Publications

Journal articles, books, and chapters. A complete list is on Google Scholar.

2026

1. Artificial Intelligence at Scale in the Chemical Industry: From Legacy to Leadership

   Chiang, L., Christiansen, D., Malloure, M. R., Briceno-Mena, L., & Kim, S. H.

   Current Opinion in Chemical Engineering, 2026

   DOI

   • AI in industry
   • Machine learning

2. The Incorporation of Qualitative Knowledge in Hybrid Modeling

   Arnese-Feffin, E., Sagar, N., Briceno-Mena, L. A., Braun, B., Castillo, I., Rizzo, C., Bui, L., Xu, J., Chiang, L. H., & Braatz, R. D.

   Computers & Chemical Engineering, 2026

   DOI

   • Hybrid modeling
   • Machine learning

2025

1. AI in Chemical Engineering: Unlocking the Power Within Data

   Romagnoli, J. A., Briceno-Mena, L., & Manee, V.

   CRC Press, 2025

   DOI

   • AI in industry
   • Machine learning

2024

1. Synergizing data-driven and knowledge-based hybrid models for ionic separations

   Olayiwola, T., Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A.

   ACS ES&T Engineering, 2024

   DOI

   • Hybrid modeling
   • Ionic separations

2. A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability

   Rahnama, A., Vaithiyanathan, M., Briceno-Mena, L., Dugas, T. M., Yates, K. L., Romagnoli, J. A., & Melvin, A. T.

   Analyst, 2024

   DOI

   • Bioanalytical methods

3. Introduction to Formulation Optimization

   Schmidt, A., Briceno-Mena, L., Rajagopalan, S., Ma, K., Reiner, B., & Braun, B.

   The Digital Transformation of Product Formulation, 2024

   • Formulated products
   • AI in industry

2023

1. Unsupervised learning: Local and global structure preservation in industrial data

   Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A.

   Computers & Chemical Engineering, 2023

   DOI

   • Process monitoring
   • Machine learning

2. Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations

   Gallage Dona, H. K., Olayiwola, T., Briceno-Mena, L. A., Arges, C. G., Kumar, R., & Romagnoli, J. A.

   Industrial & Engineering Chemistry Research, 2023

   DOI

   • Ionic separations
   • Machine learning

3. Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells

   Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A.

   Computers & Chemical Engineering, 2023

   DOI

   • Electrochemical systems
   • Machine learning
   • Hybrid modeling

4. Deconvoluting charge-transfer, mass transfer, and ohmic resistances in phosphonic acid–sulfonic acid ionomer binders used in electrochemical hydrogen pumps

   Arunagiri, K., Wong, A. J., Briceno-Mena, L., Elsayed, H. M. G. H., Romagnoli, J. A., Janik, M. J., & Arges, C. G.

   Energy & Environmental Science, 2023

   DOI

   • Electrochemical systems
   • Ionic separations

2022

1. Optimization of multi-mode classification for process monitoring

   Webb, Z. T., Nnadili, M., Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A.

   Frontiers in Chemical Engineering, 2022

   DOI

   • Process monitoring
   • Machine learning

2. Electrochemical Pumping for Challenging Hydrogen Separations

   Venugopalan, G., Bhattacharya, D., Andrews, E., Briceno-Mena, L., Romagnoli, J., Flake, J., & Arges, C. G.

   ACS Energy Letters, 2022

   DOI

   • Electrochemical systems
   • Ionic separations

3. PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

   Briceno-Mena, L. A., Romagnoli, J. A., & Arges, C. G.

   Industrial & Engineering Chemistry Research, 2022

   DOI

   • Electrochemical systems
   • Machine learning
   • Hybrid modeling

4. Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells

   Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A.

   32nd European Symposium on Computer Aided Process Engineering (ESCAPE32), 2022

   • Electrochemical systems
   • Machine learning

5. Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques

   Briceno-Mena, L. A., Nnadili, M., Benton, M. G., & Romagnoli, J. A.

   Data-Centric Engineering, 2022

   DOI

   • Process monitoring
   • Machine learning

2021

1. Machine learning for guiding high-temperature PEM fuel cells with greater power density

   Briceno-Mena, L. A., Venugopalan, G., Romagnoli, J. A., & Arges, C. G.

   Patterns, 2021

   DOI

   • Electrochemical systems
   • Machine learning

2020

1. Heterogeneous Catalytic Ozonation of Phenol over Iron-based Catalysts in a Trickle Bed Reactor

   Briceño Mena, L., & Durán Herrera, E.

   Revista Ingeniería, 2020

   DOI

   • Environmental engineering