基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

热水锅炉水冷壁管爆管事故综合分析

对在同一单位的三台热水锅炉发生多起水冷壁管爆管事故进行了综合分析,提出了相应的改进措施。     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

L型钢管混凝土芯柱耐火极限研究

火灾高温对结构安全有显著影响,为研究等肢L形钢管混凝土芯柱的耐火极限及其影响因素,利用ABAQUS软件建立合理的高温反应分析模型,在验证模型可靠性基础上,分析了荷载比、截面边长、长细比、荷载偏心率、含钢率等对L形钢管混凝土芯柱耐火极限的影响。研究结果表明:在一定参数范围内,荷载比和截面边长是构件耐火极限的主要影响参数,荷载比越小,截面边长越大,构件的耐火极限越高;长细比和荷载偏心率对其影响较大,长细比和荷载偏心率越小,构件的耐火极限越高;含钢率对其影响不显著。结果可为异形钢管混凝土芯柱的抗火安全设计提供参考。     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Application of CFD on the sensitivity analyses of some parameters of the modified Hartmann tube

The aim of this work is to determine the influence of operating parameters such as the dispersion pressure, the ignition delay and height on the dust flammability. A Computational Fluid Dynamics (CFD) simulation, based on an Euler–Lagrange approach, was developed with Ansys Fluent™ and validated experimentally. Such analysis will facilitate the choice of the most conservative conditions for a flammability test. This paper is focused on a case study performed on wheat starch with the modified Hartmann tube. The dispersion process of the powder was studied with granulometric analyses performed in situ and high speed videos. Tests were performed with injections at gas pressure ranging from 3 to 6 bars and the evolution of the particle size distribution (PSD) was recorded at different ignition heights (5, 10 and 15 cm over the dispersion nozzle). The observations highlighted the presence of agglomeration/deagglomeration processes and dust segregation. Besides, a CFD simulation analysis was aimed at evaluating the impact of a set of parameters on the PSD and the local turbulence, which are closely linked to some flammability parameters. For this computational analysis, the CFD simulation was coupled with a collision treatment based on a Discrete Element Method (DEM) in order to consider the cohesive behavior of the combustible dust. Thus the results suggest performing the injection of the gases at approximately 5 bars for the flammability tests of wheat starch in order to obtain the finest PSD at a given ignition height. It is also shown that the finest PSD are obtained at 5 cm over the dispersion nozzle. However, the local instabilities and turbulence levels are so high during the first stages of the dispersion that the flame growth can be disturbed for short ignition delays. Moreover, the stabilization of the bulk of the dust cloud requires longer periods of time when the ignition sources are located at 15 cm. As a result, the recommended height to perform a flammability test is 10 cm in this case. Finally, this study proposes some tools that might improve the procedure of dust flammability testing.     

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.     

基于神经网络分类器的城市污水处理厂水力冲击预警系统

提出了一套基于神经网络分类器的城市污水处理厂水力负荷冲击预警系统,以期对进水水量骤增现象进行提前1天的预报,使污水处理厂可根据预报结果提前采取水力冲击防护措施,从而保证各单元的平稳运行.根据进水水量的涨幅将某污水处理厂12年日进水水量监测数据分为"常规"和"冲击"两类,重点对"冲击"数据进行提前1天的预测,并采用冲击漏报率、冲击误报率和报准率对模型的预测精度进行评价;同时,基于同样的建模方法和不同的训练、验证样本建立了N(1)、N(2)和N(3)3个平行模型,以对模型的鲁棒性和建模方法的可重复性进行考察.结果显示,3个模型对2010年、2011年和2012年3年测试样本的预测效果良好,冲击漏报率和报准率两项指标数值均较为稳定,分别在0~0.167和0.981~0.995之间浮动,冲击误报率虽然在数值上的浮动较大,最低为0.143,最高为0.500,平均为0.310,但仍在工程上的可承受范围内.该结果表明,本研究基于神经网络分类器所建立的3个神经网络模型预测精度高、鲁棒性好,显示出良好的性能,有望为污水处理厂水力冲击防护工作提供有力参考.     

基于BP神经网络优化制备Cu-Ce/TiO_2及其光催化活性研究

采用Cu和Ce对TiO2进行改性,基于正交实验设计和BP神经网络研究了Cu-Ce/TiO2中Cu-Ce对TiO2的摩尔百分数、Cu-Ce/TiO2中CuCe摩尔比及Cu-Ce/TiO2烧结温度对Cu-Ce/TiO2光催化降解甲醛溶液性能的影响.同时,对Cu-Ce/TiO2制备方案进行了优化,并运用X射线衍射仪、扫描电子显微镜和紫外-可见分光光度计对最佳条件下制备的Cu-Ce/TiO2进行表征.结果表明,优化的制备条件为Cu-Ce/TiO2中Cu-Ce对TiO2的摩尔百分数为2.88%,Cu-Ce/TiO2中Cu-Ce的摩尔比为1∶1,Cu-Ce/TiO2的烧结温度为570℃.共掺杂Cu离子和Ce离子能有效避免掺杂TiO2晶格内部表层和近表层产生较多的位错,从而抑制晶格畸变增大;诱导TiO2中锐钛矿型晶体向金红石型晶体转变的能力增强,有效抑制电子-空穴对的复合,产生介电局域效应.