Browse by UCL people
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Number of items: 101.
Article
Abbasi, MR;
Galvanin, F;
Blacker, AJ;
Sorensen, E;
Shi, Y;
Dyer, PW;
Gavriilidis, A;
(2022)
Process-oriented approach towards catalyst design and optimisation.
Catalysis Communications
, 163
, Article 106392. 10.1016/j.catcom.2021.106392.
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Agunloye, Emmanuel;
Labes, Ricardo;
Chamberlain, Thomas;
Muller, Frans L;
Bourne, Richard A;
Galvanin, Federico;
(2025)
Kinetic model identification for hydrogen borrowing synthesis using a cloud platform for model-based design of experiments.
Chemical Engineering Research and Design
, 223
pp. 30-44.
10.1016/j.cherd.2025.09.005.
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Agunloye, Emmanuel Gbenga;
Petsagkourakis, Panagiotis;
Yusuf, Muhammad;
Labes, Ricardo;
Chamberlain, Thomas W;
Muller, Frans L;
Bourne, Richard;
(2024)
Automated kinetic model identification via cloud services using model-based design of experiments.
Reaction Chemistry & Engineering
10.1039/d4re00047a.
(In press).
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Aku, Michael;
Bawa, Solomon Gajere;
Lee, Ye Seol;
Galvanin, Federico;
(2025)
Optimization of Catalytic Methane Oxidation Using Hybrid
Gaussian Process and Fisher Information-Based
Experimental Design Strategies.
Chemical Engineering Transactions
, 119
pp. 553-558.
10.3303/CET25119093.
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Al-Rifai, N;
Galvanin, F;
Morad, M;
Cao, E;
Cattaneo, S;
Sankar, M;
Dua, V;
... Gavriilidis, A; + view all
(2016)
Hydrodynamic effects on three phase micro-packed bed reactor performance – gold-palladium catalysed benzyl alcohol oxidation.
Chemical Engineering Science
, 149
pp. 129-142.
10.1016/j.ces.2016.03.018.
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Bascone, D;
Galvanin, F;
Shah, N;
Garcia-Munoz, S;
(2020)
Hybrid Mechanistic-Empirical Approach to the Modeling of Twin Screw Feeders for Continuous Tablet Manufacturing.
Industrial and Engineering Chemistry Research
, 59
(14)
pp. 6650-6661.
10.1021/acs.iecr.0c0042.
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Casonato, A;
Galletta, E;
Galvanin, F;
Daidone, V;
(2021)
Von Willebrand disease type Vicenza: In search of a classification for the archetype of reduced von Willebrand factor survival.
eJHaem
10.1002/jha2.196.
(In press).
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Castaldello, C;
Galvanin, F;
Casonato, A;
Padrini, R;
Barolo, M;
Bezzo, F;
(2018)
A model‐based protocol for the diagnosis of von Willebrand disease.
Canadian Journal of Chemical Engineering
, 96
(3)
pp. 628-638.
10.1002/cjce.22964.
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Cenci, Francesca;
Pankajakshan, A;
Bawa, Solomon;
Gavriilidis, A;
Facco, Pierantonio;
Galvanin, F;
(2023)
An exploratory model-based design of experiments technique to aid parameters identification and reduce prediction uncertainty.
Computers & Chemical Engineering
, 177
, Article 108353. 10.1016/j.compchemeng.2023.108353.
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Cenci, Francesca;
Pankajakshan, Arun;
Facco, Pierantonio;
Galvanin, Federico;
(2023)
An exploratory model-based design of experiments approach to aid parameters identification and reduce model prediction uncertainty.
Computers and Chemical Engineering
10.1016/j.compchemeng.2023.108353.
(In press).
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De-Luca, R;
Galvanin, F;
Bezzo, F;
(2016)
A methodology for direct exploitation of available information in the online model-based redesign of experiments.
Computers & Chemical Engineering
, 91
pp. 195-205.
10.1016/j.compchemeng.2016.03.016.
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Deussen, P;
Galvanin, F;
(2022)
A model-based experimental design approach to assess the identifiability of kinetic models of hydroxymethylfurfural hydrogenation in batch reaction systems.
Chemical Engineering Research and Design
, 178
pp. 609-622.
10.1016/j.cherd.2021.12.028.
