神经网络预测催化超临界水氧化废水效果的研究

在间歇式反应器中进行催化超临界水氧化DDNP废水试验,考察催化剂浓度、反应温度、压力、停留时间对氧化效果的影响。在实验基础上采用BP神经网络算法,建立以催化剂浓度、反应温度、压力、停留时间作为网络模型的输入层,COD去除率作为输出层的双隐层BP神经网络预测模型,预测催化超临界水氧化废水的效果,仿真结果表明模型预测效果较好。     

矿井风流温度预测分析研究

随着煤矿机械化程度不断提高,特别是随着开采深度逐渐增加,矿井热害问题日益突显,高温环境不仅影响生产效率而且严重危害着工作人员的健康。为了更好地降低高温矿井开采时期巷道风流温度,确定合理的巷道布置方案和矿井通风系统,提出矿井巷道风流温度预测方法。以矿井通风和热力学为基础,结合网络解算,对矿井风流温度分布进行研究,总结各种类型巷道风流温度的计算方法,提出多点风流混合温度计算模型。结合具体矿井进行实际预测分析,预测结果中显示风流温度可能超限的巷道,对通风系统进行优化设计。     

区域高速公路网紧急事件救援协作体系构建

从加强高速公路紧急事件应急处置、协同管理的角度出发,给出区域高速公路网和紧急事件的定义,界定了紧急事件的分类和特征,阐明建立救援协作体系的必要性。从工作流程和环节控制入手,构建包括决策组织、信息共享、交通管控、现场救援、应急储备等内容在内的区域高速公路网紧急事件救援协作体系,提出各模块构成要件及典型运作模式,有助于统筹区域高速公路网决策指挥,优化应急资源配置和调度,密切紧急事件协同处置,提高救援效率和水平。     

有有效时间约束的最大流问题及其算法

交通运输在抗震救灾中起着非常重要的作用,无论是运输量的多少或是时间的及时与否,都直接影响着救灾的效果.而前人的单发点最短路径算法,难以满足救灾中多处救援物资及时到达和救援物资量上的需要.本文设计了确定救援站点(发点)的快速搜索程序,并在此基础上将单发点的救援路线问题扩展为多发点问题.同时考虑各条路段的最大运输量,使得出的紧急救援路线,不仅能满足救援物资的及时到达,同时又能满足对救援物资等运输量上的需求.     

阻力平衡器在除尘管网中的应用

通过对在除尘系统中作为流体阻尼元件的阻力平衡器和调节阀门的对比分析,阐述了除尘管网中采用阻力平衡器的意义, 并对阻力平衡器的结构、工作原理和设计计算进行了介绍,最后强调了阻力平衡器使用时应注意的事项.     

交通运输网络可靠性研究分析

重点分析连通可靠性、出行时间可靠性、能力可靠性、行为可靠性和潜在可靠性等评价道路网络性能的指标,在回顾和总结交通网络可靠性研究发展的基础上,针对国内外的研究现状,提出了目前研究中存在的问题,描述了该领域有待研究的方向。     

多媒体远程实时控制系统

本文给出了多媒体远程实时控制系统的分层结构模型，介绍了基于该模型，实现网络实时传输和远程监控的关键技术和实现方法。为实现实时通信，减轻网络负载，系统可以根据当前的网络状态选择合适的协议来传输通信数据，引入多线程控制和协调机制，以实现并发控制。     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

基于C/S+B/S混合模式的城市重大危险源管理系统的研究

分析城市重大危险源管理系统的功能结构,针对现阶段技术应用的局限性,笔者提出C/S+B/S混合模式的解决方案;对数据库系统、网络构架及安全性、兼容性和模型系统等关键技术进行研究;开发出基于C/S+B/S混合模式的城市重大危险源管理系统。该系统对解决城市重大危险源统一管理与布局分散的矛盾,实现重大危险源动态监管和重大事故应急指挥智能化,提高事故应急救援指挥和救援行为的效率有重要意义。     

Physical and Economic Sustainability of Water: New Approach Using the Case of the Big Lost River,Idaho

A new approach for evaluating water sustainability is introduced by comparing physical and economic sustainability. To achieve physical sustainability, water should be available in sufficient quantity and of good quality and used efficiently. The economic sustainability can be achieved by balancing between costs and values of water. The objectives of this study were to estimate the physical and economic sustainability of surface water in the Big Lost River, south–central Idaho. The study used a Bayesian network by building a graphical diagram of nodes representing all significant variables related with the sustainability, such as water demand, water quality, and the different costs and values of water. The study showed that the likelihood of the physical sustainability is less than that of the economic sustainability, which is attributed to the scarcity of water in the Big Lost River. Readers should send their comments on this paper to: BhaskarNath@aol.com within 3 months of publication of this issue.