Parameter Estimation in Population Balance through Bayesian ‎Technique Markov Chain Monte Carlo

Document Type : Research Paper


1 Graduate Program in Chemical Engineering, PPGEQ/ITEC/UFPA, Federal University of Pará, 66075-110, Belém, PA, Brazil‎

2 Graduate Program in Mathematics and Statistics, Federal University of Pará, Belém, PA, Brazil

3 Graduate Program in Natural Resource Engineering in the Amazon, PRODERNA/ITEC/UFPA, Federal University of Pará, Belém, PA, Brazil

4 Faculty of Biotechnology and Bioprocess Engineering, Federal University of Pará, Belém, PA, Brazil


In this work, the Markov Chain Monte Carlo is applied to estimate parameters that represent mechanisms that describe particles' dynamics in particulate systems from the literature's proposed models. Initially, the reduced sensitivity coefficient is evaluated to verify which parameters could be estimated simultaneously. The technique is then applied to estimate the models' parameters in different numerical scenarios to determine the rates that influence population dynamics. After the analyzes are performed, the estimates show good precision, accuracy, and a good fit between the measured and estimated state variables. The results show that the Markov chain Monte Carlo can determine the rates of population balance phenomenon.


Main Subjects

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