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Evripidou, Nikola;
Galvanin, Federico;
Angeli, Panagiota;
(2024)
Mechanistic modelling of separating dispersions in pipes using model-based design of experiments techniques.
Chemical Engineering Science
, 284
, Article 119504. 10.1016/j.ces.2023.119504.
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Ferrari, M;
Galvanin, F;
Barolo, M;
Daidone, V;
Padrini, R;
Bezzo, F;
Casonato, A;
(2018)
A Mechanistic Model to Quantify von Willebrand Factor Release, Survival and Proteolysis in Patients with von Willebrand Disease.
Thrombosis and Haemostasis
, 118
(2)
pp. 309-319.
10.1160/TH17-05-0375.
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Friso, Andrea;
Galvanin, Federico;
(2024)
An optimization-free Fisher information driven approach for online design of experiments.
Computers & Chemical Engineering
, 187
, Article 108724. 10.1016/j.compchemeng.2024.108724.
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Friso, Andrea;
Palmer, Mark;
Galvanin, Federico;
(2025)
Mathematical modelling of injection time in autoinjectors: State of the art and future perspectives.
International Journal of Pharmaceutics
, Article 126450. 10.1016/j.ijpharm.2025.126450.
(In press).
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Galletta, E;
Galvanin, F;
Bertomoro, A;
Daidone, V;
Casonato, A;
(2021)
Acquired von Willebrand syndrome in patients with monoclonal gammopathy of undetermined significance investigated using a mechanistic approach.
Blood Transfusion
10.2450/2021.0121-21.
(In press).
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Galvanin, F;
Galletta, E;
Bertomoro, A;
Daidone, V;
Casonato, A;
(2024)
Optimal design of infusion tests for the identification of physiological models of acquired von Willebrand syndrome.
Chemical Engineering Science
, 286
, Article 119660. 10.1016/j.ces.2023.119660.
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Galvanin, Federico;
Hartman, Ryan L;
Kulkarni, Amol A;
Nieves-Remacha, María José;
(2022)
Introduction to the themed collection on digitalization in reaction engineering.
Reaction Chemistry and Engineering
, 7
(4)
pp. 792-794.
10.1039/d2re90011d.
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Galvanin, Federico;
Lee, Chung Fun;
Yuxuan, Yang;
(2025)
On the development of pharmacokinetic models for the characterisation and diagnosis of von Willebrand disease.
Physical Sciences Reviews
, 10
(5-6)
pp. 497-523.
10.1515/psr-2024-0058.
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Galvanin, F;
Cao, E;
Al-Rifai, N;
Dua, V;
Gavriilidis, A;
(2015)
Optimal design of experiments for the identification of kinetic models of methanol oxidation over silver catalyst.
Chimica Oggi-Chemistry Today
, 33
(3)
pp. 51-56.
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Galvanin, F;
Cao, E;
Al-Rifai, N;
Gavriilidis, A;
Dua, V;
(2016)
A joint model-based experimental design approach for the identification of kinetic models in continuous flow laboratory reactors.
Computers and Chemical Engineering
, 95
pp. 202-215.
10.1016/j.compchemeng.2016.05.009.
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Galvanin, F;
Marchesini, R;
Barolo, M;
Bezzo, F;
Fidaleo, M;
(2015)
Optimal design of experiments for parameter identification in electrodialysis models.
Chemical Engineering Research and Design
, 105
pp. 107-119.
10.1016/j.cherd.2015.10.048.
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Galvanin, F;
Psyrraki, C;
Morris, T;
Gavriilidis, A;
(2017)
Development of a kinetic model of ethylene methoxycarbonylation with homogeneous Pd catalyst using a capillary microreactor.
Chemical Engineering Journal
, 329
pp. 25-34.
10.1016/j.cej.2017.04.059.
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Galvanin, F;
Sankar, M;
Cattaneo, S;
Bethell, D;
Dua, V;
Hutchings, G;
Gavriilidis, A;
(2018)
On the development of kinetic models for solvent-free benzyl alcohol oxidation over a gold-palladium catalyst.
Chemical Engineering Journal
10.1016/j.cej.2017.11.165.
(In press).
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Gao, Yueyang;
Fhionnlaoich, Niamh Mac;
Besenhard, Max;
Pankajakshan, Arun;
Galvanin, Federico;
Guldin, Stefan;
(2025)
Predicting Aggregation Behavior of Nanoparticles in Liquid Crystals via Automated Data-Driven Workflows.
