煤矿安全监测无线传感器网络节点定位技术

近些年来，煤矿安全事故层出不穷，这严重影响了人民群众的生命财产安全和企业的正常生产，由此做好煤矿的安全检测就显得尤为‘重要。在煤矿安全的监测中，无线传感器网络节点定位技术可以有效的提高煤矿安全生产的检测。本文从煤矿安全监测无线传感器网络基本定位机制、定位问题的表述、定位算法的分析与改进、仿真结果与讨论等四个方面论述了无线传感器网络节点定位技术在煤矿中的应用。     

无线传感器网络的3连通多跳控制集

信息时代背景下,无线传输技术已经在我们日常生活和工作中广泛应用的,对我们的日常工作和生活造成巨大冲击。无线传感器网络多为大规模静态节点随机分布,以多跳自组织方式构建分布网络。其中,网络系统信息通路及路由分配以传感器节点完成,但网络拓扑结构易受节点移动影响发生变化。以下就对无线传感器网络的3连通多跳控制集进行全面分析,旨在通过有效的设计提高网络引用质量。下文以远程温度采集为例进行分析,该系统功耗低,抗干扰能力强、数据传输安全可靠。     

基于无线传感器网络的罗茨风机温度监测节点的设计

根据水泥工业中大量使用的罗茨风机对温度监测的需求,提出了一种基于无线传感器网络的温度监测节点的设计方法并实现了数据的路由,使传感器采集的温度数据能够传输至协调器节点并由上位机进行处理,从而实现了多点温度的在线监测。此设计中把降低节点功耗作为最为关键的性能要求,选择器件时,在保证运行速度的前提下,尽可能选用功耗低的器件来完成无线传感器网络传感器节点的硬件设计和制作,对于传感器输出可能出现的尖峰,则采用了一种改进的数字滤波算法进行处理,因而取得了高可靠性和低能耗两者的平衡。     

改进型无线传感器网络安全分簇路由协议

无线传感器网络路由协议对无线传感器网络起着非常重要的作用,是无线传感器网络的关键技术之一。本文基于经典的LEACH算法提出一种改进型LEACH-R,主要体现在对簇首的考虑上。簇形成阶段引入对加入簇节点的身份验证机制以及对候选簇首广播消息的认证。簇稳定阶段引入簇内节点对簇首广播消息进行认证的机制。计算机仿真对比实验结果表明,LEACH-R协议能够延长网络寿命,使用网络的安全性得到提高,网络性能得到改善。     

基于物联网的粉尘浓度数据采集优化设计

针对无线传感器网络节点数据传输的耗能性和物联网数据的海量性特点，提出一种LEACH协议和线性回归模型相结合的PM2.5数据采集优化检测系统。该系统以PM2.5粉尘浓度传感器为检测节点，通过研究基于物联网感知层的数据融合算法，提出将线性回归模型与簇状网络结构相结合，构建系统函数，实现节点间相关参数的传输，减少了网络的总能耗，延长了网络生命周期。仿真结果表明：所提数据融合策略达到了节能高效、优化采集数据的目的。     

一种高精度湿度计节点的设计

为满足气象监测的需要,设计了一种高精度湿度计节点。采用3个不同类型的湿度传感器及无线处理器等电路构成了高精度湿度测量仪;3个传感器的湿度检测值通过数据融合处理剔除偏离数据,提高了湿度测量的可靠性和准确性。同时,将该仪表作为网络节点,与其他气象传感器一起构建无线传感器网络气象观测系统。实际应用表明：该仪表的检测精度较单传感器有了较大的提高。     

无线传感器网络均匀聚类算法研究

聚类算法可以有效延长无线传感器网络寿命,提出一种均匀聚类组网算法,针对无线传感器网络节点耗能部分,进行详细的比较和研究,使节点的归簇划分更加均匀更节省能量。     

无线传感器网络数据安全融合技术的优化

无线传感器被广泛地应用于多个领域，以无线通信技术为基础的无线网络形式，联合多种自由式节点，组成的具有保密性和安全性的网络结构，旨在为进一步提升无线传感器的网络数据安全。本文首先分析了无线传感器的网络安全机制，其次分析了数据融合中存在安全问题及需求，并进一步分析了无线传感器网络(wireless sensor networks,WSN)安全数据融合的技术实现及优化，供相关人员参考。     

