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 7 publications

(3 citation statements)

References 99 publications

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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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“…Their ability to mimic the behavior of more complex systems is transforming how engineers approach process design, analysis, and optimization. The advancement and implementation of data-driven surrogate models in PSE have been thoroughly recognized. − Moreover, data-driven surrogate models represent a significant improvement in accuracy and performance in the modeling of complex processes, including those that were not previously able to be addressed. The abilities of these surrogate models have led to more precise results, which in turn improves decision-making and benefits sustainability.…”

Section: Introductionmentioning

confidence: 99%

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

López-Flores,

Ramírez-Márquez,

Ponce-Ortega

2024

Ind. Eng. Chem. Res.

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This article studies the relevance of innovative Process Systems Engineering (PSE) tools that can reformulate trained machine learning models that are driven by advances in computational technologies, showcasing a pivotal transformation in chemical engineering methodologies. The article also delves into how trained machine learning models are reformulated and optimized to refine engineering decisions as it provides a novel analysis of tools to develop machine learning models by reformulating them, and optimizing them in PSE, thus highlighting their significance and applications. It offers a comprehensive comparison of several cutting-edge tools, including JANOS, MeLOn, ENTMOOT, reluMIP, OptiCL, Gurobi Machine Learning, OMLT, and PySCIPOpt-ML, highlighting their distinct abilities for performance and decision-making. Furthermore, challenges related to the explicit formulation of the main machine learning models are discussed. Guidance is provided to select the appropriate tool according to users' requirements. Additionally, a comparative study of the tools is presented using a case study to analyze and compare the size and type of formulations, the optimal solution, and the computation times.

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

Cited by 7 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

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

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