A semi-mechanistic model building framework based on selective and localized model extensions

Lima, Pedro Vale; Saraiva, Pedro

doi:10.1016/j.compchemeng.2006.07.006

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Lima et al 82 propose a semi‐mechanistic model building framework based on selective and localized model extensions. They use a symbolic reformulation of a set of first‐principles model equations in order to derive hybrid mechanistic–empirical models.…”

Section: Complements Sciencementioning

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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Section: Complements Sciencementioning

confidence: 99%

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

Sharma

Liu

2022

AIChE Journal

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“…In the following sections, each of these categories is explained through examples, wherever possible. It should be noted that most of the problems of real-world are a combination of one or more of these categories, as can be seen in the literature survey and summarized in Table 1 below Lumped parameter models (LPMs) [12], [16], [51], [52], [53], [54], [55], [43], [56], [57], [58], [59], [60], [22], [61], [62], [63], [64], [65], [66], [67], [64], [68], [69], [59], [60], [70], [68], [71], [66], [72], [73], [74], [75], [76] Residual modeling (RM) [30], [18], [20], [77], [78], [79], [34], [35], [33], [80], [81], …”

Section: Cstr Modelingmentioning

confidence: 99%

Integration of machine learning and first principles models

et al. 2022

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Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white‐box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black‐box models) and artificial intelligence (AI) techniques. Hybrid models (gray‐box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine‐learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.

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“…Information from the previous operation can be used in order to improve the performance via trial to trial in the sense that errors are sequentially reduced. According to Lima and Saraiva [7], by looking at the past records and sets of examples, it is possible to extract and generate important new knowledge. Such an idea has been used widely in control of the process.…”

Section: Introductionmentioning

confidence: 99%