数据协调中冗余性分析新方法

提出一种数据协调中的冗余性分析新方法 .根据尽量删除带有显著误差的变量 ,同时确保协调精度的原则 ,对所有检测出来的带有显著误差的变量 ,通过计算不同阶次的冗余度 ,按照冗余度的大小逐个选择可以删除的变量 .这样既充分利用了数据信息 ,又确保了协调精度 .仿真结果证实了该方法的有效性 .     

整体检验法与鲁棒法结合的数据协调新方法

数据协调技术能为工业过程物料平衡、生产统计、生产计划优化等提供准确、一致的数据,而仪表故障等引起的显著误差会污染数据协调的结果。今提出一种整体检验法与鲁棒法结合的数据协调新方法。该方法首先在整体检验法中基于节点残差信息找出无显著误差的"好"流股和候选显著误差流股;然后给出了基于污染正态分布模型的鲁棒数据协调权重矩阵,其中"好"流股的权重为对应方差的倒数,而候选显著误差流股的权重与数据协调值有关;最后根据权重矩阵进行鲁棒数据协调迭代求解。对蒸汽测量网络的仿真结果表明了该方法能够有效降低显著误差对数据协调结果的影响。     

节点与测量数据组合检测的数据协调及应用

提出一种新型的节点与测量数据组合检测的稳态数据协调方法。该方法通过节点检测法和测量检测法共同检测可能存在显著误差的可疑节点,以及与可疑节点相连的最可疑测量变量,并通过调整量检测法融合领域专家的先验知识判断最可疑测量变量是否存在显著误差,最终实现稳态数据协调和显著误差同步检测。该组合方法融合测量检测和节点检测方法的各自优点且克服各自的缺点。仿真研究与实际应用表明,该组合方法对有多个显著误差的系统也能给出准确的显著误差检测结果,且优于迭代测量检测方法。     

数据校正在精馏塔系中的应用

针对某芳烃联合装置中精馏塔系的双线性数据校正问题，通过引入测度因子函数，将双线性问题转化为线性数据校正问题，进行了稳态数据校正的研究，得出了该类问题的一种解法。仿真实验表明该方法迅速、准确     

化工过程中的数据协调及显著误差检测

测量数据中不可避免地会含有随机性误差及显误差。本在综合大量献的基础上，从数据协调及显误差检测两个方面系统，全面地介绍和比较了数据校正的有关方法，并对非线性及动态数据校正进行了讨论。     

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。     

一种改进的非线性动态数据协调方法

在分析传统非线性动态数据校正方法的基础上,提出了一种改进的非线性动态数据协调方法.该方法利用滑动窗口法有效地降低了动态数据校正的维数,大大提高了系统运算的效率.同时引入快速的3σ法并修改了逻辑判断,在提高检测和剔除显著误差速度的同时,消除了传统数据协调算法的时滞性,动态数据协调的效果和效率都得到了提高.最后,通过对一典型动态系统CSTR的仿真实例说明了该方法的有效性.     

数据样正在精馏塔系中的应用

针对某芳烃联合装置中精馏塔系的双线性数据校正问题，通过此入测度因子函数，将双一问题转化为线性数据校正问题，进行了稳态数据校正的研究，得出了该类问题的一种解法，仿真实验表明该方法迅速、准确。     

炼油厂两级物料平衡的数据校正方法

提出一种分两级进行物料平衡的数据校正策略。第一步,利用测量精度较高的进厂、出厂、库存计量数据进行进销存平衡数据校正;第二步,利用第一步的计算结果作为约束进行装置投入、产出和油品移动层的数据校正,该方法提高了对于显著误差的处理能力。通过一个工程数据的仿真实例验证了该方法的有效性。     

一种新的双线性数据协调方法

在流程工业数据校正中,若涉及多组分物料平衡或能量平衡时,问题则转换为一类特殊的非线性问题,即双线性数据协调.今针对双线性数据协调传统方法的不足,给出了一种新的方法.首先提出了一种消除不可观测变量的方法,通过消除不可观测变量及部分非冗余变量将协调问题降维,并将问题分解为两个子问题;然后针对分解后的子问题,利用微粒群优化算法(PSO)求解.与传统方法相比,该方法在确保高协调精度的基础上具有较好的协调运算效率,并能处理过程中含有不可观测变量的情况.今对一个实例进行了仿真,仿真结果表明该方法的有效性.     

