Department of Electrical and Electronic Engineering, College of Engineering, Yantai Nanshan University, Longkou 265713, China.
School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Math Biosci Eng. 2022 Jul 27;19(10):10687-10709. doi: 10.3934/mbe.2022500.
In the process of chlortetracycline (CTC) fermentation, no instrument can be used to measure the total sugar content of the fermentation broth online due to its high viscosity and large amount of impurities, so it is difficult to realize the optimal control of glucose feed rate in the fermentation process. In order to solve this intractable problem, the relationship between on-line measurable parameters and total sugar content (One of the parameters that are difficult to measure online) in fermentation tank is deeply analyzed, and a new soft sensor model of total sugar content in fermentation tank and a new optimal control method of glucose feed rate are proposed in this paper. By selecting measurable variables of fermentation tank, determining different fermentation stages, constructing recursive fuzzy neural network (RFNN) and applying network rolling training method, an online soft sensor model of total sugar content is established. Based on the field multi-batch data, the change trend of the amount of glucose feed required at each fermentation stage is divided, and the online prediction of total sugar content and the optimal control strategy of glucose feed rate are realized by using the inference algorithm of expert experience regulation rules and soft sensor model of total sugar content. The experiment results in the real field demonstrate that the proposed scheme can effectively predict the total sugar content of fermentation broth online, optimize the control of glucose feed rate during fermentation process, reduce production cost and meet the requirements of production technology.
在金霉素（CTC）发酵过程中，由于发酵液黏度大、杂质多，无法使用仪器在线测量其总糖含量，难以实现发酵过程中葡萄糖补料速率的优化控制。针对这一难题，深入分析发酵罐中在线可测参数与总糖含量（其中一个难以在线测量的参数）之间的关系，提出了一种发酵罐总糖含量的新型软测量模型和葡萄糖补料速率的新型优化控制方法。通过选择发酵罐的可测变量，确定不同发酵阶段，构建递推模糊神经网络（RFNN）并应用网络滚动训练方法，建立了总糖含量的在线软测量模型。基于现场多批数据，对各发酵阶段所需葡萄糖补料量的变化趋势进行划分，利用专家经验调节规则的推理算法和总糖含量软测量模型，实现了总糖含量的在线预测和葡萄糖补料速率的优化控制策略。实际现场的实验结果表明，所提出的方案能够有效在线预测发酵液的总糖含量，优化发酵过程中葡萄糖补料速率的控制，降低生产成本，满足生产工艺要求。
