A MIXED INTEGER LINEAR PROGRAMMING-BASED TECHNIQUE FOR THE ESTIMATION OF MULTIPLE GROSS ERRORS IN PROCESS MEASUREMENTS

This paper presents a new strategy for detecting, identifying, and estimating gross errors (measurement biases and leaks) in linear steady state processes. The MILP-based gross error detection and identification model is constructed aiming at identifying the minimum number of gross errors and their sizes. One significant advantage of the method is that the detection, identification, and estimation of gross errors can be performed simultaneously without using any test statistics.     

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。     

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.     

显著误差等效集及其求解方法研究

在过程工业中,显著误差检测与估计是成功实现过程数据校正的基础。无论是稳态过程还是动态过程显著误差检测,目前都没有找到高可靠性的方法,由此引发了对显著误差可识别问题的讨论。在分析显著误差可识别结果和等效集理论的基础上,对显著误差可识别问题进行了研究,提出了一种基于整数规划的显著误差最小势等效集的求解策略。理论和仿真说明了所讨论问题的存在性和所提方法的有效性。     

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     

化工过程中基于小波滤波法的过失误差侦破研究

小波滤波能有效降低化工过程测量数据的随机误差,但却无法识别测量数据中是否存在过失误差。为此,本文通过总结大量小波滤波数据校正实例中校正值、分解层数与过失误差之间存在的关系,提出了三者之间的关系公式,并根据此公式侦破识别过失误差。对Aspen Dynamic模拟产生的测量数据的校正结果表明,文中提出的公式准确的反映出了校正值、分解层数和过失误差的关系,并且利用该公式能够有效地侦破和识别过失误差。     

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.     

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.     

Dynamic bayesian approach to gross error detection and compensation with application toward an oil sands process

This paper is concerned with developing an online algorithm for detecting and estimating systematic errors (gross errors) in mass and energy balances from measurement data. This method has its application in diagnosing problems in an oil sands process. Conventional techniques for detecting gross errors presently exist for offline application. The proposed online method entitled Dynamic Bayesian Gross Error Detection (DBGED) is a dynamic Bayesian analogue of traditional gross error detection, and can be considered as a type of Switching Kalman Filter. As such, related topics such as Kalman Filtering, observability and Dynamic Bayesian Inference are discussed. In addition to detecting gross errors, the DBGED also estimates detected gross error magnitudes in real time (as an augmented state variable) so that future measurements can be corrected. When the estimate converges to yield satisfactory prediction errors, gross error estimation is stopped and instruments are corrected with a constant gross error correction term. DBGED performance is demonstrated through a simulation example and an example of an industrial application.     

A new approach to the identification of gross errors in chemical engineering measurements

This paper contributes to the problem of identification of gross errors in chemical engineering measurements. The methods hitherto suggested enable a set of quantities, suspicious as a possible source of gross errors, to be found. However, the set of suspicious quantities may be quite extensive in real problems. Described in the present paper is a new method based on the information of measurement credibility. The meaning of this term is the maximum value of a gross error that may occur in individual measurements. This method complements those developed previously and makes possible reducing the number of measured quantities which have to be checked out during the final phase of gross error elimination.     

DETECTION OF GROSS ERRORS IN NONLINEARLY CONSTRAINED DATA: A CASE STUDY

The MIMT algorithm previously developed for gross error detection in linearly constrained systems was extended to nonlinear systems. The algorithm was tested by means of computer simulation using data from an industrial grinding circuit. The overall performance of the algorithm on the nonlinear system was found to be comparable to that exhibited on a linear system of approximately the same size. The algorithm correctly detected approximately 80% of all systematic errors in the data and achieved an average reduction in total error of more than 60%. The detection rate for the more significant (gross) systematic errors was approximately 90%. These results represent the first detailed performance evaluation of a gross error detection algorithm applied to a nonlinear system of industrial significance.     

测量仪表过失误差的单结点识别方法

提出了一种新的过失误差识别方法 ,即结合仪表的可靠度、精度等级等信息 ,利用单结点的约束残差进行过失误差识别 .给出仿真实例 ,通过与其他几种常用的误差识别方法对比对这一方法进行了评价 .     

