Development of parametric reduced-order model for consequence estimation of rare events

Kumari, Pallavi; Bhadriraju, Bhavana; Wang, Qingsheng; Kwon, Joseph

doi:10.1016/j.cherd.2021.02.006

Cited by 19 publications

(2 citation statements)

References 43 publications

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“…Kumari et al 96 use data‐based reduced order methods for computational fluid dynamic modeling applied to a case study of super critical carbon dioxide rare event. They propose a k‐nearest neighbor (kNN)‐based parametric reduced‐order model (PROM) for consequence estimation of rare events to enhance numerical robustness with respect to parameter change.…”

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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“…Rare events are low-frequency events such as toxic release, explosions, and oil spills. , Rare events have been explored in various fields, including aviation, pipelines, nuclear power, and the chemical process industry (CPI), due to their high monetary, environmental, and societal implications. , In the CPI, the U.S. Chemical Safety Board reports more than 130 rare events with severe consequences in the last two decades . Rare events in the CPI results from poorly managed process faults, which are defined as deviations of observed process variables from their normal operating conditions (NOCs) .…”

Section: Introductionmentioning

confidence: 99%

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Kumari

Wang

Khan

et al. 2022

Ind. Eng. Chem. Res.

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In chemical processes, Bayesian network (BN)-based approaches have been extensively applied for process fault diagnosis. Generally, BN is learned using score and search algorithms where search algorithms create candidate networks whose fitness to data is measured by scores. However, existing approaches cannot utilize cyclic loop knowledge while learning BN. Since cyclic loops are prevalent in chemical processes, their unaccountability results in inaccurate BN and reduces diagnosis accuracy. Therefore, for accurate diagnosis, we propose direct transfer entropy (DTE)-based multiblock BN to discover cyclic loops while learning BN. First, the process is segmented into multiple blocks. Next, block-level BNs are learned using DTE-based score and Greedy search. By eliminating the common source variable effect, DTE finds correct causality between process variables and obtains accurate block-level BNs, which are fused to discover significant cyclic loops that were otherwise unachievable when finding BN. The performance of the developed methodology is demonstrated through a benchmark process.

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A Hybrid Science‐Guided Machine Learning Approach for Modeling Chemical and Polymer Processes

2023

Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing

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Development of parametric reduced-order model for consequence estimation of rare events

Cited by 19 publications

References 43 publications

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

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

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

A Hybrid Science‐Guided Machine Learning Approach for Modeling Chemical and Polymer Processes

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