Growth process and short chain alcohol separation performance of fluoride-containing NaY zeolite membrane

Growth process of the NaY zeolite membranes was investigated by fluoride-containing precursor synthesis gel.Compared with the fluoride-free precursor synthesis gel,the irregular NaY zeolite crystals were dissolved into amorphous by the fluoride-containing precursor synthesis gel initially,the amorphous contained the Y-type zeolite characteristic bands by the IR characterization.The fine square NaY zeolite crystals arose from the amorphous,which were accumulated and gradually grew into a dense NaY zeolite layer on the support surface after 6.5 h.Because the excessive NaY zeolites were dissolved by the strong alkaline and fluoride-containing precursor synthesis gel,there was plenty of amorphous on NaY zeolites layer for prolonging the crystallization time.The assynthesized NaY zeolite membranes had a good separation performance and repeatability for separation of 10 wt%methanol(MeOH)/methyl methacrylate(MMA) mixture by pervaporation,the flux and separation factor were(1.27 ± 0.07) kg·M^(-2)·h^(-1) and(4900 ± 1500) at 323 K,respectively.Besides,the NaY zeolite membranes were applied to separate the other short chain alcohol from the various alcohol/organic ester and alcohol/organic ether mixtures,the NaY zeolite membranes showed high short chain alcohol perm-selectivity.

Short-circuit process model in underwater welding using rotating arc sensor

In order to detecting and tracking along the weld seam with rotating arc sensor in underwater welding,the highpressure water environment rotating arc welding hardware platform is established and welding experiments using rotating arc sensor is done. Different radius of rotating arc sensor is used. And the corresponding welding current and voltage is obtained,which is compared with the results of rotating arc sensor short-circuit process simulation model under high-pressure water environment established in this article. The results show that under high-pressure water environment,rotating arc radius should be optimized,otherwise the short-circuit-arcing cycle will transit to a short-circuit-arcing-abruption cycle,making the welding quality poor. At last the critical radius between the short-circuit-arcing cycle and short-circuit-arcing-abruption cycle under high-pressure water environment is obtained.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

Models of the Short Interest Rate in Discrete Processes

The modeling of the term structure of interest rates is one of primary topics for researches in financial economics. Here we consider models of the short interest rate in discrete processes. Our methodology of analysis follows the framework of discrete stochastic calculus.

A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

Process disturbances monitoring and recognition of short-circuiting GMAW by fuzzy c-means system

An intelligent fuzzy c-means system for process monitoring and recognition of process disturbances during short- circuiting gas metal arc welding （GMAW） is introduced in this paper. The raw measured and statistically test data of probability density distribution （ PDD ） and class frequency distribution （ CFD ） of welding electrical parameters are further processed into a 7-dimensional array which is designed to describe various welding conditions, and is employed as input vector of the intelligent fuzzy c-means system. The fuzzy c-means system is used to conduct process monitoring and automatic recognition. The correct recognition rate of 24 test data under 8 kinds of welding condition is 92%.

Short wet-steam dyeing process of cotton fabrics with reactive dyes

The effects of Reactive Black 5 utilized to cotton fabrics by short wet-steam process on the dyeing properties were investigated. This study will provide a theoretical reference for short wet-steam process of cotton fabrics with bifunctional reactive dyes. The optimal amount of Selilao agent was 20 g/L, while the soaping and rubbing fastness of the dyed cotton fabrics were both reached to 4-5 rating.

底吹搅拌技术在电弧炉中的应用与发展

发展电弧炉短流程炼钢是钢铁工业实现“双碳”目标的重要途径。近年来,现代电弧炉技术发展迅速,电弧炉底吹搅拌技术也在不断进步和推广,对电弧炉生产的高效化和节能化作出了重要贡献。针对电弧炉底吹搅拌技术的机理、工艺特点、系统组成、气体种类、底吹元件类型及工艺布置方式进行综述,并对底吹技术的应用情况进行总结分析。模拟计算得出,远离底吹装置的区域,钢液流速明显偏低。结合炉型特点布置底吹装置及优化底吹气量,可改善熔池动力学条件,促进钢液元素传质,实现降低能耗和原材料消耗、提高生产效率的效果。深度开展底吹技术研发,与吹炼、供电、喷粉等工艺技术耦合,并结合新炉型特点制定底吹工艺制度,实现智能化底吹是发展现代电弧炉短流程技术的重要方向。

