Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

doi:10.1016/j.cjche.2015.11.011

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 2029-2036.DOI: 10.1016/j.cjche.2015.11.011

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Yongming Han, Zhiqiang Geng, Qunxiong Zhu, Xiaoyong Lin

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Engineering Research Center of Intelligent PSE, Ministry of Education in China, Beijing 100029, China

收稿日期:2015-05-20 修回日期:2015-07-17 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Zhiqiang Geng
基金资助:
Supported by theNational Natural Science Foundation of China (61374166, 6153303), the Doctoral Fund of Ministry of Education of China (20120010110010), and the Fundamental Research Funds for the Central Universities (YS1404, JD1413, ZY1502).

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Yongming Han, Zhiqiang Geng, Qunxiong Zhu, Xiaoyong Lin

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Engineering Research Center of Intelligent PSE, Ministry of Education in China, Beijing 100029, China

Received:2015-05-20 Revised:2015-07-17 Online:2015-12-28 Published:2016-01-19
Contact: Zhiqiang Geng
Supported by:
Supported by theNational Natural Science Foundation of China (61374166, 6153303), the Doctoral Fund of Ministry of Education of China (20120010110010), and the Fundamental Research Funds for the Central Universities (YS1404, JD1413, ZY1502).

摘要/Abstract

摘要： Interpretative structuralmodel (ISM) can transformamultivariate probleminto several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix,which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISMto analyze themain factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

关键词: Partial correlation coefficient, Interpretative structural model, Energy consumption, Hierarchical analysis, Ethylene production, Chemical processes

Abstract: Interpretative structuralmodel (ISM) can transformamultivariate probleminto several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix,which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISMto analyze themain factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Key words: Partial correlation coefficient, Interpretative structural model, Energy consumption, Hierarchical analysis, Ethylene production, Chemical processes

Yongming Han, Zhiqiang Geng, Qunxiong Zhu, Xiaoyong Lin. Energy consumption hierarchical analysis based on interpretative structural model for ethylene production[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2029-2036.

参考文献

[1] D.S. Guan,W.Y. Ji, Review of Sinopec's ethylene production in 2008, Ethylene Ind. 21 (1) (2009) 1-6.

[2] L.J. Zhang, J. Hu, Review of Sinopec's ethylene production in 2008, Ethylene Ind. 21 (1) (2009) 7-10.

[3] T. Ren, M. Patel, M. Blok, Olefins from conventional and heavy feedstocks: Energy use in steam cracking and alternative processes, Energy 31 (2006) 425-451.

[4] D.R.Wu, K. He, H.F. Zhu, Analysis of energy consumption and energy saving technology in ethylene complex, Chem. Eng. (China) 35 (2007) 66-71.

[5] L.J. Zhang, Application of energy conservation and emission reduction techniques in ethylene plants, Sino-Global Energy 14 (2009) 90-94.

[6] G.G. Li, X. He, Z.H. Sheng, Technology analysis and energy saving of ethylene plant, Petrochem. Technol. 38 (2009) 115-123.

[7] C.S. Yang, The design of quenching oil system for ethylene plants and technical revamping aimed at raising energy efficiency, Sino-global Energy 16 (2011) 106-111.

[8] J. Buehler, N. Genty, A successful approach to improve energy utilization in ethylene units, Ethylene producer conference Houston, USA, 2007.

[9] K. Hirata, Energy saving for ethylene process by advanced process integration, Chem. Eng. Trans. 25 (2011) 81-86.

[10] W.Q. Tang, Techno-economic assessment on ethylene fragmentation plant with light hydrocarbon as raw material, Techno-Econ. Petrochem. 5 (2004) 19-23.

[11] Q.X. Zhu, X.Y. Shi, X.B. Gu, Q. Tian, Time-series data fusion and its application to energy efficiency value for ethylene plants, J. Chem. Ind. Eng. (China) 61 (2010) 2620-2626.

