Abstract

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.

摘要

在金霉素的发酵过程中，非目标细菌对发酵液的污染是不可避免的问题。没有在线分析仪器来确定发酵液是否已被污染。仅手动采样分析的结果可用于确定发酵液是否被污染。此分析过程通常需要几个小时。为了在线预测发酵液是否被非靶标细菌污染，提出了一种软传感器建模方法，对金霉素发酵液中的污染迹象进行了研究。基于递归小波神经网络（RWNN）和高斯过程回归（GPR）方法，建立了在线测量参数和发酵液总糖含量的软传感器模型。通过深入分析发酵液总糖含量（难以在线测量的参数）与发酵过程中细菌污染迹象之间的相关性，建立了软传感器模型，并结合了发酵液中总糖含量的相关性。预测了发酵液和细菌感染的症状，以及发酵液的非目标细菌感染的症状。根据发酵过程的现场数据，预测了未感染非目标细菌，感染了细菌和感染了噬菌体的发酵液金霉素的发酵迹象。实验结果表明，所提出的软传感器模型可用于预测金霉素发酵过程中污染的发生。基于现场数据，验证了建模方法的有效性。所提出的细菌污染迹象的软传感器模型可用于预测金霉素，青霉素和相关生物发酵过程中细菌污染的发生。使现场操作人员可以及时采取有效措施，将生产损失降至最低。