On-line Monitoring Of Ethanol Fermentation Processes Based On Near Infrared Spectroscopy And Calibration Model Building

Bioethanol has a dramatically increased attention because it has the advantages of carbon dioxide emission bottom,dust emission bottom,biodegradability and renewable.Many factors have prompted the productivity of ethanol to increase year by year,and the biological fermentation process is one of the important way to produce ethanol.The monitoring and control of the biological fermentation process has a crucial impact on ensuring high yield,quality and consistency of the product.Aiming at improving the efficiency of monitoring the fermentation process,the fermentation industry needs more process analysis technology.Generally,it is possible to perform on-line detection of chemical and physical parameters of the fermentation process for control.For the biological parameters in the fermentation process,However,the offline method has the shortcomings of large time lags,time consuming and labor intensive,and easy to introduce pollution during the sampling process.Therefore,it is now necessary to rapid and effective on-line monitoring of biological parameters of the fermentation process,such as substrate concentration,biomass and product concentration.Near-infrared spectroscopy as the major technology in process analytical chemistry technology,and it has received more and more attention in online monitoring during fermentation process.In order to overcome the shortcomings of off-line detection,NIR spectroscopy has the advantages of fast measurement,no loss,no need for sample pretreatment,and good adaptability to the working environment.Based on the ethanol fermentation process,this paper studies the on-line monitoring of the fermentation process with on near-infrared spectroscopy.A robust regression model was established to calibrate a robust prediction model for the outlier in spectral data and reference data of ethanol fermentation process.This method can effectively reduce the weight of outliers in data,thus reducing the impact of outliers on prediction model.By analyzing the outliers in the data set of glucose concentration in ethanol fermentation process,the simulation results show that the method can reduce the weight of these outliers.The RMSEP is taken as the standard to prove that the model can effectively improve the accuracy of prediction.An on-line monitoring method based on MLS-SVR and near infrared spectroscopy was proposed for coupling fermentation process.The traditional method is to establish several independent models and predict each component separately.This method does not consider the characteristics of the coupling fermentation process and ignores the correlation among the components.In contrast,the establishment of joint calibration prediction model can take into account the correlation between substrate concentration,biomass and product concentration,and predict multiple components at the same time,which has better prediction accuracy.Furthermore,for the problem that the two penalty parameters and the kernel function parameters in the joint calibration model are difficult to find,the method of optimizing the parameters of the joint calibration model based on PSO algorithm is given.It can effectively reduce the time to establish the model without reducing the accuracy of the model prediction.Establishing a single model and simultaneously measuring multiple component parameters has a good application prospect in the coupling fermentation process.