Applications of Diglycolamide Based Solvent Extraction Processes in Spent Nuclear Fuel Reprocessing,Part 1: TODGA

Over the last decade there has been much interest in the applications of diglycolamide (DGA) ligands for the extraction of the trivalent lanthanide and actinide ions from PUREX high active raffinates or dissolved spent nuclear fuel. Of the DGAs, the N,N,N’,N’-tetraoctyldiglycolamide (TODGA) is the best known and most widely studied. A number of new actinide separation processes have been proposed based on extraction with TODGA. This review covers TODGA-based processes and extraction data, specifically focusing on how phase modifiers have been used to increase metal loading and thus enhance the operating process envelopes. Effects of third phase formation and the organic phase speciation are reviewed in this context. Relevant aspects of the extraction chemistry of important solvents (TODGA-modifier-diluent combinations) are described and their performances demonstrated by a consideration of the published flowsheet tests. It is seen that modifiers are successfully enabling the use of TODGA in actinide separation processes but to date the identification and testing of suitable modifiers has been rather empirical. There is a growing understanding of the fundamental chemistry occurring in the organic phase and how that affects extractant speciation and metal loading capacity but studies are still needed if TODGA-based flowsheets are to become an industrially deployable option for minor actinide (MA) recovery processes.     

Development of LTCC micro-hotplate with PTC temperature sensor for gas-sensing applications

Low temperature co-fired ceramic (LTCC) micro-hotplates show wide applications in gas sensors and micro-fluidic devices. It is easily structured in three-dimensional structures. This paper presents the low power consumption micro-hotplates which were developed with PTC (positive temperature coefficient) temperature sensor and inter-digitated electrodes. The paper presents two different structures for micro-hotplate with platinum as a heating element. The PTC temperature sensor using two different materials viz. PdAg and platinum paste are developed with micro-hotplates. The simulation has been achieved through COMSOL for LTCC and alumina micro-hotplates. The temperature variation with power consumption has been measured for the developed LTCC micro-hotplates. The change in resistance of PTC temperature sensors was measured with micro-hotplate temperature. The aim of this study was to place a temperature sensor with the gas sensor module to measure and control the temperature of micro-hotplate. A SnO₂ sensing layer is coated on LTCC micro-hotplate using screen printing and characterized for the sensing of carbon monoxide gas (CO). This study will be beneficial for designing hotplates based on LTCC technology with low power consumption and better stability of temperature for gas-sensing applications.     

Methodology for optimization of pouring variables using bottom pour for low masses of SS304 (<300 kg) in investment casting process (case study)

Bottom pour ladles with stopper rod systems are commonly used in the metal casting industry. However, stopper rod bottom-pouring systems have not yet been developed for the lower thermal masses of alloys typically used in the investment casting industry. Large thermal masses used with bottom pour systems are typically limited for ladles larger than 700 kg and to certain alloys with higher fluidity and longer solidification time like cast iron, aluminum alloys etc. In this study, bottom pour ladle designs and low thermal mass refractory systems have been developed and evaluated in production investment foundry trials with 300 kg pouring ladle. The ladles system and pouring practices used will be described along with the results from the pouring trials for SS304 that represents typical alloys used in Investment casting industries. Optimization of the variables used in an experimentation using Genetic algorithm is also explained.     

Development of mobile miniature natural gas liquefiers

With increasing consumption of natural gas (NG), small NG reservoirs, such as coalbed methane and oil field associated gas, have recently drawn significant attention. Owing to their special characteristics (e.g., scattered distribution and small output), small-scale NG liquefiers are highly required. Similarly, the mixed refrigerant cycle (MRC) is suitable for small-scale liquefaction systems due to its moderate complexity and power consumption. In consideration of the above, this paper reviews the development of mobile miniature NG liquefiers in Technical Institute of Physics and Chemistry (TIPC), China. To effectively liquefy the scattered NG and overcome the drawbacks of existing technologies, three main improvements, i.e., low-pressure MRC process driven by oil-lubricated screw compressor, compact cold box with the new designed heat exchangers, and standardized equipment manufacturing and integrated process technology have been made. The development pattern of “rapid cluster application and flexible liquefaction center” has been eventually proposed. The small-scale NG liquefier developed by TIPC has reached a minimum liquefaction power consumption of about 0.35 kW·h/Nm³. It is suitable to exploit small remote gas reserves which can also be used in boil-off gas reliquefaction and distributed peak-shaving of pipe networks.     

燃气热水器在无回水管水路中实现零冷水的方案研究

本文分析了燃气热水器行业针对无回水管水路系统实现零冷水功能的现有技术方案,指出现有技术方案存在的一些缺陷,并结合试验研究,对无回水管水路系统提出了一套全新的实现零冷水功能的技术解决方案。     

含煤盆地深层“超饱和”煤层气形成条件

中国深层煤层气资源丰富，但总体勘探和认识程度较低，尚未形成较为系统的深层煤层气地质理论。通过解剖分析准噶尔盆地白家海凸起和鄂尔多斯盆地临兴区块深层"超饱和"煤层气井的试气/生产动态，估算原地游离气的含气量，分析了深层"超饱和"煤层气的形成条件。研究表明：①深层"超饱和"煤层气储层中除吸附气外，还含有原地游离气，用常规试气方法可直接获得气流，煤层气的产出不明显依赖于排水降压；②埋藏超过一定深度，在煤阶和温度的综合作用下，煤的吸附能力将随埋深的继续增加而降低，煤层中吸附气的饱和度有增加的趋势，在达到吸附饱和后，出现原地游离气并形成"超饱和"煤层气，盆地深层具有"超饱和"煤层气形成的优势条件；③由于地温梯度和压力梯度的不同，不同盆地"超饱和"煤层气出现的临界深度不同，异常高压和异常高热流可以降低深层"超饱和"煤层气形成的临界深度；④深层"超饱和"煤层气开发具有大大缩短见气时间、充分利用地层能量和累积产水量低等优势，有望成为未来煤层气勘探开发的一个重要领域。     

Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

四川盆地高石梯-磨溪地区下二叠统气源示踪

以天然气、烃源岩及储层沥青的地球化学特征为主要依据,通过天然气-天然气、储层沥青-烃源岩、天然气-烃源岩3个方面对比,深入探讨四川盆地高石梯-磨溪地区下二叠统天然气来源。研究表明,四川盆地高石梯-磨溪地区下二叠统天然气主要来源于筇竹寺组泥岩。其5项证据分别为:下二叠统天然气乙烷碳同位素小于-30.5‰,甲、乙烷碳同位素倒转是由高演化程度烃源岩所致;多数下二叠统天然气与龙王庙组天然气特征最为相似;GS18,NC1井下二叠统天然气特征与寒武系相似,说明上下层气源一致;高石梯-磨溪地区下二叠统储层沥青地球化学特征与筇竹寺组泥岩最为相似;筇竹寺组泥岩主要为下二叠统供气,这一认识符合干酪根油气生成理论和同位素分馏规律。     

因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.