Co-chairs
Prof. Massimiliano Barolo
Dr. Massimiliano Barolo is a Professor of Chemical Engineering at the University of Padova. After graduating with honors in Chemical Engineering, he worked as a process engineer in the largest petrochemical site in Italy. He earned the Ph.D. degree in Chemical Engineering in 1994. His main research interests are in the fields of process chemometrics, and of dynamics, modeling, monitoring, and control of chemical processes and biomedical systems. He is the author or co-author of 100+ peer-reviewed papers and of some teaching monographs.
Prof. Fabrizio Bezzo
Dr. Fabrizio Bezzo is Professor of Chemical Engineering at the University of Padova. He received his degree in Chemical Engineering at the University of Padova and his PhD at Imperial College London (UK). After working for digital technology company in the UK, he joined the University of Padova in 2002. His research interests comprise process and supply chain modeling and optimization in the chemical, pharmaceutical, and energy sectors. He has published over 200 scientific papers in international research journals and conference proceedings. In 2017-2022 he co-chaired the Energy Section of the European Federation of Chemical Engineering.
Prof. Pierantonio Facco
Dr. Pierantonio Facco is Associate Professor at the University of Padova, where he received his PhD in Chemical Engineering in 2009. His research interests are related to data analytics, machine learning, statistical and deep learning to support chemical and process engineering for: processes and product quality monitoring; product formulation and process development; process understanding; process, product and technology scale-up/down; design of experiments. His main activities are concerned with multivariate statistical process and quality monitoring and control, Quality-by-Design, adaptive soft-sensing, process analytical technologies, and development of artificial vision systems. He is author of more than 80 publications, among which papers in peer reviewed journals, conference proceedings and book chapters.
Dr. Federico d’Amore
Dr. Federico d’Amore is a Junior Assistant Professor in Chemical and Process Engineering at the Department of Industrial Engineering, University of Padova, where he acquired his PhD in Chemical Engineering and his MSc Degree in Energy and Nuclear Engineering. Previously, he was a Post-Doctoral Research Associate in industrial decarbonisation at the Department of Energy, Politecnico di Milano (Italy). His research interests include the multi-scale design and optimisation of low carbon processes and integrated systems for the decarbonisation of energy and industry.
Post-doc researchers
Dr. Gianmarco Barberi
Machine learning techniques for the scale-up of biopharmaceutical processes
Dr. Marco Cattaldo
Development of machine learning techniques for product formulation and process development for a spray-drying process in the pharmaceutical industry
Dr. Margherita Geremia
Model-based design of experiments for model discrimination and identificationPhD students
Seyed Zuhair Bolourchian Tabrizi
Streamlining experimental information for carbonation process modelling
Andrea Botton
A Machine Learning approach to plant scale-up/down
Marco Brendolan
An AI-based methodology to support formulation of resins and varnishes
Mauro Davanzo
Model-based approaches to accelerate the development and scale-up of API downstream manufacturing processes
Giulio Di Carlo
Digital twins of microalgae growth for process scale-up and optimization
Tiziana Marella
A general approach to hybrid modeling in the pharmaceutical industry
Mihnea Stefan
Soft-sensing and data analytics for in-line measurements in low-carbon cement production
Edoardo Tamiazzo
Development of experimental protocols for chemical processes using machine learning and digital twins in Nitrogen Industry 4.0
Leonardo Varnier
Integrating state-of-the-art technologies for sustainable cement productionVisiting PhD students
Sena Kumcu
Capture, storage and utilization of released carbon for a sustainable industrial development
Tobias Overgaard
A data-driven approach to quantify the impact of uncertainty on process performance and product quality in large-scale pharmaceutical production
Ana Helena Valdeira Caetano
Digital quality by design with model-based design of experimentsResearch assistants
Andrea Rosolen
Development of artificial vision systems for automatic food authentication
