Research On Control Technology Of Schisandra Chinensis With Supercritical Fluid Extraction System

Supercritical fluid extraction is a new and important technology in the field of chemical extraction.The solvent is used to extract the effective components at the critical temperature and critical pressure.This method has been paid more and more attention because of its green health,high efficiency non-toxic,cost economy and recyclable.Supercritical CO₂ extraction technology has been widely used in food,chemical industry and medicine industry.But it is now facing the equipment manual or semi automated extraction,the optimum process parameters of different materials is not accurate,extraction temperature pressure or flow control precision have seriously affected the yield and quality of extraction extracts.Therefore,it is very important for the industrial application of supercritical extraction technology and energy saving while increasing the degree of automation of the system and the control precision of the system.In this paper,the principle and characteristics of supercritical extraction technology were analyzed,and the supercritical extraction equipment of HA221-30-11 was used as experimental object.The process of supercritical extraction was described systematically.The influencing factors of extraction process were studied in detail.Four major process parameters affecting the extraction process were identified.On the basis of great approximation ability and fast learning ability of RBF neural network,with Schisandra as extraction material by supercritical extraction device for experiments based on the experimental data,the extraction temperature,extraction pressure,CO₂ flow rate and extraction time as input,the extraction rate as output,the RBF neural network is trained to extract the model.The PSO-RBF extraction yield prediction model is obtained by optimizing the RBF model by using the PSO algorithm to optimize the optimization effect and convergence speed.By MATLAB validation,the model predicts good results.Finally,the PSO algorithm is used to optimize the extraction parameters,and the optimum extraction parameters are obtained.Based on the analysis of the characteristics of the parameters of the temperature control system,this paper analyzes the characteristics of the conventional PID control,but the system adaptability is relatively poor,and the Smith predictor has good lag compensation but sensitive to the parameters.The Smith-Fuzzy-PID controller is established by the fuzzy control with strong adaptability.The control system is verified by the simulation without overshoot and has good robustness and the stable time is short.This paper presents the overall scheme of the extraction system and the design of the signal acquisition process.We choose the S7-300 as the system controller,and the touch screen MP277 used to operate and monitor the system.The STEP7 is used to configure the hardware.The workflow of the system software is designed.The WinCC is used to design the monitoring and parameter setting interface,and the data transfer between MATLAB and WinCC is realized by OPC technology.We achieve the extraction temperature of the Smith-Fuzzy-PID control in the MATLAB,and then return the control signal to S7-300 to control the implementation of the device action.The system extraction temperature control accuracy is better.