发酵过程中生物量浓度的在线估计

在发酵过程中,像生物量浓度等变量都是进行实验室的离线采样分析,这往往由于存在较大的时间延迟而不能及时地进行过程控制,达不到指导生产的目的.而软测量技术为该问题提出了一个很好的解决办法.基于神经网络与最小二乘支持向量机分别建立了生物量浓度的在线检测软测量模型.模型分为两类:黑箱模型与混合模型.模型的训练与验证数据都是取自真实的实验过程诺西肽发酵.结果表明软测量方法对生物量浓度具有很好的预估性能,而且加入先验知识的混合模型精度更高.

On-Line Estimate of Biomass Concentration in Fermentation Process

In a fermentation process several variables, such as biomass concentration are conventionally determined by off-line laboratory analysis, i.e., the process control is unavailable to industrial production in time just because of time delay that often makes the analysis results inefficient. In this respect, however, soft sensing is a good solution. Based individually on neural network and LS-SVM (least square support vector machine), two on-line soft sensing models are designed to estimate the biomass concentration, i.e., the black-box model and hybrid model. The data for model training and verifying are both got from a real experiment process-the fermentation of Nosiheptide which is also used to evaluate the model performance. The results show that the way of soft sensing is good at estimating the biomass concentration, and what's more higher estimating accuracy of hybrid model can be obtained if adding prior knowledge to the model.