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    张正江, 邵之江, 陈曦, 钱积新. 化工过程系统的多层数据调和框架[J]. 华东理工大学学报(自然科学版), 2009, (3): 462-467.
    引用本文: 张正江, 邵之江, 陈曦, 钱积新. 化工过程系统的多层数据调和框架[J]. 华东理工大学学报(自然科学版), 2009, (3): 462-467.
    A Framework for Multi-layer Data Reconciliation in Chemical Process Systems[J]. Journal of East China University of Science and Technology, 2009, (3): 462-467.
    Citation: A Framework for Multi-layer Data Reconciliation in Chemical Process Systems[J]. Journal of East China University of Science and Technology, 2009, (3): 462-467.

    化工过程系统的多层数据调和框架

    A Framework for Multi-layer Data Reconciliation in Chemical Process Systems

    • 摘要: 过程数据的可靠性和一致性在化工过程系统中是非常重要的。过程的测量数据一般含有随机误差和显著误差,必须应用数据调和与显著误差检测技术来减小过程测量数据的误差。测量数据具有不同的类型。针对不同类型的测量数据的数据调和问题,提出了一种多层数据调和框架。此框架可以根据不同的测量数据选择不同层次的机理模型,进行数据调和。不同类型的测量数据的数据调和问题分为三层:第一层是基于总物料平衡层,第二层是基于物料和组分平衡层,第三层是基于严格机理模型层。在此数据调和框架中,应用加权最小二乘目标函数作为调和目标,采用鲁棒高效的显著误差检测方法。基于此框架,对于化工过程系统的测量信息的不同,均可选择合理的模型有效地对测量数据进行数据调和。联塔系统和空气分离系统的数值模拟试验说明了此框架的灵活性和有效性。

       

      Abstract: Reliable and consistent process data are very important in chemical process system. As a result of random and possibly gross errors, data reconciliation and gross error detection are needed to minimize the measurement errors. As the measured variables are usually different types in chemical process systems, the problem of data reconciliation for different types of measured variables is waited to be solved. In this paper,we present a framework for multi-layer data reconciliation in process systems, which can adjust the model to be used for data reconciliation. Data reconciliation for different types of measurements is classified into three layers in this framework. The first layer is based on the total mass balances. The second layer is based on not only the total but also the component mass balances. The last layer is based on the rigorous model. In each layer, a weighted least square objective function will be used. The strategy for gross error detection in the framework will be proposed, which is robust and efficient. With the framework, whatever types of measurements in the chemical process system present, it can adjust the model to be fit for data reconciliation. The effectiveness of this framework is demonstrated on the simulation of distillation column system and air separation system. Result shows that the framework is flexible and useful.

       

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