Research On Image Processing Of Black Impurities In Phosphogypsum Based On Machine Visio

Phosphogypsum is a solid waste residue produced by wet preparation of phosphoric acid in the industrial production process of phosphochemical plants,and the black impurities in it are the main reasons affecting the industrial value and application of phosphogypsum.Removing black impurities in phosphogypsum by flotation foam is a better way in the current research,but there are few studies on improving the efficiency of phosphogypsum flotation by machine vision,this paper summarizes the research status of flotation product quality at home and abroad,on this basis,Sichuan Chuanheng’s phosphogypsum as the main raw material,through the collector and its dosage,foaming agent and its dosage and orthogonal test to remove the black impurities and investigate the influence of various factors on the flotation index.To establish a set of machine vision-based phosphogypsum black impurity image processing online operating system,real-time detection of phosphogypsum flotation foam containing black impurities,and finally predict the flotation results by establishing a convolutional neural network soft measurement model,so as to achieve the purpose of improving flotation efficiency,this study mainly obtains the following conclusions:（1）When the flotation test of collector and its dosage and foaming agent and its dosage is carried out,the flotation effect is best when the grinding fineness is-0.075 mm accounting for 73.9%,the rotor speed is 2000 r/min,the slurry concentration is 27%,the collector is diesel-kerosene 1:1 mixture,the foaming agent methyl isobutyl methanol（MIBC）is 300 g/t,and the flotation time is 4 min.When the orthogonal test was carried out,the 5-factor 5-level orthogonal test was designed,and it was concluded that the primary and secondary order of the conditional factors affecting the flotation index was:slurry concentration>foaming agent MIBC dosage>collector kerosene-diesel 1:1 mixing dosage>grinding fineness>rotor speed.After the flotation test,the main components of black impurities in phosphogypsum were analyzed as Ca SO₄·2H₂O,SiO2,Fe S₂,eutectic phosphorus,organic matter,organic carbon and other substances.（2）By analyzing the characteristics of phosphogypsum flotation foam,the image characteristics of flotation foam containing black impurities were analyzed,and the performance requirements of the system were determined.According to the actual characteristics of phosphogypsum flotation for removing black impurities,a hardware platform was built,a light source lighting scheme and software operating system were designed,and the image acquisition,feature extraction and soft measurement model of phosphogypsum flotation foam were established and predicted.（3）When flotation foam feature extraction was carried out,the results of feature extraction were strongly correlated with the results of the experiment,indicating the effectiveness of establishing a phosphogypsum black impurity image processing system.With the continuation of the flotation time,the black impurities in phosphogypsum concentrate are gradually decreasing,and the black impurities in the flotation foam are increasing,according to the extraction results of the image features of the flotation foam,as the flotation time continues,the various aspects of feature extraction have different laws,which further shows that it is effective to establish a soft measurement model by entering the characteristics of the flotation foam.（4）In the process and results of establishing a soft measurement model,this study first uses the type of collector and its dosage and foaming agent and its dosage single-factor test data and orthogonal test data to establish a CNN neural network,using 100 groups of ten characteristic values of flotation foam as its input,the whiteness of phosphogypsum concentrate,the organic carbon content of tailings and the organic carbon removal rate as output,and the accuracy and fitting of the results after training and testing are low,the accuracy is 70.2%,After optimizing the model by optimizing the convolutional neural network and RNN using Bayesian and time series,the final BO-CNN-LSTM model is compared with the CNN-RNN model,and it can be seen that the prediction accuracy of the CNN-RNN model is higher and the fitting degree is better,among which the accuracy of the BO-CNN-LSTM model is 82.4%,and the CNN-RNN model is 96.7%.