Monitoring and diagnosis of abnormal condition in ethylene production process based on SVM-BOXPLOT

HUA Li; YU Haichen; SHAO Cheng; GONG Shixin

doi:10.11949/j.issn.0438-1157.20170907

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Monitoring and diagnosis of abnormal condition in ethylene production process based on SVM-BOXPLOT

更新时间：2025-12-10

- Monitoring and diagnosis of abnormal condition in ethylene production process based on SVM-BOXPLOT
- CIESC Journal Issue 3, Pages: 1053-1063(2018)
- 作者机构：
  
  大连理工大学先进控制技术研究所,辽宁,大连,116024
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20170907
  CLC： Q202
- Online First：05 March 2018，
  
  Published：05 March 2018
- 稿件说明：
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HUA Li, YU Haichen, SHAO Cheng, et al. Monitoring and diagnosis of abnormal condition in ethylene production process based on SVM-BOXPLOT[J]. CIESC Journal, 2018, (3): 1053-1063.
DOI：

HUA Li, YU Haichen, SHAO Cheng, et al. Monitoring and diagnosis of abnormal condition in ethylene production process based on SVM-BOXPLOT[J]. CIESC Journal, 2018, (3): 1053-1063. DOI： 10.11949/j.issn.0438-1157.20170907.

摘要

乙烯作为化工生产的重要原材料，需求量持续增加，但它也是高能耗产业，其生产运行状态直接关系到能效的高低，进而影响企业的经济效益。因此，乙烯生产运行工况的智能识别对节能降耗意义重大。针对直接影响乙烯生产过程能效水平的异常工况智能识别问题，以能够反映乙烯生产能效与能耗的关键指标——乙烯收率、丙烯收率及综合能耗为基础，使用IPSO优化SVM-BOXPLOT的方法对乙烯生产过程进行异常工况智能识别。通过机理分析与数据分析相结合的方法对监测数据降维，用SVM对生产数据进行工况分类，缩小异常识别范围，最后用BOXPLOT进行异常工况识别。将其与在线监测系统相结合应用于某石化企业生产中，所提出的异常工况监测与诊断方案模型精度更高，收敛速度更快，既实现了乙烯生产过程异常工况的监测与诊断，又满足了实际运行工况的工艺要求，保证了异常识别的实时性、准确性。

Abstract

As an important raw material for chemical production

the demands of ethylene greatly increase

but it consumes large energy. Since ethylene production and operation status is directly related to the level of energy efficiency

the economic benefits of enterprises are affected. It is great significance to realize the intelligent identification of ethylene production operating conditions for saving energy and reducing consumption. Therefore

a comprehensive method for the abnormity identification in ethylene production is presented by using the IPSO-optimized SVM-BOXPLOT method based on the key energy efficiency indicators

ethylene yield

propylene yield and comprehensive energy consumption. Specifically

the data dimensionality is reduced on the basis of the deep analysis of the ethylene production technology and the data analysis. Then the working conditions are classified by SVM for reducing the scope of abnormal recognition. Finally

the abnormal data is identified by BOXPLOT. Combined with the on-line monitoring system

the scheme is applied to the production of a petrochemical enterprise. The monitoring and diagnosis scheme for abnormal working conditions has higher model precision and faster convergence speed. The method not only realizes the monitoring and diagnosis of abnormal working conditions in ethylene production

but also meets the technological requirements of actual operating conditions

which ensures the real-time and accuracy of abnormal identification.

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