Advanced Functional Materials
, Article 2501657. 10.1002/adfm.202501657.
(In press).
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Henninger, Matthias;
Postweiler, Patrik;
Engelpracht, Mirko;
Galvanin, Federico;
Bardow, André;
(2025)
Model-based design of experiments for adsorption isotherms.
Adsorption
, 31
(8)
, Article 103. 10.1007/s10450-025-00653-0.
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Huang, C;
Cattani, F;
Piccione, PM;
Galvanin, F;
(2022)
A stochastic modelling approach for the characterisation of collision exchange processes.
Chemical Engineering Science
, 248
(A)
, Article 117207. 10.1016/j.ces.2021.117207.
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Huang, Chunbing;
Cattani, Federica;
Galvanin, Federico;
(2023)
An optimal experimental design strategy for improving parameter estimation in stochastic models.
Computers and Chemical Engineering
, 170
, Article 108133. 10.1016/j.compchemeng.2023.108133.
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Huang, C;
Cattani, F;
Piccione, PM;
Galvanin, F;
(2018)
Traveling Traders' Exchange Problem: Stochastic Modeling Framework and Two-Layer Model Identification Strategy.
Industrial and Engineering Chemistry Research
, 57
(30)
pp. 10011-10025.
10.1021/acs.iecr.8b00338.
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Iannello, Stefano;
Friso, Andrea;
Galvanin, Federico;
Materazzi, Massimiliano;
(2025)
A Hybrid Physics-Machine Learning Approach for Modeling Plastic-Bed Interactions during Fluidized Bed Pyrolysis.
Energy & Fuels
, 39
(9)
pp. 4549-4564.
10.1021/acs.energyfuels.4c05870.
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Katsoulas, Konstantinos;
Galvanin, Federico;
Mazzei, Luca;
Sorensen, Eva;
(2026)
A Diagnostic Procedure for Identifying Isotherm Models in Liquid Chromatography.
Industrial & Engineering Chemistry Research
10.1021/acs.iecr.5c03704.
(In press).
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Liu, D;
Galvanin, F;
Yu, Y;
(2018)
Formulation Screening and Freeze-Drying Process Optimization of Ginkgolide B Lyophilized Powder for Injection.
AAPS PharmSciTech
, 19
(2)
pp. 541-550.
10.1208/s12249-017-0858-2.
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Lyu, W;
Galvanin, F;
(2024)
DoE-integrated Sparse Identification of Nonlinear Dynamics for Automated Model Generation and Parameter Estimation in Kinetic Studies.
Computer Aided Chemical Engineering
, 53
pp. 169-174.
10.1016/B978-0-443-28824-1.50029-6.
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Lyu, Wenyao;
Galvanin, Federico;
(2025)
DoE-SINDy: an automated framework for model generation and selection in kinetic studies.
Computers & Chemical Engineering
, 202
, Article 109265. 10.1016/j.compchemeng.2025.109265.
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Nathanael, Konstantia;
Galvanin, Federico;
Kovalchuk, Nina;
Simmons, Mark JH;
(2023)
Development of a predictive response surface model for size of silver nanoparticles synthesized in a T-junction microfluidic device.
Chemical Engineering Science
, 279
, Article 118907. 10.1016/j.ces.2023.118907.
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Pal, S;
Pankajakshan, A;
Besenhard, MO;
Snead, N;
Almeida, J;
Abukhamees, S;
Craig, D;
... Mazzei, L; + view all
(2024)
Automated Continuous Crystallization Platform with Real-Time Particle Size Analysis via Laser Diffraction.
Organic Process Research and Development
, 28
(7)
pp. 2755-2764.
10.1021/acs.oprd.4c00110.
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Pankajakshan, A;
Petsagkourakis, P;
Galvanin, F;
(2021)
A framework for adaptive online model-based redesign of experiments in dynamic systems.
Computer Aided Chemical Engineering
, 50
pp. 1293-1298.
10.1016/B978-0-323-88506-5.50199-6.
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Pankajakshan, Arun;
Bawa, Solomon Gajere;
Gavriilidis, Asterios;
Galvanin, Federico;
(2023)
Autonomous kinetic model identification using optimal experimental design and retrospective data analysis: methane complete oxidation as a case study.
