Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

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 tr...

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Autores principales:	Teng Zhou, Rafiqul Gani, Kai Sundmacher
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Data-driven Surrogate model Machine learning Hybrid modeling Material design Process optimization Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/4fe3025fccf144449df71c6c8d716784
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spelling	oai:doaj.org-article:4fe3025fccf144449df71c6c8d7167842021-11-14T04:32:13ZHybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design2095-809910.1016/j.eng.2020.12.022https://doaj.org/article/4fe3025fccf144449df71c6c8d7167842021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2095809921001417https://doaj.org/toc/2095-8099The 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.Teng ZhouRafiqul GaniKai SundmacherElsevierarticleData-drivenSurrogate modelMachine learningHybrid modelingMaterial designProcess optimizationEngineering (General). Civil engineering (General)TA1-2040ENEngineering, Vol 7, Iss 9, Pp 1231-1238 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data-driven Surrogate model Machine learning Hybrid modeling Material design Process optimization Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Data-driven Surrogate model Machine learning Hybrid modeling Material design Process optimization Engineering (General). Civil engineering (General) TA1-2040 Teng Zhou Rafiqul Gani Kai Sundmacher Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
description	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.
format	article
author	Teng Zhou Rafiqul Gani Kai Sundmacher
author_facet	Teng Zhou Rafiqul Gani Kai Sundmacher
author_sort	Teng Zhou
title	Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
title_short	Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
title_full	Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
title_fullStr	Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
title_full_unstemmed	Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design
title_sort	hybrid data-driven and mechanistic modeling approaches for multiscale material and process design
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4fe3025fccf144449df71c6c8d716784
work_keys_str_mv	AT tengzhou hybriddatadrivenandmechanisticmodelingapproachesformultiscalematerialandprocessdesign AT rafiqulgani hybriddatadrivenandmechanisticmodelingapproachesformultiscalematerialandprocessdesign AT kaisundmacher hybriddatadrivenandmechanisticmodelingapproachesformultiscalematerialandprocessdesign
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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

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