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Blue-coke production technology and the current state-of-the-art in China

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Carbon Resources Conversion 2020年第1期3卷 82-94页

作者： yanchun feng Shaoping Xu State Key Laboratory of Fine Chemicals Institute of Coal Chemical EngineeringSchool of Chemical EngineeringDalian University of TechnologyDalian 116024China College of Chemistry and Chemical Engineering Inner Mongolia University for the NationalitiesTongliao 028000China

Blue-coke is a kind of char obtained by the low temperature pyrolysis of non-caking or weakly-caking coal with high volatile *** is generally granular with high reactivity,high electrical resistivity and high in fixed carbon but low in ash,sulfur,aluminum and phosphorus *** has low price and extensive application decided by the raw coal and the production *** paper reviews pyrolysis reactor,process principle,technology characteristic and application state of blue-coke production technology in *** includes vertical oven technology,rotary kiln technology and rotary-hearth furnace technology with external-heating,and vertical oven technology,rotary-disc furnace technology and conveyor-belt furnace technology with *** to the existing problems in industrial blue-coke production technology,perspectives in terms of dust removal from volatiles,coke quenching and breakthrough of pyrolysis technology are given.

关键词： Blue-coke Pyrolysis reactor Production technology Low temperature pyrolysis Dust removal Coke quenching

Approach to lattice-related/content-specific spectral ranges of near-infrared diffuse reflectance spectroscopy of cefazolin sodium and the construction of a quantitative model for the determination of cefazolin sodium content in different crystal forms

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Science China Chemistry 2013年第6期56卷 789-798页

作者： LIU YanYun feng yanchun JIA YanHua HU ChangQin National Institutes for Food and Drug Control Hubei Institute for Food and Drug Control Institute of Medicinal Biotechnology Chinese Academy of Medical Science and Peking Union Medical College

Cefazolin sodium can form both α- and β-form crystals. It also can form dehydrated crystalline and amorphous products through different production processes. Because different polymorphic medicines usually have different physical and chemical properties, it is critical to emphasize the crystallization control of polymorphic medicines. Near-infrared （NIR） analysis, which incorporates a combination of NIR spectroscopic techniques and multivariate chemometric methods, is considered a powerful tool for the determination of the crystallinity of polymorphic drugs. The selection of optimal spectral ranges that correlate with the lattice specificity and content specificity is crucial to obtaining a specific NIR model. In the present work, near-infrared （NIR） spectra of cefazolin sodium with different crystal forms created through different processes were studied. The results suggest that wavelengths within the range of 9102.7-8597.5 cm-1 is related to the specificity of the cefazolin sodium crystal lattice and that the range of 6001.6-5496.4 cm i is associated with the quantitative content of cefazolin sodium. The two ab- sorptions are caused by the second overtone of the C-H stretching band （3nC-H） and the first overtone of C-H stretching band （2uC-H）, respectively. Using these results, we established a suitable method of constructing a universal quantitative model by using mixed samples in different crystal forms as a calibration set, selecting a content-specific range （6001.6-5496.4 cm-l）, and adding lattice-related spectral ranges where appropriate. This may provide a framework for the construction of prediction models for polymorphic medicines.

关键词： cefazolin sodium crystal form near-infrared spectroscopy crystal-specific model universal model

A new strategy to iteratively update scalable universal quantitative models for the testing of azithromycin by near infrared spectroscopy

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Science China Chemistry 2013年第4期56卷 533-540页

作者： ZOU WenBo feng yanchun DONG JiXiong SONG DanQing HU ChangQin National Institutes for Food and Drug Control Institute of Medicinal Biotechnology Academy of Medical Science and Peking Union Medical College

The training set of a universal near infrared (NIR) model for quantitative analysis of a drug should cover as many samples of this drug in the market as possible. Inevitably the model may fail for new products that have different excipients and production processes. In such circumstances the model should be updated. We here propose a new strategy to iteratively update a universal NIR quantitative model for azithromycin. We prove that universal quantitative models generated from this new strategy are comparably effective for azithromycin injection powders and azithromycin tablets, compared to the strategy using hierarchical clustering method which we reported previously. Furthermore, we establish the correlation coefficient r between a new sample and the training set samples can be used to decide whether or not the model should be updated.

