利用啤酒厂废水污泥培养钝顶螺旋藻和普通小球藻的研究

目前国内外对藻类的生物学特征、收集、藻蛋白的利用等进行了大量的研究＾「１」「２」。存在的问题之一是培养基的费用高,经济效益低＾「３」「４」。而啤酒厂污泥富含营养物质、无毒,是培养藻类的良好基质＾「５」「６」「７」。经试验能５０％替代营养盐培养钝顶螺旋藻而保持其收得率基本不变,能８０％夫代营养盐培养普通小球藻而保持其收得率基本不变。利用啤酒厂污泥既净化环境,又降低藻类养殖成本。这将为大规模推广藻类养     

FACTOR ANALYSIS OF A SIMULATED DATA MATRIX INVOLVING AQUEOUS COMPLEX EQUILIBRIA

Abstract factor analyses were performed on databases consisting of simulated samples from aqueousequilbria.The program COMPLEX was used to generate equilibrium species in a system of three reactantmetals and five reactant bases.Reactant concentrations and pH were drawn from random-normaldistributions so that sample data vectors comprised a multivariate log-normal distribution of equilibriumconcentrations.In addition,sample groups were created containing different distributions for pH andreactant concentrations.Equilibrium species were shown to contain variance contributed by change in pH among samples aswell as change in reactant concentrations.Factor modelling revealed the qualitative relationships amongthe species and how the relationships change with pH.Factors also revealed those reactants containingvariance in the data matrix.In some cases,reactant variance obscured relationships between pH and theequilibrium species.Since factor modelling of a simulated data matrix revealed the expected chemical equilibriuminteractions,a potentially powerful tool exists for investigating the effects of outliers and error.     

Evaluation of total load sediment transport formulas using ANN

The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers.     

基于Ricker子波匹配追踪算法在薄互层砂体储层预测中的应用

我国东部大多数中、新生代陆相含油盆地大都以薄层砂、泥岩沉积为主,地层岩性和厚度横向变化均较大,而这些地层的厚度远低于常规地震勘探的垂向分辨率。为解决薄互层储层预测问题,笔者综合分析了短时傅里叶变换、连续小波变换和匹配追踪算法的优缺点,通过实验模型及实际资料分析,得到以下结论:与短时傅氏变换与连续小波变换相比,基于Ricker子波匹配追踪算法的频谱分解技术在分析薄互层储层时具有更高的时频分辨率,能够更客观地刻画地质体;从平湖油气田地震、测井数据分析,基于Ric-ker子波匹配追踪算法更有效地刻画出储层的空间形态,并且与实钻数据储层的分布吻合效果好。