Data reconciliation and optimal operation of a catalytic naphtha reformer

The naphtha reforming process converts low-octane gasoline blending components to high-octane components for use in high-performance gasoline fuels. The reformer also has an important function as the producer of hydrogen to the refinery hydrotreaters. A process model based on a unit model structure, is used for estimation of the process condition using data reconciliation. Measurements are classified as redundant or non-redundant and the model variables are classified as observable, barely observable or unobservable. The computed uncertainty of the measured and unmeasured variables shows that even if a variable is observable it may have a very large uncertainty and may thereby be practically unobservable. The process condition at 21 data points, sampled from two years of operation, was reconciled and used to optimize the process operation. There are large seasonal variations in the reformer product price and two operational cases are studied. In case 1, the product price is high and throughput is maximized with respect to process and product quality constraints. In case 2, the product price is low and the throughput is minimized with respect to a low constraint on the hydrogen production. Based on the characteristics of the optimal operation, a “self optimizing” control structure is suggested for each of the two operational cases.     

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.     

Decision models for designing and planning private communication networks

We consider the recently developed reconfigurable digital data networks consisting of T1/T3 circuits and Digital Crossconnect Systems (DCSs). A DCS is a device to patch base channels electronically from one T1/T3 circuit to another with a negligible queuing delay at the connecting node. We present new decision models for the design and circuit leasing policies of such digital backbone networks. Our model takes advantage of the special capabilities of the DCS technology and is likely to result in remarkable economic gains for the private network users. The formulation and analyses presented here simultaneously address the following problems: physical link and capacity selection, logical network configuration and channel assignment, and traffic routing on the logical network. The problem formulation results in a large-scale non-linear mixed integer program, and we propose an efficient solution methodology employing Lagrangean relaxation and subgradient optimization. Several numerical results illustrate the utility of our approach for these complex problems. We show that the economies of scale built into the tariff structure of these digital networks can be successfully exploited, and that the inherent flexibility of DCSs leads to logical networks that are dramatically different from their underlying physical topologies.     

电信网络管理的数据仓库解决方案

数据仓库技术是近几年迅速发展起来的一种技术,国际上许多电信运营企业都积极关注于数据仓库技术在电信运营部门和网络操作上的应用。本文对数据仓库技术在电信管理网(TMN)中的应用作了一些探讨,并提出了一种具体的应用方案。     

Data management in executive information systems

Data management is important in developing and maintaining executive information systems (EISs). An EIS can fail due to the lack of an adequate data infrastructure for this `data intensive' application. From the literature, discussions with developers, consulting experiences, and eight case studies, a set of key data management issues were identified. Three – data security, ownership, and standards – were further studied, using survey data collected from 85 organizations. Canonical correlation analysis was used to investigate the relationship between these issues and potentially related variables. The breadth and depth of information provided were found to be correlated with the difficulty of the issues. The degree of support from key individuals was also found to be correlated with the level of difficulty.     

Dynamically adaptive networks for integrating optimal pressure management and self-cleaning controls

This paper investigates the problem of integrating optimal pressure management and self-cleaning controls in dynamically adaptive water distribution networks. We review existing single-objective valve placement and control problems for minimizing average zone pressure (AZP) and maximizing self-cleaning capacity (SCC). Since AZP and SCC are conflicting objectives, we formulate a bi-objective design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. We approximate Pareto fronts using the weighted sum scalarization method, which uses a previously developed convex heuristic to solve the sequence of parametrized single-objective problems. The resulting Pareto fronts suggest that significant improvements in SCC can be achieved for minimal trade-offs in AZP performance. Moreover, we demonstrate that a hierarchical design strategy is capable of yielding good quality solutions to both objectives. This hierarchical design considers pressure control valves first placed for the primary AZP objective, followed by automatic flushing valves placed to augment SCC conditions. In addition, we investigate an adaptive control scheme for dynamically transitioning between AZP and SCC controls. We demonstrate these control challenges on case networks with both interconnected and branched topology.     

