High temperature combustion processes produce NOx emissions (mainly NO and NO2), which cause various health and environmental effects. Emission of NOx results in the formation of acid rain, ozone depletion and reacts with chemicals in the air to form particulate matter thereby resulting in air pollution [1]. To predict the formation of NOx in turbulent combustion, several models such as fluid dynamics, heat transfer and chemical kinetics need to be modelled. In recent years various research work have been done to reduce the NOx emissions. CFD has been proved to be an important tool in predicting the NOx emissions. But using CFD with a detailed chemistry model for modelling combustion in industrial burners requires a lot of computational effort and hence, the present study concentrates on further reduction in the computational time of the existing NOx postprocessor [2]. In this study, two optimisation approaches were investigated to further increase the performance. The existing postprocessor uses a constant temperature field, so that an optimisation approach was implemented to modify the reaction rate constant calculation method in OpenFOAM. Another optimisation approach was investigated by predicting the initial values for the postprocessor with Zeldovich mechanism [3]. Both optimisation approaches were analysed with the benchmark test case of Sandia Flame D [4]. The approaches are currently being investigated and the achieved results indicate that, even though a minimum increase in performance was achieved, further optimisation needs to be done for increasing the performance of the postprocessor.