross error detection , this thesis presented some new problems from real industrial processes and proposed the corresponding schemes . the main contributions include : 1 . review the development and the state - of - the - art of the techniques in data reconciliation and gross error detection 2 通过对以往数据协调和显著误差检测方法的分析研究，同时将理论研究同生产过程中的实际情况相结合，本文对以前的一些数据校正技术在实际情况中碰到的若干具体问题进行了剖析，并提出来相应的解决方案。
In the last section of this article , many correlations , including biasi correlation , sudo correlation , cise correlation , and 1995 look - up table , are compared with our experimental data . based on these results , evaluations on these correlations are provided , and because of lack of considering on up - stream memory effect , the correlations which mainly predict chf value based on local parameters , have considerable error 针对目前文献中存在的大量的chf经验公式以及图表，本文通过实验数据与biasi公式， sudo公式， cise公式， groenveled的95look - uptable以及hewitt & kearsey关联式计算值的对比，进行了对这些公式的评价，发现由于大部分的公式其临界的判断主要基于局部参数，没有考虑到上游记忆效应，计算结果存在显著误差。
