Application of digital technologies about water network in steel industry

To improve the efficiency of water use and reuse, a digitalized water network system was implemented in a steel industrial complex. The system consisted of four parts: online instrument and sensing devices, programmable logic controller, data communication network and control center based on expert knowledge. The system achieved real-time monitoring, diagnosis, and early warning of cooling water system using data collected inline, and enabled data connectivity and transmission of self-developed software applications on personal computer. This research used a steel enterprise as a case study. After the analysis of its cooling water system and water-saving potential using water balance test, a digitalized water network was evaluated and implemented, and we believe this network can be suitable for steel industry in general.     

Using knowledge mapping to rethink the gap between science and action

Scholars have long stressed the need to bridge the gap between science and action and seek the most efficient use of knowledge for decision making. Many contributors have attempted to consider and understand the sociopolitical forces involved in knowledge generation and exchange. We argue, however, that a model is still needed to adequately conceptualize and frame the knowledge networks in which these processes are embedded. We devised a model for knowledge mapping as a prerequisite for knowledge management in the context of conservation. Using great ape conservation to frame our approach, we propose that knowledge mapping should be based on 2 key principles. First, each conservation network results from the conglomeration of subnetworks of expertise producing and using knowledge. Second, beyond the research-management gradient, other dimensions, such as the scale of operation, geographic location, and organizational characteristics, must also be considered. Assessing both knowledge production and trajectory across different dimensions of the network opens new space for investigating and reducing the gap between science and action.     

Risk assessment of chemical process considering dynamic probability of near misses based on Bayesian theory and event tree analysis

With the development of modern automatic control systems, chemical accidents are of low frequency in most chemical plants, but once an accident happens, it often causes serious consequences. Near-misses are the precursor of accidents. As the process progresses, near misses caused by abnormal fluctuation of process variables may eventually lead to accidents. However, variables that may lead to serious consequences in the production process cannot update the risk in the life cycle of the process by traditional risk assessment methods, which do not pay enough attention to the near misses. Therefore, this paper proposed a new method based on Bayesian theory to dynamically update the probability of key variables associated with process failure risk and obtain the risk change of the near-misses. This article outlines the proposed approach and uses a chemical process of styrene production to demonstrate the application. In this chemical process, the key variables include flow rate, liquid level, pressure and temperature. In order to study the dynamic risk of the chemical process with consideration of near misses, according to the accumulated data of process variables, firstly the abnormal probability of the variables and the failure rate of safety systems associated with the variables were updated with time based on Bayesian theory. On the basis of the dynamic probability of key process variables, an event tree of possible consequences caused by variable anomalies was established. From the logical relationship of the event tree, the probability of different consequences can be obtained. The results show that the proposed risk assessment method based on Bayesian theory can overcome the shortcomings of traditional analysis methods. It shows the dynamic characteristics of the probability of different near misses, and achieves the dynamic risk analysis of chemical process accidents.     

基于轴—辐理论的皖江城市带物流网络空间构建

大力发展现代物流业和皖江城市带承接产业转移示范区建设是两大重要国家级战略.首先对轴—辐理论的国内外研究现状进行了总结和归纳,分析了轴—辐网络的结构及其优劣势.其次,设计了物流中心性指数的评价指标体系,运用主成分分析法对皖江城市带的9个地级城市进行物流节点等级划分,根据分析结果的总得分和区位特征确定合肥、芜湖、安庆3个一级物流节点,形成轴三角.分析了各级物流节点间的干线及支线物流通道,构建了皖江城市带轴—辐物流网络空间.     

Projected Climate‐Induced Habitat Loss for Salmonids in the John Day River Network,Oregon, U.S.A.

Abstract: Climate change will likely have profound effects on cold‐water species of freshwater fishes. As temperatures rise, cold‐water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate‐driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate‐induced changes in summer thermal habitat for 3 cold‐water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69–95%, 51–87%, and 86–100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.     

