Data reconciliation and optimal management of hydrogen networks in a petrol refinery

This paper describes the main problems associated to the management of hydrogen networks in petrol refineries and presents an approach to deal with them with the aim of operating the installation in the most profitable way. In particular, the problems of data reconciliation, economic optimization and interaction with the underlying basic control system are reviewed. The paper provides also a proposal for the implementation of the system and illustrates the approach with results obtained using real data from an industrial site.     

Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process

Online optimization is more and more used in the chemical industry to run a process near its optimum operating condition by providing real-time computed optimal set-points to the distributed control system. Process measurements are necessary for these applications to determine the actual state of the process and to increase the accuracy of the model with parameter estimation techniques. However, these measurements usually contain random as well as gross errors which have to be identified and eliminated before the measurements are used for online optimization. In this contribution, a data reconciliation approach was integrated into an online optimization framework for the ammonia hydrogen sulfide circulation scrubbing, a common industrial coke-oven-gas purification process. We used a rigorous rate-based model to describe this reactive absorption and desorption process. To increase the accuracy of the model, we estimated several process parameters using a sequential parameter estimation approach. Data reconciliation was performed based on simple component balances to achieve model-consistent data and to identify measurement biases. The model was then validated online on a pilot plant by connecting the estimation package through the process control system. Based on the online measured data, operating cost minimization was carried out and the computed optimal set-points realized real-time. A satisfactory agreement between measured data and optimization was achieved.     

Data reconciliation — Progress and challenges

Measurements in a chemical process are subject to errors, both random and systematic, so that the laws of conservation of mass and energy are not obeyed. In order to record the performance of the process, these measurements are adjusted in order that they conform to the conservation laws and any other constraints imposed upon them. This procedure is known as data reconciliation. Advances in the theory and application of data reconciliation are reviewed and current problems are highlighted. In addition to defining the basic problem, we discuss the detection of gross errors in data and of pre-adjustment of data, finding departures from steady state, estimation of the variance structure of the data, observability of unmeasured quantities and redundancy of measurements.     

多传感器数据最优融合

利用可信区间估计的概念,检验传感器测量值偏差,描述传感器间的支持关系,确定传感器数据的取舍,提出两种最优融合算法,仿真给果表明两种最优融合方法是有效的,其精度高于简单平均法。     

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and electronic commerce to social networking. Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains.     

Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MW_th fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.     

Robust and reliable estimation via Unscented Recursive Nonlinear Dynamic Data Reconciliation

The quality of process data in a chemical plant significantly affects the performance and benefits gained from activities like performance monitoring, online optimization and control. Since many chemical processes often exhibit nonlinear dynamics, techniques like Extended Kalman Filter (EKF) and Nonlinear Dynamic Data Reconciliation (NDDR) have been developed to improve the data quality. There are various issues that arise with the use of either of these techniques: EKF cannot handle inequality or equality constraints, while the NDDR has high computational cost. Recently a recursive estimation technique for nonlinear dynamic processes has been proposed which combines the merits of EKF and NDDR techniques. This technique, named as Recursive Nonlinear Dynamic Data Reconciliation (RNDDR), provides state and parameter estimates that satisfy bounds and other constraints imposed on them. However, the estimate error covariance matrix in RNDDR is computed in the same manner as in EKF, that is, the effects of both nonlinearity and constraints are neglected in the computation of the estimate error covariance matrix.

A relatively new method known as the Unscented Kalman Filter has been developed for nonlinear processes, in which the statistical properties of the estimates are computed without resorting to linearization of the nonlinear equations. This leads to improved accuracy of the estimates. In this paper, we combine the merits of the Unscented Kalman Filter and the RNDDR to obtain the Unscented Recursive Nonlinear Dynamic Data Reconciliation (URNDDR) technique. This technique addresses all concerns arising due to the presence of nonlinearity and constraints within a recursive estimation framework, resulting in an efficient, accurate and stable method for real-time state and parameter estimation for nonlinear dynamic processes.     

