Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

Understanding communicational behavior among rangelands’ stakeholders: application of social network analysis

Understanding communicational behavior of rangelands’ stakeholders is fundamental for effective development of rangeland management plans. This study aimed to understand differences between stakeholders’ relations among various actors involved in rangeland management using social network analysis (SNA). A survey was conducted on 334 stakeholders (89 extension agents, 110 researchers and 135 executive agents) in the Tehran province, Iran. Results showed that all the three groups of stakeholders are interested in making contact mainly within their own group. Furthermore, while the executive agents have shared the strongest technical and friendship relations with the two other groups, the extension agents established the strongest administrative interactions. The researchers, however, made a poor link especially with the extension agents. The study concluded that SNA could be an efficient tool to assess communicational behavior in rangeland management.     

An Analysis of Water Data Systems to Inform the Open Water Data Initiative

Improving access to data and fostering open exchange of water information is foundational to solving water resources issues. In this vein, the Department of the Interior's Assistant Secretary for Water and Science put forward the charge to undertake an Open Water Data Initiative (OWDI) that would prioritize and accelerate work toward better water data infrastructure. The goal of the OWDI is to build out the Open Water Web (OWW). We therefore considered the OWW in terms of four conceptual functions: water data cataloging, water data as a service, enriching water data, and community for water data. To describe the current state of the OWW and identify areas needing improvement, we conducted an analysis of existing systems using a standard model for describing distributed systems and their business requirements. Our analysis considered three OWDI‐focused use cases—flooding, drought, and contaminant transport—and then examined the landscape of other existing applications that support the Open Water Web. The analysis, which includes a discussion of observed successful practices of cataloging, serving, enriching, and building community around water resources data, demonstrates that we have made significant progress toward the needed infrastructure, although challenges remain. The further development of the OWW can be greatly informed by the interpretation and findings of our analysis.     

Dynamics of Policy Networks. The Case of Organic Farming Policy in the Czech Republic

To better understand the potential for successful and long-term establishment of environmental policy, the aim of this paper is to explore the network dynamics of a policy field that has become well established in the EU: organic farming. We look at the dynamics of the organic farming policy network in the Czech Republic over a period of 10 years by applying a comparative formal network analysis. We focused in particular on the distribution of power between actors and how capacities of policy actors have evolved between 2004 (its year of accession to the EU) and 2014. We conclude that the organic farming policy network in the Czech Republic has been highly dynamic and has changed from one that was decidedly influenced by organic sector organizations to a network centralized around the Ministry of Agriculture. However, the organic farming organization managed to maintain its good reputation for competence in organic farming policy, which creates opportunities for the organization to remain active in the policy debate.     

Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well.     

重庆市公路网络通达性格局研究

以重庆市38个区县公路网络为研究对象,以最短距离为原则,在构建公路网络拓扑结构的基础上,计算加权平均旅行时间指标和通达性系数来衡量城市通达性水平的高低,并对通达性水平的特点和空间格局进行分析。结论如下:(1)38个区县的通达性水平呈现出"放射状同心圆"特征,以重庆市主城区为圆心,随着同心圆的半径增加,通达性水平逐渐降低,形成明显的环状分异态势;(2)路网的拓扑连接水平较低,路网等级体系不够完善,网络的辐射效率不高,网络结构相对脆弱,网络的有效性较低;(3)高等级路网发育不均衡,区县间出现通达性差异很大的现象,影响区域的协调发展。     

基于粗糙集和BP神经网络的流域水资源脆弱性预测研究——以淮河流域为例

水资源是一种重要的自然资源和经济资源,对其未来的脆弱性进行预测可以预估研究区未来的水安全状况,对其脆弱性问题做出预警,从而及时采取治理措施。因此,合理科学的水资源脆弱性预测研究是缓解水资源脆弱性的有效手段。目前,水资源脆弱性研究主要是针对水资源现状进行评价,对其未来状况的预测较少。集成了粗糙集和BP神经网络两种方法,首先采用改进了的盲目删除法对构建的流域水资源脆弱性评价指标体系进行约简,其次通过BP神经网络拟合约简后的指标数据与脆弱度之间的映射关系,构建流域水资源脆弱性评价预测模型。基于之前研究的样本数据和脆弱性结果,探讨淮河流域未来的水资源脆弱性状况。结果表明:淮河流域2015年、2020年和2025年的水资源脆弱度分别为0.305、0.359和0.390,处于轻度脆弱和中度脆弱的状况,除2015年脆弱性状况有所好转以外,2020年和2025年的水资源脆弱性程度与近几年相比有所加剧,根据指标数据可知该现象主要是受年降水量、人均用水量、万元GDP废水排放量、垦殖指数、有效灌溉面积比和干旱面积受灾比6个指标的影响,为避免水资源脆弱性的加剧,应当有针对性的加强这几个方面的管理和控制。     

基于人工神经网络的半连续式混合厌氧消化产气量预测

研究采用BP神经网络和模糊神经网络(FNN)模型对逐步提高有机负荷的半连续式餐厨垃圾和猪粪混合厌氧消化试验进行日产气量预测.结果表明,BP神经网络模型的预测准确率为77.63％,FNN模型为82.33％,2种模型均可用于产气预测,但FNN模型在传统神经网络模型基础上加入了模糊控制,可提高其准确率,更适用于混合厌氧消化产气量预测.     

基于灰色模型和模糊神经网络的综合水质预测模型研究

水质状态变化趋势预测研究对水资源管理和维护具有重要的现实意义。提出了一种将灰色模型和模糊神经网络相结合的水质预测模型。首先基于改进的灰色模型预测出水体中各理化因子在未来一段时间内的指标变化,然后采用T-S模糊神经网络对各单因子的预测值进行数据融合,构建水质变化综合趋势预测模型,预测出下一时间段的水质整体状态指标。实验表明,这种方式用来预测湖泊水质变化趋势具有可行性;与BP网络模型相比,基于T-S模糊神经网络系统的模型具有预测精度高、模型系统稳定等优越性。     

苏皖交界河网区浮游植物群落结构及其与环境因子关系的研究

苏皖交界处的石臼湖是长江下游唯一的通江淡水湖,通过青弋江和姑溪河直接与长江相连,形成了复杂的河网水系。为探讨这种复杂的河网水系中浮游植物种群结构特征,2012年平水期和枯水期分别对石臼湖及其周边入湖支流进行了浮游植物调查,研究河网水系中河网与湖区浮游植物的种类组成、季节变化及与环境因子的关系。结果显示:共采集到浮游植物105种,平水期与枯水期种类数差别不大,两次调查均出现的种类约有70%相同;浮游植物丰度最高可达1×108cell/L,达到了水华暴发的标准。优势种类主要绿藻门和蓝藻门为主,但是从生物量来看硅藻门占优。排序分析显示河网区域浮游植物季节演替明显,且河网与湖区浮游植物群落结构差异明显,进一步分析则表明影响浮游植物主要的环境因子是氮、磷等营养元素。