矿井瓦斯涌出量预测的灰色建模法

简要介绍了瓦斯涌出量的常用预测方法,指出了各种预测方法的弊端,从矿山实际出发,把非等间距数列变为等间距数列,根据灰色理论提出的预测方法,利用不同采深瓦斯涌出量的原始数据建立矿井瓦斯涌出量的动态GM(1,1)模型,进行瓦斯涌出量预测,选择了合理的误差检验模型,并通过实例说明了GM(1,1)模型在预测瓦斯涌出量中的应用,结果表明预测程度较高.对矿井延深做好瓦斯涌出量预测并进行矿井安全生产具有很好的指导意义.     

试论工程地质超前预报与施工安全

塌方是较常见的矿山事故,往往造成人员伤亡和设备损坏。为此,探索了一种适用于中长期和短期工程地质超前预报相结合的方法,来预报巷道施工过程中可能塌方的地段,并提出预防措施的具体方法。这种方法,在实践中取得了较好的效果。     

采煤工作面瓦斯涌出量预测的神经网络模型

正确预测瓦斯涌出量,对于指导矿井设计和安全生产有重要意义.为此,应用神经网络理论,建立了采煤工作面瓦斯涌出量的预测模型,对其影响因素进行了权重排序,并确定了关键因素.实际应用表明,预测模型可信,精度能满足要求.     

Simple relationships between the ionization energies of one-, two- and three-electron ions of consecutive elements

The ionization energy of one-electron ions can be calculated from a well-known equation that is based on quantum mechanics and on the Bohr model, but no theoretically justified equation is available for the calculation of the ionization energies of multi-electron ions. I report here simple empirical relationships between the ionization energies of one-, two- and three-electron ions of elements whose atomic numbers are Z, Z + 1 and Z + 2. On the basis of these relationships, an equation was constructed for the calculation of the ionization energies of two- and three-electron ions (IE(2el)(Z) and IE(3el)(Z), respectively) as a function of Z only: [equation: see text] where N = the number of electrons, i.e. 2 or 3. For N = 3, this equation is only valid when Z > N, being inaccurate for the neutral Li atom. Graphs of the difference between calculated and experimental values of the ionization energies as a function of Z reveal inaccurate experimental results that are impossible to detect by inspection of the ionization energy itself. On the basis of the present results, more accurate values can be predicted for these ionization energies. A striking example is the inaccuracy of the traditional handbook value of IE(3el)(Fe).     

A new method for predicting vegetation distributions using decision tree analysis in a geographic information system

Decision tree analysis was used to predict the distribution of forest communities in an area on the south coast of New South Wales, Australia. The analysis was carried out using a geographical information system environmental data base of those topographic and geological variables thought to influence the distribution of vegetation and derived from cartographic sources. The resulting maps of forest communities are of a resolution sufficient to delimit individual forest stands and contain much ecological information.     

GIS技术在环境影响评价中的应用

在对GIS系统的组成及主要功能进行介绍的基础上,综合性地阐述了GIS技术在环境影响评价中的应用。GIS技术可对环境影响进行模拟和预测,在环境影响评价中可对环境因素的确立、环境质量的描述和预测进行分析,并为环境政策的制定提供科学依据。     

炼油厂污水处理场恶臭污染源调查及预测研究

主要对某炼油厂污水处理场恶臭污染源进行调查,确定出了重点污染源与重点污染物。以污水场曝气池为例,使用风洞采样器进行采样,三点比较式臭袋法测定恶臭浓度,确定面源源强。通过恶臭评估软件预测出一次浓度、日均浓度、夏季平均浓度和全年平均浓度,并对预测结果进行了验证。     

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 (R²), 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.     

马尔柯夫预测模型在环境预测中的应用

马尔柯夫预测模型利用系统的现在状态和发展方向去预测系统将来可能状态的概率。它在选择污染治理策略，环境状态变化的可能方向及发展趋势预测等方面地产可应用，其预测结果为某事件实现的概率即可能程度。     

长春南湖水质分析评价与污染趋势预测

本文剃用内梅罗污染指数法时2000至2010年期间长春南湖水环境质量进行了综合分析评价,选取将氨氮、硝酸盐氮、亚硝酸盐氮、总氮、总磷、COD和BOD,作为水环境评价的主要参评参数。结果表明自2000至今南湖水体内梅罗综合指数呈现增加趋势,南湖水体污染等级处于中度污染水平,并有再次爆发水华现象的可能,因此应加大南湖水体污...