ON THE CHOICE OF REGRESSION VARIABLES IN NONLINEAR DATA RECONCILIATION

Reconciliation of process data constrained by multicomponent material balance equations can be accomplished by a regression calculation in which either total flow rates and compositions (primary variables) or total and component flow rates (secondary variables) are used as the regression variables. It is shown by means of computer simulation experiments that the choice of secondary variables for regression can adversely affect the performance of an associated gross error detection algorithm. The principal effect is an increased tendency of the algorithm to make Type I errors     

含反应节点过程数据校正的扩展独立物流法

Among the techniques developed for bilinear data reconciliation problems, the method based on independent flows is well known in terms of both accuracy and efficiency. However, the independent flow method is not effective when reactor units are present in the process. In this paper, an extended independent flow method is proposed for the data reconciliation of the process with chemical reaction. By the new method, the independent flows finding algorithm is adjusted to avoid the difficulties caused by the reactors in the process, and the reaction constraints are introduced into the objective function of data reconciliation. As a result, the new method can be applied to the process with chemical reaction while retaining high solution accuracy. To test the performance, the new method and the most typical Crowe‘s projection method are used in the data reconciliation of a typical industrial process. The results show that the new method can effectively accomplish the data reconciliation of the muhicomponent process with chemical reaction and gives more accurate estimates than the Crowe‘s method.     

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 .     

CLOSED-LOOP DATA RECONCILIATION FOR THE CONTROL OF A BINARY DISTILLATION COLUMN

Data reconciliation is a procedure that makes use of process models along with process measurements to give more precise and consistent estimates for process variables. Data reconciliation has been traditionally used to provide a more representative set of data to calculate steady-state inventories and process yields. For dynamic systems, the use of data reconciliation is relatively nascent. This article examines the potential use of data reconciliation in closed-loop control as a filter to attenuate the noise in measurements of the controlled variables so that the controllers can access more accurate sets of data. Data reconciliation filters were implemented in simulations of a PID control system for a binary distillation column. Results showed that data reconciliation could efficiently reduce the propagation of measurement noise in control loops, so that the overall performance of the controller is enhanced.     

CLOSED-LOOP DATA RECONCILIATION FOR THE CONTROL OF A BINARY DISTILLATION COLUMN

Data reconciliation is a procedure that makes use of process models along with process measurements to give more precise and consistent estimates for process variables. Data reconciliation has been traditionally used to provide a more representative set of data to calculate steady-state inventories and process yields. For dynamic systems, the use of data reconciliation is relatively nascent. This article examines the potential use of data reconciliation in closed-loop control as a filter to attenuate the noise in measurements of the controlled variables so that the controllers can access more accurate sets of data. Data reconciliation filters were implemented in simulations of a PID control system for a binary distillation column. Results showed that data reconciliation could efficiently reduce the propagation of measurement noise in control loops, so that the overall performance of the controller is enhanced.     

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.     

基于一种新的鲁棒目标函数的化工过程数据校正

给出了一种基于新鲁棒目标函数的数据校正方法,分析了目标函数的性质及其影响函数,表明了该方法对显著误差具有较强的鲁棒性。对一个线性和非线性化工过程进行了仿真研究,并与常用的Huber鲁棒估计法和Fair鲁棒估计法进行了对比分析。     

一种改进的动态数据校正离群值检测法

Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation （DDR） is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor （CSTR） process verifies the effectiveness of the proposed algorithm.