在无线传感器网络中使用动态链路聚合的节点认证

无线传感器网络(W S N)是一个由无线链路组成的网络,其中每个节点互相通信,节点和自组织的动态网络和自组织性使其更容易受到影响.在无线传感器网络中,为了验证通信的有效性,主要的挑战集中在路由的传输速度上.在通信中,不安全的传输链接容易导致数据丢失或泄露.为了解决这个问题,提出了一种高效的基于开放节点的安全路由通信,使...     

无线温湿度传感器网络图示化演示系统软件设计

在已有无线温湿度传感器网络硬件平台的基础上,设计了一种无线传感器网络演示软件。该软件提供了多种图示化接口,包括数据管理、节点曲线、拓扑显示和数据收发状况显示等功能,使用户能更便捷地获取传感数据,在此基础上进行网络性能分析及网络优化,并给出了平台主要功能模块的实现方法。     

Uncertainties in the simulation of catalytic distillation process: A systematic grid refinement study

Computational fluid dynamics (CFD) methods are gradually finding applications in the simulation of distillation processes. In the discretization of the governing differential equations for the mass- and the energy-balance, finite volume method (FVM) is commonly employed. Upwind differencing scheme (UDS) and central differencing scheme (CDS) are most commonly employed in FVM to approximate the values of the variables on the control volume faces. The problems that arise while using these approximation methods are discussed here. These may be overcome by using a bounded linear deferred correction scheme (LDC). When solutions on more than one grid sizes are available, and the order of the numerical scheme is known, the exact solution may be predicted by using Richardson extrapolation technique. If the order is unknown, then the order of the scheme may be determined from grid refinement studies. However, for the determined order of the scheme to be meaningful, sufficiently fine grids must be used for these studies.     

Moving mesh generation for tracking a shock or steep moving front

For tracking a shock or steep moving front in the numerical solution of Partial Differential Algebraic Equations (PDAEs), an accurate spatial discretization method, Weighted Essentially Non-Oscillatory (WENO) scheme, is combined with moving grid techniques so that spacing of moving meshes is smoothed locally and globally. Several monitor functions, as metric criteria of node concentration, are examined. While the fixed grid method (uniform grid size) needs many mesh points to obtain enough solution accuracy, the moving grid method (non-uniform grid size) enhances accuracy even at small mesh numbers but it may be prohibitive owing to the addition of complex and non-linear mesh equations into physical PDAEs. The combination of the WENO scheme (based on an adaptive stencil idea) with the moving grid techniques improves stability and accuracy in the numerical solution over the commonly used moving grid method of central discretization. To locate adequate grid position in the moving mesh method, suitable monitor function according to problems must be selected.     

WENO scheme with static grid adaptation for tracking steep moving fronts

The numerical solution of a hyperbolic or a convection-dominated parabolic partial differential equation is challenging due to the large local gradients that are present in the solution. A possible method to track the sharp fronts that are associated with large gradients is to adapt the grid and this can be done dynamically or statically, i.e. at discrete points of time during the simulation. In this paper, a novel approach that is based on combining the high-order WENO scheme with a static moving grid method is presented. The proposed algorithm is tested on the viscid Burgers’ equation, the linear advection equation and the population balance equation that describes particle growth in emulsion polymerization. Enhancements in the performance are observed in all case studies when compared with the conventional WENO scheme on a uniform grid making it a promising alternative when dealing with similar problems.     

