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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
Engineering ( IF 12.8 ) Pub Date : 2021-04-01 , DOI: 10.1016/j.eng.2020.12.022
Teng Zhou , Rafiqul Gani , Kai Sundmacher

The world’s increasing population requires the process industry to produce food, fuels, chemicals, and consumer products in a more efficient and sustainable way. Functional process materials lie at the heart of this challenge. Traditionally, new advanced materials are found empirically or through trial-and-error approaches. As theoretical methods and associated tools are being continuously improved and computer power has reached a high level, it is now efficient and popular to use computational methods to guide material selection and design. Due to the strong interaction between material selection and the operation of the process in which the material is used, it is essential to perform material and process design simultaneously. Despite this significant connection, the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required. Hybrid modeling provides a promising option to tackle such complex design problems. In hybrid modeling, the material properties, which are computationally expensive to obtain, are described by data-driven models, while the well-known process-related principles are represented by mechanistic models. This article highlights the significance of hybrid modeling in multiscale material and process design. The generic design methodology is first introduced. Six important application areas are then selected: four from the chemical engineering field and two from the energy systems engineering domain. For each selected area, state-of-the-art work using hybrid modeling for multiscale material and process design is discussed. Concluding remarks are provided at the end, and current limitations and future opportunities are pointed out.



中文翻译:

用于多尺度材料和工艺设计的混合数据驱动和机械建模方法

世界不断增长的人口要求加工业以更高效、更可持续的方式生产食品、燃料、化学品和消费品。功能性工艺材料是这一挑战的核心。传统上,新的先进材料是根据经验或通过反复试验的方法发现的。随着理论方法和相关工具的不断改进和计算机能力的提高,现在使用计算方法来指导材料选择和设计是有效和流行的。由于材料选择与材料使用过程的操作之间存在很强的相互作用,因此必须同时进行材料和工艺设计。尽管有这种重要的联系,集成材料和工艺设计问题的解决并不容易,因为通常需要不同规模的多个模型。混合建模为解决此类复杂的设计问题提供了一个有前景的选择。在混合建模中,获得计算成本高昂的材料属性由数据驱动模型描述,而众所周知的过程相关原理由机械模型表示。本文重点介绍了混合建模在多尺度材料和工艺设计中的重要性。首先介绍通用设计方法。然后选择了六个重要的应用领域:四个来自化学工程领域,两个来自能源系统工程领域。对于每个选定的区域,讨论了使用混合建模进行多尺度材料和工艺设计的最新工作。最后给出了结论,并指出了当前的局限性和未来的机会。

更新日期:2021-04-01
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