多显著误差检测法的性能比较及改进

通过加入环境节点约束方程对多显著误差的同步识别并同步补偿法 (SEGE)进行改进研究。在多显著误差检测中的广义似然比法、同步识别并同步补偿法和顺序识别并同步补偿法等几种方法的原理对比基础上 ,考虑过程外部进料或出料流股中显著误差幅度与位于同一节点的其它显著误差幅度相近 ,容易被其它显著误差抵消的情况 ,利用这几种方法和改进的SEGE法对检测性能进行比较分析。仿真结果表明 ,改进后的SEGE法具有更好的显著误差检测性能。     

利用计算机图像处理系统测量污泥沉降比

活性污泥法是废水处理过程中应用最广泛的一种方法。污泥沉降比是污水处理过程中的主要分析项目之一,传统污泥沉降比的测量是人工操作,误差较大。利用计算机图像处理技术测量污泥沉降比可有效地避免上述问题。作者介绍了计算机图像处理系统的构成及设计原理和方法。该系统主要包括图像采集、图像预处理、图像处理和图像识别等四大模块,主要涉及图像比例变换、平移变换、彩色图像二值化等内容。还对图像处理和识别的基本理论和技术进行了研究和分析。该检测系统如果能应用于实际,可以解决人工检测时繁琐、耗时,以及读数误差大等问题,同时也可为污水处理过程的自动化控制提供一种实时检测手段。     

基于有限元离散的不规则管道几何边界红外瞬态检测识别

建立了带有不规则腐蚀内边界的管道二维瞬态传热模型,基于有限元法和共轭梯度法的导热反问题求解方法对管道内边界识别问题进行了研究,比较分析了管道外表面稳瞬态温度对内边界变化敏感程度的差异性,指出内边界瞬态检测识别比稳态检测识别更具优越性并利用数值试验进行了验证。同时,考察了测温误差、初始假设等因素对管道内边界瞬态检测识别结果的影响。在较大测温误差的情况下,采用瞬态检测条件从不同初始假设值出发都能准确地反演识别出管道内边界腐蚀后的几何形状,从而证明了算法的有效性和稳定性,表明了红外瞬态检测定量识别管道内边界的可行性。     

主元空间中的故障分离与识别方法

主元分析 (PCA)作为数据驱动的一种统计建模方法,在化工产品质量控制与故障诊断方面得到广泛研究和应用.在故障重构技术的基础上,研究了基于T²统计量的故障分离和识别问题,分别获得了主元空间中故障可分离和识别的理论条件.以双效蒸发过程为例,对该生产过程中的10种不同故障进行仿真监测分析,证实了所获理论结果的有效性.     

基于低阶特征值的结构分散化损伤识别与整体状态评估

针对大型复杂结构状态评估问题,提出了基于低阶特征值的结构分散化损伤识别与整体状态评估方法。首先通过有限元法把实际结构离散成用基本参数表示的分析模型,然后采用主从自由度法把它划分为识别部分和非识别部分,对识别部分定义误差函数并建立状态评估目标函数,推导局部损伤识别的计算公式;在此基础上提出分散化损伤识别的概念,形成服役结构整体状态评估方法。最后通过数值模拟,对识别算法性能进行了详细的分析研究,通过整体平均识别误差指标显示了所提方法的有效性和准确性。     

一种面向管道堵塞不均衡样本集的主动学习方法

针对城市排水管道堵塞检测识别过程中有标签的样本数量较少,人工标注管道数据样本成本高昂,以及管道堵塞数据集中存在明显的类别不均衡问题,提出基于主动学习的方法以解决上述问题.同时,将极限随机树作为基分类器,对未标注样本集进行分类识别;样本查询策略选择将分类熵和余弦相似度相结合的样本采样策略.该方法使得模型在主动学习的过程中...     

边界形状的红外识别算法

基于表面测温的边界形状识别是导热反问题的重要研究内容,同时也是红外无损检测技术从定性到定量发展的重要理论基础。对于边界识别类导热反问题,由于需要在迭代过程中不断改变不规则的求解区域的形状,所以其计算复杂性较大,计算时间也相对比较长,直接影响到实际算法的应用。本文基于试件整体对传热的宏观影响规律提出了基于有效热导率评估的边界识别方法。该方法将不规则求解区域上的边界识别问题转化为一个规则求解区域上的有效热导率的识别问题,从而大大降低了边界形状迭代求解的复杂性和计算所需时间。数值算例证明了算法的有效性。边界的初始假设可以忽略。算法没有明显放大温度测量的误差。     

重载货车轴箱橡胶减振垫对称度检具设计

轴箱橡胶减振垫在铁路重载货车运行过程中起到定位、减振和吸收冲击能量的作用。减振垫对称度直接影响其能否顺利装配和功能实现。本文分析产品结构特点,根据对称度检测原理,将立体空间对称度检测等效转换为平面检测。可调节的定位组件设计,可消除铁路重载货车零部件本身较大的尺寸误差对对称度检测结果的影响。该检具结构简单、操作方便、检测效率、准确性高,适合对批量产品连续检测。等效转换的设计思路,为简化此类立体空间尺寸检测提供一种新的设计理念。     

催化裂化反应动力学参数识别与优化

以十二集总动力学模型为基础，讨论了催化裂化反应的参数识别思想，应用参数识别方法，对轻烷烃和轻燃料油催化裂化反应网络中的反应速率常数和时变失活函数进行了计算，并对模型计算值与实验值作了比较，二者吻合较为满意，在此基础上，研究了基于集总模型的优于问题用所建立的优化方法，得出了优化参数，提高了催化裂化装置的效率。     

基于DMD哈达玛变换近红外光谱仪的汽油辛烷值检测

应用近红外光谱分析检测技术建立一种汽油研究法辛烷值的快速定量分析方法。收集来自不同地区共100个汽油样品,应用化学计量学方法建立近红外光谱原始数据信息与研究法辛烷值之间的定量分析模型,结果表明:对原始光谱进行归一化处理后,采用偏最小二乘回归建立数学模型,其校正集与预测集相关系数分别为0.9300和0.9322,校正集均方根误差与预测集均方根误差分别为0.6700和0.6577,表明模型准确可靠,可应用于汽油辛烷值的快速检测。     

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.     

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.     

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.     

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.     

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     

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.     

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。     

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。     

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 .