Reaction Chemistry & Engineering
10.1039/d3re00156c.
(In press).
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Pankajakshan, Arun;
Pal, Sayan;
Snead, Nicholas;
Almeida, Juan;
Besenhard, Maximilian O;
Abukhamees, Shorooq;
Craig, Duncan QM;
... Galvanin, Federico; + view all
(2024)
MLAPI: A framework for developing machine learning-guided drug particle syntheses in automated continuous flow platforms.
Chemical Engineering Science
, 302
(Part A)
, Article 120780. 10.1016/j.ces.2024.120780.
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Pankajakshan, A;
Waldron, C;
Quaglio, M;
Gavriilidis, A;
Galvanin, F;
(2019)
A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model-Identification Platforms.
Engineering
10.1016/j.eng.2019.10.003.
(In press).
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Petsagkourakis, Panagiotis;
Sandoval, Ilya Orson;
Bradford, Eric;
Galvanin, Federico;
Zhang, Dongda;
Rio-Chanona, Ehecatl Antonio del;
(2022)
Chance constrained policy optimization for process control and optimization.
Journal of Process Control
, 111
pp. 35-45.
10.1016/j.jprocont.2022.01.003.
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Petsagkourakis, P;
Galvanin, F;
(2020)
Safe model-based design of experiments using Gaussian processes.
Computers & Chemical Engineering
10.1016/j.compchemeng.2021.107339.
(In press).
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Quaglio, M;
Bezzo, F;
Gavriilidis, A;
Cao, E;
Al-Rifai, N;
Galvanin, F;
(2019)
Identification of kinetic models of methanol oxidation on silver in the presence of uncertain catalyst behavior.
AIChE Journal
, 65
(10)
, Article e16707. 10.1002/aic.16707.
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Quaglio, M;
Fraga, E;
Cao, E;
Gavriilidis, A;
Galvanin, F;
(2018)
A model-based data mining approach for determining the domain of validity of approximated models.
Chemometrics and Intelligent Laboratory Systems
, 172
pp. 58-67.
10.1016/j.chemolab.2017.11.010.
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Quaglio, M;
Fraga, E;
Galvanin, F;
(2018)
Model-based design of experiments in the presence of structural model uncertainty: an extended information matrix approach.
Chemical Engineering Research and Design
, 136
pp. 129-143.
10.1016/j.cherd.2018.04.041.
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Quaglio, M;
Fraga, ES;
Galvanin, F;
(2020)
A diagnostic procedure for improving the structure of approximated kinetic models.
Computers & Chemical Engineering
, 133
, Article 106659. 10.1016/j.compchemeng.2019.106659.
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Quaglio, M;
Roberts, L;
Bin Jaapar, MS;
Fraga, ES;
Dua, V;
Galvanin, F;
(2020)
An artificial neural network approach to recognise kinetic models from experimental data.
Computers & Chemical Engineering
, 135
, Article 106759. 10.1016/j.compchemeng.2020.106759.
|
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Quaglio, M;
Waldron, C;
Pankajakshan, A;
Cao, E;
Gavriilidis, A;
Fraga, ES;
Galvanin, F;
(2019)
An online reparametrisation approach for robust parameter estimation in automated model identification platforms.
Computers & Chemical Engineering
, 124
pp. 270-284.
10.1016/j.compchemeng.2019.01.010.
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Quaglio, M;
Waldron, C;
Pankajakshan, A;
Cao, E;
Gavriilidis, A;
Fraga, ES;
Galvanin, F;
(2019)
On the Use of Online Reparametrization in Automated Platforms for Kinetic Model Identification.
Chemie Ingenieur Technik
, 91
(3)
pp. 268-276.
10.1002/cite.201800095.
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Sangoi, E;
Cattani, F;
Padia, F;
Galvanin, F;
(2025)
Foliar uptake of biocides: Statistical assessment of compartmental and diffusion-based models.
Chemical Engineering Science
, 317
, Article 121984. 10.1016/j.ces.2025.121984.
(In press).
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Sangoi, Enrico;
Quaglio, Marco;
Bezzo, Fabrizio;
Galvanin, Federico;
(2024)
An optimal experimental design framework for fast kinetic model identification based on artificial neural networks.
Computers and Chemical Engineering
, 187
, Article 108752. 10.1016/j.compchemeng.2024.108752.
