Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In t...

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Autores principales:	Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Artificial intelligence Machine learning Reaction engineering Process engineering Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/5297e32d3e9d477d95ca985730c8e9e8
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spelling	oai:doaj.org-article:5297e32d3e9d477d95ca985730c8e9e82021-11-14T04:32:13ZMachine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats2095-809910.1016/j.eng.2021.03.019https://doaj.org/article/5297e32d3e9d477d95ca985730c8e9e82021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2095809921002010https://doaj.org/toc/2095-8099Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.Maarten R. DobbelaerePieter P. PlehiersRuben Van de VijverChristian V. StevensKevin M. Van GeemElsevierarticleArtificial intelligenceMachine learningReaction engineeringProcess engineeringEngineering (General). Civil engineering (General)TA1-2040ENEngineering, Vol 7, Iss 9, Pp 1201-1211 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Machine learning Reaction engineering Process engineering Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Artificial intelligence Machine learning Reaction engineering Process engineering Engineering (General). Civil engineering (General) TA1-2040 Maarten R. Dobbelaere Pieter P. Plehiers Ruben Van de Vijver Christian V. Stevens Kevin M. Van Geem Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
description	Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.
format	article
author	Maarten R. Dobbelaere Pieter P. Plehiers Ruben Van de Vijver Christian V. Stevens Kevin M. Van Geem
author_facet	Maarten R. Dobbelaere Pieter P. Plehiers Ruben Van de Vijver Christian V. Stevens Kevin M. Van Geem
author_sort	Maarten R. Dobbelaere
title	Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
title_short	Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
title_full	Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
title_fullStr	Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
title_full_unstemmed	Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
title_sort	machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5297e32d3e9d477d95ca985730c8e9e8
work_keys_str_mv	AT maartenrdobbelaere machinelearninginchemicalengineeringstrengthsweaknessesopportunitiesandthreats AT pieterpplehiers machinelearninginchemicalengineeringstrengthsweaknessesopportunitiesandthreats AT rubenvandevijver machinelearninginchemicalengineeringstrengthsweaknessesopportunitiesandthreats AT christianvstevens machinelearninginchemicalengineeringstrengthsweaknessesopportunitiesandthreats AT kevinmvangeem machinelearninginchemicalengineeringstrengthsweaknessesopportunitiesandthreats
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Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

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