关键词： near infrared spectroscopy universal quantitative model model updating azithromycin

高效液相色谱法测定β-内酰胺抗生素中残留乙酸的含量

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中国抗生素杂志 2024年第4期49卷 388-393页

作者：冯艳春裴文莉王晨朱俐田冶崇小萌宋暤昀姚尚辰宁保明中国食品药品检定研究院北京102629

目的建立测定β-内酰胺类抗生素原料中乙酸残留量的HPLC方法。方法采用多因素组内设计方法,确定色谱流动相的组成、比例和pH值。参考ICH Q2,从专属性、检出限和定量限、线性、准确度、精密度和耐用性等6个方面对方法进行验证。用所建立... 详细信息

目的建立测定β-内酰胺类抗生素原料中乙酸残留量的HPLC方法。方法采用多因素组内设计方法,确定色谱流动相的组成、比例和pH值。参考ICH Q2,从专属性、检出限和定量限、线性、准确度、精密度和耐用性等6个方面对方法进行验证。用所建立的方法对19种β-内酰胺类抗生素原料进行了测定。结果采用C_(18)色谱柱,流动相:甲醇-0.02 mol/L磷酸二氢钾,梯度洗脱;柱温25℃;检测波长220 nm。供试品、溶剂和流动相对乙酸测定没有明显干扰;乙酸的最低检出限约为0.029μg,最低定量限约0.12μg;从定量限到限度2倍浓度范围内,方法的线性关系良好,拟合直线方程为y=388866.7186x-177.5720,r2=0.9999;低中高浓度加样回收率平均分别为93%,96%和99%;方法的重复性和中间精密度分别为2.37%和2.11%;在3根不同C_(18)色谱柱上的耐用性良好。19种β-内酰胺抗生素原料中有3种筛查出残留乙酸。结论所建方法可以实现对β-内酰胺类抗生素原料中残留乙酸的准确定量,为该类原料的生产过程控制和标准物质研制提供了依据。

关键词： β-内酰胺类抗生素原料乙酸反相高效液相色谱法

Effects of Exogenous Trehalose on the Metabolism of Sugar and Abscisic Acid in Tomato Seedlings Under Salt Stress

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Transactions of Tianjin University 2019年第5期25卷 451-471页

作者： yanchun feng Xiuyu Chen Yulong He Xiaohong Kou Zhaohui Xue School of Chemical Engineering and Technology Tianjin University

Salt stress a ects the growth and development of plants, which results in a decrease in crop quality and yield. In this study, we used tomato seedlings treated with salt and trehalose as experimental materials and analyzed them using the technique for order preference by similarity to ideal solution analysis to select the optimal trehalose concentration for treatment. We also determined the contents of sugar and abscisic acid (ABA) and detected the expression of genes involved in the metabolism of sugar and ABA by quantitative real-time PCR. Results showed that the optimal trehalose concentration was 2 mmol/L for tomato seedlings under salt stress. Exogenous trehalose decreased the starch content and increased the soluble sugar con- tent by a ecting the expression of genes related to the metabolism of starch and soluble sugar. Exogenous trehalose altered the accumulation and distribution of sugar by inducing the upregulation of sugar transporter genes. Furthermore, trehalose increased the ABA content to induce salt stress response by regulating the expression of genes related to the synthesis and metabolism of ABA. In conclusion, trehalose can e ectively alleviate salt stress and enhance salt tolerance of tomato. These ndings provide a novel perspective and a better resource to investigate the salt tolerance mechanism and a new method for alleviating salt stress in tomato.

关键词： Tomato Trehalose Salt stress Sugar metabolism Sugar transporter Abscisic acid

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基于加热卷烟降温材料的预折叠收束系统设计

烟草科技 2024年第3期57卷 103-107页

作者：代怡薄立朗汪金鹏柴祎迪龙思宇冯炎春闫信四川中烟工业有限责任公司成都卷烟厂成都市610068 华中农业大学工学院武汉市430070

为解决加热卷烟降温基础棒横截面孔隙过大、圆度超标等问题,设计了一种降温材料预折叠收束系统。该系统由热压装置、折叠装置、收束装置、后段送料装置等部分组成,安装在开卷褶切机与ZL22D型滤棒成型机组之间,使多级相变降温膜纸在进入... 详细信息

为解决加热卷烟降温基础棒横截面孔隙过大、圆度超标等问题,设计了一种降温材料预折叠收束系统。该系统由热压装置、折叠装置、收束装置、后段送料装置等部分组成,安装在开卷褶切机与ZL22D型滤棒成型机组之间,使多级相变降温膜纸在进入烟枪前经过预热、折叠、压缩收束至近似降温基础棒的圆柱形态,以降低膜纸横截面的不规则应力。以ZL22D滤棒成型机组生产的9J11中式烤烟降温基础棒为对象进行测试,结果表明:预折叠收束系统应用后,降温基础棒圆度平均合格率增加72百分点,端面孔隙合格率达到100%。该技术可为提高加热卷烟滤棒质量提供支持。

关键词：加热卷烟多级相变降温膜纸折叠收束基础棒圆度滤棒成型机组

SpectraTr:A novel deep learning model for qualitative analysis of drug spectroscopy based on transformer structure

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Journal of Innovative Optical Health Sciences 2022年第3期15卷 107-117页

