For any given large-scale solid substrate fermentation (SSF) bioreactor, to assess how well a control system will work in practice requires the most realistic model possible. This model needs to account fully for complicated dynamic reactor behaviour and, in addition, has to include a specific noise model that is capable of reproducing the disturbances observed in SSF bioreactor measurements. In this work, noisy data collected historically from SSF pilot scale fermentations was used to develop such a model. Applying standard signal processing techniques, each measured variable was separated into deterministic and noise signals. Deterministic signals were used to calibrate a previously developed phenomenological model of the bioreactor. Noise signals were used to construct a realistic noise model for each measured variable in turn. Finally, the two models were combined to attain simulations that compared well with real measurements. This integrated model will provide realistic simulations that will prove useful in the design of effective control systems for intermittently mixed SSF bioreactors.