Soft-sensing Modeling And Research Of Observation System For Marine Alkaline Protease MP Fermentation Process

In recent years,the rapid development of marine biotechnology makes marine microbiological engineering occupy an important proportion in the national economic system.Marine low temperature alkaline protease MP is a new kind of source of protease,compared with other terrestrial protease,can effectively solve such problems as high requirement of enzyme activity,instability and inactivation in harsh environment in industrial production.These excellent characteristics make it widely used in washing industry,environmental protection,food processing and national defense.In the actual fermentation process,to increase the production efficiency and product quality of enzyme preparation and reduce the economic cost,the environmental parameters of the fermentation process should be controlled in a specific range.However,the process of cell growth,reproduction,and metabolic enzyme production is extremely reflected by the external environment.There is a complex nonlinear dynamic relationship among various state parameters of the fermentation process,which is difficult to decouple,and the key parameters reflecting the quality of fermentation have serious defects in measurement stability and price.which have become a bottleneck problem restricting the optimal control of marine alkaline protease.The thesis mainly includes the following research contents.Firstly,based on the material balance relationship of the fermentation process,a "gray box" dynamic model of the continuous fermentation process of marine protease is established,and an inverse expansion model is constructed by analyzing the existence of the inverse system and introducing the characteristic information of the fermentation process;Then use the fitting ability of the least squares support vector machine(LSSVM)to identify the initial inverse expansion model offline,and to reduce the model deviation,the artificial bee colony algorithm(ABC)is used to correct the initial inverse expansion model;Finally,the corrected inverse expansion model is connected in series after the marine alkaline protease MP fermentationprocess to form a composite pseudo-linear system,thereby realize real-time online estimation of key biological parameters of the fermentation process.Taking the marine alkaline protease MP fermentation process as an example,the simulation experiment shows that,compared with the traditional support vector machine soft-sensing method,this soft-sensing modeling method can solve the online prediction of key biological parameters in the fermentation process,and has higher accuracy and generalization ability.Then,to facilitate the monitoring of the marine low-temperature alkaline protease MP fermentation process,a marine alkaline protease MP fermentation process monitoring system was designed.And use the ABC-MLSSVM inverse soft-sensing algorithm prediction module to complete the online prediction function of key variables(cell concentration,substrate concentration and relative enzyme activity).Finally,the main research contents and results of this dissertation are summarized,and the deficiencies in the dissertation and the improvement direction of related research in the future are proposed.