Data aggregation in sensor networks: Balancing communication and delay costs

In a sensor network the sensors, or nodes, obtain data and have to communicate these data to a central node. Because sensors are battery powered they are highly energy constrained. Data aggregation can be used to combine data of several sensors into a single message, thus reducing sensor communication costs at the expense of message delays. Thus, the main problem of data aggregation is to balance the communication and delay costs.     

Reducing energy usage in drive storage clusters through intelligent allocation of incoming commands

Although significant research has been undertaken to reduce high level energy consumption in a data centre, there has been very little focus on reducing storage drive energy consumption via the intelligent allocation of workload commands at the file system level. This paper presents a method for optimising drive energy consumption within a custom built storage cluster containing multiple drives, using multi-objective goal attainment optimization. Significantly, the model developed was based on actual power consumption values (from current/voltage sensors on the drives themselves), which is rare in this field.The results showed that command energy savings of up to 87% (17% overall energy) could be made by optimising the allocation of incoming commands for execution to drives within a storage cluster for different workloads. More significantly, the transparency of the method meant that it showed exactly how such savings could be made and on which drives. It also highlighted that whilst it is well known that solid state drives use less energy than traditional hard disk drives, the difference is not consistent for different sizes of data transfers. It is far larger for small data transfers (less than or equal to 4 kB) and our algorithm utilised this.Significantly, it highlights how much larger energy savings can be made through using the optimisation results to show which drives can be safely put into a low power state without affecting storage cluster performance.     

Investigation of decision making process: a hypermedia approach

Research in decision making has concentrated mainly on the decision outcome (choice) rather than on the process of the decision making. This pattern is primarily due to the lack of an acceptable unobtrusive tool for investigation of the decision process. Further, the lack of a consistent decision aid in empirical works has hindered the comparison of results. The purpose of this paper is to present a flexible and adaptable computer-based system for empirical examination of information processing. The main thrust of the proposed system is to provide guidelines to the restrictive/channeling approach to system design. This system can be easily modified to suit different experimental research requirements. It should support more realistic decision tasks, which often are criticized in decision experiments. Furthermore, availability of this system should encourage more research work on the process of decision making. The nature of this support system is linked to the literature, and specifications and components of the system are provided.     

基于数据仓库的失业保险决策支持系统的研究与实现

文章论述了在失业保险信息系统基础上建立基于数据仓库的决策支持系统的有关问题。包括数据仓库和多维分析模型的建立,决策支持系统的体系结构及其实现的关键技术。     

A comparison study on a chemical reactor modelling with a physical model and PLRBF networks

The paper presents the modelling practice and considerations for a multivariable process with a first-principle model and three redial basis function (RBF) networks. The process is a laboratory-scaled three-input three-output chemical reactor rig. The RBF networks used are standard RBF networks, pseudo-linear RBF (PLRBF) networks and adaptive PLRBF networks. The first-principle model, the network structures and training algorithms are briefly reviewed. Real data collection with the design of the excitation signal is described. The four models are evaluated by multi-step-ahead prediction errors and the comparison is made. The methods and considerations provide useful experience for deriving data-driven models for industrial processes.     

Business intelligence approach to supporting strategy-making of ISP service management

The recent deregulation of telecommunication industry by the Taiwanese government has brought about the acute competition for Internet Service Providers (ISP). Taiwan’s ISP industry is characterized by the heavy pressure for raising revenue after hefty capital investments of last decade and the lack of knowledge to develop competitive strategies. To attract subscribers, all ISP dealers are making an all-out effort to improve their service management. This study proposes a Business Intelligence process for ISP dealers in Taiwan to assist management in developing effective service management strategies. We explore the customers’ usage characteristics and preference knowledge through applying the attribute-oriented induction (AOI) method on IP traffic data of users. Using the self-organizing map (SOM) method, we are able to divide customers into clusters with different usage behavior patterns. We then apply RFM modeling to calibrate customers’ value of each cluster, which will enable the management to develop direct and effective marketing strategies. For network resource management, this research mines the facility utilization over various administrative districts of the region, which could assist management in planning for effective network facilities investment. With actual data from one major ISP, we develop a BI decision support system with visual presentation, which is well received by its management staff.     

