Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants

Qun Xiong Zhu; Li Na Chang; Yan Lin He; Yuan Xu

doi:10.1109/CAC.2018.8623569

Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants

Qun Xiong Zhu
, Li Na Chang
, Yan Lin He
, Yuan Xu

Beijing University of Chemical Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Due to the complex reaction mechanisms and high coupling between variables in the Ethylene production process, it is difficult for manufacturers to configure the energy structure and achieve the optimal production status in experience. However, the traditional data envelopment analysis method (DEA) cannot perform well in distinguishing the effective and inefficient samples. Therefore, in this paper, a novel method integrating DEA with virtual sample generation (VSG-DEA) is proposed to improve the effective discrimination. The proposed DEA method applies the improved extreme learning machine (ELM) which utilized feature scaling of the hidden layer outputs to generate appropriate virtual samples from original samples. Then the mixed samples with virtual samples are evaluated using DEA. In order to validate the performance, the proposed VSG-DEA is utilized to analyze the energy efficiency of the ethylene production process. It is proved that the discrimination of the ethylene production unit is effective in the simulation experiments. Furthermore, the energy-saving potential can be obtained by analyzing the simulation results.

Original language	English
Title of host publication	Proceedings 2018 Chinese Automation Congress, CAC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3030-3034
Number of pages	5
ISBN (Electronic)	9781728113128
DOIs	https://doi.org/10.1109/CAC.2018.8623569
Publication status	Published - 2 Jul 2018
Externally published	Yes
Event	2018 Chinese Automation Congress, CAC 2018 - Xi'an, China Duration: 30 Nov 2018 → 2 Dec 2018

Publication series

Name	Proceedings 2018 Chinese Automation Congress, CAC 2018

Conference

Conference	2018 Chinese Automation Congress, CAC 2018
Country/Territory	China
City	Xi'an
Period	30/11/18 → 2/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

DEA
Energy efficiency analysis
Energy-saving
Ethylene production plants
Virtual sample generation

Access to Document

10.1109/CAC.2018.8623569

Cite this

Zhu, Q. X., Chang, L. N., He, Y. L., & Xu, Y. (2018). Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants. In Proceedings 2018 Chinese Automation Congress, CAC 2018 (pp. 3030-3034). Article 8623569 (Proceedings 2018 Chinese Automation Congress, CAC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC.2018.8623569

@inproceedings{4cfdf1ca37264717a630e2d31a3c90d2,

title = "Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants",

abstract = "Due to the complex reaction mechanisms and high coupling between variables in the Ethylene production process, it is difficult for manufacturers to configure the energy structure and achieve the optimal production status in experience. However, the traditional data envelopment analysis method (DEA) cannot perform well in distinguishing the effective and inefficient samples. Therefore, in this paper, a novel method integrating DEA with virtual sample generation (VSG-DEA) is proposed to improve the effective discrimination. The proposed DEA method applies the improved extreme learning machine (ELM) which utilized feature scaling of the hidden layer outputs to generate appropriate virtual samples from original samples. Then the mixed samples with virtual samples are evaluated using DEA. In order to validate the performance, the proposed VSG-DEA is utilized to analyze the energy efficiency of the ethylene production process. It is proved that the discrimination of the ethylene production unit is effective in the simulation experiments. Furthermore, the energy-saving potential can be obtained by analyzing the simulation results.",

keywords = "DEA, Energy efficiency analysis, Energy-saving, Ethylene production plants, Virtual sample generation",

author = "Zhu, \{Qun Xiong\} and Chang, \{Li Na\} and He, \{Yan Lin\} and Yuan Xu",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Chinese Automation Congress, CAC 2018 ; Conference date: 30-11-2018 Through 02-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/CAC.2018.8623569",

language = "English",

series = "Proceedings 2018 Chinese Automation Congress, CAC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3030--3034",

booktitle = "Proceedings 2018 Chinese Automation Congress, CAC 2018",

address = "United States",

}

Zhu, QX, Chang, LN, He, YL & Xu, Y 2018, Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants. in Proceedings 2018 Chinese Automation Congress, CAC 2018., 8623569, Proceedings 2018 Chinese Automation Congress, CAC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3030-3034, 2018 Chinese Automation Congress, CAC 2018, Xi'an, China, 30/11/18. https://doi.org/10.1109/CAC.2018.8623569

Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants. / Zhu, Qun Xiong; Chang, Li Na; He, Yan Lin et al.
Proceedings 2018 Chinese Automation Congress, CAC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3030-3034 8623569 (Proceedings 2018 Chinese Automation Congress, CAC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Energy Efficiency Analysis Using a Novel VSG Based DEA

T2 - 2018 Chinese Automation Congress, CAC 2018

AU - Zhu, Qun Xiong

AU - Chang, Li Na

AU - He, Yan Lin

AU - Xu, Yuan

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Due to the complex reaction mechanisms and high coupling between variables in the Ethylene production process, it is difficult for manufacturers to configure the energy structure and achieve the optimal production status in experience. However, the traditional data envelopment analysis method (DEA) cannot perform well in distinguishing the effective and inefficient samples. Therefore, in this paper, a novel method integrating DEA with virtual sample generation (VSG-DEA) is proposed to improve the effective discrimination. The proposed DEA method applies the improved extreme learning machine (ELM) which utilized feature scaling of the hidden layer outputs to generate appropriate virtual samples from original samples. Then the mixed samples with virtual samples are evaluated using DEA. In order to validate the performance, the proposed VSG-DEA is utilized to analyze the energy efficiency of the ethylene production process. It is proved that the discrimination of the ethylene production unit is effective in the simulation experiments. Furthermore, the energy-saving potential can be obtained by analyzing the simulation results.

AB - Due to the complex reaction mechanisms and high coupling between variables in the Ethylene production process, it is difficult for manufacturers to configure the energy structure and achieve the optimal production status in experience. However, the traditional data envelopment analysis method (DEA) cannot perform well in distinguishing the effective and inefficient samples. Therefore, in this paper, a novel method integrating DEA with virtual sample generation (VSG-DEA) is proposed to improve the effective discrimination. The proposed DEA method applies the improved extreme learning machine (ELM) which utilized feature scaling of the hidden layer outputs to generate appropriate virtual samples from original samples. Then the mixed samples with virtual samples are evaluated using DEA. In order to validate the performance, the proposed VSG-DEA is utilized to analyze the energy efficiency of the ethylene production process. It is proved that the discrimination of the ethylene production unit is effective in the simulation experiments. Furthermore, the energy-saving potential can be obtained by analyzing the simulation results.

KW - DEA

KW - Energy efficiency analysis

KW - Energy-saving

KW - Ethylene production plants

KW - Virtual sample generation

UR - https://www.scopus.com/pages/publications/85062777607

U2 - 10.1109/CAC.2018.8623569

DO - 10.1109/CAC.2018.8623569

M3 - Conference contribution

AN - SCOPUS:85062777607

T3 - Proceedings 2018 Chinese Automation Congress, CAC 2018

SP - 3030

EP - 3034

BT - Proceedings 2018 Chinese Automation Congress, CAC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 30 November 2018 through 2 December 2018

ER -

Energy Efficiency Analysis Using a Novel VSG Based DEA: A Case Study of Ethylene Production Plants

Abstract

Publication series

Conference

UN SDGs

Keywords

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