共查询到20条相似文献,搜索用时 203 毫秒
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DCS在化学工业中应用概况 总被引:2,自引:0,他引:2
郑名登 《化工自动化及仪表》1996,23(4):3-10
综述了国外DCS的发展动向;我国化工经的历史和发展,DCS在我国化学工业应用情况;提出了发展我国化工自动化的几点意见。 相似文献
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绿色化学的发展与广州化工的对策 总被引:3,自引:1,他引:2
绿色化学是 2 1世纪化学的主要内容 ,是一门从源头上阻止污染的化学。本文对绿色化学发展的三个方面 :绿色的原料路线、绿色的催化技术、绿色的化学产品进行了评述 ,并结合广州化工经济发展提出了几点对策 相似文献
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化学工业是运用化学方法从事产品生产的工业是在国民经济中占重要地位的工业部门,随着我国改革开放的深入和发展,人民生活水平的不断提高,人们的衣、食、住、行各方面几乎都离不开化工产品.所以化工企业像雨后春笋般迅速发展起来,本文分析了导致化工事故的几个因素特点及抢险救援工作所面临的新问题,初步探讨了化工事故抢险救援工作的可行性,分别从对化工事故抢险救援的预案的制定,如何提高对化工事故抢险救援工作能力等,提出了对当前化工事故抢险救援工作的几点认识. 相似文献
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自20世纪80年代以来,面向对象的软件开发生成技术发展迅速并且得到了广泛的应用,在面向对象信息的分析、设计技术以及面向对象信息的程序设计语言方面都有了丰富的研究成果,在当前,面向对象的软件开发方法已经应用的较为普遍。面向对象的方法是一种把面向对象信息应用软件开发的全过程,并在软件开发当中起到指导性作用的方法。《化学化工软件应用教程》对化学化工行业当中需要的各种化学化工工具软件进行了详细的阐述,并将这些软件进行有机结合,便于化学化工专业学生对于化学化工工具软件的掌握和运用。 相似文献
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从高炉煤气生产的实际工况出发,对异常数据产生的原因和特点进行分析。针对现有异常检测方法运算效率低下的问题,提出一种改进的局部异常因子检测算法。该算法首先利用五数总括法剔除掉大量的正常数据,然后再用一种相对k距离的比值表示剩余离群点的异常程度,进而判断异常数据。仿真实验表明:改进方法检测所需的时间比传统的局部异常因子方法检测所需的时间更少,且检测效果更加准确、直观。 相似文献
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David Appelhaus Yan Lu René Schenkendorf Stephan Scholl Katharina Jasch 《化学,工程师,技术》2021,93(12):1976-1986
The analysis of process and equipment operational data in chemical engineering regularly requires a high level of expert knowledge. This work presents a Machine Learning-based approach to evaluate and interpret process data to support robust operation of a thermosiphon reboiler. By applying an outlier detection, potentially interesting and unstable operating conditions can be identified quickly. A multidimensional regression allows to forecast the circulating mass flow. The results obtained fit well into the current state of research and manual evaluation of thermosiphon reboilers. 相似文献
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Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation (DDR) is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor (CSTR) process verifies the effectiveness of the proposed algorithm. 相似文献
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Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood‐based method in finite samples and indicates that the proposed methods are preferable when dealing with the non‐Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood. 相似文献
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Shu Xu Michael Baldea Thomas F. Edgar Willy Wojsznis Terrence Blevins Mark Nixon 《American Institute of Chemical Engineers》2015,61(2):419-433
A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier and innovational outlier detection problems in dynamic process dataset. Compared with current outlier detection methods, the new method enjoys the following advantages: (a) no prior knowledge of the process model is needed; (b) it is easy to tune; (c) it can be applied to both univariate and multivariate outlier detection; (d) it is applicable to both on‐line and off‐line operation; (e) it cleans outliers while maintains the integrity of the original dataset. © 2014 American Institute of Chemical Engineers AIChE J, 61: 419–433, 2015 相似文献
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This work investigates outlier detection and modelling in non‐Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets. 相似文献
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提出一种不等长的多模态间歇过程故障检测方法。首先,运用局部加权算法对不等长批次数据进行预处理。在训练样本中确定不等长数据的最大可保留长度,利用k近邻信息,通过加权重构出不等长批次缺失的数据点。其次,对等长的训练集构造局部近邻标准化矩阵,运用K-means算法进行模态聚类,使用局部离群因子方法确定第一控制限,并剔除离群样本。最后,对各个模态建立MPCA模型并确定第二控制限。根据各个模态控制限的匹配系数计算统一的统计量和控制限,在统一的控制限下进行多模态故障检测。将提出方法应用于半导体工业过程,仿真结果表明,与传统的故障检测算法相比,本文算法提高了故障检测率,验证了该方法的有效性。 相似文献
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一种新型融合离群点的稳态检测方法 总被引:1,自引:0,他引:1
针对带有离群点的数据稳态检测,采用分布图法对离群点进行剔除;为了保持数据的完整性,提出用灰色预测值替代离群点值;最后用3δ法则进行稳态检验。如此,数据的稳态与非稳态便会区分开来。与现有稳态检测方法相比,分布图法快速有效地克服了离群点对稳态检测结果不准确的影响,降低了过程中个别异常数据带来的误诊率;灰色预测方法使离群点的替代值更贴近真实值,从而得到的过程数据比现有方法得到的数据更可靠。仿真结果证实了该方法的有效性和优越性。 相似文献
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The aim of this study is to propose a novel partial least squares with outlier detection (PLS_OD) calibration method and show its usefulness in calibration successfully with data containing outlying objects. We apply this method in gasoline spectral analysis to predict gasoline properties. In particular, a comparative study of PLS_OD and other five methods is presented. The performances of the proposed method are illustrated on spectral data set with and without outliers. The obtained results suggest that the proposed method can be used for constructing satisfactory gasoline prediction model whether there are some outliers or not. 相似文献
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复杂化工过程通常具有多个操作模态,而且采集的数据不服从单一的高斯或非高斯分布。针对化工过程的多模态和复杂数据分布问题,将局部标准化(local standardized,LS)策略应用于邻域保持嵌入(neighborhood preserving embedding,NPE)算法,提出了一种新的基于局部标准化邻域保持嵌入(local standardized neighborhood preserving embedding,LSNPE)算法的故障检测方法。首先,使用LSNPE算法提取高维数据的低维子流形,进行维数约减,同时保持邻域结构不变。其次,通过特征空间中样本的局部离群因子(local outlier factor,LOF)构造监控统计量并确定其控制限。相较于监控多模态化工过程的多模型策略,提出的LSNPE方法不需要过程先验知识的支持,只需建立一个全局的监控模型。最后,通过数值仿真及Tennessee Eastman(TE)过程仿真研究验证了本文提出方法的有效性。 相似文献