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
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.
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
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.
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
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
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
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
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
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
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
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
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
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.
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
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
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
Published in Computers \& Chemical Engineering, 1900
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. (Under review). The Incorporation of Qualitative Knowledge in Hybrid Modeling. Computers & Chemical Engineering.
Published in Molecular Systems Design \& Engineering, 1900
Driving Innovation of Formulated Products with DOW Predictive Intelligence
Recommended citation: Mukhopahyay, S., Christiansen, D., Briceno-Mena, L.A., Pradhan, A., Aguirre, F., & Cookson, P. (Under review). Driving Innovation of Formulated Products with DOW Predictive Intelligence. Molecular Systems Design & Engineering.
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.