基于主成分分析法的危险货物运输企业安全评价

为更准确地评估危险货物运输企业安全等级以降低其运输风险,提出基于主成分分析法(PCA)的危险货物运输企业BP神经网络安全评价模型;在从人-机-物-环境-管理角度构建危险货物运输企业安全评价指标的基础上,分别利用该模型和其他3种模型对3家实例企业进行仿真评价和对比分析。结果表明,该模型的评价结果与期望值间的相对误差约为0.5%～1.2%,计算精度优于其他模型,且具有计算量小等特点。     

煤层透气性系数的优化和简化计算方法

煤层透气性系数是用以考察保护层开采效果和设计瓦斯抽采系统的重要参数之一,钻孔径向流量法是求取该系数最常用的算法。利用理论推导和图形分析方法,探讨流量准数、时间准数、煤层透气性系数与参数A,B间的关系。在此基础上,针对科研工作和现场管理的实际情况,提出求解透气性系数的2种新算法及其算法流程,并对时间准数-参数A,B函数关系中的间断点问题进行讨论和分析,解决此区间内求解透气性系数相应计算公式等问题。理论和实例分析表明:算法1在不更改最终计算公式和结果的前提下,省去了繁琐的试算过程,既保证了可靠性也优化了计算流程;算法2以稍减精度的代价对计算流程进行进一步的简化,其结果仍有较高精度。     

基于BP神经网络算法的北京市地铁站应急疏散能力仿真评估模型

地铁站的应急疏散能力是地下铁道安全运营设计中非常重要的部分,对于维持社会稳定,保障人民安全具有重大意义.本文利用BP神经网络计算方法和MATLAB软件,选取可能影响地铁站疏散能力的相关因素作为评价指标,建立了应急疏散指标体系,并以北京北三环区域周边地铁站作为主要研究对象,对北京市地铁站应急疏散能力进行了仿真评估,提出了增加疏散通道、疏散楼梯、安全出口等数量,以提高地铁站应急疏散能力等一些可供参考的建议.     

环境模型参数识别与不确定性分析

在对水文模型实例的参数不确定性分析基础上,分别采用传统灵敏度分析方法、HSY算法、线性回归等方法对模型参数特性进行了识别与比较研究.结果表明参数优化算法与传统灵敏度分析方法不能解释模型结构复杂性特征,采用不确定性分析方法对环境模型参数进行识别提供了深入分析与理解模型系统的有效途径.     

Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

水平管内不同煤质煤尘爆炸火焰传播特性实验研究

为研究水平管道空间不同煤质煤尘爆炸火焰传播特性，选取褐煤、长焰煤、不粘煤、气煤4种煤尘，对爆炸火焰焰峰特性、火焰加速传播特性、火焰传播距离与持续时间展开研究。研究结果表明:褐煤在500 ms内焰峰的形状由尖锐向平滑再向钝化不断演变，长焰煤与不粘煤在375 ms时焰峰前端出现明显焰体分离现象，分析认为这与管体冷壁效应、空间尺度效应及空间氧气消耗直接相关；气煤在375 ms时焰峰出现大面积火焰碎纹，说明气煤爆炸火焰猛烈传播的持续时间相对较短，整体爆炸强度相对较弱；褐煤与长焰煤爆炸火焰存在2次间断性加速，分析认为这与管体空间受限、常温管壁散热、局部助燃氧气瞬间不足等因素有关；褐煤在爆炸后400～600 ms内火焰2次加速完全，火焰传播距离达740 mm，明显大于长焰煤、不粘煤与气煤，说明低变质褐煤爆炸火焰持续时间更长，火焰传播距离更远且传播更剧烈；虽然气煤火焰最远传播距离比长焰煤大30 mm，但由于气煤火焰在375 ms左右出现大片火焰碎纹，因此气煤整体的爆炸强度小于长焰煤。     

综合拉底、扩漏和出矿全过程的底部结构安全性颗粒流模拟技术研究

为探明自然崩落法从拉底崩落至出矿全过程中底部结构节理连续扩展过程与破坏过程规律，采用PFC2D软件，结合RocLab软件反演节理岩体参数，建立229 m×129 m的岩体扩大模型，对拉底崩落至出矿过程进行连续综合计算。研究结果表明:模拟结果及破坏现象与现场监测结果相近，建模与细观参数反演方法适用于该类岩体工程研究；裂纹扩展贯通导致岩体失稳破坏，破坏过程与裂纹扩展具有相对应的阶段特征，裂纹扩展分为崩落前稳定扩展期，崩落与聚矿槽开挖过程加速扩展期和后续持续扩展期；结构破坏分为蕴育过程，扰动失稳过程和宏观破坏过程；较大范围的宏观破坏主要在出矿阶段。该模拟方法与结果可为底部结构维护以及节理岩体长期稳定性研究提供参考。     

Flame propagation in lycopodium/air mixtures below atmospheric pressure

Industrial processes are often operated at conditions deviating from atmospheric conditions. Safety relevant parameters normally used for hazard evaluation and classification of combustible dusts are only valid within a very narrow range of pressure, temperature and gas composition. The development of dust explosions and flame propagation under reduced pressure conditions is poorly investigated. Standard laboratory equipment like the 20 l Siwek chamber does not allow investigations at very low pressures. Therefore an experimental device was developed for the investigations on flame propagation and ignition under reduced pressure conditions. Flame propagation was analysed by a video analysis system the actual flame speed was measured by optical sensors. Experiments were carried out with lycopodium at dust concentrations of 100 g/m³, 200 g/m³ and 300 g/m³. It was found that both flame shapes and flame speeds were quite different from those obtained at atmospheric pressure. Effects like buoyancy of hot gases during ignition and flame propagation are less strong than at atmospheric conditions. For the investigated dust concentrations the flame reaches speeds that are nearly an order of a magnitude higher than at ambient conditions.     

AHP与GA-SVM耦合模型在滑坡预警中的应用*

为了减少滑坡造成的损失，提高滑坡预测的准确性，通过搭建灾害模拟平台获得滑坡的实验数据，在获得多组模拟实验数据后，分析各变量的特性。首先，通过层次分析(Analytic Hierarchy Process，AHP)算法，对滑坡进行危险度划分；然后，通过支持向量机(Support Vector Machine，SVM)建立模型，遗传算法(Genetic Algorithm,GA)再优化SVM参数，提出1种层次分析法与GA-SVM相耦合的模型。研究结果表明:AHP方法划分后的数据，通过GA与SVM结合建立的模型精度较好，实验预测结果与实际结果较为吻合，与单一SVM相比，精度更高，结果更好，更加适用于多变量的复杂非线性滑坡预警。     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.