基于 IEC ６１９７０ 的调度运行管理系统设计

针对电网运行智能化要求,建立了针对多维度调度数据的运行管理系统.首先分析了目前调度运行管理中在数据处理和分析方面存在的问题,并说明了设计系统应满足的功能.接着基于面向服务构架理念,设计了基于 IEC ６１９７０ 的调度运行管理系统,分析了其功能和系统特点,从数据采集方案、数据整合流程、数据安全构架等方面分析了数据管理方案.最后从调度报表的应用情况说明了所设计系统的有效性.     

ZnO压敏防雷芯片工频耐受与通流能力的相关性

为了研究ZnO压敏防雷芯片的工频过电压耐受特性和8/20μs通流能力的相关性,在生产线上抽取同批次产品进行工频过电压耐受试验和8/20μs通流能力试验。研究结果表明:工频过电压老化后会使压敏电阻的通流能力下降,但经过8/20μs雷电流峰值20 kA冲击后,压敏电阻的工频过电压耐受能力会增强,在冲击15次左右时达到最大值,随着冲击次数的增多,工频过电压耐受能力会降低。     

硅灰对超轻水泥基复合保温材料性能的影响

超轻水泥基复合保温材料(UCIM)是以水泥为胶凝材料,膨胀聚苯乙烯泡沫塑料(EPS)颗粒、掺合料、泡沫剂、改性剂和水等为主要原料,采用物理发泡工艺制备而成。UCIM由EPS颗粒与泡沫混凝土基体互穿构成,不同品种的掺合料等效替代水泥后,能不同程度影响水泥浆体对EPS颗粒的包裹性,从而影响UCIM结构的均匀性与制品性能。通过设计不同掺量的掺合料,对比硅灰、偏高岭土及矿粉所制备的UCIM的均匀性及强度,结果表明,当采用硅灰时,UCIM未产生分层离析现象且制品强度试验结果较好;通过微孔拍摄及强度、热工性能测试,系统研究了硅灰掺量对UCIM的泡沫混凝土基体的孔结构、强度和导热系数的影响,结果表明,适宜掺量的硅灰能提高UCIM的力学性能,使UCIM的泡沫混凝土基体的平均孔径减小,进而有利于降低UCIM导热系数。     

人为Pb,Cd污染条件下土壤磁性与重金属关系

该研究通过对昆明呈贡区某一实验样地人为添加Pb、Cd污染,将其分为未添加Pb、Cd和分别添加Cd、Pb三组,采用单因子污染指数和内梅罗综合污染指数法进行污染评价,结果显示研究区存在一定程度的土壤重金属污染问题,人为活动是主要污染原因。同时利用Pearson相关和主成分分析方法探讨人为Pb,Cd污染条件下土壤磁性参数(χ_lf、χ_fd、ARM、SIRM)与土壤重金属(Pb、Cd、Hg、As、Cr、Cu、Fe、Zn、Al₂O₃)之间的关系。研究结果表明：研究区土壤人为添加Pb时,磁参数与重金属之间的关系影响不大,而人为添加Cd时,磁参数与重金属之间的相关性有所变化,除重金属Cu、Zn一直与磁参数具有相关性,其他重金属元素要么一直与磁参数不相关,要么两者关系均有所改变。     

碳中和背景下燃煤烟气重金属污染物控制技术展望

双碳背景下,我国能源结构将构建新的电力能源体系。在今后较长一段时期,燃煤火电是我国能源安全的基石,清洁低碳是发展方向,烟气重金属排放控制是其关键研究领域。光催化、双金属硫化物脱除等重金属净化技术方兴未艾,多种污染物协同脱除具有广阔的应用前景。     

Determination of Six Paraben Residues in Fresh-cut Vegetables Using QuEChERS with Multi-walled Carbon Nanotubes and High-Performance Liquid Chromatography–Tandem Mass Spectrometry

In this study, an optimized QuEChERS sample preparation method was developed to analyze residues of six parabens: methyl-, ethyl-, n-propyl-, isopropyl-, n-butyl-, and isobutylparaben in five fresh-cut vegetables (potato, broccoli, carrot, celery, and cabbage) with high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS). Homogenized samples were extracted using acetonitrile, and the extracts were cleaned with the novel sorbent multi-walled carbon nanotubes (MWCNTs). MWCNTs provided 84–94% removal of chlorophyll and lower matrix effects (MEs) compared to commonly used primary-secondary-amine (PSA) sorbent. Selected parabens were separated by HPLC with isocratic elution using acetonitrile and 0.1% (v/v) formic acid solution and determined by triple quadrupole MS/MS. The method validation results showed that recoveries were at 70–120% with RSDs <20%. Calibration curves showed linear responses of six parabens with R ² > 0.99. Fifty fresh-cut vegetable samples from different farmer markets in Beijing, China were collected to measure the paraben residues, and only one sample was tested positive with methylparaben concentration at 81 μg/kg.     

水平井产能影响因素综合分析

目前就如何准确预测水平井产能这一问题,国内外还存在着很大的分歧。综述了目前国内外预测水平井产能的5种计算方法,并给出了当储层存在着渗透率各向异性和地层损害时修正的产能计算公式。对水平井产能公式进行了分析,并对其相关计算中排驱面积、水平井段长度、油层厚度、渗透率各向异性、地层损害及一些采油工艺等因素对水平井产能造成的影响进行了综合评价,最后提出了相关建议。     

马来酰亚胺类杀菌剂的制备与性能评价

报道了2种马来酰亚胺类化合物Ⅳ-苯基马来酰亚胺和Ⅳ-苄基马来酰亚胺的制备过程及其对石化污水中的三种主要菌类即异养菌、铁细菌和硫酸盐还原菌的静态杀菌性能,并将其与目前常用的异噻唑啉酮和“1227”(十二烷基二甲基苄基氯化铵)进行了比较。结果表明,Ⅳ-苄基马来酰亚胺是一种高效杀菌剂。其性能优于异噻唑啉酮,与“1227”相比,其1h的杀菌效果稍弱,但其持久杀菌效果远好于“1227”。N-苯基马来酰亚胺对异养菌和铁细菌有一定的杀菌效果。但对硫酸盐还原菌无效。     

An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

Deep learning rapidly promotes many fields with successful stories in natural language processing. An architecture of deep neural network (DNN) combining tree-structured long short-term memory (Tree-LSTM) network and back-propagation neural network (BPNN) is developed for predicting physical properties. Inspired by the natural language processing in artificial intelligence, we first developed a strategy for data preparation including encoding molecules with canonical molecular signatures and vectorizing bond-substrings by an embedding algorithm. Then, the dynamic neural network named Tree-LSTM is employed to depict molecular tree data-structures while the BPNN is used to correlate properties. To evaluate the performance of proposed DNN, the critical properties of nearly 1,800 compounds are employed for training and testing the DNN models. As compared with classical group contribution methods, it can be demonstrated that the learned DNN models are able to provide more accurate prediction and cover more diverse molecular structures without considering frequencies of substructures.