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Taverna, B;
Casonato, A;
Bezzo, F;
Galvanin, F;
(2019)
A framework for the optimal design of a minimum set of clinical trials to characterize von Willebrand disease.
Computer Methods and Programs in Biomedicine
, 179
, Article 104989. 10.1016/j.cmpb.2019.104989.
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Waldron, C;
Pankajakshan, A;
Quaglio, M;
Cao, E;
Galvanin, F;
Gavriilidis, A;
(2019)
Closed-Loop Model-Based Design of Experiments for Kinetic Model Discrimination and Parameter Estimation: Benzoic Acid Esterification on a Heterogeneous Catalyst.
Industrial & Engineering Chemistry Research
, 58
(49)
pp. 22165-22177.
10.1021/acs.iecr.9b04089.
|
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Waldron, C;
Pankajakshan, A;
Quaglio, M;
Cao, E;
Galvanin, F;
Gavriilidis, A;
(2019)
Model-based design of transient flow experiments for the identification of kinetic parameters.
Reaction Chemistry & Engineering
10.1039/c9re00342h.
(In press).
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Waldron, C;
Pankajakshan, A;
Quaglio, M;
Cao, E;
Galvanin, F;
Gavriilidis, A;
(2019)
An autonomous microreactor platform for the rapid identification of kinetic models.
Reaction Chemistry and Engineering
10.1039/C8RE00345A.
(In press).
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Ward, Oliver;
Galvanin, Federico;
Jurado, Nelia;
Clements, Chris;
Abdallah, Mohamad;
Blackburn, Daniel;
Fraga, E;
(2023)
Optimization of a PID Controller within a Dynamic Model of a Steam Rankine Cycle with Coupled Energy Storage.
Chemical Engineering Transactions
, 99
(2023)
pp. 583-588.
10.3303/CET2399098.
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Ward, Oliver MG;
Fraga, Eric S;
Galvanin, Federico;
Jurado, Nelia;
Blackburn, Daniel;
Warren, Robert J;
Acres, Jack;
(2025)
A dynamic model of a power conversion system with indirect thermal energy storage for a pulsed fusion tokamak for use in design optimisation.
Fusion Engineering and Design
, 219
, Article 115289. 10.1016/j.fusengdes.2025.115289.
(In press).
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Book chapter
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Bezzo, F;
Galvanin, F;
(2018)
On the Identifiability of Physiological Models: Optimal Design of Clinical Tests.
In: Manca, D, (ed.)
Computer Aided Chemical Engineering.
(pp. 85-110).
Elsevier: Amsterdam, Netherlands.
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Friso, A;
Palmer, M;
Bano, G;
Galvanin, F;
(2024)
On the development of hybrid models to describe delivery time in autoinjectors.
In:
Computer Aided Chemical Engineering.
(pp. 157-162).
Elsevier
|
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Galvanin, F;
Bezzo, F;
(2018)
Advanced Techniques for the Optimal Design of Experiments in Pharmacokinetics.
In: Manca, D, (ed.)
Computer Aided Chemical Engineering.
(pp. 65-83).
Elsevier: Amsterdam, Netherlands.
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Galvanin, F;
Cao, E;
Al-Rifai, N;
Gavriilidis, A;
Dua, V;
(2015)
Model-based design of experiments for the identification of kinetic models in microreactor platforms.
In:
12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering.
(pp. 323-328).
Elsevier: Amsterdam, The Netherlands.
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Sangoi, E;
Cattani, F;
Padia, F;
Galvanin, F;
(2024)
Foliar Uptake Models for Biocides: Testing Structural and Practical Identifiability.
In:
Computer Aided Chemical Engineering.
(pp. 37-42).
Elsevier
|
Proceedings paper
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Agunloye, Emmanuel;
Gavriilidis, Asterios;
Galvanin, Federico;
(2025)
Hybrid Modelling Framework for Reactor Model Discovery
Using Artificial Neural Networks Classifiers.
In:
Proceedings.
(pp. p. 11).
MDPI: Basel, Switzerland.
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Aku, Michael;
Bawa, Solomon Gajere;
Pankajakshan, Arun;
Lee, Lauren Ye Seol;
Galvanin, Federico;
(2025)
Optimizing Methane Conversion in a Flow Reactor System Using Bayesian Optimization and Model-Based Design of Experiments Approaches: A Comparative Study.
