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.     

Military humanitarian and disaster governance networks in Southeast Asia: framework and analysis

Behind humanitarian assistance and disaster relief (HADR) efforts are governance networks of actors working together to facilitate, coordinate, and deliver joint aid and response initiatives. Militaries are key stakeholders in these networks because of their unique capabilities, particularly in Southeast Asia. This paper explores the role of military governance networks in shaping HADR affairs. It presents a framework for examining military-to-military relational structures in humanitarian and disaster contexts using network theory and social network analysis. It also assesses the nature of military ties and their influence on two capability areas essential to humanitarian activities in the region: (i) the distribution of military assets and equipment used in HADR; and (ii) the coordination of HADR exercises and training. This paper seeks to provide insights into how governance network features and properties affect the capacity of ASEAN (Association of Southeast Asian Nations) militaries to use available resources efficiently and to achieve shared objectives in regional disaster response.     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

Effect of pipes in heat pump system on electric vehicle energy saving

ABSTRACT

Refrigerant pressure drop and temperature change in pipes are normally ignored in the thermodynamic analysis of traditional vehicle air conditioning system, this will result in serious errors. In this Paper, pressure drop and temperature difference are simulated in different pipes of electric vehicle (EV) heat pump system to analysis the effects of pipes in the actual EV heat pump system. The results indicate that the greater the mass flow, the faster pressure drop increases, the temperature difference decreases. Pressure drop of saturated liquid refrigerant is smaller than that of saturated gas refrigerant at the same saturation pressure and mass flow rate. The higher the refrigerant pressure (no phase change), the slower pressure drop decreases, the faster the temperature difference decreases. Pressure drop decreases with the increment of bending angle of the pipe. For EV heat pump system, suitable valves and less branches are helpful for energy saving of the system. Shortening the pipe between compressor and condenser can reduce temperature change obviously. Pressure drop per unit length in the pipe between evaporator and compressor is large especially in heating mode because of lower refrigerant density. It even reaches to over 100 times of that in the pipe between condenser and throttle valve in heating mode and has negative effects on the performance of the system. If the evaporator is closer to the compressor and the number of branches is less, then pressure drop will decrease a lot, which will be advantageous for energy saving of the heat pump system.     

基于VB6.0的供水管网震害预测方法和软件

为了提高供水管网震害预测的效率,修订了现有供水管网震害预测模型,应用Visual Basic 6.0软件平台,开发了供水管网震害预测软件。震害预测软件提供了两方面的预测功能,分别为不同地震烈度下供水管段基于三态的破坏等级预测和供水管网基于五态的破坏等级预测。软件实现了批量供水管段及整个管网的震害预测,且可以进行管网在设定地震下的震害预测,提高了预测效率。经算例分析,验证了软件的可靠性。     

Application of dynamic risk analysis in offshore drilling processes

Process safety is the common global language used to communicate the strategies of hazard identification, risk assessment and safety management. Process safety is identified as an integral part of process development and focuses on preventing and mitigating major process accidents such as fires, explosions, and toxic releases in process industries. Accident probability estimation is the most vital step to all quantitative risk assessment methods. Drilling process for oil is a hazardous operation and hence safety is one of the major concerns and is often measured in terms of risk. Dynamic risk assessment method is meant to reassess risk in terms of updating initial failure probabilities of events and safety barriers, as new information are made available during a specific operation. In this study, a Bayesian network model is developed to represent a well kick scenario. The concept of dynamic environment is incorporated by feeding the real-time failure probability values (observed at different time intervals) of safety barriers to the Bayesian network in order to obtain the corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and quantitatively shows the effect of deterioration of barrier performance on kick consequence probabilities. The Macondo blowout incident is used to demonstrate how early warnings in barrier probability variations could have been observed and adequately managed to prevent escalation to severe consequences.     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

Bibliometric analysis of simulated driving research from 1997 to 2016

Objective: The objective of this study was to explore the evolution footprints of simulated driving research in the past 20 years through rigorous and systematic bibliometric analysis, to provide insights regarding when and where the research was performed and by whom and how the mainstream content evolved over the years.

Methods: The analysis began with data retrieval in Web of Science with defined search terms related to simulated driving. BibExcel and CiteSpace were employed to conduct the performance analysis and co-citation network analysis; that is, probe of the performance of institutes, journals, authors, and research hotspots.

Results: A total of 3,766 documents were filtered out and presented an exponential growth from 1997 to 2016. The United States contributed the most publications as well as international collaborations followed by Germany and China. In addition, several universities in The Netherlands and the United States dominated the list of contributing institutes. The leading journals were in transportation and ergonomics. The leading researchers were also recognized among the 8,721 contributing authors, such as J. D. Lee, D. L. Fisher, J. H. Kim, and K. A. Brookhuis. Finally, the co-citation analysis illuminated the evolution of simulated driving research that covered the following topics roughly in chronological order: task-induced stress, drivers with neurological disorders, alertness and sleepiness while driving, trust toward driving assistance systems, driver distraction, the effect of drug use, the validity of simulators, and automated driving.

Conclusions: This article employed bibliometric tools to probe the contributing countries, institutes, journals, authors, and mainstream hotspots of simulated driving research in the past 20 years. A systematic bibliometric analysis of this field will help researchers realize the panorama of global simulated driving and establish future research directions.     

产业园区碳排放核算方法研究

产业园区是一个协同实现经济效益和环境效益的重要单元,同时也是重点碳排放源,其转型和发展对实现国家和地区的低碳战略起到关键作用。碳排放核算方法影响园区碳排放水平的评估,是制定有效碳减排策略的基础。产业园区碳排放核算需要明确核算对象、核算结果表征和核算范围。本研究首先界定核算对象,辨析"二氧化碳量"与"二氧化碳当量"两种表征方式,认为二氧化碳量可以反映产业园区能源结构,而二氧化碳当量则能够量化园区的不同排放过程和环境效应。同时,从价值链角度出发,将园区碳排放核算划分为三个Scope,分别反映园区直接排放和上下游关联企业的间接排放。然后,从排放活动和部门关联出发,分析三种典型碳排放核算方法(清单分析法、投入产出分析和生态网络分析),进一步划分为流量分析模式和结构分析模式。流量分析模式主要从排放量角度对园区内部及其上下游产业链相关的碳排放活动进行核算;结构分析模式则聚焦园区相关碳排放部门和价值链的组织形式,从投入产出或者网络关联角度开展分析。研究发现清单分析法可以自下而上量化产业园区碳排放活动的流量,投入产出法则自上而下揭示碳排放单元的直接和间接流量以及平行结构,而生态网络分析则采用混合式核算模式刻画碳排放单元之间的网状关联结构。最后,本研究初步提出了产业园区碳排放核算方法的研究框架,力求提高排放因子精度,改进核算模式和建立动态核算模型,对碳排放总量和结构进行预测,从而实现产业园区的低碳发展。