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中文摘要：
实时优化要求准确的过程模型与过程数据,然而通过仪表测量获取过程数据不仅存在随机误差而且有时还存在过失误差,直接影响实时优化的准确性。根据流程工业过程系统的特点,提出了基于大规模严格机理模型的数据校正,构造了随机误差与过失误差的隶属函数并根据它们的隶属度大小来诊断过失误差。当测量信息丰富时,可同时对进料的流量、组分及压力等多种测量数据同时进行数据校正。将基于大规模严格机理模型的数据校正应用于大规模乙烯分离系统进行仿真模拟,测量值仅存在随机误差时,经过数据校正后,满足严格机理模型。测量值引入过失误差时,可准确地诊断出过失误差。模拟计算结果证实了基于大规模严格机理模型的数据校正与过失误差诊断方法的有效性。
英文摘要：
Reliable process model and process data are required in the real-time optimization. As a result of random and possible gross errors existing in the measured process data, the real-time optimization is not efficient and accurate. According to the characteristics of process system, data reconciliation based on large scale rigorous model was proposed in this paper. The membership functions of gross error and random error were constructed and used to detect the gross error. When there is plenty of measurement information, the reconciliation is available for the process measurement of flow rate, components, pressure, etc. The proposed method for data reconciliation and gross error detection was used in the ethylene separation process system. If only random errors exist in the process data, the reconciled data satisfy the process model. If both random and gross errors exist in the process data, the gross errors can be detected accurately, and the process model is also satisfied after data reconciliation. The effectiveness of the method proposed was demonstrated by the simulation calculation.
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