Parameter Estimation in Population Balance through Bayesian ‎Technique Markov Chain Monte Carlo - Carlos H.R. Moura; Bruno M. Viegas; Maria Tavares; Emanuel N. Macêdo; Diego C. Estumano; João N.N. Quaresma (Journal of Applied and Computational Mechanics, 2021)
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.