数据驱动绿色催化技术的商业化道路:机器学习在技术生命周期管理中的应用
1School of Economics and Management, Jiujiang Polytechnic University of Science and Technology, Jiujiang, Jiangxi, 332020, PR China.
Environmental research
|February 10, 2026
概括
数据驱动的方法,特别是机器学习,可以通过优化研发和管理其生命周期来加速绿色催化技术的商业化. 本综述探讨了机器学习的应用,以克服开发可持续化学工艺的传统瓶.
科学领域:
- 催化剂是一种催化剂.
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 绿色催化技术对于可持续发展和碳中和性至关重要.
- 传统的研发模式面临着诸如高成本,长周期和多层次因素分析困难等挑战.
- 商业化绿色催化技术需要克服复杂的系统优化问题.
研究的目的:
- 探索数据驱动的范式,特别是机器学习,如何重塑绿色催化技术的全链管理.
- 在商业化每个阶段分析挑战和数据需求.
- 为加速绿色技术商业化提供系统的框架和指导.
主要方法:
- 从技术生命周期管理的角度进行系统审查.
- 在催化材料设计,反应机制分析,反应器优化和生命周期评估中分析机器学习应用.
- 评估不同的机器学习范式 (监督,无监督,强化学习,生成模型).
主要成果:
- 机器学习为智能设计,选,机制分析,运动模拟和流程优化提供解决方案.
- 各种ML模型在解决技术生命周期中的特定挑战方面表现有前途.
- 识别的常见挑战包括数据稀缺性,模型可解释性和跨规模集成.
结论:
- 数据驱动的方法,特别是机器学习,可以显著提高绿色催化技术商业化的效率和可预测性.
- 未来的方向包括基于物理的机器学习,标准化数据平台和智能决策支持系统.
- 跨学科的合作是推动绿色制造系统的关键.
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