融合Mar-GLSTM的流程生产工艺质量预测算法

针对流程生产连续性强、时序耦合复杂等特点,传统神经网络不具备长期记忆能力,且在深层次网络训练时易出现训练参数灾难、梯度爆炸等问题,提出基于马尔可夫优化的融合门控循环单元(GRU)与长短期记忆网络(LSTM)的组合预测模型(Mar-G LSTM)。首先在循环神经网络结构中融入门控机制构建深度LSTM神经网络模型,对流程生产时序数据信息进行选择性记忆,学习时序数据序列的信息依赖,进而解决训练过程中的梯度爆炸问题;同时结合马尔可夫链对GRU-LSTM模型的预测结果进行修正优化,在降低模型的复杂度的情况下进一步提高了模型的预测精度。最后,结合某流程生产线的工艺数据进行分析验证,结果表明,Mar-G LSTM算法在预测精度上较随机森林模型、门控循环单元神经网络模型(GRU)、长短期记忆神经网络模型(LSTM)和卷积神经网络与门控循环单元网络组合模型(CNN-GRU)分别提高了37.42%、21.32%、17.91%和12.56%,所提Mar-G LSTM算法可实现流程生产质量的准确预测,为降低工艺参数调控任务的完成时间提供了思路和实现途径。

基于跳聚集现象随机波动率短期利率模型的影响研究

构建了具有自我激励机制跳的随机波动率短期利率模型,应用Hawkes过程描述自我激励机制的跳,从而刻画了跳的聚集现象。基于微分算子展开给出精确的矩函数,进一步应用广义矩方法给出模型的参数估计值和统计推断。实证结果揭示了在随机波动模型条件下,引入自我激励机制跳的模型将不会明显地改变了拟合效果,但是在统计意义上接受强度满足Hawkes过程,而且所构建的模型也能很好地刻画跳的聚集现象。最后,使用过滤方法给出随机波动率、跳的幅度、跳的概率和随机跳强度的估计,特别是跳的概率估计值可作为市场压力测试的一个重要指标。

基于双重分解和双向长短时记忆网络的中长期负荷预测模型

针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。

基于注意力机制的短视频助力导论类课程教学改革研究——以人工智能导论课程为例

以人工智能导论为代表的导论类课程具有课时少、知识点多、算法难、学生基础差异大等特点,给教学带来诸多难点,如难以平衡深度和广度,难以持续吸引注意力等。利用短视频的多形式表达、多通道传输、高密度信息等优点可以解决导论类课程教学难题。该文在剖析课堂注意力特点和短视频吸引注意力机制的基础上,运行注意分配的认知理论,提出一套基于注意力机制的短视频助力导论类课程的教学方法,可为导论类教学工作者提供参考。

The Validity of the Thermohydrogravidynamic Theory Concerning the Predicted Dates of the Maximal Temporal Intensifications of the Global Seismotectonic Processes of the Earth during the Range 2020 - 2023 AD

We present (on the 13th International Conference on Geology and Geophysics) the convincing evidence that the strongest earthquakes (according to the U.S. Geological Survey) of the Earth (during the range 2020 - 2023 AD) occurred near the predicted (calculated in advance based on the global prediction thermohydrogravidynamic principles determining the maximal temporal intensifications of the global seismotectonic, volcanic, climatic and magnetic processes of the Earth) dates 2020.016666667 AD (Simonenko, 2020), 2021.1 AD (Simonenko, 2019, 2020), 2022.18333333 AD (Simonenko, 2021), 2023.26666666 AD (Simonenko, 2022) and 2020.55 AD, 2021.65 AD (Simonenko, 2019, 2021), 2022.716666666 AD (Simonenko, 2022), respectively, corresponding to the local maximal and to the local minimal, respectively, combined planetary and solar integral energy gravitational influences on the internal rigid core of the Earth. We present the short-term thermohydrogravidynamic technology (based on the generalized differential formulation of the first law of thermodynamics and the first global prediction thermohydrogravidynamic principle) for evaluation of the maximal magnitude of the strongest (during the March, 2023 AD) earthquake of the Earth occurred on March 16, 2023 AD (according to the U.S. Geological Survey). .

基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。

基于空间信息关注和纹理增强的短小染色体分类方法

染色体分类是核型分析中的重要任务之一。尽管残差神经网络已经在染色体分类领域取得了显著成就,但由于部分染色体具有长度较短、分类特征难以识别以及形态相似度较高的特点,使得其分类仍然具有挑战性。提出了基于空间信息关注和纹理增强的染色体分类模型(SIATE-Net),以Inception_ResNetV2模型作为骨干网络提取染色体的深层特征,自注意力机制和深度可分离卷积的引入能够更好地关注和保留短小染色体的空间信息。染色体长度较短易造成显带信息混淆,模型融入了纹理增强机制以扩大染色体间的差异性,为分类任务增加更多的判定依据。SIATE-Net模型分别在私人数据集与公开数据集上进行验证,分类性能明显优于其他方法,尤其是短小染色体。在私人数据集上,SIATE-Net模型表现出了最佳的总体分类准确率98.05%,短小染色体分类精度高达97.42%。在公开数据集上,SIATE-Net模型的总体分类准确率为98.95%,而短小染色体也达到了98.51%。实验结果表明,具有较强针对性的自注意力模块、深度可分离卷积和纹理增强模块在不降低整体分类准确性的前提下,能够有效地解决短小染色体分类任务。

