基于神经网络的煤与瓦斯突出预测模型

在全面分析了煤与瓦斯突出影响因素的基础上 ,提出了煤与瓦斯突出预测的人工神经网络模型。介绍了突出特征指标的选取及表示方法与推理过程。实例分析表明 ,模型精度很高 ,可用于工作面煤与瓦斯突出预测 ,并分别给出图 2 ,表 3,文献 5     

雾霾天气预测中支持向量机的应用分析

支持向量机在对非线性复杂问题进行处理的过程中,展现出来的优势特征非常突出,本文针对雾霾天气预测中支持向量机的应用做出了进一步探究,对支持向量机的概念、支持向量机的基本思想、建立雾霾预测模型、预测试验给出了详细的分析。     

基于粒子群优化的 PI 预测函数励磁控制器

将PI控制器与预测函数算法相结合,利用粒子群算法优化整定其参数,提出了一种新型的基于粒子群优化的PI预测函数励磁控制器的设计方法。该算法具有广义上的PI控制器的两个参数,同时具有预测函数算法对模型要求低和上升速度快等优点,同时利用粒子群算法优化PI参数,使其克服了传统PI控制器参数难以整定的缺点。仿真结果表明该算法不仅能有效的解决机端电压偏移问题,还能有效的抑制电网低频振荡,且具有较好的鲁棒性,同时PI参数整定方便,为电力系统稳定性控制提供了一种新型有效的方法。     

Development of models for predicting toxicity from sediment chemistry by partial least squares-discriminant analysis and counter-propagation artificial neural networks

There is strong interest in developing tools to link chemical concentrations of contaminants to the potential for observing sediment toxicity that can be used in initial screening-level sediment quality assessments. This paper presents new approaches for predicting toxicity in sediments, based on 10-day survival tests with marine amphipods, from sediment chemistry, by means of the application of Partial Least Squares-Discriminant Analysis (PLS-DA) and Counter-propagation Artificial Neural Networks (CP-ANNs) to large historical databases of chemical and toxicity data. The exploration of the internal structure of the developed models revealed inherent limitations of predicting toxicity from common chemical analyses of bulk contaminant concentrations. However, the results obtained in the validation of these models combined relevant values of non-error classification rate, sensitivity and specificity of, respectively, 76, 87 and 73% with PLS-DA and 92, 75 and 97% with CP-ANNs, outperforming the results reported for previous approaches.     

地下商业街建筑人员疏散预测

利用网络控制原理地下商业街建筑人员疏散行为进行模拟,建立疏散预测模型,通过计算机软件的开发预测建筑人员疏散所需时间动态模拟疏散过程。并根据预测结果对建筑方案及疏散计划方案加以修改完善,以保证建筑中所有人员在火灾发生时得以安全疏散。     

220 kV高压输电线电磁辐射水平及防护距离预测

依据<500 kV超高压送变电工程电磁辐射环境影响评价技术规范>(HJ/T 24-1998)中的预测模式对监测点周围地面1.5 m处工频电场进行了验证性监测,表明实际测定结果与理论计算结果基本吻合.通过对220 kV双回同相、双回逆相和单回线路下地面1.5 m、4.5 m、和7.5 m处工频电场变化趋势分析,预测了220 kV高压输电线产生的电磁辐射水平,以及不同房屋结构的防护距离.提出了220 kV高压输电线电磁辐射的防护措施.     

Study of fuel oxygenates solubility in aqueous media as a function of temperature and tert-butyl alcohol concentration

Methyl tert-butyl ether (MTBE) is the most widely used oxygenate in gasoline blending and has become one of the world’s most widespread groundwater and surface water pollutants. Alternative oxygenates to MTBE, namely ethyl tert-butyl ether (ETBE), tert-amyl ether (TAME) and diisopropyl ether (DIPE) have been hardly studied yet. The solubility of these chemicals is a key thermodynamic information for the assessment of the fate and transport of these pollutants. This work reports experimental data of water solubility at the range from 278.15 to 313.15 K and atmospheric pressure of ethers used in fuels (MTBE, ETBE, TAME and DIPE) due to the strong influence of temperature on its trend. From the experimental data, temperature dependent polynomials were fitted, thermodynamic parameters were calculated and theoretical models were used for prediction. Finally, the tert-butyl alcohol (TBA) influence in the solubility of MTBE and ETBE in aqueous media was studied.     

厦门市空气质量臭氧预报和评估系统

为了评价和预测厦门市区空气中臭氧的污染水平,运用2006～2009年的监测数据对臭氧的污染成因及其变化规律进行研究。通过风向、风速、气温、湿度等气象因子对臭氧浓度影响的分析,进而运用多元线性回归法建立厦门市臭氧预报及评估系统。     

Flash points measurements and prediction for binary miscible mixtures

Flash point is one of the most important parameters used to characterize the potential fire and explosion hazards for flammable liquids. In this study, flash points of twenty eight binary miscible mixtures comprised eighteen flammable pure components with different compositions were measured by using the closed cup apparatus. The obtained experimental data are further employed to develop simple and accurate models for predicting the flash points of binary miscible mixtures. Based on the vapor–liquid equilibrium theory, the normal boiling point, the standard enthalpy of vaporization, the average number of carbon atoms, and the stoichiometric concentration of the gas phase were selected as the dominant physicochemical parameters that were relevant to the overall flash point property of liquids. With these parameters for pure components as well as the compositions of mixtures, the new form of characteristic physicochemical parameters for mixtures were developed and used as the input parameters for the flash point prediction of mixtures. Both the modeling methods of multiple linear regression (MLR) and multiple nonlinear regression (MNR) were employed to model the possible quantitative relationships between the parameters for mixtures and the flash points of binary miscible mixtures. The resulted models showed satisfactory prediction ability, with the average absolute error for the external test set being 2.506 K for the MLR model and 2.537 K for the MNR model, respectively, both of which were within the range of the experimental error of FP measurements. Model validation was also performed to check the stability and predictivity of the presented models, and the results showed that both models were valid and predictive. The models were further compared to other previously published models. The results indicated the superiority of the presented models and revealed which can be effectively used to predict the FP of binary miscible mixtures, requiring only some common physicochemical parameters for the pure components other than any experimental flash point or flammability limit data as well as the use of the Le Chatelier law. This study can provide a simple, yet accurate way for engineering to predict the flash points of binary miscible mixtures as applied in the assessment of fire and explosion hazards and the development of inherently safer designs for chemical processes.