油罐扬沸火灾预测方法的研究及安全预警系统的建立

通过研究扬沸前兆噪声特性及其声发射机理，并与典型环境噪声特性差异作了分 析、比较，提取出一组识别特征物理量，以便在扬沸火灾早期能诊断预报扬沸的发生。     

降雨型泥石流（水石流）预报模型研究

根据泥石流暴发具有某种周期性的特点，在获取了大量野外调查资料和历史资料的基础上，对降雨型泥石流进行了预报模型研究，并以北京市北部山区密云县为例作了泥石流预报尝试。以层次分析法和多元回归方法所得出的泥石流空间预报和时间预报模型对于泥石流的预报具有重要意义。     

上海市生活垃圾的处理现状及对策探讨

阐述了上海市目前生活垃圾收运及处理情况，对２０００年上海生活垃圾的产量作了预测，对生活垃圾处理对策的分析中，提出技术，资金和管理等方面的支持条件。     

曲线拟合对空气污染物浓度的预测

通过对深圳市1991年－2000年环境空气中SO2浓度月均值,年均值变化规律的分析,得出了其数学表达式所需的函数特征,建立了曲线拟合方程,并通过实例计算及一系列统计回归检验,表明SO2浓度的月均值、年均值实际变化与拟合曲线结果基本一致。     

邕江水源地上游水环境容量计算及预测

通过计算邕江上游的水环境容量,对邕江水环境容量的价值做进一步的讨论,确保水源地的安全。分析了邕江水源地上游河段的水文特征,以具有代表性的COD和NH3-N为控制因子,选用水环境容量二维模型,按照水情保证率95%进行计算。得出2007年COD和NH3-N的已利用容量为2751.37t/a和117.63t/a。在对邕江水质进行评价的基础上,还预测出邕江水源地上游河段的COD和NH3-N剩余水环境容量呈上升趋势,这表明水源地上游水质安全。     

灰色马尔柯夫模型在地表水体DO浓度预测中的应用

灰色马尔柯夫模型是一种随机预测模型，被广泛的应用于社会，经济的预测中，本文利用灰色马尔柯夫模型对某平原河段DO浓度进行了预测。经验证，灰色马尔柯夫模型适用于对地表水体DO浓度预测，并具有相当高的精度，在此基础上，作者提出了改进的建议，使模型更具有实用性和可操作性。     

人工神经网络法在大气污染预报中的应用

以鞍山市为例,应用人工神经网络方法,模拟人脑的思维方式,建立了大气污染物浓度的神经网络预报模型,并将计算结果与监测值进行了对比验证,计算结果表明,BP模型应用于大气污染物浓度预报具有较高的预报精度。     

A decision support system for wet soil cleaning

Newly developed software can predict the essential characteristics of the sand product resulting from the ex-situ washing of contaminated soil, dredging sludge or breaker sand. The system was designed for the Dutch situation and it works on the basis of readily available information. The primary output of the system is an assessment of the reusability of the sand product, which is obtained by comparing the prediction of the residual contamination and engineering quality with accepted levels and standards for building materials. The system also provides a ranking of potential applications for the sand with respect to treatment cost, the amount of recycled material or the environmental quality of the product. The software integrates soil cleaning expertise and unit process modelling in a way that allows varying levels of refinement of input data, ranging from a simple identification of the pollution source to a detailed particle size distribution of the contaminated soil. Tests on a database containing information on 117 processed batches of soil, dredging sludge and breaker sand show an 80% success rate in predicting cleanability, even though the input contamination levels were predominantly taken from (relatively inaccurate) in-situ data.     

A new method for the prediction of flash points for ternary miscible mixtures

The flash point is one of the most important physicochemical parameters used to characterize the fire and explosion hazard for flammable liquids. The flash points of ternary miscible mixtures with different components and compositions were measured in this study. Four model input parameters, being normal boiling point, the standard enthalpy of vaporization, the average number of carbon atoms and the stoichiometric concentration of the gas phase for mixtures, were employed and calculated based on the theory of vapor–liquid equilibrium. Both multiple linear regression (MLR) and multiple nonlinear regression (MNR) methods were applied to develop prediction models for the flash points of ternary miscible mixtures. The developed predictive models were validated using data measured experimentally as well as taking data on flash points of ternairy mixtures from the literature. Results showed that the obtained average absolute error of both the MLR and the MNR model for all the datasets were within the range of experimental error of flash point measurements. It is shown that the presented models can be effectively used to predict the flash points of ternary mixtures with only some common physicochemical parameters.