| An autonomous microreactor platform for the rapid identification of kinetic models C Waldron, A Pankajakshan, M Quaglio, E Cao, F Galvanin, A Gavriilidis Reaction Chemistry & Engineering 4 (9), 1623-1636, 2019 | 59 | 2019 |
| Model-based design of transient flow experiments for the identification of kinetic parameters C Waldron, A Pankajakshan, M Quaglio, E Cao, F Galvanin, A Gavriilidis Reaction Chemistry & Engineering 5 (1), 112-123, 2020 | 38 | 2020 |
| An artificial neural network approach to recognise kinetic models from experimental data M Quaglio, L Roberts, MSB Jaapar, ES Fraga, V Dua, F Galvanin Computers & Chemical Engineering 135, 106759, 2020 | 24 | 2020 |
| Closed-loop model-based design of experiments for kinetic model discrimination and parameter estimation: benzoic acid esterification on a heterogeneous catalyst C Waldron, A Pankajakshan, M Quaglio, E Cao, F Galvanin, A Gavriilidis Industrial & Engineering Chemistry Research 58 (49), 22165-22177, 2019 | 24 | 2019 |
| An online reparametrisation approach for robust parameter estimation in automated model identification platforms M Quaglio, C Waldron, A Pankajakshan, E Cao, A Gavriilidis, ES Fraga, ... Computers & Chemical Engineering 124, 270-284, 2019 | 21 | 2019 |
| A model-based data mining approach for determining the domain of validity of approximated models M Quaglio, ES Fraga, E Cao, A Gavriilidis, F Galvanin Chemometrics and intelligent laboratory systems 172, 58-67, 2018 | 19 | 2018 |
| Model-based design of experiments in the presence of structural model uncertainty: an extended information matrix approach M Quaglio, ES Fraga, F Galvanin Chemical Engineering Research and Design 136, 129-143, 2018 | 10 | 2018 |
| A multi-objective optimal experimental design framework for enhancing the efficiency of online model identification platforms A Pankajakshan, C Waldron, M Quaglio, A Gavriilidis, F Galvanin Engineering 5 (6), 1049-1059, 2019 | 8 | 2019 |
| Identification of kinetic models of methanol oxidation on silver in the presence of uncertain catalyst behavior M Quaglio, F Bezzo, A Gavriilidis, E Cao, N Al-Rifai, F Galvanin AIChE Journal 65 (10), 2019 | 7 | 2019 |
| A diagnostic procedure for improving the structure of approximated kinetic models M Quaglio, ES Fraga, F Galvanin Computers & Chemical Engineering 133, 106659, 2020 | 5 | 2020 |
| Constrained model-based design of experiments for the identification of approximated models M Quaglio, ES Fraga, F Galvanin IFAC-PapersOnLine 51 (15), 515-520, 2018 | 5 | 2018 |
| On the use of online reparametrization in automated platforms for kinetic model identification M Quaglio, C Waldron, A Pankajakshan, E Cao, A Gavriilidis, ES Fraga, ... Chemie Ingenieur Technik 91 (3), 268-276, 2019 | 4 | 2019 |
| Statistical diagnosis of process-model mismatch by means of the Lagrange multiplier test M Quaglio, ES Fraga, F Galvanin Computer Aided Chemical Engineering 46, 679-684, 2019 | 2 | 2019 |
| Optimal Design of Experiments Based on Artificial Neural Network Classifiers for Fast Kinetic Model Recognition E Sangoi, M Quaglio, F Bezzo, F Galvanin Computer Aided Chemical Engineering 49, 817-822, 2022 | 1 | 2022 |
| Experimentally Driven Guaranteed Parameter Estimation: a Way to Speed up Model-Based Design of Experiments Techniques A Pankajakshan, M Quaglio, F Galvanin Computer Aided Chemical Engineering 43, 355-360, 2018 | 1 | 2018 |
| Optimal Design of Experiments for Artificial Neural Network-based Kinetic Model Recognition E Sangoi, M Quaglio, F Galvanin | | 2022 |
| Novel techniques for kinetic model identification and improvement M Quaglio UCL (University College London), 2020 | | 2020 |
| The Evolution of Approximated Kinetic Model Structures M Quaglio, ES Fraga, F Galvanin 2019 AIChE Annual Meeting, 2019 | | 2019 |
| 提高在线模型识别平台效率的多目标最优实验设计框架 A Pankajakshan, C Waldron, M Quaglio, A Gavriilidis, F Galvanin Engineering, 2019 | | 2019 |
| An evolutionary approach to kinetic modelling inspired by Lamarckian inheritance M Quaglio, ES Fraga, F Galvanin | | 2019 |