Computation of optimal composite re-parameterizations

Rational re-parameterizations of a polynomial curve that preserve the curve degree and [0,1] parameter domain are characterized by a single degree of freedom. The “optimal” re-parameterization in this family (that comes closest under the L₂ norm to arc-length parameterization) can be identified by solving a quadratic equation, but may exhibit too much residual parametric speed variation for motion control and other applications. Closer approximations to arc-length parameterizations require more flexible re-parameterization functions, such as piecewise-polynomial/rational forms. We show that, for fixed nodes, the optimal piecewise-rational parameterization of the same degree is defined by a simple recursion relation, and we analyze its convergence to the arc-length parameterization. With respect to the new curve parameter, this representation is only of C⁰ continuity, although the smoothness and geometry of the curve are unchanged. A C¹ parameterization can be obtained by using continuity conditions, rather than optimization, to fix certain free parameters, but the objective function is then highly non-linear and does not admit a closed-form optimization. Empirical results from implementations of these methods are presented.     

The influence of social norms and social consciousness on intention reconciliation

Research on resource-bounded agents has established that rational agents need to be able to revise their commitments in light of new opportunities. In the context of collaborative activities, rational agents must be able to reconcile their intentions to do team-related actions with other, conflicting intentions. The SPIRE experimental system allows the process of intention reconciliation in team contexts to be simulated and studied. Initial work with SPIRE examined the impact of environmental factors and agent utility functions on individual and group outcomes in the context of one set of social norms governing collaboration. This paper extends those results by further studying the effect of environmental factors and the agents' level of social consciousness and by comparing the impact of two different types of social norms on agent behavior and outcomes. The results show that the choice of social norms influences the accuracy of the agents' responses to varying environmental factors, as well as the effectiveness of social consciousness and other aspects of agents' utility functions. In experiments using heterogeneous groups of agents, both sets of norms were susceptible to the free-rider effect. However, the gains of the less responsible agents were minimal, suggesting that agent designers would have little incentive to design agents that deviate from the standard level of responsibility to the group.     

清远供电局数据存储优化的思考

数据库系统的性能决定数据库的可用性和生命力。通常数据库系统在业务数据增加后都会存在性能瓶颈,主要涉及数据库服务器、应用程序、操作系统、数据库参数等方面。其中数据库性能调整与优化涉及到多个层面,通过统一规划、系统性调整,可以提高数据库的稳定性和可用性,保障系统高效地运行,解决系统瓶颈。通过建立并实施数据库后台存储过程不断改进的管理机制和设计合理的数据存储过程工作指引,来不断提升整个系统性能,实现对现有信息系统的优化,达到提升业务运营和经营决策效率的目标。     

Enhanced Dynamic Hierarchical Replication and Weighted Scheduling Strategy in Data Grid

The Data Grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to the limitation of available resources in the grid as well as production of large volumes of data, efficient use of the Grid resources becomes an important challenge. Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner. Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available. In this paper two strategies are proposed, first a novel job scheduling strategy called Weighted Scheduling Strategy (WSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in a queue, the location of the required data for the job and the computing capacity of the sites Second, a dynamic data replication strategy, called Enhanced Dynamic Hierarchical Replication (EDHR) that improves file access time. This strategy is an enhanced version of the Dynamic Hierarchical Replication strategy. It uses an economic model for file deletion when there is not enough space for the replica. The economic model is based on the future value of a data file. Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time. So, it is considered in this paper. The proposed strategies are implemented by OptorSim, the European Data Grid simulator. Experiment results show that the proposed strategies achieve better performance by minimizing the data access time and avoiding unnecessary replication.     

振动试验仿真系统中的数据通讯实现

振动试验仿真系统是一套全数字仿真系统,由三台计算机组成,形成一个Client／Sever模式的网络体系。该文在分析了振动试验仿真系统的数据通讯要求后,设计了仿真系统数据通讯方案,采用VB的Winsock控件技术实现了客户机的数据通讯编程。采用C语言设计Winsock API网络通讯程序,在MATLAB环境下编译成M函数,直接嵌入到仿真回路中,实现了MATLAB环境下的Winsock API的无缝链接,从而解决了服务器端的数据通讯问题。     

