The role of situation awareness in accidents of large-scale technological systems

In the last two decades, several serious accidents at large-scale technological systems that have had grave consequences, such as that at Bhopal, have primarily been attributed to human error. However, further investigations have revealed that humans are not the primary cause of these accidents, but have inherited the problems and difficulties of working with complex systems created by engineers. The operators have to comprehend malfunctions in real time, respond quickly, and make rapid decisions to return operational units to normal conditions, but under these circumstances, the mental workload of operators rises sharply, and a mental workload that is too high increases the rate of error. Therefore, cognivitive human features such as situation awareness (SA)—one of the most important prerequisite for decision-making—should be considered and analyzed appropriately. This paper applys the SA Error Taxonomy methodology to analyze the role of SA in three different accidents: (1) A runaway chemical reaction at Institute, West Virginia killing two employees, injuring eight people, and requiring the evacuation of more than 40,000 residents adjacent to the facility, (2) The ignition of a vapor cloud at Bellwood, Illinois that killed one person, injured two employees, and caused significant business interruption, and (3) An explosion at Ontario, California injuring four workers and caused extensive damage to the facility. In addition, the paper presents certain requirements for cognitive operator support system development and operator training under abnormal situations to promote operators’ SA in the process industry.     

岩溶塌陷倾向性等级的KPCA-SVM预测模型

为了快速、有效地预测岩溶塌陷倾向性等级,在统计分析大量观测实例的基础上,选取岩性系数、岩体结构系数、地下水系数、覆盖层系数、地形地貌系数和环境条件系数作为特征指标。利用核主成分分析(KPCA)方法在高维空间提取岩溶塌陷影响因子的主成分,将获取的主成分作为支持向量机(SVM)的特征向量,建立基于KPCA的岩溶塌陷倾向性等级的SVM预测模型。将12组观测数据作为学习样本对模型进行训练。采用回代估计法进行回检,误判率为0。利用训练好的模型对2组待判样本进行预测。结果表明:经KPCA后指标个数减少,相关性降低,SVM运算的复杂度降低。用该模型所得预测结果的准确率为100%。     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

烧结机共处置危险废物过程中重金属Pb、Zn的挥发特性

利用化学试剂PbCl2、ZnCl2来模拟危险废物,研究了烧结机共处置危险废物过程中Pb、Zn的挥发特性.首先选取PbCl2、ZnCl2两种化合物的纯化学试剂分别进行了热重分析实验,然后对掺有一定比例PbCl2、ZnCl2的烧结矿原料进行煅烧,采集分析煅烧烟气和煅烧所得烧结矿中Pb和Zn的总量.实验结果表明,在刚开始煅烧的一段时间内,Pb、Zn两种重金属挥发特性相似,均是随着时间的增加以及温度的升高而挥发率逐渐上升,而当煅烧超过一定时间后,其挥发和固化反应会达到平衡,当煅烧的温度为1200℃时,Pb和Zn的挥发率分别为97.13%和65.04%.运用化学反应动力学理论对Pb、Zn两种重金属在烧结机共处置过程中的挥发规律进行动力学模拟,模拟结果表明,PbCl2中Pb挥发的动力学方程可表示为α=f(T,t)=1-exp[-7.90exp(-5595/T)t];而ZnCl2中的Zn为α=f(T,t)=1-exp[-0.534exp(-3210/T)t].     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

战场环境下装备保障信息系统安全风险与防护

目的研究战场环境下装备保障信息系统的安全问题。方法从装备保障及装备保障信息系统的概念、分类和复杂战场环境下装备保障信息系统所面临的安全威胁入手,依照五分法把装备保障信息系统安全风险划分为5个等级,推导出风险评估模型,提出实施装备保障信息系统安全防护的技战术策略。结果战场环境下,为确保整个作战进程中装备保障活动安全、有效、稳定地进行,装备保障信息系统防护必须遵循统一协调、综合利用的积极防护原则,采用有效的风险控制措施加以应对。结论通过积极探索和发展装备保障信息安全与防护理论,找准战场环境信息防护中存在的薄弱环节,制定了装备保障信息安全防护策略,加强装备保障信息系统的安全管理工作,最终提高我军装备保障信息系统的防护能力。     

