Understanding the uncertainty of model parameters is crucial for building predictive models. Within the field of spontaneous ignition a slight variation in the model parameters can cause a significant variation in our ability to determine if ignition occurs. We consider this problem through an application to the steel industry. A byproduct of the steelmaking process is stockpiled where oxidation can induce ignition. The resulting ignition process sinters the filter improving the durability. Understanding this process requires careful modelling and consideration of the uncertainty in the reaction kinetics. We examine some experimental data on the filter cake to determine these reaction kinetics. Due to the complex nature of the filter cake, standard estimation techniques are difficult to apply and the uncertainty in our parameters cannot be an input into the larger stockpiles. We apply a Bayesian framework for parameter estimation that considers a distribution for the parameters rather