Industrial data science – a review of machine learning applications for chemical and process industries

Literature Information

Publication Date 2022-04-21
DOI 10.1039/D1RE00541C
Impact Factor 4.239
Authors

Max Mowbray, Mattia Vallerio, Carlos Perez-Galvan, Antonio Del Rio Chanona


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Abstract

In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to start with examples that are irrelevant to process engineers (e.g. classification of images between cats and dogs, house pricing, types of flowers, etc.). However, process engineering principles are also based on pseudo-empirical correlations and heuristics, which are a form of ML. In this work, industrial data science fundamentals will be explained and linked with commonly-known examples in process engineering, followed by a review of industrial applications using state-of-art ML techniques.

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Source Journal

Reaction Chemistry & Engineering

Reaction Chemistry & Engineering
CiteScore: 0
Self-citation Rate: 8.8%
Articles per Year: 284

Reaction Chemistry & Engineering is an interdisciplinary journal reporting cutting-edge research focused on enhancing the understanding and efficiency of reactions. Reaction engineering leverages the interface where fundamental molecular chemistry meets chemical engineering and technology. Challenges in chemistry can be overcome by the application of new technologies, while engineers may find improved solutions for process development from the latest developments in reaction chemistry. Reaction Chemistry & Engineering is a unique forum for researchers whose interests span the broad areas of chemical engineering and chemical sciences to come together in solving problems of importance to wider society. All papers should be written to be approachable by readers across the engineering and chemical sciences. Papers that consider multiple scales, from the laboratory up to and including plant scale, are particularly encouraged.

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