A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

Analytical mapping of research on disaster management,types and role of ICT during 2011–2018

Catastrophic disasters like earthquake and flood cause widespread destruction and financial devastation. This has brought disaster management into limelight making it a burgeoning academic research field. The remarkable rise of ICT (Information and Communication Technology) has instigated the scientific world to incorporate these technologies in disaster management. This study presents scientometric analysis to identify the status quo of research on the management of various disasters and role of ICT in it. This paper uses bibliographic data retrieved from Scopus for the observation period from 2011 to 2018. We provide extensive insights into growth of publications, citation pattern and their connectedness with other subject disciplines. Furthermore, we identify most productive and influential countries, institutes and journals. Our study analyses co-occurrence of keywords using Visualization of Similarities (VOS) Viewer. This structured overview will enhance the understanding of this field leading to more focussed and purposeful research.     

长江三峡区间数字流域水系的构建

基于全球陆地一公里基础高程GLOBE数据 ,采用Martz和Garbrecht研制的数字高程流域水系模型自动提取长江三峡万县～宜昌区间的河网水系、各子流域分水线、河网与子流域编码及河网结构拓扑关系 ,从而构成该区域的数字流域和数字水系。结果表明该模型生成的水系是可以接受的 ,与 1:10万地形图上长江三峡万县～宜昌区间的水系一致 ,这为空间分布式模型的建立提供了必备的空间信息数据。最后 ,探讨了数字流域水系在流域生态学中的应用前景 ,构建的数字水系可为中尺度以上流域水体和水生生物的资源保护与合理利用提供决策平台 ,服务于社会经济的可持续发展。     

Phosphate recovery with granular acid-activated neutralized red mud: Fixed-bed column performance and breakthrough curve modelling

Granular acid-activated neutralized red mud (AaN-RM) has been successfully prepared with good chemical stability and physical strength. However, its potential for industrial application remains unknown. Therefore, the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated. The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters, such as the bed depth, flow rate, initial solution pH and initial phosphate concentration. With the optimal empty-bed contact time (EBCT) of 24.27 min, the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g, respectively. Then, the saturated fixed-bed column could be effectively regenerated with a 0.5 mol/L HCl solution. The desorption efficiency remained as high as 83.45% with a low weight loss of 3.57% in the fifth regeneration cycle. In addition, breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network (ANN) could be effectively applied for the optimization of the fixed-bed adsorption system; the coefficient of determination (R²) and the root mean square error (RMSE) evaluated on the validation-testing data were 0.9987 and 0.0183, respectively. Therefore, granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water.     

旅游目的地网络空间结构及其复杂性研究——野三坡旅游地案例实证

复杂性是旅游目的地网络化发展的必然趋势,是认识旅游地网络空间结构与组织特征的重要研究手段。论文以河北省野三坡旅游地为例,通过构建旅游联系强度模型,建立旅游地空间网络,并运用TOP网络空间分析以及复杂网络理论,揭示旅游地网络空间结构与组织的复杂性特征。研究发现：1）旅游地网络具有空间集聚性,形成了以核心景区为中心,由中心城镇、乡村社区和特色园区共同构成的空间集聚单元,并体现出“邻近连接”与“择优连接”的双重特性;2）旅游地网络服从幂律分布,具有无标度网络特性;3）每个节点的前三名和前五名旅游联系强度网络（Top3和Top5网络）具有较小的平均路径长度、较大的集聚系数,呈现出典型的“小世界网络”特征;4）旅游地网络是典型的异配型网络,度-度呈负相关,而网络节点的集聚系数与其度值之间存在近似的倒数关系,因此具有层次性。     

基于SOM-SVM组合分类器的涂层防护性能研究

目的为避免EIS,EN技术可能出现的问题,建立一个准确、高效的评价模型,以探究现役军用有机涂层防护性能。方法利用电化学阻抗谱(EIS)、电化学噪声(EN)技术分析了两种军车有机涂层在循环暴露试验中的腐蚀行为,提取低频阻抗模值|Z|_(0.1 Hz)与涂层噪声电阻R_n两种电化学评价参数作为自组织神经网络(SOM)的输入训练样本,同时结合支持向量机(SVM)方法建立涂层防护性能组合分类器。结果将涂层失效过程自适应地分为涂层防护性能良好、防护性能下降、基本失效三个阶段。结论所建立的SOM-SVM组合分类器对于辅助分析涂层防护性能具有可行性。     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.     

Land Degradation Monitoring in Namibia: A First Approximation

This paper presents development of a first approximation of a Namibian, national level, land degradation monitoring system. The process involved a large number of stakeholders and led to the definition of four primary indicators that were regarded as related to land degradation in Namibia: population pressure, livestock pressure, seasonal rainfall and erosion hazards. These indicators were calculated annually for the period 1971–1997. Annual land degradation risk maps were produced for the same period by combining the indicators. A time series analysis of results generated by indicators was undertaken at two sites. The analysis revealed a general trend towards an increased land degradation risk over the period 1971–1997. A decrease in annual rainfall and an increase in livestock numbers caused this negative trend at one site, while decreased annual rainfall and increased human population were the causes at a second site. Evaluation of resulting maps through direct field observations and long-term monitoring at selected study sites with different conditions relevant for the indicators defined, is an essential next step.