Personale docente

Pierantonio Facco

Professore associato


Indirizzo: VIA F. MARZOLO, 9 - PADOVA . . .

Telefono: 0498275470


  • Il Venerdi' dalle 10:00 alle 11:00
    presso My office at DII: via Marzolo 9, Padova
    Please, contact me by email ( to schedule a meeting. Telematic meetings can be sheduled, as well.

Pierantonio Facco is Associate Professor at the Department of Industrial Engineering of the University of Padova. He is a Chemical Engineer and PhD in Chemical Engineering. Dr. Facco teaches Data analytics and design of industrial experiment for the curriculum of Chemical Engineering, and Quality control and data analytics in food production for the curriculum of Food and Health at the University of Padova.
His research interests deal with the development of data-driven methodologies to support chemical and process engineering activities for: process and product quality monitoring; product formulation and process development; process understanding and troubleshooting; process continuous improvement; process, product and technology scale-up/down and transfer; design of experiments. His main activities are concerned with data analytics, machine learning, deep learning, hybrid models, multivariate statistical process and quality monitoring and control, Quality-by-Design, process and product development, adaptive soft-sensing, process analytical technologies, and development of artificial vision systems.
He is author of 100+ publications, among which 45 papers in peer reviewed journals, also as corresponding author, with the following bibliometric indices: H-index=16 on Scopus with 781 citations in 615 documents; 18 on Google Scholar with 1039 citations; 16 on Web of Science with 690 citations in 528 documents [data assessed on April 14th 2022]. He attended several international congresses, presenting different works, also as an invited speaker. He is referee of several prestigious scientific journals. He is involved in national and international academic collaborations (Imperial college, Louisiana State University, Pukyong National University, Polytechnic University of Valencia, Rutgers University, University College London) and industrial partnerships (GlaxoSmithKline, Buhler, BASF, Versalis-Eni, Eli Lilly, Pfizer, Fresenius Kabi, Merial/Sanofi, Novamont, Safilo, Unox, Sirca).
Dr. Facco is also a certified Internal Auditor for the system of evaluation of the Quality.
The main skills of Dr. Facco’s are: the ability of transferring research innovations to industrial world, good communication ability, great attitude to teamwork and to conceive innovative solutions and in the development of new research ideas.

Download Curriculum_Facco.pdf

15.Facco, P., S. Zomer, R. Rowland-Jones, D. Marsh, P. Diaz-Fernandez, G. Finka, F. Bezzo, M. Barolo (2020). Using data analytics to accelerate biopharmaceutical process scale-up. Biochem. Eng. J., 164, 107791.
14. Destro, F., P. Facco, S. García-Muñoz, F. Bezzo, M. Barolo (2020). A hybrid framework for process monitoring: enhancing data-driven methodologies with state and parameter estimation. J. Process Control., 92, 333-351
13. A hybr Palacì-López, D., P. Facco, A. Ferrer, M. Barolo (2019). New tools for the design and manufacturing of new products based on Latent Variable Model Inversion. Chemom. Intell. Lab. Sys., 194, 103848.
12. Benedetti, A., J. Khoo, S. Sharma, P. Facco, M. Barolo, S. Zomer (2019). Data analytics on raw material properties to accelerate pharmaceutical drugs development. Int. J. Pharma., 563, 122-134.
11. Bano, G., Z. Wang, P. Facco, F. Bezzo, M. Barolo, M. Ierapetritou (2018). A novel and systematic approach to identify the design space of pharmaceutical processes. Comp. Chem. Eng., 115, 309-322.
10. Dal Pastro, F., P. Facco, F. Bezzo, E. Zamprogna, M. Barolo (2017). Model-based approach to the design and scale-up of wheat milling operations - Proof of concept. Food and Bioproducts Processing, 106, 127-136.
9. Bano, G., P. Facco, N. Meneghetti, F. Bezzo, M. Barolo (2017). Uncertainty back-propagation in PLS model inversion for the determination of the design space of a new pharmaceutical product. Comput. Chem. Eng., 101, 110-124.
8. Facco, P., A. C. Santomaso, M. Barolo (2017). Artificial vision system for particle size characterization from bulk materials. Chem. Eng. Sci., 164, 246-257 (corresponding author).
7. Dal Pastro, F., P. Facco, F. Bezzo, E. Zamprogna, M. Barolo (2016). Data-driven modeling of milling and sieving operations in a wheat milling process. Food and Bioproducts Processing, 99, 99-108.
6. Facco, P., M. Largoni, E. Tomba, F. Bezzo, M. Barolo (2014). Transfer of process monitoring models between plants: batch systems. Chemical Engineering Research and Design, 92, 273-284.
5. Facco, P., E. Tomba, F. Bezzo, S. García-Muñoz, M. Barolo (2012).Transfer of process monitoring models between different plants using latent variable techniques. Ind. Eng. Chem. Res., 51, 7327-7339.
4. Facco, P., A. Masiero, F. Bezzo, A. Beghi, M. Barolo (2011). Improved multivariate image analysis for product quality monitoring. Chemom. Intell. Lab. Sys., 109, 42-50.
3. Facco, P., E. Tomba, M. Roso, M. Modesti, F. Bezzo, M. Barolo (2010). Automatic characterization of nanofiber assemblies by image texture analysis. Chemom. Intell. Lab. Sys., 103, 66-75.
2. Facco, P., R. Mukherjee, F. Bezzo, M. Barolo, J. A. Romagnoli (2009). Monitoring roughness and edge shape on semiconductors through multiresolution and multivariate image analysis. AIChE J., 55, 1147-1160.
1. Facco, P., F. Doplicher, F. Bezzo, M. Barolo (2009). Moving-average PLS soft sensor for online product quality estimation in an industrial batch polymerization process. J. Process Control, 19, 520-529.

