Knowledge discovery from process operational data for assessment and monitoring of operator's performance

Sebzalli, Y.M.; Li, R.F.; Chen, F.Z.; Wang, X.Z.

doi:10.1016/s0098-1354(00)00430-0

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…PCA is a multivariate statistical method. Its core idea is to reduce the dimensionality of a data set composed of a large number of related variables, while retaining the changes in the data set as much as possible [23]. In The data is visualized, and the distribution diagram of the first two principal components is shown in Fig.…”

Section: Principal Component Analysis Of Volatile Compounds In Seven Cultivars Of Melonmentioning

confidence: 99%

Characteristic volatile organic compounds in “HeTao” melon and other cultivars grown in Hetao region analyzed by HS-GC-IMS

Liu

Sun

et al. 2021

SN Appl. Sci.

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The headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to compare the volatile organic components of the Hetao melon and six other cultivars of melon grown in the Hetao region of China. The results showed that the common VOCs that could be qualitatively identified from the 7 different melon samples were 35 monomers and dimers of certain compounds, mainly including alcohols, esters, aldehydes, terpenes, acids and pyridines. Hexyl acetate, 3-methylbutyl acetate, ethyl acetate and ethyl formate were predominant VOCs in seven melon cultivars. Among them, Xizhoumi No. 25 (XZM25) had 3 unique volatile organic components: 3-methylbutanal, benzaldehyde and nonanal. Xizhoumi No. 17 (XZM17) had 3 unique volatile organic components: alpha-pinene, linalool and (E)-2-hexenol. Jinhongmi (JHM) had 1 unique volatile organic component: ethyl pentanoate. The Hetao melon (HLS) contained 3 unique volatile organic components: heptanal, 2-ethyl-6-methyl pyrazine and 3-methyl valeric acid. Yinmi (YM) had 2 unique volatile organic components: 3-methylbutanol and 1-butanol, and Huangjinmi (HJM) had 1 unique volatile organic component: limonene. YM, GMB2010, HLS and JHM were similar based on the principal component analysis. This research analyzed the flavor components of different melon cultivars grown in the Hetao region of China for the first time.

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Section: Principal Component Analysis Of Volatile Compounds In Seven Cultivars Of Melonmentioning

confidence: 99%

Characteristic volatile organic compounds in “HeTao” melon and other cultivars grown in Hetao region analyzed by HS-GC-IMS

Liu

Sun

et al. 2021

SN Appl. Sci.

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“…Sebzalli et al (2000) Brought to you by | University of California -A training simulator "CRACKER" developed by them had a user-friendly graphic interface for the convenient use.…”

Section: Speedupmentioning

confidence: 99%

Operator training simulators in the chemical industry: review, issues, and future directions

Patle¹,

Ahmad²,

Rangaiah

2014

Reviews in Chemical Engineering

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An inevitable need for the skilled operators to increase the safety and productivity is not new to the chemical industry. Consequently, the training of operators is considered as a very important activity in the chemical industry. Conventional training methodologies are ineffective in training the operators for seldom-occurring perilous situations. The operator training simulator (OTS) is an alternative to train operators without actually endangering the plant and personnel. This contribution covers and discusses the need for OTS, applications of OTS in the chemical industry, issues related to OTS, salient features of a good OTS, commercial software packages used to build OTS, and training configurations. In this article, applications of OTS in the chemical industry reported in the open literature from 1990 to mid-2013 are also reviewed briefly. The review shows that OTS has been successfully used in many chemical industries. Finally, this article concludes by outlining the future directions. Overall, it provides a deeper understanding of many issues about the OTS to interested researchers, vendors, modeling engineers, and application engineers aiming to stimulate further developments in this area leading to improved OTS and their increased usage in the chemical industry.

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“…Changing the opacity of data points and displacing data points that address the issue of overlapping are among overlap reducing techniques [4]. Changing the opacity of data points enables the identification of small learning and data mining [19][20][21][22][23][24]. Typically, existing clustering techniques used for clutter reduction eliminate overlaps by representing a group of data points or a group of lines by means of a single data point or line [2,3,25,26].…”

Section: Introductionmentioning

confidence: 99%

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

et al. 2016

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Big data are visually cluttered by overlapping data points. Rather than removing, reducing or reformulating overlap, we propose a simple, effective and powerful technique for density cluster generation and visualization, where point marker (graphical symbol of a data point) overlap is exploited in an additive fashion in order to obtain bitmap data summaries in which clusters can be identified visually, aided by automatically generated contour lines. In the proposed method, the plotting area is a bitmap and the marker is a shape of more than one pixel. As the markers overlap, the red, green and blue (RGB) colour values of pixels in the shared region are added. Thus, a pixel of a 24-bit RGB bitmap can code up to 2 24 (over 1.6 million) overlaps. A higher number of overlaps at the same location makes the colour of this area identical, which can be identified by the naked eye. A bitmap is a matrix of colour values that can be represented as integers. The proposed method updates this matrix while adding new points. Thus, this matrix can be considered as an up-to-time knowledge unit of processed data. Results show cluster generation, cluster identification, missing and out-of-range data visualization, and outlier detection capability of the newly proposed method.

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Knowledge discovery from process operational data for assessment and monitoring of operator's performance

Cited by 13 publications

References 9 publications

Characteristic volatile organic compounds in “HeTao” melon and other cultivars grown in Hetao region analyzed by HS-GC-IMS

Characteristic volatile organic compounds in “HeTao” melon and other cultivars grown in Hetao region analyzed by HS-GC-IMS

Operator training simulators in the chemical industry: review, issues, and future directions

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

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