[12] Z.Q. Geng, X.Y. Shi, X.B. Gu, Q.X. Zhu, Hierarchical linear optimal fusion algorithm and its application in ethylene energy consumption indices acquisition, J. Chem. Ind. Eng. (China) 61 (2010) 2056-2060.

[13] Z.Q. Geng, Y.M. Han, X.B. Gu, Q.X. Zhu, Energy efficiency estimation based on data fusion strategy: Case study of ethylene product industry, Ind. Eng. Chem. Res. 51 (2012) 8526-8534.

[14] J. Fu, Q. Xu, Simultaneous study on energy consumption and emission generation for an ethylene plant under different start-up strategies, Comput. Chem. Eng. 56 (2013) 68-79.

[15] C. Liu, Q. Xu, Emission source characterization for proactive flaremini-mization during ethylene plant start-ups, Ind. Eng. Chem. Res. 49 (2010) 5734-5741.

[16] J.N. Warfield, Binarymatrices in systemmodeling, IEEE Trans. Syst.Man Cybern. 3 (5) (1973) 441-449.

[17] T.C. Kuo, H.Y. Ma, S.H. Huang, A.H. Hu, C.S. Huang, Barrier analysis for product service system using interpretive structural model, Int. J. Adv. Manuf. Technol. 49 (4) (2010) 407-417.

[18] M.N. Faisal, Analyzing the barriers to corporate social responsibility in supply chains: An interpretive structural modelling approach, Int. J. Logist. Res. Appl. 13 (3) (2010) 179-195.

[19] F. Talib, Z. Rahman,M.N. Qureshi, Analysis of interaction among the barriers to total quality management implementation using interpretive structural modeling approach, Benchmarking Int. J. 18 (4) (2011) 563-587.

[20] S. Chandramowli, M. Transue, F.A. Felder, Analysis of barriers to development in landfill communities using interpretive structural modeling, Habitat Int. 35 (2) (2011) 246-253.

[21] K. Govindan, M. Palaniappan, Q. Zhu, D. Kannan, Analysis of third party reverse logistics provider using interpretive structural modeling, Int. J. Prod. Econ. 140 (1) (2012) 204-211.

[22] N.L. Yu, D.Y. Yi, X.Q. Tu, Analyze auto-correlations and partial-correlations function in time series, Math. Theory Appl. 27 (1) (2007) 54-57.

[23] A. Vargha, L.R. Bergman, H.D. Delaney, Interpretation problems of the partial correlation with nonnormally distributed variables, Qual. Quant. (2011) 1-12.

[24] Y. Fan, L.S. Sirish, D.Y. Xiao, Signed directed graph modeling of industrial processes and their validation by data-based methods, 2010 Conference on Control and Fault Tolerant Systems 2010, pp. 387-392.

[25] R. Hildebrand,M. Gevers, G. Solari, Closed-loop optimal experiment design: The partial correlation approach, 49th IEEE Conference on Decision and Control (CDC), IEEE 2010, pp. 2855-2862.

[26] W. Bergsma, A Note on the Distribution of the Partial Correlation Coefficient With Nonparametrically Estimated Marginal RegressionsarXiv, preprint arXiv:1101.4616 2011.

[27] J.-M. Kim, Y.-S. Jung, C. Taeryon, A.S. Engin, Partial correlation with copulamodeling, Comput. Stat. Data Anal. 55 (2011) 1357-1366.

[28] W. Hopkins, A new view of statistics, http://www.sportsci.org/resource/stats/2002- 08-21.

[29] H. Zhang, T.S. Tong, F. Zhang, H.L. Liu, Consensusmeasurement inmicro-inertial sensors, J. Transducer Technol. 10 (2001) 40-41.

[30] Calculation Method for Energy Consumption in Petrochemical Engineering Design SH/T 3110, 2011.

[31] X.L. Wang, The dynamic benchmarkingmethod for energy consumption of ethylene production process, Comput. Appl. Chem. 27 (9) (2010) 1166-1170.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

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