Recent trends on hybrid modeling for Industry 4.0

Sansana, Joel; Joswiak, Mark N.; Castillo, Iván; Wang, Zhenyu; Rendall, Ricardo; Chiang, Leo H.; Reis, Marco S.

doi:10.1016/j.compchemeng.2021.107365

Cited by 149 publications

(66 citation statements)

References 163 publications

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“…Most modern industries utilise modelling and simulations for process monitoring, control, diagnosis, optimisation, and design. Industry 4.0 and massive digitisation have made it possible to collect and process large arrays of data, resulting in the development of data-driven decisions and modelling tools [ 81 ]. It is worth mentioning that data-driven, statistical, or empirical models do not require broad initial knowledge about the studied system, but strongly rely on the presence of data collected from the process [ 82 ].…”

Section: Ingredients Of An Industry 40 Healthcare Systemmentioning

confidence: 99%

Industry 4.0 and Digitalisation in Healthcare

et al. 2022

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Industry 4.0 in healthcare involves use of a wide range of modern technologies including digitisation, artificial intelligence, user response data (ergonomics), human psychology, the Internet of Things, machine learning, big data mining, and augmented reality to name a few. The healthcare industry is undergoing a paradigm shift thanks to Industry 4.0, which provides better user comfort through proactive intervention in early detection and treatment of various diseases. The sector is now ready to make its next move towards Industry 5.0, but certain aspects that motivated this review paper need further consideration. As a fruitful outcome of this review, we surveyed modern trends in this arena of research and summarised the intricacies of new features to guide and prepare the sector for an Industry 5.0-ready healthcare system.

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Section: Ingredients Of An Industry 40 Healthcare Systemmentioning

confidence: 99%

Industry 4.0 and Digitalisation in Healthcare

et al. 2022

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“…14 Over the years, we have seen a growing number of applications of hybrid modeling in bioprocessing and chemical engineering as part of the advances in smart manufacturing. [15][16][17] In their 2021 paper, Sansana et al 16 discuss mechanistic modeling, data-based modeling, hybrid modeling structures, system identification methodologies, and applications. They classify their hybrid model into parallel, series, surrogate models (which are simpler mathematical representations of more complex models and similar to reduced-order models that we discuss below), and alternate structures (which include gray-box modeling mentioned above).…”

Section: Applications Of Hybrid Sgml Approach In Bioprocessing and Ch...mentioning

confidence: 99%

“…Over the years, we have seen a growing number of applications of hybrid modeling in bioprocessing and chemical engineering as part of the advances in smart manufacturing 15–17 …”

Section: Applications Of Hybrid Sgml Approach In Bioprocessing and Ch...mentioning

confidence: 99%

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Sharma

Liu

2022

AIChE Journal

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This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.

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“…49 Applications of surrogate models range from efficient modeling nonlinear dynamic systems, conducting advanced design of experiments for parameter estimation and sensitivity analysis of complex systems, and surrogate-based optimization. 8,50,51 Derivative-free optimization approaches are common in surrogate-based optimization. 52 Recently, methodologies and frameworks aimed at efficient optimization using surrogate, for example, ALAMO 53 or gray-box models have been developed like ARGO-NAUT.…”

Section: Recently Aykol Et Al Included Additional Arrangements That I...mentioning

confidence: 99%

Towards artificial intelligence at scale in the chemical industry

et al. 2022

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In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision‐making is shown. Then state‐of‐the‐art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI‐ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.

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Recent trends on hybrid modeling for Industry 4.0

Cited by 149 publications

References 163 publications

Industry 4.0 and Digitalisation in Healthcare

Industry 4.0 and Digitalisation in Healthcare

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Towards artificial intelligence at scale in the chemical industry

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