化工领域人工智能的多尺度革命

IF 4.5 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Ying Li, Quanhu Sun, Zutao Zhu, Huaqiang Wen, Saimeng Jin, Xiangping Zhang, Zhigang Lei, Weifeng Shen
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引用次数: 0

摘要

随着第四次技术革命的到来,新一代人工智能(AI)为化学工程领域的动量、热量和质量传递以及化学反应过程建模提供了新的意义和机会。人工智能技术在化学工业中得到广泛应用,并不断发展,为应对实际挑战提供更有效的解决方案。本文探讨了化学工业从传统的数字模拟到先进的基于人工智能的方法的转变,目标是从分子到工厂的整个规模的高效率和低碳排放。特别强调的是在沈伟峰课题组内进行的研究。在分子水平上,分子特征的智能捕捉和结构-性质关系的精确测定已经达到成熟阶段。此外,通过基于人工智能的高通量筛选和生成模型,已经完成了溶剂、反应试剂和其他物质的多功能驱动反分子设计。为了提高化工分离过程的安全性、环保性和减碳性能,人们提出了一系列创新的强化策略,主要是对溶剂设计进行系统优化。在实际生产的过程规模上,经常出现构建的机制模型与实际系统行为不一致的情况,从而制约了模型的产业化应用。为了解决这一问题,机制-数据混合驱动框架已经成功开发,利用人工智能增强了对复杂分离系统的预测、诊断、优化和控制。最后,作为连接大数据智能技术和实际工业过程的桥梁,讨论了动态数字孪生模型在提高化工行业效率和可持续性方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-scale revolution of artificial intelligence in chemical industry

With the advent of the fourth technological revolution, the new generation of artificial intelligence (AI) has imparted new significance and opportunities to the modeling of momentum, heat, and mass transfer, as well as chemical reaction processes with the realm of chemical engineering. AI techniques are being widely employed in the chemical industry and are constantly evolving to offer more effective solutions for tackling practical challenges. This review delves the transformation of the chemical industry from traditional digital simulations to advanced AI-based approaches, targeting high efficiency and low carbon emissions across the scale from molecules to factories. Particular emphasis is mainly placed on the research carried out within the research group of Weifeng Shen. At the molecular level, the intelligent capture of molecular characteristics and the precise determination of structure-property relationships have reached a mature stage. Furthermore, multifunction-driven reverse molecular design for solvents, reaction reagents, and other substances has been accomplished through AI-based high-throughput screening and generative models. To improve the safety, environmental friendliness, and carbon reduction performance of chemical separation processes, a series of innovative reinforcement strategies have been put forward, with a primary focus on the systematic optimization of solvent design. On the process scale of actual production, it frequently occurs that the constructed mechanism model fails to align with the actual system behavior, thereby restricting the industrial application of the model. To solve this issue, mechanism-data hybrid-driven frameworks have been successfully developed, leveraging AI-enhanced prediction, diagnosis, optimization, and control for complex separation systems in practice. Finally, as a bridge connecting big data intelligent technology and actual industrial processes, dynamic digital twin modeling is discussed for its potential to boost efficiency and sustainability in the chemical industry.

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来源期刊
CiteScore
7.60
自引率
6.70%
发文量
868
审稿时长
1 months
期刊介绍: Frontiers of Chemical Science and Engineering presents the latest developments in chemical science and engineering, emphasizing emerging and multidisciplinary fields and international trends in research and development. The journal promotes communication and exchange between scientists all over the world. The contents include original reviews, research papers and short communications. Coverage includes catalysis and reaction engineering, clean energy, functional material, nanotechnology and nanoscience, biomaterials and biotechnology, particle technology and multiphase processing, separation science and technology, sustainable technologies and green processing.
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