Energy Optimization Strategies in Low-carbon Manufacturing

DOI

10.2991/978-94-6463-823-3_4How to use a DOI?

Keywords

Energy optimization; Low-carbon manufacturing; Data-based algorithm

Abstract

This article introduces 5 optimization strategies to enhance energy efficiency and reduce carbon emissions in low-carbon manufacturing. Key focus areas include algorithmic optimization models and technological frameworks. The Grey Wolf Algorithm (GWO) and Chromosome Hierarchical Coding Genetic Algorithm (GA) are highlighted as advanced multi-objective optimization tools for minimizing energy consumption and emissions in manufacturing processes. Practical case studies are listed to demonstrate their effectiveness. To be specific, GWO achieved a 12–15% improvement in Pareto front solutions for gear manufacturing, while GA reduced standby energy by 83% in mechanical part production. The National Energy Technology-Aluminum (NET-AL) model addresses China’s aluminum industry. It plans a 5.3% cumulative CO₂ reduction by 2050 through phased technology deployment and energy structure optimization. Digital transformation (DT) is emphasized as a trigger for low-carbon innovation due to its role in reconstructing and sharing knowledge, thereby enhancing sustainability outcomes. The Design for Energy Minimization (DfEM) framework integrates energy metrics into product development, achieving 15–20% energy savings in automotive and plastics sectors through simulation tools and supply chain collaboration. Collectively, these strategies underscore the potential of data-driven algorithms, digital integration and policy alignment to balance energy-carbon trade-offs while maintaining industrial productivity.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xinrui Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Energy Optimization Strategies in Low-carbon Manufacturing
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 34
EP  - 43
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_4
DO  - 10.2991/978-94-6463-823-3_4
ID  - Zhang2025
ER  -