In:
Systems and Control Transactions.
(pp. pp. 1228-1236).
PSE Press
|
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Bascone, D;
Galvanin, F;
Shah, N;
Garcia-Munoz, S;
(2019)
Semi-empirical model of twin screw feeders for continuous pharmaceutical tablet manufacturing process.
In:
Proceedings of the The Foundations of Computer-Aided Process Design (FOCAPD 2019).
FOCAPD: Frisco, CO, USA.
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Bascone, D;
Galvanin, F;
Shah, N;
Garcia-Munoz, S;
(2019)
Modelling and Nonlinear Model Predictive Control of a Twin Screw Feeder.
In: Danciu, D, (ed.)
Proceedings of the 15th IFAC Workshop on Time Delay Systems TDS 2019.
Elsevier: Sinaia, Romania.
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Bortoli, A;
Bezzo, F;
Galvanin, F;
(2019)
Optimal design of experiments for the identification of kinetic models of 5-hydroxymethylfurfural hydrogenation.
In: Kiss, Anton and Zondervan, Edwin and Lakerveld, Richard and Ozkan, Leyla, (eds.)
Proceedings of the 29th European Symposium on Computer Aided Process Engineering (ESCAPE-29).
(pp. pp. 685-690).
Elsevier
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Castaldello, C;
Gubert, A;
Galvanin, F;
Casonato, A;
Padrini, R;
Barolo, M;
Bezzo, M;
(2017)
A Model-Based Support for Diagnosing von Willebrand Disease.
In:
Computer Aided Chemical Engineering.
(pp. pp. 2779-2784).
Elsevier: Amsterdam, Netherlands.
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Cenci, Francesca;
Pankajakshan, Arun;
Bawa, Solomon;
Gavriilidis, Asterios;
Facco, Pierantonio;
Galvanin, Federico;
(2023)
An exploratory model-based design of experiments technique to aid parameters identification and reduce prediction uncertainty.
In: Kokossis, Antonios C and Georgiadis, Michael C and Pistikopoulos, Efstratios, (eds.)
33rd European Symposium on Computer Aided Process Engineering.
(pp. pp. 1-6).
Elsevier
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De Luca, R;
Galvanin, F;
Bezzo, F;
(2015)
A framework for a direct exploitation of available information in the online model-based redesign of experiments.
In: Bertran, MO and Bisgaard, T and Frauzem, F, (eds.)
Proceedings of the 25th European Symposium on Computer Aided Process Engineering.
(pp. pp. 30-31).
European Federation of Chemical Engineering: Copenhagen, Denmark.
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Deussen, Philipp;
Galvanin, Federico;
(2023)
A joint model-based design of experiments approach for the identification of Kriging models in geological exploration.
In: Kokossis, Antonios and Georgiadis, Michael and Pistikopoulos, Stratos, (eds.)
Computer Aided Chemical Engineering.
(pp. pp. 789-794).
Elsevier: Athens, Greece.
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Deussen, P;
Galvanin, F;
(2021)
On the practical identifiability of kinetic models of hydroxymethylfurfural hydrogenation in batch reaction systems.
In:
Computer Aided Chemical Engineering.
(pp. pp. 859-865).
Elsevier
|
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Evripidou, N;
Galvanin, Federico;
Angeli, Panagiota;
(2023)
Effect of coalescence models on the prediction of the separation of dispersed oil-water pipe flows.
In:
Computer Aided Chemical Engineering.
(pp. pp. 1101-1196).
Elsevier
|
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Friso, Andrea;
Galvanin, Federico;
(2023)
An optimization-free Fisher information driven approach for online design of experiment.
In: Kokossis, Antonios and Georgiadis, Michael and Pistikopoulos, Stratos, (eds.)
Computer Aided Chemical Engineering.
(pp. pp. 13-18).
Elsevier: Athens, Greece.
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Galvanin, F;
Cao, E;
Al-Rifai, N;
Gavriilidis, A;
Dua, V;
(2015)
A model-based experimental design study for the development of kinetic models of methanol oxidation on silver catalyst.
In:
Proceedings of the CAPE Forum 2015.
(pp. pp. 87-93).
EFCE Working Party on Computer Aided Process Engineering (CAPE-WP): Paderborn, Germany.
