Abstract

Successful scaling up of Solid Substrate Cultivation (SSC) bioreactors has been hampered by the lack of reliable models that describe such processes satisfactorily. Even though experimental data may be available for model development, data analysis is hindered by system heterogeneity and noisy measurements. This work presents a data processing procedure for periodically agitated SSC fixed bed reactors. The procedure considers several steps. First, all measurements were pre-processed on-line during the cultivation using a low pass fourth order Butterworth digital filter. Then, using this preprocessed data, the average bed temperature, evaporation rate, removed heat, and CO2 production rate were computed off-line. The variables used to compute the evaporation rate and the removed heat were smoothed off-line with a peak shaving algorithm and a non-delay inducing forward/backward moving average scheme. Variables associated with biomass growth (CO2 and metabolic heat) are known to evolve slowly. Hence, these were reprocessed with a smoothing procedure in order to diminish the effects of bioreactor heterogeneity. Here, moving average smoothing was applied using a larger window than for other variables, and determined empirically in order to smooth the pre-processed data and extract its real trend. The whole procedure was assessed with data from a 200 kg capacity SSC bioreactor in the cultivation of a filamentous fungus (Gibberella fujikuroi) on wheat bran.