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题名: 钢铁企业蒸汽管网流量测量数据校正方法研究
其他题名: On the Data Rectification Approaches for the Flow Rate Measurements of Steam Pipe Networks in Iron& Steel Plants
作者: 罗先喜
导师: 王宏 ; 苑明哲
分类号: TP393.07
关键词: 蒸汽管网 ; 流量 ; 数据校正 ; 显著误差检测 ; 数据协调
页码: 107页
学位专业: 机械电子工程
学位类别: 博士
答辩日期: 2013-02-04
授予单位: 中国科学院沈阳自动化研究所
作者部门: 信息服务与智能控制技术研究室
中文摘要: 针对钢铁企业蒸汽管网流量测量数据的质量问题,本文提出了蒸汽管网流量数据校正实施方案,重点研究了蒸汽管网异常数据监控、蒸汽管网流量建模、流量数据显著误差检测和数据协调等数据校正的方法。本文的主要工作与贡献是: (1)为了监控蒸汽管网的异常测量数据和状态变化,本文采用统计过程控制,提出了确定单变量和多变量控制极限的方法。 针对单变量的控制极限,考虑钢铁企业蒸汽状态数据不服从正态分布,通过历史数据建立该变量的经验分布,依据“3σ”原则确定控制极限对应的概率,其在经验分布函数对应的位置即为控制极限。由此确定的控制极限是非对称的,比经典“3σ”法确定的控制极限更符合生产实际情况。 针对多变量的控制极限,考虑状态数据具有多工况的实际情况,先按表现出多个峰值的单变量经验分布对样本数据分组,再用PCA(Principal Components Analysis, PCA)的方法建立每组样本的统计模型,分别确定每种状态下Hotelling’s T2及平方预测误差SPE(Squared Prediction Error, SPE)报警极限的方法。仿真结果证实:与不区分工况的多变量统计过程监控相比,本方法对蒸汽管网变量和状态监控具有较高的灵敏度,且误报警率较低。 (2)针对流量测量数据不完整的问题,改进或建立蒸汽管网流量的计量、认证和计算模型,用以估算未测量流量或判定为不准确的流量测量数据,使管网流量数据趋于完整。 改进了蒸汽流量测量模型,该模型采用IF97公式确定蒸汽的密度、流出系数和可膨胀系数,并采用迭代计算方法获得流量计量值,从测量原理上减小了流量计量误差,可用于校正流量仪表的测量数据。 利用生产环节影响蒸汽产生量或使用量的已测量变量和参数,按照机理、统计和经验相结合的方法,针对稳定蒸汽源、余热回收和蒸汽消耗三种设备或工序,分别建立了蒸汽流量的认证模型。该模型用于估算未测量的流量值或校正已测量流量值的固定偏差。 提出了利用管道节点处压力、温度及管网特性数据,根据蒸气在管网中传输的水力与热力学方程计算各管段蒸汽流量的计算模型,并用搜索的方法计算存在中间节点的蒸汽管网流量。计算模型用于管网中没有流量测量管段的流量估计。 (3)针对流量数据精度低的问题,提出了基于TBM(Transferable Blief Model, TBM)的合成显著误差检测方法。 该方法用TBM证据理论对MT(Measurement Test, MT)和NT(Nodal Test, NT)检测结果进行决策,解决了单独使用MT或NT无法准确定位显著误差的问题,实例证实该方法能准确地判断和定位显著误差。 (4)针对流量数据一致性差的问题,提出在蒸汽管网流量测量中实施数据协调的方法,改进了间接确定加权系数矩阵的方法。 提出用Taylor 展开式的方法确定管网泄漏与蒸汽损耗,并修正约束方程的方法。提出采用改进的间接法确定加权系矩阵Q。实例证实计入损耗、采用间接法确定Q能显著改善数据协调效果。 (5)提出了钢铁企业蒸汽管网流量数据校正的完整方案。设计开发了蒸汽管网流量数据校正软件模块,并在大型联合钢铁企业能源管理系统EMS(Energy Management System, EMS)中应用。 通过本文的研究,实现了钢铁企业蒸汽管网异常数据与状态自动监控,解决了流量数据的完整性、精确性和一致性差的问题,为企业蒸汽管网优化控制、能源成本核算提供了条件,对稳定钢铁企业生产、降低吨钢综合能耗和提高经济效益产生积极的作用。
英文摘要: For the data quality problem of being incomplete, inconsistent and inaccurate, the approaches to rectify the flow rate data are proposed. In the paper, the problem of abnormal varialbles monitoring, the flow rate measurement modelling, gross errors detecting and data reconciliation are researched. The main works and contributions of the paper are as follows. (1) In order to monitor the abnormal data and state shifting, the technology of Statistic Process Control (SPC) is applied and the methods to determine the control limits for single variable and muti-variables are proposed. For single varialbe not following normal distribution, the history data are adopted to setup the empirical distribution. By the principle of “3σ”, the corresponding probabilities determined by the limits are confirmed. With these probabilities, the control limits can be located in the empirical distribution respectively. The control limits determined by this way are asymmetrical, and they are more conformable to the actual cases than ones determined by classical methods of “3σ”. For the multi-variables, the samples are grouped by the empirical distribution with multi-peaks. For each group, the principal components analysis (PCA) is applied to determine the control limit of Hotelling’s T2 and squared prediction error (SPE). The simulation results testify that the control limits make the monitor more sensitive and the false alarm rate are lower. (2) For the problem of data being incomplete, the measurement model, authentication model and computation model of the steam flow rates are innovated or established to estimate the unmeasured data or determined