Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2022, 5(13); doi: 10.25236/AJETS.2022.051303.

Energy Efficiency Optimization Based on Digital Twin Workshops


Guoshun Wang, Zhigang Bing, Zhaohao Hou, Ying Li

Corresponding Author:
Zhigang Bing

School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China


In order to realize the optimization of energy saving for the digital processing workshop, reducing the energy consumption of equipment in the workshop and improving the energy efficiency of the workshop is also imperative. Energy efficiency optimization based on digital twin workshop is proposed. According to the characteristics of the whole processing process and energy consumption of the workshop, the dynamic energy efficiency model of the digital twin workshop is established to realize the visualization platform of control substantiation and management. The NSGA-II algorithm is used to analyse the energy efficiency analysis method of machine tool scheduling and plan the digital twin energy efficiency scheduling scheme.


Digital twin, Energy consumption, NSGA-II, Scheduling scheme

Cite This Paper

Guoshun Wang, Zhigang Bing, Zhaohao Hou, Ying Li. Energy Efficiency Optimization Based on Digital Twin Workshops. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 13: 15-25. https://doi.org/10.25236/AJETS.2022.051303.


[1] Strategic Consulting Center of Chinese Academy of Engineering. Green Manufacturing [M]. Beijing: Electronic Industry Press, 2016: 2-5.

[2] Liu Fei, Wang Qiulian, Liu Gaojun. Content system and development trend of energy efficiency research on mechanical processing system [J]. Journal of Mechanical Engineering, 2013,49 (19): 87-94.

[3] Zhang M, Zuo Y, Tao F. Equipment energy consumption management in digital twin shop-floor: A framework and potential applications[C]// 2018:1-5.

[4] Xu W, Ji Z, Ma Y, et al. Digital twin-driven energy-efficient assessment service[M]//Digital Twin Driven Service. Academic Press, 2022: 139-171.

[5] Tao Fei, Liu Weiran, Zhang Meng, et al. Five-dimension digital twin model and its ten applications [J]. Computer Integrated Manufacturing System, 2019, 25 (01): 1-18.

[6] Deb K, Agrawal S, Pratap A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II[C]//International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg, 2000: 849-858.

[7] Li Yunlong, Luo Guofu, Wen Xiaoyu, et al. A Flexible Job Shop Scheduling Scheme Based on Hybrid Genetic Algorithm in Cloud Manufacturing Environment [J] Journal of Light Industry, 2020, 35 (3): 99.

[8] Chen R, Yang B, Li S, et al. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem[J]. Computers & Industrial Engineering, 2020, 149: 106778.

[9] Pauker F, Frühwirth T, Kittl B, et al. A systematic approach to OPC UA information model design[J]. Procedia CIRP, 2016, 57: 321-326.

[10] Xu Bingbing. Design and Implementation of Key Modules of Data Acquisition and Monitoring System based on OPC UA [D]. Xidian University,2017.