On-line Detection System For Component Content Of Pr/Nd Extraction Process Based On Improved Just-in-time Learning Algorithm

China’s rare earth resources are abundant,and there are many rare earth separation enterprises,whose scale and output of which are in the forefront of the world.However,the extraction and separation controls of rare earth enterprises in the extraction and separation production process are still in the off-line analysis and manual control stage,the level of automation isn’t high,resulting in low resource utilization rate and unstable product quality.On-line detection of rare earth element content is an important prerequisite for the company obtains stable and qualified rare earth products.At present,the research of component content prediction uses soft measurement technology stays in the off-line global model,and the effective prediction cannot be made when the rare earth extraction condition changes suddenly.Aiming at the problem of on-line detection of praseodymium/neodymium(Pr/Nd)element contents in the rare earth extraction process,this paper proposes a component contents prediction model based on an improved just-in-time learning algorithm,and develops an on-line detection system of praseodymium / neodymium content,which integrates automatic sample,image process,and information extraction,component content detection,data management and human-computer interaction.The main research contents are as follows:1.Combined with the on-site detection requirements of rare earth extraction production,the overall scheme for the online detection system was determined,the system hardware structure and software function designs were completed,and the online collection of rare earth mixed solution images was realized.The image process algorithm was designed using HACLON software to convert the color space of the image.The value resolution reduces the noise interference generated during the environment and image transmission.The feature region is obtained by adaptive threshold segmentation,and the first-order moment is used as the color feature of the solution image and extracts information from the effective area of the image solution.2.The mutual information weighted similarity criterion,data updating strategy and adaptive threshold model updating strategy are introduced to improve the traditional just-in-time learning algorithm,and proposes a Pr/Nd element content prediction model based on the improved just-in-time learning algorithm.Furthermore,the improved just-in-time learning algorithm,the traditional just-in-time learning algorithm and the WLSSVM component content prediction models were simulated and compared.The results show that thecomprehensive prediction performance index of the model proposed in this paper is the best,which meets the prediction requirements of component content under the sudden change of extraction conditions.3.In order to invoke the image processing algorithm developed by HALCON and the component content prediction model established by MATALB,build the system development environment on MFC platform,and complete the functional requirements of the Pr/Nd element component online detection system software and the development of human-computer interaction interface.Perform test experiments on field test samples to analyze performance indicators such as prediction accuracy,repeatability,and real-time performance of the system.Algorithm simulation experiments and system test experiments show that the prediction model of component content established on-line with improved just-in-time learning algorithm can solve the Pr/Nd component content under sudden change of working conditions,and the system’s performance indexes can meet the requirements of rapid detection in the field.