Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.