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Galvanin, F;
Al-Rifai, N;
Cao, E;
Sankar, M;
Hutchings, G;
Gavriilidis, A;
Dua, V;
(2016)
Merging information from batch and continuous flow experiments for the identification of kinetic models of benzyl alcohol oxidation over Au-Pd catalyst.
In: Kravanja, Zdravko and Bogataj, Miloš, (eds.)
26th European Symposium on Computer Aided Process Engineering.
(pp. pp. 961-966).
Elsevier: Amsterdam, The Netherlands.
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Galvanin, F;
Fraga, ES;
Quaglio, M;
(2018)
Constrained model-based design of experiments for the identifcation of approximated models.
In:
IFAC-PapersOnLine.
(pp. pp. 515-520).
IFAC (International Federation of Automatic Control)
(In press).
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Galvanin, F;
Psyrraki, C;
Morris, T;
Gavriilidis, A;
(2016)
A ranking of experiments approach for the identification of kinetic models of ethylene methoxycarbonylation in capillary microreactors.
In:
(Proceedings) XXII International conference on Chemical Reactors (CHEMREACTOR-22).
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Galvanin, F;
Quaglio, M;
Fraga, E;
(2017)
Model-based design of experiments under structural model uncertainty.
In: Espuña, A and Graells, M and Puigjaner, L, (eds.)
Computer Aided Engineering.
(pp. pp. 145-150).
Elsevier: Barcelona, Spain.
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Huang, C;
Piccione, PM;
Cattani, F;
Galvanin, F;
(2017)
A two-layer identification strategy for the development of stochastic models of the travelling traders' exchange problem.
In:
Computer Aided Chemical Engineering.
(pp. pp. 265-270).
Elsevier: Barcelona, Spain.
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Huang, C;
Piccione, PM;
Cattani, F;
Galvanin, F;
(2016)
Travelling traders' exchange problem: stochastic simulation and sensitivity analysis.
In:
Proceedings of the 2016 AIChE Annual Meeting.
(pp. 245e-245e).
AIChE: San Francisco, CA, USA.
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Lyu, Wenyao;
Galvanin, Federico;
(2025)
Automated Identification of Kinetic Models for Nucleophilic Aromatic Substitution Reaction via DoE-SINDy.
In:
Systems and Control Transactions.
(pp. pp. 179-185).
PSE Press
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Pankajakshan, A;
Quaglio, M;
Galvanin, F;
(2018)
Experimentally Driven Guaranteed Parameter Estimation: a Way to Speed up Model-Based Design of Experiments Techniques.
In: Friedl, A and Klemeš, JJ and Radl, S and Varbanov, PS and Wallek, T, (eds.)
Proceedings of the 28th European Symposium on Computer Aided Process Engineering.
(pp. pp. 355-360).
Elsevier: Graz, Austria.
(In press).
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Pankajakshan, A;
Quaglio, M;
Waldron, C;
Cao, E;
Gavriilidis, A;
Galvanin, F;
(2018)
Online model-based redesign of experiments for improving parameter precision in continuous flow reactors.
In:
IFAC-PapersOnLine.
(pp. pp. 359-364).
Elsevier: Stockholm, Sweden.
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Petsagkourakis, P;
Galvanin, F;
(2020)
Backoff-Based Model-Based Design of Experiments Under Model Mismatch.
In: Pierucci, S and Manenti, F and Bozzano, GL and Manca, D, (eds.)
Computer Aided Chemical Engineering.
(pp. pp. 1777-1782).
Elsevier: Milan, Italy.
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Quaglio, M;
Bezzo, F;
Gavriilidis, A;
Cao, E;
Galvanin, F;
(2016)
A MINLP approach to model-based data mining for the quick development of nonlinear dynamic models.
In:
Proceedings of the 2016 AIChE Annual Meeting.
American Institute of Chemical Engineers (AIChE): San Francisco, CA, USA.
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Quaglio, M;
Fraga, E;
Galvanin, F;
(2019)
Statistical diagnosis of process-model mismatch by means of the Lagrange multiplier test.
In: Kiss, Anton and Zondervan, Edwin and Lakerveld, Richard and Ozkan, Leyla, (eds.)
Proceedings of the 29th European Symposium on Computer Aided Process Engineering (ESCAPE-29).
