"Green + Intelligent" Dual-Core Drive: Restructuring and Practice of the Training Model for Outstanding Engineers in the Field of Textile Chemistry and Dyeing and Finishing Engineering
DOI:
https://doi.org/10.70088/jzw1dv57Keywords:
green manufacturing, intelligent manufacturing, engineering education, textile chemistry, innovation capacityAbstract
Facing profound global industrial adjustments and urgent national strategic demands, China's textile printing and dyeing industry increasingly requires high-level, specialized engineering talents to effectively support its comprehensive green and intelligent transformation. By critically analyzing the existing constraints and bottlenecks prevalent in professional postgraduate education—such as the persistent disconnection between theoretical instruction and practical application, as well as the often superficial nature of industry-academia collaboration—this study proposes an innovative "Green + Intelligent" dual-core training model. This comprehensive framework systematically restructures the traditional curriculum to seamlessly integrate cutting-edge interdisciplinary knowledge, thereby ensuring that students are equipped with the latest technological advancements. Furthermore, the model significantly deepens industry collaboration through the implementation of a substantive dual-supervisor system, bridging the gap between academic research and industrial practice. It actively enhances students' engineering innovation capacity through immersive participation in specialized workshops, high-level academic competitions, and integrated industry platforms. Concurrently, pedagogical teaching methods and academic evaluation systems are fundamentally reformed to strongly emphasize project-based learning, critical thinking, and tangible practical value. Extensive practical application demonstrates that this optimized dual-core model substantially improves postgraduates' complex problem-solving skills, technological innovation ability, and overall professional competence. Ultimately, this approach supplies vital, high-quality talent essential for the sustainable upgrading of the textile industry, while simultaneously offering valuable theoretical insights and practical paradigms for the broader reform of modern engineering education.References
M. Maksimovic, "Greening the future: Green Internet of Things (G-IoT) as a key technological enabler of sustainable development," in Internet of Things and Big Data Analytics Toward Next-Generation Intelligence, Cham: Springer International Publishing, 2017, pp. 283-313.
H. M. Li and Z. M. Feng, "Analysis of practical ability improvement for teachers based on the 'excellence plan'," *International Journal of Cognitive Research in Science, Engineering and Education*, vol. 2, no. 1, pp. 31-35, 2014.
X. Wei, F. Jiang, Y. Chen, and W. Hua, "Towards green development: the role of intelligent manufacturing in promoting corporate environmental performance," Energy Economics, vol. 131, p. 107375, 2024.
H. Ji, X. Zeng, and F. Zhou, "Intelligent manufacturing and green innovation efficiency: Perspective on the agglomeration effect," Sustainability, vol. 17, no. 11, p. 4929, 2025.
S. Yin, N. Zhang, K. Ullah, and S. Gao, "Enhancing digital innovation for the sustainable transformation of manufacturing industry: a pressure-state-response system framework to perceptions of digital green innovation and its performance for green and intelligent manufacturing," Systems, vol. 10, no. 3, p. 72, 2022.
Z. Xu and R. Pan, "Effects of intelligent manufacturing on the high-quality development of manufacturing industry: The mediating role of green technology innovation," Scientific Reports, vol. 14, no. 1, p. 26145, 2024.
X. Hao, Y. Li, K. Wang, Q. Sun, and H. Wu, "Eco-intelligent production: Intelligent manufacturing and industrial green transition," Environment, Development and Sustainability, pp. 1-31, 2025.
X. Xu, J. Pan, and X. Meng, "Intelligent Manufacturing and Green Innovation—Evidence from China’s Listed Manufacturing Firms," Sustainability, vol. 16, no. 23, p. 10376, 2024.
T. Feng, "Do Intelligent manufacturing concerns promote corporate sustainability? Based on the perspective of green innovation," Sustainability, vol. 15, no. 14, p. 10958, 2023.
S. Chen, D. Gao, and L. Tan, "Smarter and Greener: How Does Intelligent Manufacturing Empower Enterprises’ Green Innovation?," Sustainability, vol. 17, no. 16, p. 7230, 2025.
Z. Yang and Y. Shen, "The impact of intelligent manufacturing on industrial green total factor productivity and its multiple mechanisms," Frontiers in Environmental Science, vol. 10, p. 1058664, 2023.
Z. Pei, T. Yu, W. Yi, and Y. Li, "Twenty‐year retrospection on green manufacturing: A bibliometric perspective," IET Collaborative Intelligent Manufacturing, vol. 3, no. 4, pp. 303-323, 2021.
X. Li, B. Wang, T. Peng, and X. Xu, "Greentelligence: Smart manufacturing for a greener future," Chinese Journal of Mechanical Engineering, vol. 34, no. 1, p. 116, 2021.
L. Torres-Treviño, I. Escamilla-Salazar, and B. González-Ortíz, "On developing a green and intelligent manufacturing system," Expert Systems with Applications, vol. 243, p. 122876, 2024.
H. Tieng, T. C. Ou, T. H. Tsai, Y. Y. Li, M. H. Hung, and F. T. Cheng, "I4.2-GiM: A novel green intelligent manufacturing framework for net zero," IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 18030-18050, 2023.
J. Chen, W. Zhang, and H. Wang, "Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing," Journal of Intelligent Manufacturing, vol. 32, no. 3, pp. 745-756, 2021.
S. Mao, B. Wang, Y. Tang, and F. Qian, "Opportunities and challenges of artificial intelligence for green manufacturing in the process industry," Engineering, vol. 5, no. 6, pp. 995-1002, 2019.
W. Zhang, P. Zhang, W. Zhang, and O. Grebinevych, "Towards digital intelligence: A sustainable approach for green and intelligent manufacturing," Journal of Environmental Management, vol. 393, p. 126925, 2025.
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Copyright (c) 2026 Liran Zhang, Fanglan Guan, Wenxia Li, Lihong Bao, Liping Zhang, Jinmei Nie, Chuanjiang Tang, Changhuan Zhang, Xiaochun Wang, Shengnan Min (Author)

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