The quality of online measured operational data is usually not satisfactory for the performance monitoring of olefin plants, due to the low accuracy of measuring instrument. Data reconciliation (DR) is data preprocessing method which can improve the accuracy of measured data through process modeling, optimization, and can also be applied for gross error detection together with the statistical test method. In this study, DR and gross error detection are used to an industrial olefin plant. DR simulation results showed that the relative root means squared errors of the primary flow measurements, namely the inlet mass flow rate of feed to F-101, F-102, and F-104 are reduced by 40.3%, 13.4%, and 21.4%. After that, the authors applied Global Test (GT) and serial elimination strategies to gross error detection and validation in the measurement of existed olefin plant and was successfully detected and validated by onsite inspection of the olefin plant.

由于测量仪器的精度低，在线测量的操作数据的质量通常对于烯烃工厂的性能监控而言并不令人满意。数据对账（DR）是一种数据预处理方法，可以通过过程建模，优化来提高测量数据的准确性，也可以与统计测试方法一起用于粗差检测。在这项研究中，DR和总错误检测用于工业烯烃工厂。DR模拟结果表明，一次流量测量的相对均方根误差，即F-101，F-102和F-104进料的入口质量流速降低了40.3％，13.4％和21.4％ 。之后，