Sun Yumei, Tang Lingtong, Sun Qiaoyan, Wang Meichun, Han Xiang, Chen Xiangguang
College of Electronic Engineering, Yantai Nanshan University, Longkou, Shandong 265713, China.
School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Technol Health Care. 2019;27(S1):205-215. doi: 10.3233/THC-199020.
How to accurately predict the occurrence of contamination in the fermentation process of Chlortetracycline? How to prompt field operators to take effective measures in time? This is a difficult problem that the fermentation process of Chlortetracycline has not been solved well.
The aim of this paper is to effectively predict whether the fermentation process of Chlortetracycline is contaminated or not.
A Gaussian process regression soft sensor modeling method with real time integration learning is studied in depth by combining two local learning strategies, namely just-in-time learning (JITL) method and integrated learning method, and a multi-model weighted Gaussian process regression (MWGPR) soft sensor modeling method based on real-time integration learning is proposed in the paper. This soft sensing method was used to study the relationship between the viscosity of fermentation broth and the contamination in fermentation process. A soft-sensing model based on the viscosity of fermentation broth for predicting the signs of contamination is established.
The validity of this method is verified by field data. The experimental results demonstrate that the soft sensing model proposed in this paper can effectively determine whether the fermentation broth is infected by hybrid bacteria.
The method proposed in this paper is innovative and practical so that field operators can issue early warning and take effective measures.
如何准确预测金霉素发酵过程中污染的发生？如何及时促使现场操作人员采取有效措施？这是金霉素发酵过程中尚未很好解决的难题。
本文旨在有效预测金霉素发酵过程是否受到污染。
通过结合即时学习（JITL）方法和集成学习方法这两种局部学习策略，深入研究一种具有实时集成学习的高斯过程回归软测量建模方法，提出一种基于实时集成学习的多模型加权高斯过程回归（MWGPR）软测量建模方法。运用该软测量方法研究发酵液黏度与发酵过程中污染之间的关系，建立基于发酵液黏度预测污染迹象的软测量模型。
通过现场数据验证了该方法的有效性。实验结果表明，本文提出的软测量模型能够有效判定发酵液是否受到混合菌感染。
本文提出的方法具有创新性和实用性，可为现场操作人员发出预警并采取有效措施。
