Multi-objective optimisation (MOO) of super-structured process designs are expensive in CPU- time because of the high number of potential configurations and operation conditions to be calculated. Thus single process units are generally represented by simple models like equilibrium based (chemical or phase equilibrium) or specific short cut models. In the development of new processes, kinetic effects or mass transport limitations in certain process units may play an important role, especially in multiphase chemical reactors. Therefore, it is desirable to represent such process unit by experimentally derived RBMs (rate based models) in the process flowsheet simulators used for the extensive MOO. This increases the trust engineers have in the results and allows enriching the process simulations with newest experimental findings. As most RBMs are iteratively solved, a direct incorporation would cause higher CPU-time that penalises the use of MOO. A global surrogate model (SUMO) of a RBM was successfully generated to allow its incorporation into a process design & optimisation (PDO) tool which makes use of an evolutionary MOO. The methodology was applied to a fluidised bed methanation reactor in the process chain from wood to Synthetic Natural Gas (SNG). Two types of surrogate model, an ordinary Kriging interpolation and an artificial neural network, were generated and compared to its underlying rate based model and the chemical equilibrium model. The analysis showed that kinetic limitations have significant influence on the result already for standard bulk gas chemical components. From experimental experience it can be stated that a significant amount of chemical compounds, which are near to complete conversion according the thermodynamic equilibrium, are measured in the gas phase after the reactor. Thus including RBMs in PDO improves the quality of results. This approach allows a significant improvement of information exchange between process design & optimisation workflow and experimental development of PUs in early stages of process development.