Process intelligence
DoE, response-surface and phenomenological modelling, and machine learning to extract more insight from experiments and optimise complex processes.
Industrial chemistry · University of Bologna
I develop catalytic materials and process strategies that turn renewable feedstocks and industrial residues into useful products — with Design of Experiments, process modelling, and machine learning guiding better decisions.

Research focus
My work connects catalyst design with Design of Experiments (DoE), process modelling, and machine learning—always with industrial relevance and circularity in view.
DoE, response-surface and phenomenological modelling, and machine learning to extract more insight from experiments and optimise complex processes.
Heterogeneous and multifunctional catalysts for upgrading biomass-derived platform molecules into fuels, solvents, and chemical intermediates.
Non-reductive carbon dioxide conversion and the reuse of industrial alkaline residues as resources for lower-impact materials.
Latest work
The most recent Scopus-indexed articles, refreshed automatically every week.
Subscribe via RSSPublication data and citation counts from Scopus, updated 24 August 2026. Citation counts link to their Scopus records and change over time.