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Energy efficiency assessment and resource optimization using novel DEA model: evidence from complex chemical processes

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Abstract

Energy efficiency assessment and resource allocation optimization are conducive to improve the production and reduce carbon emissions in complex chemical processes. And the traditional energy efficiency assessment method based on data envelopment analysis (DEA) does not distinguish the effective decision-making units (DMUs) better. Therefore, this paper proposed a novel DEA method combining the cosine similarity (CS) (DEA-CS) to estimate the energy efficiency and optimize the resource of complex chemical plants. The ineffective and effective DMUs can be obtained by the DEA. Then, the CS can further distinguish the effective DMUs to obtain the optimal DMU, which makes the DEA model have the better discriminating ability. Moreover, through the input and output of the optimal DMU, the resource of ineffective DMUs can be optimized. Finally, the proposed model is used in energy efficiency assessment and resource optimization of ethylene and purified terephthalic acid (PTA) production plants in complex chemical processes. The experiments show that the energy-saving potential of ethylene and PTA production plants is improved by about 13.61% and 1.22%, respectively. Meanwhile, the average carbon emission–saving potential of the ethylene production plants is reduced by 12.58%, approximately.

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Abbreviations

DEA:

Data envelopment analysis

CS:

Cosine similarity

DEA-CS:

Data envelopment analysis combining cosine similarity

DMUs:

Decision-making units

PTA:

Purified terephthalic acid

GDP:

Gross domestic product

CO2 :

Carbon dioxide

IDA:

Index decomposition analysis

SFA:

Stochastic frontier approach

AP:

Affinity propagation

SBM:

Slack-based measure

AHP:

Analytic hierarchy process

PCA:

Principal component analysis

SO-CSLN:

Self-organizing cosine similarity learning network

MVO:

Multi-verse optimization

d r :

Input vector of the rth DMU

e r :

Output vector of the rth DMU

v :

Weight coefficients of the pth input

u :

Weight coefficients of the qth output

η :

The effective value of input associated with the output

μ:

The optimal solution

ε:

Non-Archimedes infinitesimal

\( {g}_t^{-} \) :

Redundancy of pth input

\( {g}_t^{+} \) :

Deficiency of qth output

e t :

Unit vector

a · b :

The dot product of the two vectors

‖A‖:

The modulo of vector A

cosθ :

Cosine of two vectors

GJ:

GJ/ton ethylene

PX:

p-Xylene

AA:

Acetic acid

TSDT:

The top of the solvent dehydration tower

FANN:

Fuzzy artificial neural network

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Funding

This work is partly funded by the National Natural Science Foundation of China (21978013), the Fundamental Research Funds for the Central Universities (XK1802-4), the Science and Technology Major Project of Guizhou Province (Guizhou Branch [2018]3002), the Shenzhen Fundamental Research Program (JCYJ20170413164102261), and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2016ZT06D631).

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Author notes

  1. Kai Chen and Shuang Liu contributed equally to this work.

Authors and Affiliations

  1. Guizhou Academy of Science, Guiyang, 550001, China

    Kai Chen

  2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Shuang Liu, Yongming Han & Zhiqiang Geng

  3. Sichuan Technology and Business University, Chengdu, 611745, China

    Yang Zhang

  4. College of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Lin Liu

  5. Bionic Sensing and Intelligence Center (BSIC), Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Tao Peng

  6. BoHuaXinZhi Technology, Inc., Beijing, China

    Yifan Ding

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  1. Kai Chen
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Correspondence to Yongming Han or Zhiqiang Geng.

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Kai Chen and Shuang Liu contributed to the work equally and should be regarded as co-first authors.

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Chen, K., Liu, S., Han, Y. et al. Energy efficiency assessment and resource optimization using novel DEA model: evidence from complex chemical processes. Energy Efficiency 13, 1427–1439 (2020). https://doi.org/10.1007/s12053-020-09892-2

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