Intelligent Control Of Flotation Process Based On Data-driven

The coal slurry process flotation is complicated,and there are many influencing factors.Detection methods of some variables are backward,and there are some phenomena such as low measurement accuracy or poor timeliness.The process model is in low accuracy due to the low dimensionality of the input variables and the lack of some variables.This paper studies the intelligent control of flotation process based on online and offline data through three aspects: process variable collection,drug prediction model and control system.The paper takes the coal slurry flotation process as the research object,and analyzes the process variables.According to the actual production status,the paper determines the collection method and the detection equipment of flotation process variable,and the actuator for drug addition.Based on the data acquisition of variables,a prediction model of flotation reagent based on DNN algorithm was established.An intelligent control system of flotation process based on data-driven was constructed to realize the intelligent addition of medicament in the flotation process.Combined with the actual situation of the flotation workshop,the foam image acquisition system was established through hardware selection and geometric position layout.In the experiment,it was found that when the frame rate of the industrial camera reached 30 fps,the image acquired by the global exposure method was clear and of high quality.The paper pretreats flotation froth image collected,extracts and analyzes the foam image's features,determines main feature values.The average gradient is used as the image sharpness evaluation function,and the median filtering with the kernel size of 3 is used to complete the image preprocessing.The paper extracts color features by grayscale of the image,and extracts texture features by the gray level co-occurrence matrix,and extracts size features by the marked watershed segmentation method,and extracts dynamic features by feature point matching.The paper analyzes correlation between the characteristics of bubble image extracted and the drug added,and determine the gray mean,entropy,variance,average area,and moving speed as the main related variables.The paper proposes the soft measurement method based on image features for flotation tailings ash,and designed the tailings image acquisition system and hardware,and determined the main features of the image.Image acquisition was determined at a light intensity of 12400 lux with a stirring rate of 830 r/min.The grayscale features of the image are extracted for correlation analysis,and the six variables of gray mean,variance,smoothness,skewness,energy,entropy and ash are determined as the input of the soft measurement model.Based on tailings image features,the paper established the soft-measurement model of tailings,and designed ash automatic prediction platform for tailings images.Based on the 81 sets of data collected by experiments,a soft measurement model for flotation tailings ash based on GA-SVM and PSO-SVM algorithm was established.The error data analysis shows that the PSO-SVM prediction has higher accuracy,the absolute error is below 6%,and the stability of the model is stronger,which is suitable for soft measurement of tailings ash.Based on the PSO-SVM tailings soft measurement model,an automatic prediction platform for tailings ash based on Labview and Matlab was developed.Based on different algorithms,a prediction model for flotation agent dosage was established,and the most suitable model was determined through comparative analysis.Ash content of raw coal,raw coal quantity,feed concentration,feed concentration,clean coal ash,tailings ash,and the gray scale mean,entropy,variance,average area,moving speed of foam image as input,collector dosage and frother dosage are outputs.Using the collected 120 sets of data,the GRNN,SVM and DNN prediction models for the flotation agent dosage are established respectively.The data error indicates that the prediction effect based on the DNN model is stronger than the prediction effect of GRNN and SVM,and the prediction error of collector dosage is below 12% and the prediction error of the frother dosage is below 8%.Based on the established DNN drug additive quantity prediction model,an intelligent control system of flotation process based on data-based was established.Through the design of the fuzzy controller,the predicted dosage of the medicament is compensated and fine-tuned;and the accurate flow of the medicament is achieved by calibrating the flow rate of the pump.Through the design of the acquisition program,variable acquisition and interaction are realized,and the specific control process is realized through the design of the lower computer of the upper computer.