深入探索智能算法与反应网络研究的融合

反应网络是化工过程机理在微观分子尺度上的表达方式，但网络的复杂性为深入认识生产过程提出了挑战。本文提出了探索智能算法与反应网络研究融合的思路，基于物质转化的“透明工程”的概念，深入剖析反应网络的结构统计指标、结构拓扑特征、节点性质特征、机理动态演化、建模应用性能等特点。随后阐述了使用“数据结构化、智能优化与分析、智能代理建模”三步结合的机理数值化反应网络研究方法，既实现了在微观层面的局部放大，又实现了在工业应用中的准确预测。文中指出，智能算法融合反应网络后可以对实际工业过程执行可视化、可解释性的建模、分析与优化，为相关工业生产提质增效提供决策依据，并进一步帮助人类突破认知的极限，更深入地理解反应过程，提取关键的反应规律，助力化学工业的智能化。

Going deep into the integration of intelligent algorithms and reaction network research

Reaction network is a micromolecular-scale expression of the chemical process mechanism, but the complexity of the network poses a challenge for in-depth understanding of the production process. This paper propose the idea of exploring the integration of intelligent algorithm and reaction network, of which the structural statistical indicators, structural topological characteristics, node properties, mechanism dynamic evolution, modeling application performance are analyzed based on the conception of “transparency engineering”. Subsequently, a three-step mechanism-digital reaction network research method combining data structuring, intelligent optimization and analysis, and intelligent surrogate modeling is used, which provide a partial enlargement in micro scale and an accurate prediction in industrial application. The integration of the intelligent algorithms and reaction network implement a visible, explainable modeling, analysis and optimization of the industrial practice, which further provide decision-making recommendations for quality and efficiency enhancement. Additionally, these attempts could help humans to break through the limit of cognition and go deeper into the understanding of the reaction mechanisms, extraction of the key reactions, and intellectualization of the chemical industry.