复杂断块油藏构造建模及应用

以地震、测井解释成果、小层对比的井点数据,先断面骨架模型再断层网格模型,进而以井点分层数据及断层属性数据实现复杂断块油藏构造模型的建立.复杂断块油藏与常规油藏最大的不同在于油藏被断层复杂化,因此复杂断块地质建模关在于建立较精准的断层及构造模型,只有这样才能形成储层物性参数赖以分布的地质模型,才能为油藏数值模拟提供较为实际的数据场.本文使用与此思路相适应的Petrel软件实现了某断块储层的精准构造模型的建立,取得了良好的效果.     

动态实时优化策略下的分层式预测控制及其在化工过程中的应用

针对化工过程中那些因存在批处理、含有物料回流环节而很难达到稳态的过程以及一些因扰动的存在而很难精确地操作在一个设定点处的非线性过程,采用常规的稳态优化会产生低效或失效优化解的问题,提出一种动态实时优化策略。即在多层控制结构中的RTO层采用动态优化而非常规的稳态优化,依照过程的优化操作信息在满足过程动态规律和物料、产品市场价格变化的条件下实现生产的经济利润最优,事例仿真结果表明该方法的可行性和有效性。     

Total plant performance evaluation based on big data: Visualization analysis of TE process

The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.     

An adaptive refined grid search strategy for assessing operational flexibility and application on refrigerant selection

A novel adaptive refined grid search strategy is developed for representative characterization of process feasible region boundaries and accurate estimation of its hypervolume. In particular, a linked list data structure adopted from the field of computer science is used to maintain the grid connectivity information. A uniform perturbation scheme is used to refine the search only near the boundaries. The volumetric flexibility index (FI_V) is calculated directly from a summation of feasible hypercubes in the grid, without the need to apply shape reconstruction techniques. The proposed adaptive grid search strategy can capture complex region shapes with reduced sampling costs and without randomness, increasing reproducibility. Operational flexibility is optimized traditionally at a process scale. A case study on refrigerant selection is presented to demonstrate that the developed strategy can be combined within a computer-aided molecular design framework for operational flexibility optimization at the molecular scale.     

Integrating process and power grid models for optimal design and demand response operation of giga-scale green hydrogen

Electrolysis-based hydrogen production can play a significant role in industrial decarbonization, and its economic competitiveness can be promoted by designing demand response operating schemes. Nevertheless, the scale of industrial supply plants may be significantly large (on the order of gigawatts), meaning that electricity prices cannot be treated as an input for scheduling problems, that is, the “price taker” approach. This article presents a framework for the optimization of a large-scale, electricity-powered hydrogen production facility considering its integration with the power grid. Using a computational case study, we present an iterative scheme for integrating the process model with a model for power grid optimization and capacity expansion, taking the popular GenX model as an example.     

Model reference optimal steady-state adaptive computer control of plastics extrusion processes

The first true hierarchical automatic optimal control scheme for plastics extrusion has operated satisfactorily in the laboratory. The process operator is only required to specify desired output rate and die inlet melt temperature set-points. The control variable set-points are continually set by computer in line with an optimal policy pre-programmed from off-line solutions using models for extruder and die behavior. Feedback and feedforward control strategies are used to hold the melt temperature and pressure at die inlet within close tolerances using control actions calculated via process models and applied by a hierarchical cascade controller. The methodology is described by reference to a specific 40 mm diameter screw extruder fitted with a variable geometry annular blow molding die head, processing specific polymers. However, the control procedures are completely general and can be applied to any extruder-die combination.     

Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control

Fuzzy models within the framework of orthonormal basis functions (OBF fuzzy models) have been introduced in previous works and shown to be a very promising approach to the areas of nonlinear system identification and control, since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. As fuzzy models, however, they exhibit the dimensionality problem which is the main drawback to the application of neural networks and fuzzy systems to the modeling and control of large-scale systems. This problem has successfully been dealt with in the literature by means of hierarchical structures composed of submodels connected in cascade. In the present paper a hierarchical fuzzy model within the OBF framework is presented. A data-driven hybrid identification method based on genetic and gradient-based algorithms is described in details. A model-based predictive control scheme is also presented and applied to control of a complex industrial process for ethyl alcohol (ethanol) production.