基于人工神经网络的自然语言处理技术研究

文章探讨了基于人工神经网络的自然语言处理技术,首先,阐述了人工神经网络的定义、结构、工作原理,以及与深度学习的关系。其次,详细研究了基于人工神经网络的自然语言处理技术,包括神经网络模型、词嵌入技术、循环神经网络、长短期记忆网络、转换器模型与自注意力机制等,并分析了这些技术面临的挑战。最后,通过实验设计与结果分析验证了所提出方法的有效性。文章研究内容对于推动自然语言处理技术的发展和应用具有重要意义。

多用户复杂网络信息流短时预测方法

当前的多用户复杂网络信息流短时预测模型多为单一结构,预测的范围较小,为此提出多用户复杂网络信息流短时预测方法。根据实时的信息流预测需求及标准的变化,设定最大预测误差范围,采用多阶的方式构建多阶短时预测模型,建立短时模糊预测流程,构建多用户复杂网络信息流短时预测模型,采用自适应修正处理,实现信息流预测。测试结果表明,设计方法的信息流的短时预测F值均可以达到0.95以上,表明该方法的泛化能力与针对性均得到增强,可以大范围地精准预测信息流。

厌氧氨氧化工艺在主流污水处理中的应用及其调控研究进展

厌氧氨氧化工艺(Anammox)具有低能耗、低产泥量、高脱氮性能等特点,在处理垃圾渗滤液、污泥消化液等领域的应用与研究得到了广泛重视。尽管如此,受制于氨氮浓度低、有机物与水量波动大、低温等问题,影响了其在城市污水主流工艺的应用。本文在介绍厌氧氨氧化工艺的基础上,总结了以厌氧氨氧化为基础的组合工艺处理城市污水案例,分析了当前厌氧氨氧化工艺应用于城市污水处理的限制性因素以及新型调控手段与策略。进一步展望了厌氧氨氧化应用于城市污水主流工艺的发展方向,以期为厌氧氨氧化应用到主流污水提供理思路。

基于电子鼻和电子舌与1D-CNN-LSTM模型的花椒产地快速溯源检测

针对不同产地花椒产品的溯源问题,提出一种基于电子鼻和电子舌结合一维卷积神经网络(One Dimension-Convolutional Neural Networks,1D-CNN)-长短期记忆网络(Long Short-Term Memory,LSTM)混合模型的花椒产地快速检测方法。以5个不同产地的花椒为试验对象,采用电子舌和电子鼻分别采集花椒样本的味觉和嗅觉指纹图谱信息,根据信号特点分别设计1D-CNN提取味觉和嗅觉信号中的局部空间特征,然后采用LSTM捕捉信号的时间序列特征,最后采用多层感知机融合两种特征并进行分类识别。实验结果表明,电子鼻与电子舌信息融合对不同产地花椒的分辨准确率优于单一设备,与其他深度模型相比,所提的模型分类准确性更高,其准确率、精确率、召回率、F1分数分别达到99.0%、99.1%、99.0%、0.989。以上研究将为不同产地花椒的快速鉴定提供新的方法,并为其他农产品的产地溯源检测提供新的研究思路。

异养/硫自养集成短程反硝化工艺影响因素研究

为保证厌氧氨氧化(Anammox)过程的亚硝态氮(NO2--N)稳定来源,提升自养脱氮系统的运行效能。本研究采用上流式厌氧污泥床(UASB)反应器,构建异养/硫自养集成短程反硝化系统,探究碳源浓度、pH、水力停留时间(HRT)、温度对系统的影响。结果表明,碳源浓度与系统短程反硝化效能成反比。碳源浓度为200 mg/L时NO2--N积累率(NAR)最高,此时NO_(2)^(-)-N平均积累率及其速率分别为76.99%和1.63 mg/(L·h),异养反硝化功能菌属Thauera丰度由3.26%升至19.73%。系统短程反硝化效能随pH的升高先升后降,pH为8.0±0.5时NAR最高,此时NO2--N平均积累率及其速率分别为79.46%和1.90 mg/(L·h);HRT与系统短程反硝化效能成反比。当HRT=9.7左右时NAR最高,此时NO2--N平均积累率及其速率分别为77.35%和1.68 mg/(L·h)。温度与系统短程反硝化效能成正比,与Thauera、Terrimonas等反硝化功能菌属丰度成反比。当温度在35℃左右时NAR最高,此时NO_(2)^(-)-N平均积累率及其速率分别为71.21%和1.68 mg/(L·h)。集成短程反硝化为自养脱氮工艺提供NO2--N的过程具有NAR高且运行稳定性强等优点,更适用于自养脱氮工艺稳定高效运行,为该领域提供更多思考。