作者： Pengyou Fu Yue Wen Yuke Zhang Lingqiao Li yanchun feng Lihui Yin Huihua Yang School of Computer Science and Information Security Guilin University of Electronic Technology 1 Jinji Road Guilin 541004P.R.China School of Artīcial Intelligence Beijing University of Posts and Telecommunications 10 Xitucheng Road Beijing 100876P.R.China School of International Beijing University of Posts and Telecommunications 10 Xitucheng Road Beijing 100876P.R.China National Institutes for Food and Drug Control 10 Tiantanxili Road Beijing 100050P.R.China

The drug supervision methods based on near-infrared spectroscopy analysis are heavily dependent on the chemometrics model which characterizes the relationship between spectral data and drug *** preliminary application of convolution neural network in spectral analysis demonstrates excellent end-to-end prediction ability,but it is sensitive to the hyper-parameters of the *** transformer is a deep-learning model based on self-attention mechanism that compares convolutional neural networks(CNNs)in predictive performance and has an easy-todesign model ***,a novel calibration model named SpectraTr,based on the transformer structure,is proposed and used for the qualitative analysis of drug *** experimental results of seven classes of drug and 18 classes of drug show that the proposed SpectraTr model can automatically extract features from a huge number of spectra,is not dependent on pre-processing algorithms,and is insensitive to model *** the ratio of the training set to test set is 8:2,the prediction accuracy of the SpectraTr model reaches 100%and 99.52%,respectively,which outperforms PLS DA,SVM,SAE,and *** model is also tested on a public drug data set,and achieved classification accuracy of 96.97%without preprocessing algorithm,which is 34.85%,28.28%,5.05%,and 2.73%higher than PLS DA,SVM,SAE,and CNN,*** research shows that the SpectraTr model performs exceptionally well in spectral analysis and is expected to be a novel deep calibration model after Autoencoder networks(AEs)and CNN.

关键词： Near-infrared spectroscopy analysis drug supervision transformer structure deep learning chemometrics

Multi-manufacturer drug identification based on near infrared spectroscopy and deep transfer learning

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Journal of Innovative Optical Health Sciences 2020年第4期3卷 39-50页

作者： Lingqiao Li Xipeng Pan Wenli Chen Manman Wei yanchun feng Lihui Yin Changqin Hu Huihua Yang School of Automation Beijing University of Posts and Telecommunications 10 Xitucheng Road Beijing 100876P.R.China School of Computer Science and Information Security Guilin University of Electronic Technology 1 Jinji Road Guilin 541004P.R.China National Institutes for Food and Drug Control 10 Tiantanxili Road Beijing 100050 P.R.China

Near infrared(NIR)spectrum analysis technology has outstanding advantages such as rapid,nondestructive,pollution-free,and is widely used in food,pharmaceutical,petrochemical,agricultural products production and testing *** neural network(CNN)is one of the most successful methods in big data analysis because of its powerful feature ex-traction and abstraction ability,and it is especially suitable for solving multi-classification ***-based transfer learning is a machine learning technique,which migrates para-meters of trained model to the new one to improve the *** transfer learning strategy can speed up the learning efficiency of the model instead of learning from *** view of the difficulty in acquisition of drug NIR spectral data and high labeling cost,this paper proposes three simple but very effective transfer learning methods for multi-manufacturer identification of drugs based on one-dimensional *** with the original CNN,the transfer learning method can achieve better classification performance with fewer NIR spectral data,which greatly reduces the dependence on labeled NIR spectral *** the same time,this paper also compares and discusses three different transfer learning methods,and selects the most suitable transfer learning model for drug NIR spectral data *** with the current popular methods,such as SVM,BP,AE and ELM,the proposed method achieves higher classification accuracy and scalability in multi-variety and multi-manufacturer NIR spectrum classification experiments.

关键词： Near-infrared spectroscopy transfer learning drug identification multi-manufacturer

SEOE: an option graph based semantically embedding method for prenatal depression detection

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Frontiers of Computer Science 2024年第6期18卷 186911-186911页

作者： HAN Xiaosong CAO Mengchen XU Dong feng Xiaoyue LIANG yanchun LANG Xiaoduo GUAN Renchu College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center University of Missour Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Science and Technology Jilin Provincial Institute of Population Science and Technology

Prenatal depression, which can affect pregnant women’s physical and psychological health and cause postpartum depression, is increasing dramatically. Therefore, it is essential to detect prenatal depression early and conduct an attribution analysis. Many studies have used questionnaires to screen for prenatal depression, but the existing methods lack attributability. To diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires, we present the semantically enhanced option embedding (SEOE) model to represent questionnaire options. It can quantitatively determine the relationship and patterns between options and depression. SEOE first quantifies options and resorts them, gathering options with little difference, since Word2Vec is highly dependent on context. The resort task is transformed into an optimization problem involving the traveling salesman problem. Moreover, all questionnaire samples are used to train the options’ vector using Word2Vec. Finally, an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from depression. To verify the model, we compare it with other deep learning and traditional machine learning methods. The experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8. The most relevant factors of depression found by SEOE are also verified in the literature. In addition, our model is of low computational complexity and strong generalization, which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression