Identification of aroma components of Spanish ‘Verdejo’ wine

Aroma components of ine resulting from fermentation of grape must from the Verdejo cultivar ere isolated by continuous liquid-liquid extraction and further fractionation on a silica gel column. One hundred and thirty-to substances, including alcohols, esters, carbonyl compounds, terpenes, acids and sulphur compounds, ere identified by using GC-MS; some had not been previously detected in ines. Data are given hich could lead to the characterisation of the Verdejo cultivar and the effect of the elaboration of young ines. The sensory contribution of some volatile compounds to the flavour of Verdejo ine is also studied.     

BP神经网络在致密砂岩储层识别中的应用

川西须家河组地层岩性复杂,属于超致密低孔渗储层,所以储层识别是该地层天然气勘探中所面临的关键问题和难点之一。针对常规储层识别准确率不高的状况,提出利用BP神经网络进行储层的气水干层识别,利用模糊聚类和产层测试结果标定建模样本,采取随机抽样形成建模集与测试集,建立BP神经网络模型对23口井的储层进行气水干层预测,正确率达77.9%以上,明显提高了该地区的测井解释精度,并提供了一种准确率较高的储层预测方法。     

Determination of Flow Curves at High Strain Rates using the Electromagnetic Forming Process and an Iterative Finite Element Simulation Scheme

The knowledge of reliable material parameters is an essential requirement for the numerical analysis and planning of forming processes, especially when using high speed forming processes in industry. Therefore, a method was developed and implemented to determine the relationship between yield stress, plastic strain, and strain rate of aluminium alloys at very high strain rates of up to 10,000 s-1 by using the electromagnetic forming process (EMF) of tubular specimens. To determine the associated flow curves, an inverse engineering strategy combining an online measurement technique and multi-physical coupled finite element simulations is used. The results are discussed with regard to the anisotropic behaviour of specific aluminium alloys.     

测井相识别模型及其在东海油气勘探中的应用

文章提出一种测井相识别模型,该模型用常规测井信息,定量地确定测井相在钻遇地层中的空间位置,建立测井相。配合东海油气勘探工作,用该识别模型对苏堤构造带的钻遇地层进行了测井相分析。     

陆相碎屑岩储层隔夹层成因、特征及其识别

陆相储层隔夹层是形成储层流体流动非均质的主要因素之一，也是储层精细表征的重要内容，油田勘探开发实践表明，陆相储层隔夹层主要可分为泥质隔夹层、钙质隔夹层和物性隔夹层三大类。根据这种分类，深入分析和总结了这三类隔夹层成因、岩性特征、分布特征和测井识别特征，并比较其区别，这对以后新老区块隔夹层研究有参考和适用价值。     

陆相断陷湖盆中层序边界的形成机理及其识别特征研究

陆相断陷湖盆中层序边界是由于沉积物表面高出沉积基准面或盆地基准面、沉积物发生剥蚀或无沉积作用所形成的，其产生机理主要有边界断层停止活动、断块翘倾活动、湖盆整体抬升和湖平面下降。层序边界在地震特征上、测井曲线上、钻井剖面中的岩性、岩相特征、古生物组合、微量元素的变化上均有明显的特征。利用这些特征，在济阳坳陷下第三系中识别出9个三级层序。     

谷氨酰胺(Gln)活性肽营养液的研究进展

对G1n小肽在氨基酸营养液中的使用以及G1n小肽的稳定性和代谢机制进行了论述。G1n是一种“条件必需氨基酸”，但Cln的水溶液不稳定，因而在氨基酸输液或氨基酸营养被中，用G1n小肽代替G1n单体使用。G1n小肽含有取代的α—NH2，使Cln小肽的稳定性大大增加。     

Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.     

致密砂岩储集层裂缝综合录井识别技术研究

四川盆地油气勘探结果表明，盆地油气储集层普遍为致密砂岩非常规储集层，有效性主要取决于孔、洞、缝发育程度，因此在油气钻井中如何识别致密储集层的孔、洞、缝并确定其所在井段尤其重要。针对综合录井仪的0．1m钻时及相关参数的资料采集技术要求，通过对大量录井数据资料的统计和演算，建立起储集层裂缝发育程度及裂缝发育位置的3种储集层裂缝识别模型，即直接识别模型、地层可钻性（A值）识别模型、钻时回归（RWSR）值识别模型。系统阐述了致密砂岩储集层裂缝综合录井识别技术方法原理，详细介绍了致密砂岩储集层裂缝识别的3个模型在实际生产中的应用情况。该技术方法的研究在国内尚属首次，为油气勘探中储集层评价提供了一个新的方法和手段，可最大限度地发挥现代录井技术在油气勘探开发过程中的作用。     

Discrete-time adaptive sliding mode power system stabilizer with only input/output measurements

In this paper a discrete-time adaptive sliding mode control method is newly developed and applied to the power system stabilization problem. A controllable canonical form of state space realization is constructed using the parameters identified by the on-line recursive least squares method and the system state is estimated from the input/output measurements and the simple state transformations. The identified parameters and the estimated state are then used by the discrete-time sliding mode control, which is suitable for the digital equipment. The most important advantage of the proposed power system stabilizer (PSS) is that it is able to maintain its regulating performance with a slower sampling period than that of the conventional sliding mode PSS because it is developed in a pure discrete-time domain. Another advantage of the proposed PSS is that it needs neither a mathematical model of the power system nor the full-state measurements because they are identified through on-line identifications. Several computer simulations for the linear power system are performed to verify the performance of the proposed PSS. In the computer simulations for various circumstances which are probable in a power system are considered, such as transitions of the active and reactive powers, change of parameters of the synchronous machine, line-to-ground faults and measurement noise. As a result, a new power system stabilizer which can operate in a wide range of operating conditions and can overcome various disturbances and measurement noises is proposed.