as unaccurate data. By these models, the flow rate data of the steam pipe network tends to be complete. The steam flow rate measurement model is innovated. The model directly uses IF97 formula to determine the steam density, discharge coefficient and expansion coefficient, and applies the iterative calculation algorithm. The model is improved on the base of measurement principle to reduce the measurement errors. The model can be used to rectify the flow rate measurement data. Applying the parameters and measured variables in the production section, and combined with the mechanism, statistical modeling approaches and experiences, the authentication models for the flow rates data are established for stable steam sources, heat recycling steam sources and steam consumption sections. The models are used to estimate unmeasured flow rate values or rectify the instruments whose readings are suspected containing fixed deviation. The calculation model for steam flow rates is proposed according to the hydraulic and thermodynamic equations. By the model, applying the pressures and temperatures of the pipe joints, and the properties of the pipes the steam flow rates can be calculated. In addition, a searching algorithm is proposed to solve the problem of the networks with unmeasured intermediate nodes. Calculation model can be used to estimate the flow rates of the main pipes without flow meters. (3) For the problem of data being inaccurate, the synthesis methods of gross data detection based on TBM (Transferable Blief Model, TBM) are proposed. In the method, the theory of TBM is applied to decide the gross errors by combining the results of Measurement Test (MT) and Nodal Test (NT), which can not correctly show the exact positions where the gross errors locate respectively. The example confirms the methods can decide and locate the gross errors correctly. (4) For the problem of data being inconsistent, the method of applying data reconciliation the measured flow rate data in steam pipe network is proposed. Especially, the indirect method to determine the weighted matrix is innovated. Taylor expansion is applied to identify the steam loss and leakage, and the constrained equation is modified. The innovated indirect method to determine the weighted matrix Q are put forward as well. The example proves that data reconciliation considering the loss and leakage, using the innovated indirect method to determine the Q can improve the data reconciliation effect. (5) The complete proposal to rectify the flow rate data of the steam networks in Iron& Steel plants is proposed in the paper. The steam pipe network flow rate data rectification software module has been developed and applied in the EMS of a large scale iron&steel corporation. Through the present research, the automatic monitoring the abnormal data and the state shift is realized, and the data problem of being incomplete, inaccurate and inconsistent is solved. The condition for optimization control and energy cost calculation is satisfied. It’s important to stabilize the production, reduce the comprehensive energy consumption of per ton steel, and raise economic benefits of Iron& Steel plants.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/10746
Appears in Collections:信息服务与智能控制技术研究室_学位论文

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Recommended Citation:
罗先喜.钢铁企业蒸汽管网流量测量数据校正方法研究.[博士学位论文].中国科学院沈阳自动化研究所.2013
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