Research On Fault Diagnosis And Control Method Of Scraped Film Molecular Distillation Process

In today’s society,molecular distillation technology is becoming more and more perfect,due to its liquid film thickness is small,separation efficiency is high,has been widely studied and applied in the food industry,fine chemicals,pharmaceutical industry and other fields,has become an indispensable extraction and separation technology.However,in the actual distillation extraction process,the uncertainty of the system or the improper operation of the personnel may lead to the failure of the scraping film process,which in turn affects the purity and yield of the product,and even causes an immeasurable impact on the production process.Therefore,for scraping film molecular distillation,rapid detection,classification and control of faults in the scraping film process is the key to improving product purity and yield.To this end,this paper focuses on the fault diagnosis of scraping film molecular distillation:Independent component analysis method is a calculation method for separating multiple signals into additive sub-components,which has good processing ability for the non-Gaussian nature of process data,and due to the difficulties of its independent component selection,the use of this method in the field of fault detection is limited.Therefore,this paper proposes an improved independent composition method.Firstly,the independent component analysis method is modeled offline to obtain the correlation matrix;Secondly,the online sample information obtained during online modeling is used to find the independent component with the lowest probability density based on the kernel density estimation method.Further search for independent components with high similarity based on the correlation matrix,construct statistics,and determine control limits.Finally,the simulation experiment of the scraping film process is used to prove the effectiveness of the method.Fault classification identification is also one of the key technologies for fault diagnosis.In this paper,combined with the classification model of the support vector machine and the characteristics of the wiped film evaporation process,the detected faults are classified and identified,and an optimized support vector machine fault classification method with improved whale algorithm is proposed.The whale algorithm is optimized using a reverse learning strategy and an adaptive weight factor to optimize the parameters of the support vector machine model on this basis.In order to verify the effectiveness of the method,this paper compares the method with the particle swarm/gray wolf algorithm optimization support vector machine,and the results show that the improved whale algorithm optimizes the fault classification method of the support vector machine with higher accuracy.As the core of the distillation equipment,the stability of its performance can not only ensure the safety of the distillation process,but also ensure the key to the distillation yield and purity.If it is unstable,the probability of problems with the Hall sensor in the motor is the greatest,so in order to ensure the safety of the motor,a fault-tolerant compensation strategy for the Hall sensor is proposed.By constructing a simulation model of fault-tolerant control of the motor and using the first-order compensation strategy to simulate the fault part,the results verify the reliability of the fault-tolerant compensation method.