Hydrocracking is a crucial refinery process that transforms heavy molecules (i.e. vacuum gas oil (VGO)) into lighter and highly-valued products such as naphtha, kerosene and diesel. It is a two-step process. The hydrotreatment (HDT) reactor uses a more robust catalyst, which essentially serves to remove heteroatoms from the VGO feed in order to satisfy product quality constraints and avoid poisoning of the more delicate zeolite-based HCK catalysts. The second, hydrocracking (HCK) reactor uses a commercial zeolite catalyst with a carefully selected balance of acid and metallic sites. For hydrotreatment simulation, the kinetic model is decomposed in several ODE (Ordinary Differential Equation). Catalyst vendors develop more and more catalysts. For each new catalyst (new generation), the kinetic parameters must be refitted. This task is costly and time consuming. In this paper, in order to reduce the required number of experimental points, a Bayesian transfer approach is proposed to fit the parameters of catalyst (n+1), using the past knowledge of catalyst (n) to add more information. A method for the choice of the prior is proposed and can be used for any type of parametric model. This approach is applied and shows an improvement in the prediction performance and robustness compared to a classical fitting method. In our case, only 10 pilot plant points on catalyst (n+1) are requested to refit a HDN kinetic model.