Maintaining potable water resources is a challenge that humanity cannot ignore, given the growing increase in potential contaminants of different classes. In the scenario of viable alternatives to the remediation of this environmental setback, adsorption stands out for its effectiveness, relative operational simplicity, and low cost. The use of models in a simulated environment that represents the adsorptive dynamics in a fixed bed column is presented to anticipate viable scenarios and execution conditions before elaborating a full-scale project. Analytical models are often used due to their simplicity of application. However, their parameters remain limited to the specific experimental curve used in their adjustment. In this sense, this work adopts the mechanistic approach to using a model capable of providing more detailed information about an adsorptive bed, such as axial dispersion and mass transfer in the solid phase. We carried out the verifications presented here from the perspective of Bayesian inference. It allows the use of all previous knowledge of the phenomenon and experimental measurements and their associated uncertainties. The mean and standard deviation of the prior probability distribution of the parameters were varied. We adopted the Monte Carlo method via Markov Chains (MCMC) to obtain the estimates and implemented them according to the Metropolis-Hastings algorithm. Estimates showed that information obtained at bed outlet could predict breakthrough curves at other points in the column where measurements are not available.