数据挖掘与人才素质评价

根据数据挖掘技术的概念、数据的组织方式和系统的设计方法,利用k-中心算法对人才素质进行分类,借助判定树归纳分类法对人才素质进行分析和评价,提出了混合数据挖掘算法,提高人才素质评价的高效性和科学性。     

基于数据仓库的OLAP在决策支持系统中的应用研究

介绍DSS的数据组织、数据仓库的特征和结构模型、OLAP的特征和体系结构,及其实现决策分析的过程等方面的基本知识,以某大企业的基于数据仓库的销售分析系统为实例作分析.     

数据仓库和数据挖掘技术在ERP中的应用

数据仓库和数据挖掘是近几年迅速发展起来的技术,主要用于构建企业的决策支持系统。文章根据数据仓库和数据挖掘技术的这个特点,并针对传统ERP系统在决策方面的不足,提出把数据仓库和数据挖掘应用到ERP中去,以数据仓库作为企业底层的数据源,再配合以各种数据挖掘技术,以提高ERP中的决策支持功能。     

数据仓库和数据挖掘在高校决策支持中的应用研究

运用高校学生成绩、学籍等相关数据,创建高校学生成绩分析的数据仓库,运用ID3算法实现基于学生成绩等级的决策树挖掘模型的构建,由决策树提取分类规则,并利用Analysis Services工具进行挖掘验证.     

Improving the management of microfinance institutions by using credit scoring models based on Statistical Learning techniques

A wide range of supervised classification algorithms have been successfully applied for credit scoring in non-microfinance environments according to recent literature. However, credit scoring in the microfinance industry is a relatively recent application, and current research is based, to the best of our knowledge, on classical statistical methods. This lack is surprising since the implementation of credit scoring based on supervised classification algorithms should contribute towards the efficiency of microfinance institutions, thereby improving their competitiveness in an increasingly constrained environment. This paper explores an extensive list of Statistical Learning techniques as microfinance credit scoring tools from an empirical viewpoint. A data set of microcredits belonging to a Peruvian Microfinance Institution is considered, and the following models are applied to decide between default and non-default credits: linear and quadratic discriminant analysis, logistic regression, multilayer perceptron, support vector machines, classification trees, and ensemble methods based on bagging and boosting algorithm. The obtained results suggest the use of a multilayer perceptron trained in the R statistical system with a second order algorithm. Moreover, our findings show that, with the implementation of this MLP-based model, the MFI? misclassification costs could be reduced to 13.7% with respect to the application of other classic models.     

数据挖掘及隐私保护在医学中的应用

数据挖掘对医学数据分析具有非常重要的意义,首先阐述了数据挖掘在医学中的应用,然后改进了一种非常有效的保护医学数据的方法,该方法不会影响数据分析的结果,同时它又满足医学数据对安全性和准确性的需求。     

Application-aware integration of data collection and power management in wireless sensor networks

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into sensor databases that reside at (more powerful) servers. A natural tradeoff exists between resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. In order to adapt to these variations while at the same time optimizing the energy consumption of sensors, this paper proposes three different models and corresponding data collection protocols. We analyze all three models with a Markov state machine formulation, and either derive closed forms for the operation point of the data collection application or suggest algorithms for estimating this operating point to achieve a minimal energy consumption. We observe that the operating point depends on environmental characteristics and application quality requirements, which the proposed algorithms aim to accommodate. Our experimental results show significant energy savings compared to the naive approach to data collection.     

Towards the fast and robust optimal design of wireless body area networks

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.