Satyajeet Bhonsale
Satyajeet Bhonsale
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Zitiert von
Crystal growth kinetics of an industrial active pharmaceutical ingredient: Implications of different representations of supersaturation and simultaneous growth mechanisms
GL Quilló, S Bhonsale, B Gielen, JF Van Impe, A Collas, C Xiouras
Crystal Growth & Design 21 (9), 5403-5420, 2021
Optimal experiment design under parametric uncertainty: A comparison of a sensitivities based approach versus a polynomial chaos based stochastic approach
P Nimmegeers, S Bhonsale, D Telen, J Van Impe
Chemical Engineering Science 221, 115651, 2020
Global sensitivity analysis of a spray drying process
S Bhonsale, CA Muñoz López, J Van Impe
Processes 7 (9), 562, 2019
An analysis of uncertainty propagation methods applied to breakage population balance
S Bhonsale, D Telen, B Stokbroekx, J Van Impe
Processes 6 (12), 255, 2018
Pomodoro: A novel toolkit for dynamic (multiobjective) optimization, and model based control and estimation
S Bhonsale, D Telen, D Vercammen, M Vallerio, J Hufkens, ...
IFAC-PapersOnLine 51 (2), 719-724, 2018
Solace: An open source package for nolinear model predictive control and state estimation for (bio) chemical processes
SS Bhonsale, M Vallerio, D Telen, D Vercammen, F Logist, J van Impe
Computer Aided Chemical Engineering 38, 1971-1976, 2016
Numerical simulation of particle dynamics in a spiral jet mill via coupled CFD-DEM
S Bhonsale, L Scott, M Ghadiri, J Van Impe
Pharmaceutics 13 (7), 937, 2021
Assessment of the parameter identifiability of population balance models for air jet mills
SS Bhonsale, B Stokbroekx, J Van Impe
Computers & Chemical Engineering 143, 107056, 2020
Manifold learning and clustering for automated phase identification and alignment in data driven modeling of batch processes
CA Muñoz López, S Bhonsale, K Peeters, JFM Van Impe
Frontiers in Chemical Engineering 2, 582126, 2020
Optimal experiment design for dynamic processes
S Bhonsale, P Nimmegeers, S Akkermans, D Telen, I Stamati, F Logist, ...
Simulation and Optimization in Process Engineering, 243-271, 2022
Comparison of numerical solution strategies for population balance model of continuous cone mill
SS Bhonsale, D Telen, B Stokbroekx, J Van Impe
Powder Technology 345, 739-749, 2019
Dynamic optimisation of beer fermentation under parametric uncertainty
S Bhonsale, W Mores, J Van Impe
Fermentation 7 (4), 285, 2021
Multi-objective optimization under parametric uncertainty: A Pareto ellipsoids-based algorithm
W Mores, P Nimmegeers, I Hashem, SS Bhonsale, JFM Van Impe
Computers & Chemical Engineering 169, 108099, 2023
Design, implementation and simulation of a small-scale biorefinery model
M Sbarciog, V De Buck, S Akkermans, S Bhonsale, M Polanska, ...
Processes 10 (5), 829, 2022
Metabolic reaction network-based model predictive control of bioprocesses
P Nimmegeers, D Vercammen, S Bhonsale, F Logist, J Van Impe
Applied Sciences 11 (20), 9532, 2021
On the implementation of generalized polynomial chaos in dynamic optimization under stochastic uncertainty: a user perspective
S Bhonsale, P Nimmegeers, D Telen, JA Paulson, A Mesbah, J Van Impe
Computer Aided Chemical Engineering 46, 541-546, 2019
Towards quality by design in pharmaceutical manufacturing: modelling and control of air jet mills
S Bhonsale, D Telen, B Stokbroekx, J Van Impe
EPJ Web of Conferences 140, 07003, 2017
Quantitative methods to predict the effect of climate change on microbial food safety: A needs analysis
L Katsini, S Bhonsale, S Akkermans, S Roufou, S Griffin, V Valdramidis, ...
Trends in Food Science & Technology 126, 113-125, 2022
Risk averse model predictive control of bioreactors
S Bhonsale, M Descamps, MI Sbarciog, P Sopasakis, J Van Impe
IFAC-PapersOnLine 55 (7), 928-933, 2022
Towards nonlinear model predictive control with integrated experiment design
D Telen, M Vallerio, S Bhonsale, F Logist, J Van Impe
2016 American Control Conference (ACC), 942-947, 2016
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