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portfolio
SimWiz (contributor)
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publications
Artificial Intelligence at Scale in the Chemical Industry: From Legacy to Leadership
Published in Current Opinion in Chemical Engineering, 1900
Artificial Intelligence at Scale in the Chemical Industry: From Legacy to Leadership
Recommended citation: Chiang, L., Christiansen, D., Malloure, M., Briceno-Mena, L. A., & Kim, S. H. (Under review). Artificial Intelligence at Scale in the Chemical Industry: From Legacy to Leadership. Current Opinion in Chemical Engineering.
Heterogeneous Catalytic Ozonation of Phenol over Iron-based Catalysts in a Trickle Bed Reactor
Published in Revista Ingeniería, 2020
Heterogeneous Catalytic Ozonation of Phenol over Iron-based Catalysts in a Trickle Bed Reactor
Recommended citation: Briceño Mena, L., & Durán Herrera, E. (2020). Heterogeneous Catalytic Ozonation of Phenol over Iron-based Catalysts in a Trickle Bed Reactor. Revista Ingeniería, 30(2), 1-13. https://doi.org/10.15517/ri.v30i2.39236
Machine learning for guiding high-temperature PEM fuel cells with greater power density
Published in Patterns, 2021
Machine learning for guiding high-temperature PEM fuel cells with greater power density
Recommended citation: Briceno-Mena, L. A., Venugopalan, G., Romagnoli, J. A., & Arges, C. G. (2021). Machine learning for guiding high-temperature PEM fuel cells with greater power density. Patterns, 2(2), 100187. https://doi.org/10.1016/j.patter.2020.100187
Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques
Published in Data-Centric Engineering, 2022
Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques
Recommended citation: Briceno-Mena, L. A., Nnadili, M., Benton, M. G., & Romagnoli, J. A. (2022). Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques. Data-Centric Engineering, 3. https://doi.org/10.1017/dce.2022.21
Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells
Published in 32nd European Symposium on Computer Aided Process Engineering (ESCAPE32), 2022
Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells
Recommended citation: \Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A. (2022). Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells. In 32nd European Symposium on Computer Aided Process Engineering (ESCAPE32), Toulouse, France.
PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems
Published in Industrial \& Engineering Chemistry Research, 2022
PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems
Recommended citation: Briceno-Mena, L. A., Romagnoli, J. A., & Arges, C. G. (2022). PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems. Industrial & Engineering Chemistry Research, 61(9), 3350-3357. https://doi.org/10.1021/acs.iecr.1c04237
Electrochemical Pumping for Challenging Hydrogen Separations
Published in ACS Energy Letters, 2022
Recommended citation: Gokul Venugopalan, Deepra Bhattacharya, Evan Andrews, Luis Briceno-Mena, José Romagnoli, John Flake, and Christopher G. Arges ACS Energy Lett. 2022, 7, XXX, 1322–1329 DOI: 10.1021/acsenergylett.1c02853. https://pubs.acs.org/doi/full/10.1021/acsenergylett.1c02853
Electrochemical Pumping for Challenging Hydrogen Separations
Published in ACS Energy Letters, 2022
Electrochemical Pumping for Challenging Hydrogen Separations
Recommended citation: Venugopalan, G., Bhattacharya, D., Andrews, E., Briceno-Mena, L., Romagnoli, J., Flake, J., & Arges, C. G. (2022). Electrochemical Pumping for Challenging Hydrogen Separations. ACS Energy Letters, 7(4), 1322-1329. https://doi.org/10.1021/acsenergylett.1c02853
Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques
Published in Data-Centric Engineering, 2022
Recommended citation: Briceno-Mena, L., Nnadili, M., Benton, M., & Romagnoli, J. (2022). Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques. Data-Centric Engineering, 3, E18. doi:10.1017/dce.2022.21 https://www.cambridge.org/core/journals/data-centric-engineering/article/data-mining-and-knowledge-discovery-in-chemical-processes-effect-of-alternative-processing-techniques/6E58B1133A9390177130A292422F4786
Optimization of multi-mode classification for process monitoring
Published in Frontiers in Chemical Engineering, 2022
Optimization of multi-mode classification for process monitoring
Recommended citation: Webb, Z. T., Nnadili, M., Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A. (2022). Optimization of multi-mode classification for process monitoring. Frontiers in Chemical Engineering, 4. https://doi.org/10.3389/fceng.2022.900083