数据库应用系统性能与数据查询优化

数据库查询的效率是数据库应用系统性能的重要指标，本文好数据查询优化的几种方式，并以实例说明查询优化的过程及其在数据库应用系统中的应用。     

仿真应用中数据获取与管理的途径分析

结合仿真应用中数据获取与管理的目的和特点，提出了在软件开发中采用基于数据库技术的通用数据源进行仿真应用软件设计的思想，从理论上论述了其必然性，并从应用出发，分析了通用数据源的选择及设计情况。     

基于分布式数据库的查询优化技术及应用

分布式数据库系统的分布和冗余使查询处理复杂化,因此分布式查询处理的优化显得尤为重要。本文结合自己实践经验,从查询策略选择的角度讨论了查询优化技巧,重点介绍了基于关系代数等价变换的优化算法和半联接的查询技术及其应用。     

大气环境仿真中数据模型及数据库的设计与实现

大气环境对飞行器有着至关重要的影响,不仅影响着飞行器本身的飞行和作战性能,而且也影响着飞行器操纵人员的决策.所以,大气环境仿真是高逼真度飞行仿真器设计的一个的重要组成部分.该文基于数据场的概念建立了大气环境数据模型,然后根据这个大气环境数据模型设计了一个具有四叉树结构的大气环境数据库,很好地解决了多分辨率的问题.最后将大气环境数据库映射到SEDRIS表示.     

Data quality awareness: a case study for cost optimal association rule mining

The quality of discovered association rules is commonly evaluated by interestingness measures (commonly support and confidence) with the purpose of supplying indicators to the user in the understanding and use of the new discovered knowledge. Low-quality datasets have a very bad impact over the quality of the discovered association rules, and one might legitimately wonder if a so-called “interesting” rule noted LHS→ RHS is meaningful when 30% of the LHS data are not up-to-date anymore, 20% of the RHS data are not accurate, and 15% of the LHS data come from a data source that is well-known for its bad credibility. This paper presents an overview of data quality characterization and management techniques that can be advantageously employed for improving the quality awareness of the knowledge discovery and data mining processes. We propose to integrate data quality indicators for quality aware association rule mining. We propose a cost-based probabilistic model for selecting legitimately interesting rules. Experiments on the challenging KDD-Cup-98 datasets show that variations on data quality have a great impact on the cost and quality of discovered association rules and confirm our approach for the integrated management of data quality indicators into the KDD process that ensure the quality of data mining results.     

面向公共安全数据处理的浪涌模型研究应用

近年来,随着平安城市和智慧城市项目的建设和发展,视频监控系统已经成为公安机关治安管控、打击犯罪、预防应急突发事件的有效手段。随着网络通信技术的迅速发展以及移动智能终端(如智能手机、平板电脑等)的快速普及,智能终端已经普遍携带视频监控、音频、加速传感器等感知设备。部分高端智能终端所能携带的视频设备已经超过部分低端的视频监控设备。智能终端的大量普及使得构建以人为中心的感知与计算网络成为可能。对不同信息空间的信息进行有效融合,可以加强对于公共安全事件的有效感知与检测。针对公共安全事件多源信息的融合问题,提出了数据浪涌模型,并对该模型进行了定义。同时利用该模型对人证合一系统进行了实例验证。开发的人证合一系统已经应用于北京市的多个长途车站与火车站。     

遗传算法及其在数据挖掘中的应用

遗传算法是一种模拟生物进化过程的自适应全局优化算法,是解决现代非线性优化问题的一种重要方法。作为一种全局优化算法,遗传算法很适合于数据挖掘工作。在介绍遗传算法的基本原理和特点的基础上,对遗传算法在数据挖掘中的应用问题作了一些初步的探讨,最后给出一个应用遗传算法进行数据挖掘的具体实例。     

稳态在线数据校正在炼油厂气体分离装置上的应用

文章系统地研究了稳态过程在线数据校正技术,在具体实现中采用均值法进行稳态检测,修正系数法进行误差的侦破、识别,通过两层次变换进行数据分类。开发了稳态过程在线数据校正软件,并将校正后的数据作为输入值用于某炼油厂气体分离系统产品质量的在线预测,应用结果表明,采用校正后的数据作为输入值进行产品质量在线预测比直接用原始数据更稳定、更符合实际情况。