Use of Support Vector Machine to Fault Detection in Biomethanation Process. - Juan C. Acosta-Pavas; Carlos E. Robles-Rodríguez; C. Dumas; Arnaud Cockx; Jérôme Morchain; Cesar Arturo Aceves-Lara (DCAI (1), 2022)

Fault Detection in Biological Methanation Process Using Machine Learning: A Comparative Study of Different Algorithms. - Juan C. Acosta-Pavas; David Griol; Zoraida Callejas; David Camilo Corrales; Carlos Eduardo Robles-Rodríguez; Jérôme Morchain; César Arturo Aceves-Lara (SOCO (1), 2023)

A Soft Sensing Approach for Efficient Monitoring of Nanobody-Based Scorpion Antivenom Production. - Juan C. Acosta-Pavas; David Camilo Corrales; Irene Martínez-Menéndez; Susana María Alonso Villela; Balkiss Bouhaouala-Zahar; Georgios K. Georgakilas; Konstantinos Mexis; Stefanos Xenios; Theodore Dalamagas 001; Antonis Kokosis; Michael O'donohue; Luc Fillaudeau; et al. (DCAI (1), 2024)

Comparison of Machine Learning-Enhanced Dynamic Hybrid Models for a Nanobody Scorpion Antivenom Production with Escherichia Coli. - Irene Martínez-Menéndez; Juan C. Acosta-Pavas; David Camilo Corrales; Susana María Alonso Villela; Balkiss Bouhaouala-Zahar; Georgios K. Georgakilas; Konstantinos Mexis; Stefanos Xenios; Theodore Dalamagas 001; Antonis Kokosis; Michael O'donohue; Luc Fillaudeau; et al. (DCAI (1), 2024)

Soft sensors based on interpretable learners for industrial-scale fed-batch fermentation: Learning from simulations. - Juan C. Acosta-Pavas; Carlos Eduardo Robles-Rodríguez; David Griol; Fayza Daboussi; Cesar Arturo Aceves-Lara; David Camilo Corrales (Comput. Chem. Eng., 2024)

Towards a machine learning operations (MLOps) soft sensor for real-time predictions in industrial-scale fed-batch fermentation. - Brett Metcalfe; Juan C. Acosta-Pavas; Carlos Eduardo Robles-Rodríguez; Georgios K. Georgakilas; Theodore Dalamagas 001; Cesar Arturo Aceves-Lara; Fayza Daboussi; Jasper J. Koehorst; David Camilo Corrales (Comput. Chem. Eng., 2025)