RESUMO
During Chlortetracycline fermentation, contamination of fermentation broth by non-target bacteria is an unavoidable problem. There is no online analytical instrument to determine whether the fermentation broth has been contaminated. Only the results of manual sampling analysis can be used to determine whether the fermentation broth is contaminated. This analysis process usually takes several hours. In order to predict online whether the fermentation broth is contaminated by non-target bacteria, a soft sensor modeling method for the signs of contamination in Chlortetracycline fermentation broth was proposed in this paper. Based on recursive wavelet neural network (RWNN) and Gaussian process regression (GPR) method, the soft sensor model of online measurable parameters and total sugar content of fermentation broth was established. By deeply analyzing the correlation between the total sugar content (it is a parameter that is difficult to measure online) of fermentation broth and the signs of bacterial contamination during fermentation, a soft sensor model was established combining with the correlation between the total sugar content of fermentation broth and the symptoms of bacterial infection, and the symptoms of non-target bacterial infection of fermentation broth were predicted. Based on the field data of the fermentation process, the different signs of Chlortetracycline fermentation were predicted for the fermentation broth uninfected with non-target bacteria, infected with bacilli and infected with phages. The experimental results showed that the proposed soft sensor model could be used to predict the occurrence of contamination during Chlortetracycline fermentation. Based on the field data, the validity of the modeling method is verified. The proposed soft sensor model of signs of bacterial contamination can be used to predict the occurrence of bacterial contamination in Chlortetracycline, Penicillin and related biological fermentation processes. So that the site operators can take effective measures in time to reduce production losses to a minimum.
