Name: Federico
Surname: Pelagagge
Title: Msc. Chemical Engineering; PhD Student
Research laboratory: CPC-Lab
Lab address: Largo L. Lazzarino No.2 56126 Pisa
Department to which it belongs: Department of Civil and Industrial Engineering (DICI)
Contacts
– Phone N.: 3203322851
– Email: federico.pelagagge@phd.unipi.it
– Skype:
– LinkedIn profile:https://www.linkedin.com/in/federico-pelagagge-1a546ab3/
– Research gate profile:
– ORCID:
Short BIO:
I was born in Pisa in 1994. I graduated in Chemical Engineering in 2016 (Bachelor’s degree) from the University of Pisa, where I also received the master’s degree in Chemical Engineering in 2019. I am currently a PhD candidate at the Smart Industry program in Pisa.
Interests:
Big Data, automation, cyber physical system, machine learning, Model Predictive Control (MPC), Real Time Optimization (RTO)
Research topic:
My main research activities are within the area of process modeling, control, and economic optimization. A First contribution is the study of economic-MPC algorithms that combine MPC and RTO into a single dynamic optimization and control module. I aimed at studying recent proposals, as well as defining new algorithms, that included disturbance estimation and the novel technique carried by the Dynamic-RTO of modifier adaptation, to improve robustness and applicability in process control problems.
A central contribution is the study of the data-driven techniques for MPC. Nonlinear data-driven models will be deployed and updated on-line to achieve optimality of the control action despite the inaccuracy of the available non-linear model and to guarantee the safety of operation.
The subsequent contribution will be the application of the proposed methodologies into a chosen process industry.
First-year: Analysis of scientific literature in the subjects of real-time optimization, model predictive control, optimization algorithms, machine learning, and cyber-physical system. Identification of interest in existing solutions.
Second-year: Theoretical development and validation of the proposed algorithm.
Third-year: Software implementation in a “candidate” industry.
Other activities:
Conference paper
Current status: Accepted as Invited session paper.
Marco Vaccari, Federico Pelagagge, Dominique Bonvin, Gabriele Pannocchia*
Estimation technique for offset-free economic MPC based on modifier adaptation
Conference: 21st IFAC World Congress, 2020.