A MULTIVARIABLE COMPUTATIONAL FLUID DYNAMICS ANALISYS METHOD BASED IN BAYESIAN NETWORKS APPLIED IN A BIOREACTOR

Computational Fluid Dynamics is a technique that has been used in recent years to simulate and obtain numerical approximations of the physical conditions within bioreactors. Although this technique has been able to increase the accuracy and realism of the models, it is necessary to establish their accuracy with respect to the real data obtained from sensors. However, one of the problems that present, when discretizing a domain control to calculate the balance of energy, is the impossibility of handling many variables and parameters established at the same time. Then it is necessary other tools to analyze these models. In recent years, Bayesian networks have been employed for analysis the multivariable models. We develop a Computational Fluid Dynamics model which was analyzed using Bayesian Networks to show the relations between methane and CO2 concentrations. It has been possible to quantify these relationships, calculating inferences in dependent probability distributions.