1. Facco, P., N. Meneghetti, F. Bezzo, M. Barolo (2018). Mining information from developmental data: process understanding, design space identification, and product transfer. In: Multivariate Analysis in the Pharmaceutical Industry (A. P. Ferreira, J. Cardoso Menezes, M. Tobyn Eds.), Elsevier, 267-292.

Download Pubblicazioni_Facco.pdf

Current and future research interests involve:
• novel methodologies for both the product formulation and the process, product and technology transfer between different production scales or different industrial sites, with particular attention to the application of the Quality-by-Design paradigm and the implementation of Process Analytical Technologies in the pharmaceutical industry
• development of technologies for the real time process, product, and quality monitoring based on data fusion (adaptive process monitoring, soft sensing, artificial vision systems, hyperspectral data management)
a) development of anti-fraud and anti-sophistication technologies for food characterization, authentication and labelling
b) adoption of multivariate Gaussian Mixture models and Hidden Markov models for process monitoring and soft sensing
c) exploration of kriging methodologies for soft sensing
d) comparison among non-linear multivariate statistical techniques and classical non-linear methodologies, such as neural networks and support vector machines
e) development of high level structures for the performance monitoring of control systems
f) development of methodologies for the predictive maintenance in different industrial applications
g) development of novel multi-block and multivariate statistical techniques for managing in an appropriate manner different types of variables such as manipulated variables, measured variables and response variables
• innovative methodologies for the multivariate Design of Experiments (DoE)
a) study of response hyper-surface methodologies from latent variable modelling
b) comparison of classical statistical DoE methodologies with multivariate DoE and latent variable inversion models
c) analysis and extension of methodologies for dynamic DoE
• development and implementation of methodologies for Big Data analytics in the Industry 4.0 perspective
a) application of multivariate methods for Big Data volume compression
b) application of pattern recognition techniques for the joint analysis and the visualization of wide volumes of Big Data with differentiated varieties
c) application of methodologies for the real-time updating of models for Big Data velocity treatment
d) assessing the veracity of Big Data dealing with multivariate statistical model parameter uncertainty
• development of strategies for multivariate exploratory analysis and data correlation extraction, with particular attention to biological processes and biomedical applications
a) development and industrial implementation of a framework to aid both the process scale-up and scale-down in biopharmaceutical processes and cell line selection
b) study of the circadian features in RNA/DNA expression
c) data based modelling of the role of neuroblastoma-derived exosomes in cancer dissemination

• Optimization of biopharmaceutical production protocols through Dynamic Design of Experiment on digital twins.
• Data augmentation through digital twins to support process monitoring and quality prediction in the biopharmaceutical industry.
• Development of artificial vision system for the diagnosis and classification of pneumonia.
• LLDPE-C4 spot price prediction to plan the purchase strategies through machine learning (in Italian).
• Comparison between different multivariate regression techniques for quality real-time estimation of product quality in batch processes.
• Development of multivariate statistical techniques for data reconciliation in refinery heat exchangers.
• Data-driven reaction modelling to accelerate drug substance development.
• Comparison between different multivariate regression techniques for quality real-time estimation of product quality in batch processes.
• Development of multivariate statistical techniques for data reconciliation in refinery heat exchangers.
• Multivariate and multi-resolution soft-sensor development in fed-batch fermenters for the production of penicillin.
• On the equivalence of null space and orthogonal space in multivariate statistical latent variable models.
• Real-time monitoring of the transesterification reaction in biodiesel production
• Multivariate data analysis to approach process reliability through sensors-data smart use
• Characterization of high pressure CO2 pasteurized food through machine learning and deep learning image analysis.
• Scale-up of a biopharmaceutical process by Data Analytics techniques.
• Design of experiments data analysis: a comparison among different commercial softwares
• Experimental Design in biotechnological and biopharmaceutical applications
• Development of codes for image analysis for the quantification of important characteristics in biological samples
• Realtime tracking of data-driven model performance to improve pharmaceutical process operations
• Pharmaceutical development and manufacturing in a Quality-by-Design perspective: Techniques for design space description.
• Quality-by-Design (QbD) through multivariate latent structures
• Data analytics for powder feeding modelling on continuous secondary pharmaceutical manufacturing processes
• Comparison among different response surface strategies for the statistical design of experiments
• Design of dynamic experiments for the identification of data driven dynamic models.
• Characterization of the particle size distribution in granulated mixtures through multivariate image analysis