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Structure Department of Industrial Engineering
Telephone 0498275470
Qualification Ricercatore a tempo determinato di tipo B
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Office hours
Friday from 10:00 to 11:00 My office at DII: via Marzolo 9, Padova Please, contact me by email (pierantonio.facco@unipd.it) to schedule a meeting.
(updated on 23/04/2018 11:35)

Proposals for thesis
• 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

Research areas
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

1. Facco, P., A. C. Santomaso, M. Barolo (2017). Artificial vision system for particle size characterization from bulk materials. Chem. Eng. Sci., 164, 246-257.
2. 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.
3. Meneghetti, N., P. Facco, F. Bezzo, C. Himawan, S. Zomer and M. Barolo (2016). Knowledge management in secondary pharmaceutical manufacturing by mining of data historians – A proof-of-concept study. Int. J. Pharmaceutics, 505, 394-408.
4. Largoni, M., P. Facco, D. Bernini, F. Bezzo, M. Barolo (2015). Quality by-Design approach to monitor the operation of a batch bioreactor in an industrial avian vaccine manufacturing process. Journal of Biotechnology, 211, 87-96.
5. Facco, P., F. Dal Pastro, N. Meneghetti, F. Bezzo, M. Barolo (2015). Bracketing the design space within the knowledge space in pharmaceutical product development. Ind. Eng. Chem. Res., 54, 5128-5138.
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.
7. Tomba, E., P. Facco, F. Bezzo, M. Barolo (2013). Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing – A review. Int. J. Pharmaceutics, 457, 283-297.
8. Ottavian, M., L. Fasolato, P. Facco, M. Barolo (2013). Foodstuff authentication from spectral data: toward a species-independent discrimination between fresh and frozen-thawed fish samples. Journal of Food Engineering, 119, 765-775.
9. Facco, P., A. Masiero, A. Beghi (2013). Advances on Multivariate Image Analysis for Product Quality Monitoring. J. Process Control, 23, 89-98. (invited paper in IFAC World Congress Special Issue).
10. 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.
11. 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.
12. 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.
13. Facco, P., F. Bezzo, M. Barolo (2010). Nearest neighbour method for the automatic maintenance of multivariate statistical soft sensors in batch processing. Ind. Eng. Chem. Res., 49, 2336-2347.
14. 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.
15. 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.

List of taught course units in A.Y. 2019/20
Degree course code (?) Degree course track Course unit code Course unit name Credits Year Period Lang. Teacher in charge
IN0530 COMMON INP8083337 6 1st Year Second