基于参数估计的动态系统过失误差侦破与识别

The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a powerful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in efficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the presence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be estimated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of decision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.     

Bayesian method for simultaneous gross error detection and data reconciliation

Process measurements collected from daily industrial plant operations are essential for process monitoring, control, and optimization. However, those measurements are generally corrupted by errors, which include gross errors and random errors. Conventionally, those two types of errors were addressed separately by gross error detection and data reconciliation. Solving the simultaneous gross error detection and data reconciliation problem using the hierarchical Bayesian inference technique is focused. The proposed approach solves the following problems in a unified framework. First, it detects which measurements contain gross errors. Second, the magnitudes of the gross errors are estimated. Third, the covariance matrix of the random errors is estimated. Finally, data reconciliation is performed using the maximum a posteriori estimation. The proposed algorithm is applicable to both linear and nonlinear systems. For nonlinear case, the algorithm does not involve any linearization or approximation steps. Numerical case studies are provided to demonstrate the effectiveness of the proposed method. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3232–3248, 2015     

双线性约束过程的鲁棒自适应数据校正方法

采用污染正态分布模型进行数据校正,相对于传统的最小二乘方法具有较好的鲁棒性,然而参数估计结果的精确度依赖于误差发生概率和方差比值两个先验模型参数的选取,这在实际生产中难以获得,采用固定的方差比也不符合实际,因而其应用受到了限制。本文针对污染正态分布模型的不足,提出了一种鲁棒自适应误差分布模型,该模型具有与标准正态分布模型相似的分布密度函数,不同之处在于采用鲁棒自适应可变权重因子调节误差方差,通过放大显著误差方差,减小其对参数估计的影响。将该模型用于双线性约束数据校正问题,并采用Lagrange乘子法得到鲁棒自适应最小二乘分析解,同时还对鲁棒自适应数据校正中的测量数据相关性问题进行了研究。仿真结果证实了该方法的有效性。     

The maximum-power test for gross errors in the original constraints in data reconciliation

The detection of gross errors in the reconciliation of process measurement data is an important step in removing their distorting effects on the corrected data. Tests of maximum power (MP), based on the normal distribution, are known for the detection of gross errors in the measurements and for the constraints, but only for those remaining after the removal of unmeasured flows. Here, the MP tests are derived for the original constraints, which allows the direct detection of gross errors in species balances around individual process units. It is shown that the square of the MP test statistic is precisely equal to the reduction in the weighted sum of squares of the adjustments which results from the deletion of that constraint. The test is illustrated with two examples.     

一种混杂系统数据校正新方法

对于既包含连续生产过程又包含离散事件的混杂系统,尤其是对于带有生产方案切换的实际生产过程,通过在物料平衡模型中引入随机调度方程,从而构造出包含随机调度方程参数变量θ的新型协调模型,然后利用一种不确定模型的协调算法对此模型进行求解,最后,通过仿真研究证实了该方法的有效性和鲁棒性.     

Gross error modeling and detection in plant linear dynamic reconciliation

This paper presents a method to identify and estimate gross errors in plant linear dynamic data reconciliation. An integral dynamic data reconciliation method presented in a previous paper (Bagajewicz and Jiang, 1997) is extended to allow multiple gross error estimation. The dynamic integral measurement test is extended to identify hold-up measurements as suspects of gross error. A series of theorems are used to show the equivalencies of gross errors and to discuss the issue of exact identification. A serial approach for gross error identification and estimation is then presented. Gross errors are identified without the need for measurement elimination. The strategy is capable of effectively identifying a large number of gross errors.     

基于贝叶斯网络的数据校正方法

精确的物料平衡模型是数据校正技术的基础，但实际上，频繁发生的调度事件动态地改变着物料的流向，目前的研究中往往容易被忽视，为此，从工程实践的角度出发提出一种新的处理方法.依据专家经验选择贝叶斯网络关键变量，利用大量的历史数据学习出贝叶斯网络，继而利用贝叶斯网络的诊断功能实现对调度事件的实时跟踪，最后建立精简模型, 增强了数据校正的可行性.仿真研究证实了该方法的有效性.     

稳态系统的过失误差识别

数据校正包括数据协调和过失误差侦破与识别两部分，其中过失误差的侦破与识别一直是数据校正的重点和难点所在。针对系统偏差型的过失误差，研究了稳态系统中含有多个这失误差情况下的过失误差侦破与识别问题。提出了系统的过失误差可识别性的概念，分析了稳态系统的特性，指出了系统过失误差可识别的条件，并提出了过失误差的参数估计识别方法。计算实例表明，此方法可以准确地识别出系统所含的多个过失误差，具有很重要的理论意义。