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

Dobbelaere, Maarten R.; Plehiers, Pieter; Vijver, Ruben Van de; Stevens, Christian V.; Geem, Kevin Van

doi:10.1016/j.eng.2021.03.019

Cited by 158 publications

(82 citation statements)

References 120 publications

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“…The application of AI to chemical engineering disciplines such as catalysis, material science, energy, and fuels has gained significant attention in recent years [18,59,60]. The progress of ML in chemical processes has been explicitly described by Venkatasubramanian [61], as summarized in Fig.…”

Section: Insights From "In Silico" Modellingmentioning

confidence: 99%

“…The adoption of ML and its representative subset in chemical engineering and other subfields has increased in a sustained way in the last decade and have been addressed by Tkatchenko and Dobbelaere [18,73]. One of the major areas with notable progress in this field is "in silico" catalyst synthesis, drugs, and materials design, among others [74,75].…”

Section: Machine Learning For Plastic Pyrolysismentioning

confidence: 99%

“…To address these gaps, "data-driven" tools, based on machine learning (ML) and quantum mechanics (QM) have been widely applied to unravel reaction mechanisms and kinetics triplets [15][16][17][18][19]. For this reason, the following sections of this review explore the recent advances of computational tools, such as ML and QM, applied to obtain kinetics and mechanisms information of plastic pyrolysis.…”

Section: Introductionmentioning

confidence: 99%

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Application of computational approach in plastic pyrolysis kinetic modelling: a review

Armenise

Wong

Ramírez-Velásquez

et al. 2021

Reac Kinet Mech Cat

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During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

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Section: Insights From "In Silico" Modellingmentioning

confidence: 99%

Section: Machine Learning For Plastic Pyrolysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of computational approach in plastic pyrolysis kinetic modelling: a review

Armenise

Wong

Ramírez-Velásquez

et al. 2021

Reac Kinet Mech Cat

View full text Add to dashboard Cite

show abstract

“…In fact, it is generally challenging to find works in chemical engineering that apply machine learning models and evaluate the issues related to the predictors. As pointed by Dobbelaere et al (2021) [17]: "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.". Therefore, this work addresses part of this issue, providing a comprehensive guideline for ML application in this field.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

et al. 2022

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A misusage of machine learning (ML) strategies is usually observed in the process systems engineering literature. This issue is even more evident when dynamic identification is performed. The root of this problem is the gradient explode and vanishing issue related to the recurrent neural networks training. However, after the advent of deep learning, these issues were mitigated. Furthermore, the problem of data structuration is often overlooked during the machine learning model identification in this field. In this scenario, this work proposes a guideline for identifying ML models for the different applications in process systems engineering, which are usually for simulation or prediction purposes. While using the proposed guideline, the work also identifies a virtual analyzer for a pressure swing adsorption unit. In these types of adsorption separations, it is usual that the measurement of the main properties is not done online. Therefore, the virtual analyzer is another contribution of this manuscript. The overall results demonstrate that even though the test provides good performance during the ML model identification, its quality might degenerate over the application domain if the model application is overlooked.

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“…Chemical engineering is one example of an area that uses data‐driven techniques but is still an auxiliary tool. As argued by Dobbelaere et al [ 2 ] at some level, the inappropriate use of data‐driven techniques in this field has hampered the advancement of these strategies in solving chemical engineering problems. On the other hand, the consolidated knowledge about the physics of the systems makes the phenomenological models a reliable source of information that is desirable to be used as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Using scientific machine learning to develop universal differential equation for multicomponent adsorption separation systems

et al. 2022

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Universal differential equations are a concept in scientific machine learning that leverages the potential of the universal approximator theorem and the physical knowledge of a given system. Creating this level of hybridization within a stiff partial differential equation system is a challenge. On the other hand, adsorption phenomenological models have sink/source terms that describe the adsorption equilibrium through a well‐known simplified model (e.g., Langmuir; Sips; and Brunauer, Emmet, Teller [BET]). These suitable mechanistic assumptions are identified through experiments, providing the parameters of the sink/source model. However, these mechanistic assumptions are a simplification of the system phenomenology. Therefore, the resulting model is limited by its premises. In this scenario, the universal ordinary differential equations (UODE) is presented as an approach that conciliates the potential of artificial neural networks to learn given phenomena without conceptual simplifications. On the other hand, keeping into consideration the system physics. This work proposes a UODE system to solve the multicomponent separation by adsorption in a fixed bed column. Experimental data is used to identify the hybrid model. The required amount of data used in the model identification demonstrates that hybrid models can use a few data points to precisely describe the system. Furthermore, the obtained model can describe competitive adsorption with higher precision than the Langmuir model.

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

Cited by 158 publications

References 120 publications

Application of computational approach in plastic pyrolysis kinetic modelling: a review

Application of computational approach in plastic pyrolysis kinetic modelling: a review

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

Using scientific machine learning to develop universal differential equation for multicomponent adsorption separation systems

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