溶剂抽提-选择性加氢脱硫组合技术的开发及工业应用

针对以硫含量和烯烃含量高、芳烃含量低的催化裂化汽油为原料加氢脱硫生产满足车用汽油（Ⅴ）标准的汽油（简称国Ⅴ标准汽油）时辛烷值损失偏大的问题，开发了催化裂化汽油溶剂抽提-选择性加氢脱硫组合技术（简称RCDS技术）。中试结果表明，采用RCDS技术处理具有上述特点的催化裂化汽油生产国Ⅴ标准汽油时的RON损失比单独采用选择性加氢脱硫技术时减少0.9～1.9个单位。工业应用结果表明，采用RCDS技术处理硫质量分数为418～460 μg/g、烯烃体积分数为27.6%～27.9%、芳烃体积分数为19.2%～19.3%的清江石化催化裂化汽油，当产品硫质量分数降低至7 μg/g时，汽油RON损失仅为1.0～1.3个单位，且装置汽油收率高达99.9%。     

生物预处理技术在天然产物提取中的应用研究进展

生物预处理技术运用于天然产物提取是借助微生物或生物酶的作用，在适当条件下对天然资源进行处理，破坏植物原料的细胞壁，促进天然产物释放到提取介质中，以提高分离效率，有助于保证天然产物活性。生物预处理技术按形式可以分为微生物发酵技术和生物酶处理技术。针对2种生物预处理技术的作用机制和应用进行综述，旨在为天然产物开发利用提供参考。     

Isolation of peanut protein aggregates using aqueous extraction processing combined with membrane separation

A two-stage microfiltration and ultrafiltration (MF/UF) for peanut protein recovery and water recycling from aqueous extraction processing (AEP) were investigated. Peanut protein aggregates and alkaline water were obtained using flat and wound MF/UF and alkaline water recycled in AEP. With this approach, most of the wastewater could be recycled to produce reusable water to solve wastewater problems in aqueous oil extraction processing from oilseeds. The result showed that a little oil and protein were lost (1.21% and 4.35%, respectively) during the recycling of permeate in AEP, which was still acceptable. Peanut protein aggregations (PPAs) were characterised by analysis with Fourier-transform infrared spectroscopy (FTIR), sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE), high-performance gel filtration chromatography (HPGFC) and binding force analysis. Solubility analysis suggested that PPA had molecular structures with more hydrophobic groups on the surface than in the peanut protein isolate (PPI). In vitro, PPA was digested by both pepsin and trypsin more than PPI. FTIR profiles demonstrated there were more β-sheet and less α-helix in PPA, which means much more aggregation. In addition, binding force analysis showed hydrophobic interaction was the major force that restricted dissolution of PPA and disulfide bonds were of second importance, which provided a possible physical treatment opportunity for improving the solubility of PPA.     

用于盐湖卤水吸附法提锂的连续离子交换工艺研究

采用连续离子交换技术用于盐湖卤水的吸附法提锂。针对青海一里坪盐湖老卤体系,开发了连续离子交换吸附提锂工艺,研究了操作参数对连续离子交换系统提锂性能的影响,并在优化的工艺条件下进行了长周期的稳定性评价。结果表明,在转动步进周期为20 min、卤水进料量为3.2 BV/h、淋洗水量为2.9 BV/h、解吸水量为9.3 BV/h、解吸温度为15~25 ℃时,连续离子交换系统可以稳定获得镁锂质量浓度比（简称镁锂比）在3左右、锂质量浓度接近1.1 g/L的合格液,锂回收率为98.5%以上。     

磷酸脲母液制备工业级磷酸二氢铵工艺研究

湿法磷酸制备磷酸脲过程中会副产大量磷酸脲母液,母液含有金属杂质及五氧化二磷含量较高。采用溶剂萃取法回收磷酸脲母液中五氧化二磷制备工业级磷酸二氢铵（MAP）,分别考察了包括萃取、洗涤、中和、精制、结晶等工艺流程对产品收率及纯度的影响,确定了最佳工艺条件：萃取相比（有机相与水相体积比）=3:1,萃取时间为5 min,萃取温度为50 ℃,洗涤相比（有机相与洗涤剂体积比）=10:1,中和pH为4.5,复合酸性萃取剂PO8皂化率为20%,精制相比（萃取剂与水相体积比）=1:1,浓缩比为1.36,结晶温度为30 ℃。在该条件下制备的工业级MAP纯度可达99.45%,母液中五氧化二磷回收率为85.34%。该工艺对环境友好,原料中五氧化二磷回收率高,具有较高的经济效益。     

β-环糊精协同乙烯基咪唑离子液体脱除溶剂油中的萘

采用1-乙烯基-3-正丁基溴代咪唑离子液体（[VBIM]Br）为脱除剂，在β-环糊精协同作用下萃取溶剂油中的萘。本实验考察了不同单因素条件在[VBIM]Br离子液体与β-环糊精共同作用下对溶剂油中脱萘率的影响，同时通过响应面法优化脱除工艺条件。实验结果表明：[VBIM]Br离子液体与β-环糊精共同作用下对萘的萃取率比单独利用[VBIM]Br离子液体提高了15%以上，萃取率达到90%以上。离子液体简单回收后，重复使用5次过后，脱除率仍在80%以上。实验通过红外分析和紫外光谱分析了[VBIM]Br离子液体协同β-环糊精脱萘的机理，与[VBIM]Br离子液体协同作用下，β-环糊精对萘存在包合作用，实现了对溶剂油中萘的脱除。     

特厚煤层综放工作面瓦斯抽采技术研究

为解决蒋家河煤矿采空区瓦斯涌出量大、上隅角瓦斯超限和回风平巷风排瓦斯量大等问题,提出了本煤层预抽、专用瓦斯抽放巷抽采和上隅角埋管抽采瓦斯相结合的方法对该矿ZF202综放工作面进行瓦斯治理,进行了现场实测和瓦斯抽采效果分析。结果表明,本煤层预抽后,瓦斯含量由7.92 m^3/t下降为4.21 m^3/t,瓦斯压力由0.72 MPa下降为0.38 MPa;上隅角瓦斯浓度由0.78%下降至0.4%左右。通过对比,专用瓦斯抽放巷的抽采纯量是高位钻孔的2.5倍,抽采效果好于高位钻孔,使工作面和上隅角瓦斯浓度保持较低水平,有效地解决了特厚煤层综放工作面瓦斯超限问题,为安全生产提供了重要保障。     

基于多线结构光的弱纹理物体形貌测量方法

大多数现有的主流结构光测量装置在被测物体的表面上投射单条激光,通过移动装置实现物体表面的扫描重建。为了实现物体表面的在线快速重建,将多线结构光和双目立体视觉理论相结合,设计了一种新的三维测量方法。提出了一种结合形态滤波和Zhang-Suen细化算法的光条骨架提取方法和基于光条序列关系的光条匹配算法,并通过极线约束实现光条上特征点的精确匹配。通过实验验证了方法的有效性,相对误差在3%以内。     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.