Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)

Zavala, Ví­ctor M.

doi:10.1021/acs.iecr.3c01565

Cited by 6 publications

(2 citation statements)

References 99 publications

(160 reference statements)

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“…Interdisciplinary papers may reflect collaborations between academics working on theory or experimental research, and/ortrue to I&ECR ’s nameindustrial practitioners, spanning multiple length and time scales. Examples include simultaneous design of materials and processes in reaction engineering and separations, integrated process design and operation, computational design of functional materials accompanied by experimental validation, data science applications in chemical engineering (in the sense discussed in a recent invited commentary by Associate Editor Victor Zavala). We will also maintain our strong pipeline of festschriften and special issues, encouraging the members of the editorial boards and authors to propose relevant themes on an ongoing basis.…”

Section: Creating Compelling Contentmentioning

confidence: 99%

I&ECR: A Vision for Growth

Baldea

2024

Ind. Eng. Chem. Res.

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Section: Creating Compelling Contentmentioning

confidence: 99%

I&ECR: A Vision for Growth

Baldea

2024

Ind. Eng. Chem. Res.

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“…For an extensive discussion on the impact of AI in chemical engineering, we recommend reading Venkatasubramanian [285]. Regarding the specific impact of machine learning, several works have been published [16,[288][289][290][291][292][293][294][295].…”

Section: Machine Learningmentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

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Constrained continuous-action reinforcement learning for supply chain inventory management

Burtea,

Tsay

2024

Computers & Chemical Engineering

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Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)

Cited by 6 publications

References 99 publications

I&ECR: A Vision for Growth

I&ECR: A Vision for Growth

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Constrained continuous-action reinforcement learning for supply chain inventory management

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