A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.     

对江南煤矿井上下立体化抽采工艺分析

为了解决对江南煤矿“先抽后建”以及煤与煤层气协调开发的问题,根据对江南煤矿采掘部署以及首采煤层M78煤体结构好、气含量高、渗透性较好的特点,对首采面提出了地面水平井和丛式井相结合的方式进行高效地面预抽瓦斯,而对较晚开采的备用面和准备面采用丛式井地面预抽方式。模拟显示,地面抽采5年后,首采面M78煤层瓦斯有了大幅度降低,最高降低幅度超过50%。在地面预抽后井下采用底板排水巷定向长钻孔抽采技术,实现井上下立体化抽采。该工艺降低了煤矿瓦斯治理投资,提高了煤炭安全生产的效率,同时可充分有效地利用煤层气资源。     

Digital Extraction Column: Measurement and Modeling Techniques

The digitization of extraction columns requires a profound knowledge of the present hydrodynamics/mass transport interaction as well as appropriate measurement techniques for the detection of relevant input and target values. In this article, the different techniques for droplet size distribution as well as concentration determination are presented and new methods for online evaluation are discussed. In combination with the simulation of droplet size, holdup and solute concentration distribution, an online-capable process tool for controlling and optimizing extraction columns will be obtained.     

基于VMD与时间序列分析的滚动轴承故障特征提取方法

为了在非线性、非平稳的滚动轴承故障振动信号中有效提取出敏感的故障特征,提出了基于变分模态分解(VMD)与时间序列分析相结合的特征提取方法。首先通过VMD将原始信号分解为不同预设尺度的本征模态分量(IMF),对各个IMF分量建立时间序列预测模型,通过叠加重构得到最终的预测模型,比较评价指标确定最优参数的选取。最后,通过仿真信号与滚动轴承实际故障数据分析,并与经验模式分解(EMD)进行对比,结果表明该方法能够有效的提取到故障特征频率。     

Parameters extraction of PEMFC's model using manta rays foraging optimizer

In this article, a recently developed bio-inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non-linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic-based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H-12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.     

Autochthonous white grape pomaces as bioactive source for functional jams

Seeds and skins from grape pomaces of Pecorello and Mantonico cv underwent extraction (ultrasound–assisted or maceration), in order to obtain added-value ingredients for the production of a functional pear jam. The antioxidant features of the extracts were tested by in vitro colorimetric assays. Among seeds, Mantonico by maceration (MSC) showed the best results, as well as Mantonico by ultrasound-assisted extraction (MBs) among skin extracts. The selected extracts were fully characterised by NMR and MS techniques, confirming the presence of many polyphenols, flavonoids and tannins among others. Pectin was then derivatised by the grafting procedure with the active molecules of MBs and MSC. The latter produced the best antioxidant polymer also without toxicity evaluated using Caco-2 cells and was used for the jam preparation. The functional pear jam showed improved antioxidant performances in comparison with its non-functional counterparts as well as its maintenance over time (15 days).     

Extraction of Trivalent Rare Earths and Minor Actinides from Nitric Acid with N,N,N’,N’-Tetradodecyldiglycolamide (TDdDGA) by Using Mixer-Settler Extractors in a Hot Cell

Single-stage batch experiments to reveal the extraction properties of N,N,N’,N’-tetradodecyldiglycolamide (TDdDGA) for Y, La, Eu, Nd, and Am in nitric acid were carried out. The distribution ratios of Y, Eu, Nd, and Am exceeded 10 when the nitric acid concentration was 1–2 mol/dm³ (M), and the distribution ratio of La was 5.5 when the nitric acid concentration was 2 M. A continuous counter-current experiment using 0.1 M TDdDGA diluted with n-dodecane was performed using mixer-settler extractors installed in a hot cell. Nitric acid with a concentration of 2.1 M containing minor actinides (MAs: Am and Cm), rare earths (REs: Y, La, Nd, and Eu), and other fission products (Sr, Cs, Zr, Mo, Ru, Rh, and Pd) was fed to the extractor. TDdDGA effectively extracted MAs and REs from the feed, while other fission products were barely extracted. The extracted MAs and REs were back-extracted by bringing them in contact with 0.02 M nitric acid, and they were collected as the MA–RE fraction. The results indicated that more than 98% of Am and Cm in the feed were recovered in the MA–RE fraction. The proportions of Y, La, Nd, and Eu in the MA–RE fraction were 94.0%, 99.9%, 99.9%, and 86.9%, respectively.     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

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.