(pp. pp. 679-684).
Elsevier: Cham, Switzerland.
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Sangoi, E;
Cattani, F;
Padia, F;
Galvanin, F;
(2024)
Foliar Uptake Models for Biocides: Testing Practical Identifiability of Diffusion-Based Models.
In: Klapa, Maria and Bernaerts, Kristel and Vignoni, Alejandro, (eds.)
IFAC-Papers Online.
(pp. pp. 73-78).
Elsevier: Amsterdam, The Netherlands.
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Sangoi, Enrico;
Quaglio, Marco;
Bezzo, Fabrizio;
Galvanin, Federico;
(2022)
Optimal Design of Experiments Based on Artificial Neural Network Classifiers for Fast Kinetic Model Recognition.
In:
Proceedings of the 14th International Symposium on Process Systems Engineering (PSE 2021+).
(pp. pp. 817-822).
Elsevier B.V.
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Taverna, B;
Casonato, A;
Bezzo, F;
Galvanin, F;
(2018)
Towards the optimal design of a minimum set of clinical trials for the identification and characterization of VWD.
In: Gavin, ME and Towler, G and Ierapetritou, M, (eds.)
Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018.
Elsevier
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Tillmann, Maerthe Theresa;
Galvanin, Federico;
(2023)
Development of a holistic Python package for optimal selection of experimental design criteria in kinetic model discrimination.
In: Kokossis, Antonios and Georgiadis, Michael and Pistikopoulos, Stratos, (eds.)
Computer Aided Chemical Engineering.
(pp. pp. 631-636).
Elsevier: Athens, Greece.
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Ward, Oliver;
Galvanin, Federico;
Jurado Pontes, Nelia;
Clements, Chris;
Abdallah, Mohamad;
Blackburn, Daniel;
Fraga, E;
(2023)
Lumped-Parameter Heat Exchanger Models for the Robust Dynamic Modelling of Power Generation Cycles.
In:
PROCEEDINGS OF THE 33 rd European Symposium on Computer Aided Process Engineering (ESCAPE33).
Elsevier B.V: Athens, Greece.
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Ward, Oliver MG;
Galvanin, Federico;
Jurado, Nelia;
Blackburn, Daniel;
Warren, Robert J;
Fraga, Eric S;
(2025)
Optimization of the Power Conversion System for a Pulsed Fusion Power Plant with Multiple Heat Sources using a Dynamic Process Model.
In: Van Impe, Jan and Léonard, Grégoire and Bhonsale, Satyajeet and Polańska, Monika and Logist, Filip, (eds.)
Systems and Control Transactions.
(pp. pp. 1114-1119).
PSE Press: Ghent, Belgium.
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Conference item
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Galvanin, Federico;
(2025)
Development of Autonomous Digital Platforms for Resilient and Sustainable Manufacturing in Industry 5.0.
Presented at: CAIREES 2025, Bournemouth, UK.
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Galvanin, Federico;
(2023)
Development of autonomous platforms for kinetic model identification.
Presented at: 15th Continuous Flow Reactor Technology for Industrial Applications (CFRT), Dublin, Ireland.
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Huang, Chunbing;
Cattani, Federica;
Galvanin, Federico;
(2022)
An optimal experiment design strategy for improving parameter estimation in stochastic models.
Presented at: ChemEngDayUK2022, London, UK.
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Poster
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Deussen, Philipp;
Galvanin, Federico;
(2022)
On the effect of parametric uncertainty in Gaussian Processes applied to geostatistical estimation: an in-silico case study.
Presented at: Sargent Centre Industrial Consortium Members Annual Meeting 2022, London, UK.
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Sangoi, Enrico;
Cattani, Federica;
Galvanin, Federico;
(2023)
On the development and application of a general model identification framework to biological systems.
Presented at: 2023 Symposium on Model Based Design of Experiments, London, UK.
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Sangoi, Enrico;
Quaglio, Marco;
Galvanin, Federico;
(2022)
Optimal Design of Experiments for Artificial Neural Network-based Kinetic Model Recognition.
Presented at: Sargent Centre Industrial Consortium Members Annual Meeting 2022, London, UK.
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Thesis
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Quaglio, Marco;
(2020)
Novel techniques for kinetic model identification and improvement.
Doctoral thesis (Ph.D), UCL (University College London).
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