Analysis of Energy Consumption and Carbon Emissions in Traditional versus Smart Manufacturing Practices: A Case Study in the Steel Industry

Authors

  • Mohit Beniwal Research Scholar, Department of Mechanical Engineering, Rabindranath Tagore University, Bhopal, Madhya Pradesh, India Author
  • Dr. Lalit Narayan Assistant Professor, Department of Mechanical Engineering, Rabindranath Tagore University, Bhopal, Madhya Pradesh, India Author

Keywords:

Energy consumption, carbon emissions, smart manufacturing, traditional manufacturing, steel industry, comparative analysis, statistical tests, sustainability

Abstract

This study investigates the disparities in energy consumption and carbon emissions between traditional manufacturing processes and smart manufacturing practices in the steel industry. Utilizing a comparative analysis approach, the research employs quantitative methods, including descriptive and inferential statistics, to scrutinize differences in energy usage and emissions levels. Data collected from a stratified sample of manufacturing facilities are analyzed using independent samples t-tests and effect size calculations. Ethical considerations guide the research methodology, ensuring compliance with privacy protection and informed consent. The findings reveal that while there are observable variations in energy consumption and carbon emissions between the two manufacturing paradigms, these differences are not statistically significant. The study contributes to the understanding of sustainability challenges in manufacturing and informs evidence-based decision-making for industry stakeholders and policymakers.

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References

Chen, D., Chen, Y., & Zhao, Z. (2020). Big data-driven smart manufacturing for complex product and system development: a review. Robotics and Computer-Integrated Manufacturing, 62, 101864. doi:10.1016/j.rcim.2019.101864

Cimini, C., Ciasullo, M. V., & Saggese, S. (2018). A framework for Industry 4.0 adoption in steel industry. Procedia Manufacturing, 17, 1343-1350. doi:10.1016/j.promfg.2018.10.174

Ding, Y., & Ren, L. (2019). Deep learning for smart manufacturing: methods and applications. Journal of Manufacturing Systems, 53, 261-270. doi:10.1016/j.jmsy.2019.04.017

Gao, R., Ai, T., & Yang, X. (2017). Cyber-physical system enabled intelligent maintenance for smart manufacturing. Procedia CIRP, 63, 10-15. doi:10.1016/j.procir.2017.03.025

Goyal, S., Singh, R. K., & Dutt, V. (2019). A review on the application of artificial intelligence techniques in Indian steel industry. Materials Today: Proceedings, 18, 521-527. doi:10.1016/j.matpr.2019.06.600

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Industry 4.0 in steel manufacturing: a systematic review and future research directions. Journal of Manufacturing Technology Management, 31(3), 544-576. doi:10.1108/JMTM-09-2019-0359

Lee, J., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & services innovation. Procedia CIRP, 38, 3-7. doi:10.1016/j.procir.2015.07.011

Li, C., & Chen, X. (2019). Integration of Internet of Things and big data analytics in smart manufacturing: a case study in steel industry. Journal of Manufacturing Systems, 50, 134-141. doi:10.1016/j.jmsy.2018.08.007

Li, L., Lu, Y., & Ouyang, S. (2020). Digital twin-driven product lifecycle management for smart manufacturing: a review. Journal of Manufacturing Systems, 55, 407-419. doi:10.1016/j.jmsy.2020.01.014

Li, S., Xu, L., & Wang, L. (2020). Smart manufacturing in steel industry: a review. International Journal of Advanced Manufacturing Technology, 108(3-4), 927-942. doi:10.1007/s00170-020-05154-9

Liu, J., Gao, R., & Chen, Y. (2018). A cyber-physical system approach to intelligent manufacturing: a review. Engineering, 4(11), 11-20. doi:10.1016/J.ENG.2018.11.013

Lu, Y., & Morris, K. C. (2017). A review of cyber-physical systems in steel manufacturing: modeling, control, optimization, and data analytics. Engineering, 3(4), 434-448. doi:10.1016/J.ENG.2017.04.019

Sharma, P., & Gupta, A. D. (2020). Internet of Things (IoT)-enabled smart manufacturing: a review. Materials Today: Proceedings, 29(9), 3304-3308. doi:10.1016/j.matpr.2020.03.867

Wang, Y., Yang, X., & Zhao, Y. (2018). Research on smart manufacturing mode of steel industry based on Internet of Things technology. IOP Conference Series: Materials Science and Engineering, 440(1), 012040. doi:10.1088/1757-899X/440/1/012040

Xu, X., & Panchal, J. H. (2017). A review of data-driven approaches for prognostics and health management in smart manufacturing systems. Journal of Manufacturing Science and Engineering, 139(7), 070801. doi:10.1115/1.4036703

Xu, X., & Yoon, S. H. (2017). A review of production planning and control: from a holistic perspective. Journal of Cleaner Production, 143, 1168-1187. doi:10.1016/j.jclepro.2016.12.023

Zeng, D., Luo, X., & Wang, H. (2016). Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Computer-Aided Design, 72, 1-18. doi:10.1016/j.cad.2015.10.006

Zhu, Z., & Song, H. (2019). Development of a predictive model for energy consumption in steel manufacturing using machine learning algorithms. Journal of Cleaner Production, 229, 365-378. doi:10.1016/j.jclepro.2019.05.335

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Published

31-01-2025

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Section

Research Articles

How to Cite

Analysis of Energy Consumption and Carbon Emissions in Traditional versus Smart Manufacturing Practices: A Case Study in the Steel Industry. (2025). International Journal of Scientific Research in Science and Technology, 12(1), 318-329. https://ijsrst.com/index.php/home/article/view/IJSRST25121177

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