Study On The Method Of Improving At-line Spectral Model Performance In The Fermentation Process

Fermentation engineering is a complicated change of biological.It's necessary to measure the fermentation parameters by on-line method and analysis these parameters real-time,in order to ensure the stability of fermentation engineering.However,some of the important parameters in the fermentation process are measured by traditional chemical method.There are some disadvantages of traditional chemical method,such as:large lag,off-line measurement and so on,so it is not suitable for industrial production.In this case infrared spectrometry technology came into being.The measurement of infrared spectrometry has the advantages of high efficiency,fast,low cost,no damage and green environmental protection.However,there are still some problems in the application of infrared spectrometry in the fermentation process.The purpose of this study is put mid-infrared measurement technology in industrial online production.This paper are aimed at this propose at three aspects of research:Firstly,the impact of biomass on spectral measurement.Biomass is an important parameters in fermentation process.This paper answer this question by analyzing the absorbance intensity and a PLS regression of cells,the measurement of biomass by MIR is not due to specific absorption by bacteria.So the online measurement of fermentation process can be achieved.Secondly,sample selection.To guarantee accurate predictions,representative samples are needed when building a calibration model for spectroscopic measurements.In industrial production engineering,the sample size is too large to measured one by one.This paper use YR method and net signal(NAS)method to judge whether the set of samples need to be added to an existing sample.Take the cost and applicability into account,NAS method is more effective than other methods can save the measurement of reference value of the measurement.Thirdly,Spectral modeling.Partial Least Squares Regression(PLS)can not only realize the analysis of spectral matrix X,but also realize the analysis of concentration matrix Y,which is effective for process monitoring and processing of collinear variable modeling.In the industrial production process,the data sample size is large,occupy the computer memory.In order to reduce the duplication of computing and improve the efficiency of the computer,the need for data batch,that is,the sample block.In order to improve the generalization performance of the model,to improve the model self-calibration,reduce the impact of data drift and adapt to online production needs.It is necessary to increase the number of samples with new information and eliminate the samples with less information to establish the dynamic partial least squares(DPLS)method.Dynamic block partial least squares is an algorithm that adapts to change processes and processes large numbers of data samples off-line.Can improve the applicability of the model and improve the accuracy of the model.