Data processing for solid substrate cultivation bioreactors

Successful scaling up of Solid Substrate Cultivation (SSC) bioreactors has been hampered by the lack of reliable models that describe such processes s atisfactorily. Even though experimental data may be available for model dev elopment, data analysis is hindered by system heterogeneity and noisy measu rements. This work presents a data processing procedure for periodically ag itated 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 prepro cessed data, the average bed temperature, evaporation rate, removed heat, a nd CO2 production rate were computed off-line. The variables used to comput e the evaporation rate and the removed heat were smoothed off-line with a p eak shaving algorithm and a non-delay inducing forward/backward moving aver age scheme. Variables associated with biomass growth (CO2 and metabolic hea t) are known to evolve slowly. Hence, these were reprocessed with a smoothi ng procedure in order to diminish the effects of bioreactor heterogeneity. Here, moving average smoothing was applied using a larger window than for o ther variables, and determined empirically in order to smooth the pre-proce ssed 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 filamen tous fungus (Gibberella fujikuroi) on wheat bran.