有机化学品不同温度下(过冷)液体蒸气压预测模型的建立与评价

(过冷)液体蒸气压(PL)是评价化学品在环境中分配、迁移和归趋行为的重要参数。PL具有较强的温度依附性。发展一种能够精确预测不同环境温度下化学品PL的方法,有助于填补化学品生态风险评估的大量数据缺失。本研究收集整理了661种有机化合物在不同温度下(200~830 K)共计10 478个log PL值。在此基础上,采用偏最小二乘(PLS)回归和支持向量机(SVM)方法,构建了PL的线性和非线性预测模型。结果表明:2种模型均具有良好的拟合度、稳健性及预测能力,SVM模型的预测性能略高于PLS模型(PLS:R2adj.tra=0.912,RMSEtra=0.477,Q2ext=0.910;SVM:R2adj.tra=0.997,RMSEtra=0.092,Q2ext=0.967)。机理分析表明,温度是影响PL的主要因素,温度越高,蒸气压越大;其次,X1sol也影响PL大小,X1sol用来描述分子间的色散作用,分子间色散力越小,蒸气压越大;此外,化合物的氢键个数、极性和分子构型等因素也影响PL大小。采用Wiliams plot方法表征了PLS模型应用域。所建立的模型可用来预测烷烃、烯烃、醇、酮、羧酸、苯、酚、联苯、卤代芳香烃、含N化合物及含S化合物在不同温度下的PL数据。     

Prioritisation of citizen‐centric information for disaster response

In the aftermath of a disaster event, and in the absence of trained professionals, many responsibilities are assumed by uninjured citizens who are willing and able to help, such as care of the injured or search and rescue. These citizens are constrained by communications and logistics problems but are less equipped to deal with them as most often they are cut off from any coordinated assistance. The method proposed in this study would increase the survivability of those injured or trapped by a disaster event by providing a facility to allow citizens to coordinate and share information among themselves. This is facilitated by the proposed deployment and the autonomous management of an ad hoc infrastructure that liaises directly with survivors without central control. Furthermore, as energy concerns present critical constraints to these networks, this research proposes a system of categorising information elements within the network to ensure efficient information exchange.     

Land-use forecasting and hydrologic model integration for improved land-use decision support

This paper develops a methodology for integrating a land-use forecasting model with an event scale, rainfall-runoff model in support of improving land-use policy formulation at the watershed scale. The models selected for integration are loosely coupled, structured upon a common GIS platform that facilitates data exchange. The hydrologic model HEC-HMS is calibrated for a specific storm event that occurred within central Washington State. The land-use forecasting model, What If? is implemented to forecast future spatial distributions of low-density residential land-uses under low and high population growth estimates. Forecasted land-use distribution patterns for the years 2015, 2025, and 2050 are then used as land-use data input for the calibrated hydrologic model, keeping all other parameters constant. Impacts to the stream discharge hydrograph are predicted as the study area becomes increasingly developed as forecasted by What If?. The initial results of this integration process demonstrate the synergy that can be generated through the linkage of the selected models. The ability to quantifiably forecast the potential hydrologic implications of proposed land-use policies before their implementation offers land-use decision-makers a valuable tool for discerning which proposed land-use alternatives will be effective at minimizing storm water runoff.     

蒙脱土原位生长MoS2复合材料光催化降解罗丹明B的机理研究

通过在Na⁺-MMT表面生长MoS₂来提高窄带隙半导体光生电子分离速率及稳定性.利用阳离子填充法及水热法成功制备了复合光催化剂Na⁺-MMT/MoS₂,并通过FT-IR、SEM、TEM、Raman、XRD、TG、XPS、UV-DRS和ESR等表征进一步证明了材料的成功负载及光、电化学性能. 同时,以有机染料罗丹明B为待降解染料来评价光催化剂的催化性能,发现其在80 min可有效降解罗丹明B,降解率达96%.经过5次循环使用后,Na⁺-MMT/MoS₂复合光催化剂仍具有较好的光催化性能.因此,利用MMT的表面电负性及稳定的片层结构负载MoS₂,可形成光生电子迁移通道进一步提高电荷迁移速率及光催化剂的稳定性.本研究可为黏土材料调控窄带隙半导体制备环境友好型光催化剂提供新思路.