The Pr/Nd Extraction Process Component Content Prediction Based On Jit Learning

Rare earth elements play an important role in the industrial production.The rare earth elements can improve the performance of the products,especially the performance of the Sophisticated products.For this reason,many country is competing to reserve rare earth resources.At present,the production technology of rare earth elements has gradually matured.At present,the rare earth industry mainly uses multistage cascade extraction method to extract rare earth elements,the extraction process is complex,also,there are many control variables.It is difficult to control the process of rare earth extraction.To ensure the production process is highly efficient and stable content,accurate component testing methods are used to detect the content of mixed solution of rare earth.In this paper,the machine vision technology is used to establish the prediction model to solve all above problems,based on the just in time learning strategy to detect the content of mixed solution of rare earth.The main research contents are as follows:1.HSI color space is used to describe the image characteristics of mixed solution of rare earth.Take the Pr/Nd extraction process as the research object,analysis the differences between the RGB and HSI color space when they are used to describe the characteristics of the image.Take H,S value of images as the input of the model to establish the Nd content prediction model.2.The use of JIT learning strategies for nonlinear processes to build multiple sub models.Based on historical data clustering,the nonlinear process is divided into several sub spaces,in each sub space,useing the JIT learning algorithm establish model for the input data.The activation sub model is used to predict the output of the model,the method of multi model modeling is suitable for rapid detection of component content of mixed solution of rare earth.3.An online prediction model for the content of mixed solution of rare earth is established by using the JIT learning strategy.According to the neighborhood selection criterion of k-VNN,the historical data similar to the input data is selected as the sample,and the recursive linear model is used to predict the content of Nd.Finally,the relative error of the online model is verified by the field data to meet the accuracy requirement of component content detection.