Deconvoluting charge-transfer, mass transfer, and ohmic resistances in phosphonic acid–sulfonic acid ionomer binders used in electrochemical hydrogen pumps
Published in Energy \& Environmental Science, 2023
Deconvoluting charge-transfer, mass transfer, and ohmic resistances in phosphonic acid–sulfonic acid ionomer binders used in electrochemical hydrogen pumps
Recommended citation: Arunagiri, K., Wong, A. J., \textbfBriceno-Mena, L. Energy \& Environmental Science 2023 16, 5916-5932 https://doi.org/10.1039/D3EE01776A
Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells
Published in Computers \& Chemical Engineering, 2023
Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells
Recommended citation: Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A. (2023). Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells. Computers & Chemical Engineering, 171, 108159. https://doi.org/10.1016/j.compchemeng.2023.108159
Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations
Published in Industrial \& Engineering Chemistry Research, 2023
Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations
Recommended citation: Gallage Dona, H. K., Olayiwola, T., Briceno-Mena, L. A., Arges, C. G., Kumar, R., & Romagnoli, J. A. (2023). Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations. Industrial & Engineering Chemistry Research, 62(24), 9533-9548. https://doi.org/10.1021/acs.iecr.3c00636
Unsupervised learning: Local and global structure preservation in industrial data
Published in Computers \& Chemical Engineering, 2023
Unsupervised learning: Local and global structure preservation in industrial data
Recommended citation: Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A. (2023). Unsupervised learning: Local and global structure preservation in industrial data. Computers & Chemical Engineering, 178, 108378. https://doi.org/10.1016/j.compchemeng.2023.108378
Introduction to Formulation Optimization
Published in The Digital Transformation of Product Formulation, 2024
Introduction to Formulation Optimization
Recommended citation: Schmidt, A., Briceno-Mena, L., Rajagopalan, S., Ma, K., Reiner, B., & Braun, B. (2024). Introduction to Formulation Optimization. In A. Schmidt & K. Wallace (Eds.), The Digital Transformation of Product Formulation. CRC Press.
A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability
Published in Analyst, 2024
A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability
Recommended citation: Rahnama, A., Vaithiyanathan, M., Briceno-Mena, L., Dugas, T. M., Yates, K. L., Romagnoli, J. A., & Melvin, A. T. (2024). A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability. Analyst, 149, 4256-4266. https://doi.org/10.1039/D4AN00619D
AI in Chemical Engineering - Unlocking the Power Within Data
Published in CRC Press, 2024
AI in Chemical Engineering - Unlocking the Power Within Data
Recommended citation: Romagnoli, J. A., Briceno-Mena, L., & Manee, V. (2024). AI in Chemical Engineering - Unlocking the Power Within Data. CRC Press.
Synergizing data-driven and knowledge-based hybrid models for ionic separations
Published in ACS ES\&T Engineering, 2024
Synergizing data-driven and knowledge-based hybrid models for ionic separations
Recommended citation: Olayiwola, T., Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A. (2024). Synergizing data-driven and knowledge-based hybrid models for ionic separations. ACS ES&T Engineering, 4. https://doi.org/10.1021/acsestengg.4c00405
The Incorporation of Qualitative Knowledge in Hybrid Modeling
Published in Computers & Chemical Engineering, 2025
The Incorporation of Qualitative Knowledge in Hybrid Modeling
Recommended citation: Arense-Feffin, E., Sagar, N., Briceno-Mena, L. A., Braun, B., Castillo, I., Rizzo, C., Bui, L., Xu, J., Chiang, L., & Braatz, R. (2025). The Incorporation of Qualitative Knowledge in Hybrid Modeling. Computers & Chemical Engineering, 109484.
talks
Heterogeneous Catalytic Ozonation of Phenol in a Trickle Bed Reactor over Fe (II) Based Catalysts
Published:
IA+PSE 10 Lecciones Aprendidas
Published:
Bridging the length scales in ionic separations via data-driven science and machine learning
Published:
teaching
Assistant Professor
Undergraduate courses, Universidad de Costa Rica, School of Chemical Engineering, 2019
Courses
Kinetics and Chemical Reactors, Laboratory of Measurements and Experimental Data Analysis, Thermodynamics I, Laboratory of Separation Operations of Phases
Graduate Teaching Assistant
Undergraduate and graduate courses, Louisiana State University, Cain Department of Chemical Engineering, 2022