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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Perspectives on the integration between first-principles and data-driven modeling
William A. Bradley, Jinhyeun Kim, Zachary Kilwein, et al.
Computers & Chemical Engineering (2022) Vol. 166, pp. 107898-107898
Open Access | Times Cited: 97

Showing 1-25 of 97 citing articles:

Machine learning for industrial sensing and control: A survey and practical perspective
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, et al.
Control Engineering Practice (2024) Vol. 145, pp. 105841-105841
Open Access | Times Cited: 13
Machine learning in process systems engineering: Challenges and opportunities
Pródromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, et al.
Computers & Chemical Engineering (2023) Vol. 181, pp. 108523-108523
Open Access | Times Cited: 26
Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
Afrânio Melo, Maurício Melo Câmara, José Carlos Pinto
Processes (2024) Vol. 12, Iss. 2, pp. 251-251
Open Access | Times Cited: 10
Machine learning prediction of bio-oil production from the pyrolysis of lignocellulosic biomass: Recent advances and future perspectives
Hyojin Lee, Il-Ho Choi, Kyung-Ran Hwang
Journal of Analytical and Applied Pyrolysis (2024) Vol. 179, pp. 106486-106486
Open Access | Times Cited: 8
Advanced Modeling and Optimization Strategies for Process Synthesis
Efstratios N. Pistikopoulos, Yuhe Tian
Annual Review of Chemical and Biomolecular Engineering (2024) Vol. 15, Iss. 1, pp. 81-103
Closed Access | Times Cited: 8
Challenges in data-based reactor modeling: A critical analysis of purely data-driven and hybrid models for a CSTR case study
Luisa Peterson, Jens Bremer, Kai Sundmacher
Computers & Chemical Engineering (2024) Vol. 184, pp. 108643-108643
Open Access | Times Cited: 6
Sparse Mass-Constrained Nonlinear Dynamic Model Building from Noisy Data Using a Bayesian Approach
Samuel Adeyemo, Debangsu Bhattacharyya
Industrial & Engineering Chemistry Research (2025)
Closed Access
A comparative study of dimensional and non-dimensional inputs in physics-informed and data-driven neural networks for single-droplet evaporation
Narjes Malekjani, Abdolreza Kharaghani, Evangelos Tsotsas
Chemical Engineering Science (2025), pp. 121214-121214
Open Access
Beyond the fourth paradigm of modeling in chemical engineering
John R. Kitchin, Victor Alves, Carl D. Laird
Nature Chemical Engineering (2025)
Closed Access
A hybrid time series and physics-informed machine learning framework to predict soil water content
Amirsalar Bagheri, Andres Patrignani, Behzad Ghanbarian, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110105-110105
Closed Access
Integrating Order-of-Magnitude Analysis to Physics-Informed Neural Networks for Linear Chromatographic Models
Yucheng Chen, Shan‐Jing Yao, Dong‐Qiang Lin
Industrial & Engineering Chemistry Research (2025)
Closed Access
Hygienic design and cleaning-in-place (CIP) systems in breweries
Ben Connolly, Scott Davies, Trevor Sykes, et al.
Elsevier eBooks (2025), pp. 177-192
Closed Access
A review on full-, zero-, and partial-knowledge based predictive models for industrial applications
Stefano Zampini, Guido Parodi, Luca Oneto, et al.
Information Fusion (2025), pp. 102996-102996
Open Access
The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering
Zhi‐Yong Wu, Huan Wang, Chang He, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 44, pp. 18178-18204
Closed Access | Times Cited: 18
Modeling and Control of a Chemical Process Network Using Physics-Informed Transfer Learning
Ming Xiao, Zhe Wu
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 42, pp. 17216-17227
Closed Access | Times Cited: 15
Accelerating heat exchanger design by combining physics-informed deep learning and transfer learning
Zhi‐Yong Wu, Bingjian Zhang, Haoshui Yu, et al.
Chemical Engineering Science (2023) Vol. 282, pp. 119285-119285
Closed Access | Times Cited: 15
Toward Physics-Informed Machine-Learning-Based Predictive Maintenance for Power Converters—A Review
Youssof Fassi, Vincent Heiries, J. Boutet, et al.
IEEE Transactions on Power Electronics (2023) Vol. 39, Iss. 2, pp. 2692-2720
Closed Access | Times Cited: 15
A review and perspective on hybrid modeling methodologies
Artur M. Schweidtmann, Dongda Zhang, Moritz von Stosch
Digital Chemical Engineering (2023) Vol. 10, pp. 100136-100136
Open Access | Times Cited: 15
Computational toolkits for model-based design and optimization
Damian T. Agi, Kyla D. Jones, Madelynn J. Watson, et al.
Current Opinion in Chemical Engineering (2023) Vol. 43, pp. 100994-100994
Open Access | Times Cited: 14
A hierarchical Bayesian network-based semi-mechanistic model for handling data variabilities in dynamical process systems
Mohammad Alauddin, Faisal Khan, Syed Imtiaz, et al.
Computers & Chemical Engineering (2024) Vol. 185, pp. 108659-108659
Closed Access | Times Cited: 5
Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations
Elisavet Anglou, Y. A. Chang, William A. Bradley, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 24, pp. 9003-9017
Open Access | Times Cited: 5
Physics-Informed Neural Networks for Process Systems: Handling Plant-Model Mismatch
Farshad Moayedi, Aswin Chandrasekar, S.K. Rasmussen, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 31, pp. 13650-13659
Closed Access | Times Cited: 4
Artificial intelligence perspectives: A systematic literature review on modeling, control, and optimization of fluid catalytic cracking
Mustapha Kamel Khaldi, Mujahed Al‐Dhaifallah, Othman Taha
Alexandria Engineering Journal (2023) Vol. 80, pp. 294-314
Open Access | Times Cited: 14
Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps
Massimiliano Manfren, Karla M. Gonzalez-Carreon, P.A.B. James
Energies (2024) Vol. 17, Iss. 4, pp. 881-881
Open Access | Times Cited: 4
Computer-aided chemical engineering research advances in precision fermentation
Tom Vinestock, Michael Short, Keeran Ward, et al.
Current Opinion in Food Science (2024) Vol. 58, pp. 101196-101196
Open Access | Times Cited: 3
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