| 1 |
Machine Learning Supporting Experimental Design for Product Development in the Lab
Babutzka J, Bortz M, Dinges A, Foltin G, Hajnal D, Schultze H, Weiss H
Chemie Ingenieur Technik, 91(3), 277, 2019 |
| 2 |
Sequential Markov Chain Monte Carlo (MCMC) model discrimination
Masoumi S, Duever TA, Reilly PM
Canadian Journal of Chemical Engineering, 91(5), 862, 2013 |
| 3 |
Design of experiments for discrimination of rival models based on the expected number of eliminated models
Alberton AL, Schwaab M, Lobao MWN, Pinto JC
Chemical Engineering Science, 75, 120, 2012 |
| 4 |
Experimental design for the joint model discrimination and precise parameter estimation through information measures
Alberton AL, Schwaab M, Lobao MWN, Pinto JC
Chemical Engineering Science, 66(9), 1940, 2011 |
| 5 |
Sequential experimental design based on multiobjective optimization procedures
Alberton AL, Schwaab M, Biscaia EC, Pinto JC
Chemical Engineering Science, 65(20), 5482, 2010 |
| 6 |
Sequential experimental design for estimation and analysis of thermal parameters in a fixed bed
de Oliveira SC, Dechechi EC, Maciel R, Freire JT, Bueno JMC
Canadian Journal of Chemical Engineering, 79(6), 874, 2001 |
| 7 |
Optimal and asymptotically optimal decision rules for sequential screening and resource allocation
Pronzato L
IEEE Transactions on Automatic Control, 46(5), 687, 2001 |
| 8 |
Data and knowledge based experimental design for fermentation process optimization
Berkholz R, Rohlig D, Guthke R
Enzyme and Microbial Technology, 27(10), 784, 2000 |
| 9 |
On the costs of parameter uncertainties. Effects of parameter uncertainties during optimization and design of experiments
Pinto JC
Chemical Engineering Science, 53(11), 2029, 1998 |