Rectification of process measurement data in the presence of gross errors

A systematic strategy is developed for the location of the source and the rectification of gross, biased measurement errors in a chemical process. The proposed strategy proceeds in three levels: (a) A structural analysis of the balance equations identifies subsets of balances with measurements which are suspected to possess gross errors. (b) A sequential analysis of the balance equations with suspect measurements further reduces the size of the problem. Statistical criteria are used in this step. (c) Finally, a sequential analysis of the suspect measurements appearing in the reduced set of balances leads to the identification of the source of the gross errors.The proposed strategy: (i) reduces the size of the data reconciliation problem significantly, even for large-scale chemical processes, (ii) is computationally simple and (iii) it conforms with the general process of variable monitoring in a chemical plant.Numerical examples are presented to clarify the elements of the procedure involved and demonstrate their value and effectiveness in dealing with realistic situations.     

Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor

In a previous study, a nonlinear dynamic data reconciliation procedure (NDDR) based on the particle swarm optimization (PSO) method was developed and validated in line and in real time with actual industrial data obtained for an industrial polypropylene reactor (Prata et al., 2009, Prata et al., 2008b). The procedure is modified to allow for robust implementation of the NDDR problem with simultaneous detection of gross errors and estimation of model parameters. The negative effects of the less frequent gross errors are eliminated with the implementation of the Welsch robust estimator, avoiding the computation of biased estimates and implementation of iterative procedures for detection and removal of gross errors. The performance of the proposed procedure was tested in line and in real time in an industrial bulk propylene polymerization process. A phenomenological model of the real process, based on the detailed mass and energy balances and constituted by a set of algebraic-differential equations, was implemented and used for interpretation of the actual plant behavior. The resulting nonlinear dynamic optimization problem was solved iteratively on a moving time window, in order to capture the current process behavior and allow for dynamic adaptation of model parameters. Results indicate that the proposed procedure, based on the combination of the PSO method and the robust Welsch estimator, can be implemented in real time in real industrial environments, allowing for the simultaneous detection of gross errors and estimation of process states and model parameters, leading to more robust and reproducible numerical performance.     

化工过程数据校正方法的探讨

简要地介绍了数据校正中的基本概念;然后从数据协调、显著误差检测、数据校正三个方面较系统地、全面地总结了数据校正方法的研究进展和成就;最后探讨了这一领域中值得进一步研究的问题和可能的发展方向。     

A review of techniques for instrumentation design and upgrade in process plants

This article is a review of the area of optimal instrumentation location in process plants for both grassroots designs and upgrades. Emphasis is put in methods for monitoring purposes and based on the cost minimization model. These methods are mostly related to data reconciliation paradigms and include especial consideration to the detection of gross errors. Connections between this model and other models based on other objective functions and goals are analyzed. Finally, the emerging field of sensor design for efficient process fault detection is briefly covered.     

带显著误差集的改进MILP数据协调方法

Mixed integer linear programming (MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applied in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to generate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objective function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.     

基于粗差判别的参数优化自适应加权最小二乘支持向量机在PX氧化过程参数估计中的应用

针对软测量建模过程中数据可能存在粗大误差以及粗差数据对模型的性能产生的影响,提出了一种基于粗差判别的自适应加权最小二乘支持向量机回归方法(WLS-SVM)。 该方法首先根据3δ法则检测出样本中的显著误差并加以剔除,然后根据样本误差的大小自适应地调整权值,使得非显著误差对模型性能的影响大大降低。另外,由于最小二乘支持向量机的正则化参数和核宽度参数对模型的拟合精度和泛化能力有较大的影响,一般依靠经验和试算的方法进行估计,耗时且不准确,本文将模型的参数作为进化算法的优化问题,应用自适应免疫算法(AIGA)对参数进行优化选择。仿真实验表明,该方法对非线性系统的建模具有很好的效果。同时,将该方法应用于工业PX氧化建模过程中动力学参数的估计中,结果表明,基于粗差判别的参数优化自适应最小二乘支持向量机预测精度高,取得了较好的效果。     

基于时间冗余的显著测量误差处理方法

提出基于时间冗余的不可删显著误差处理方法。对于该类特殊变量，采用线性回归的方法，根据历史数据进行预测，进而代替实际到量值进行协调运算。大大降低显著误差对协调数据的污染，确保协调物度。通过实例数据仿真计算证实该方法的有效性。