As environmental policies drive to increase the renewables participation in the transportation sector, biomass-derived fuels are becoming an attractive alternative to replace, totally or partially, fossil ones. Taking into account the sustainability concerns about first generation biofuels, attention is paid to second generation ones. Their main drawbacks are the high CAPEX and OPEX required if high quality fuels are required as final product due to the current deployment of its technology. In this way, co-processing of biomass-derived feedstocks in oil refineries can be a good solution and can pave the way for the introduction of biofuels in the market, since it reduces environmental impacts from fossil fuels, decreases the oil dependency of countries and, the most important one, takes advantage of existing facilities avoiding the erection of new plants. In this work, the co-processing of hydrodeoxigenated pyrolysis oil (FIDC:1-oil, from lignocellulosic biomass) with vacuum gas oil (VGO) in a FCC unit is modelled and implemented in a calculation block in MS Excel inside Aspen Plus. 40 pseudocomponents and 10 real components are defined to represent both feedstocks and their possible cracking products. These components are used in a pseudoreaction mechanism of more than 12,000 single reactions, developed from a probabilistic reaction system. Such a large number of reactions is handled by considering key parameters for the estimation of each reaction rate constant and integrated along the riser which is modelled using a finite volume method. Tuning parameters are used to fit the model depending on the feedstock and the riser conditions, and have to be estimated using experimental data. Thereby, the kinetic reactor model allows to work with different feedstocks and conditions. Material, heat and hydrodynamic balances are performed in all volume elements of the modelled riser reactor, which results in a composition and temperature profiles along the riser. The model is validated with experimental data, showing a low relative error. Therefore, this model can be used to predict product yields, according to different feedstocks and experimental conditions. (C) 2017 Elsevier Ltd. All rights reserved.