危化品道路运输事故应急救援中心选址模型研究

为快速提高危化品道路运输事故应急救援能力,降低事故后果,文章基于目前应急救援中心选址模型的不足,提出了一种新的双目标决策救援中心选址模型。通过综合考虑应急响应时间及应急处理能力对救援能力的影响,建立了救援中心点的最大网络覆盖模型;并采用较优的邻域搜索算法求解救援中心点的合理位置。最后以某地危化品运输路网为例,利用所建模型对事故应急救援中心的合理布局计算求解。结果表明:考虑应急响应时间及应急处理能力限制的应急救援中心选址模型,能够有效提高救援中心的综合应急能力,为有关部门合理规划危化品道路运输事故应急救援中心布局提供理论参考和决策依据。     

Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle

Relocation is one of the strategies used by conservationists to deal with problem cheetahs in southern Africa. The success of a relocation event and the factors that influence it within the broader context of long-term viability of wild cheetah metapopulations was the focus of a Bayesian Network (BN) modelling workshop in South Africa. Using a new heuristics, Iterative Bayesian Network Development Cycle (IBNDC), described in this paper, several networks were formulated to distinguish between the unique relocation experiences and conditions in Botswana and South Africa. There were many common underlying factors, despite the disparate relocation strategies and sites in the two countries. The benefit of relocation BNs goes beyond the identification and quantification of the factors influencing the success of relocations and population viability. They equip conservationists with a powerful communication tool in their negotiations with land and livestock owners, which is key to the long-term survival of cheetahs in southern Africa. Importantly, the IBNDC provides the ecological modeller with a methodological process that combines several BN design frameworks to facilitate the development of a BN in a multi-expert and multi-field domain.     

东部发达城市智慧水务建设探析

在互联网技术飞速发展,于各传统行业运用的建设大背景下,"智慧水务"的构想应运而生。结合当前水务发展遇到的实际问题和国家战略部署阐述了智慧水务的建设。最后提出应落实发展信息技术,加快技术融合,为智慧城市的建设添砖加瓦。     

Dynamic failure probability analysis of urban gas pipeline network

The gas pipeline network is an essential infrastructure for a smart city. It provides a much-needed energy source; however, it poses a significant risk to the community. Effective risk management assists in maintaining the operational safety of the network. The risk management of the network requires reliable dynamic failure probability analysis. This paper proposes a methodology of condition monitoring and dynamic failure probability analysis of urban gas pipeline network. The methodology begins with identifying key design and operational factors responsible for pipeline failure. Subsequently, a causation-based failure model is developed as the Bowtie model. The Bowtie model is transformed into a Bayesian network, which is analyzed using operational data. The key contributory factors of accident causation are monitored. The monitored data is used to analyze the updated failure probability of the network. The gas pipeline network's dynamic failure probability is combined with the potential consequences to assess the risk. The application of the approach is demonstrated in a section of the urban gas pipeline.     

Bayesian Decision Analysis for Evaluating Management Options to Promote Recovery of a Depleted Salmon Population

Abstract:  The endangered population of sockeye salmon (Oncorhynchus nerka) in Cultus Lake, British Columbia, Canada, migrates through commercial fishing areas along with other, much more abundant sockeye salmon populations, but it is not feasible to selectively harvest only the latter, abundant populations. This situation creates controversial trade-offs between recovery actions and economic revenue. We conducted a Bayesian decision analysis to evaluate options for recovery of Cultus Lake sockeye salmon. We used a stochastic population model that included 2 sources of uncertainty that are often omitted from such analyses: structural uncertainty in the magnitude of a potential Allee effect and implementation uncertainty (the deviation between targets and actual outcomes of management actions). Numerous state-dependent, time-independent management actions meet recovery objectives. These actions prescribe limitations on commercial harvest rates as a function of abundance of Cultus Lake sockeye salmon. We also quantified how much reduction in economic value of commercial harvests of the more abundant sockeye salmon populations would be expected for a given increase in the probability of recovery of the Cultus population. Such results illustrate how Bayesian decision analysis can rank options for dealing with conservation risks and can help inform trade-off discussions among decision makers and among groups that have competing objectives.