{"title":"基于 NRBO-CNN-LSTM 的浮选回收洁净煤灰分预测研究","authors":"Yujiao Li, Haizeng Liu, Fucheng Lu","doi":"10.3390/min14090894","DOIUrl":null,"url":null,"abstract":"Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of flotation. In recent years, the development of machine vision and deep learning has made it possible to detect ash content in flotation-recovered clean coal. Therefore, a prediction method for ash content in flotation-recovered clean coal based on image processing of the surface characteristics of flotation froth is studied. A convolutional neural network –long short-term memory (CNN-LSTM) model optimized by Newton–Raphson is proposed for predicting the ash content of flotation froth. Initially, the collected flotation froth video is preprocessed to extract the feature dataset of flotation froth images. Subsequently, a hybrid CNN-LSTM network architecture is constructed. Convolutional neural networks are employed to extract image features, while long short-term memory networks capture time series information, enabling the prediction of ash content. Experimental results indicate that the prediction accuracy on the training set achieves an R value of 0.9958, mean squared error (MSE) of 0.0012, root mean square error (RMSE) of 0.0346, and mean absolute error (MAE) of 0.0251. On the test set, the prediction accuracy attains an R value of 0.9726, MSE of 0.0028, RMSE of 0.0530, and MAE of 0.0415. The proposed model effectively extracts flotation froth features and accurately predicts ash content. This study provides a new approach for the intelligent control of the flotation process and holds broad application prospects.","PeriodicalId":18601,"journal":{"name":"Minerals","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Prediction of Ash Content in Flotation-Recovered Clean Coal Based on NRBO-CNN-LSTM\",\"authors\":\"Yujiao Li, Haizeng Liu, Fucheng Lu\",\"doi\":\"10.3390/min14090894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of flotation. In recent years, the development of machine vision and deep learning has made it possible to detect ash content in flotation-recovered clean coal. Therefore, a prediction method for ash content in flotation-recovered clean coal based on image processing of the surface characteristics of flotation froth is studied. A convolutional neural network –long short-term memory (CNN-LSTM) model optimized by Newton–Raphson is proposed for predicting the ash content of flotation froth. Initially, the collected flotation froth video is preprocessed to extract the feature dataset of flotation froth images. Subsequently, a hybrid CNN-LSTM network architecture is constructed. Convolutional neural networks are employed to extract image features, while long short-term memory networks capture time series information, enabling the prediction of ash content. Experimental results indicate that the prediction accuracy on the training set achieves an R value of 0.9958, mean squared error (MSE) of 0.0012, root mean square error (RMSE) of 0.0346, and mean absolute error (MAE) of 0.0251. On the test set, the prediction accuracy attains an R value of 0.9726, MSE of 0.0028, RMSE of 0.0530, and MAE of 0.0415. The proposed model effectively extracts flotation froth features and accurately predicts ash content. This study provides a new approach for the intelligent control of the flotation process and holds broad application prospects.\",\"PeriodicalId\":18601,\"journal\":{\"name\":\"Minerals\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Minerals\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/min14090894\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minerals","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/min14090894","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Research on Prediction of Ash Content in Flotation-Recovered Clean Coal Based on NRBO-CNN-LSTM
Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of flotation. In recent years, the development of machine vision and deep learning has made it possible to detect ash content in flotation-recovered clean coal. Therefore, a prediction method for ash content in flotation-recovered clean coal based on image processing of the surface characteristics of flotation froth is studied. A convolutional neural network –long short-term memory (CNN-LSTM) model optimized by Newton–Raphson is proposed for predicting the ash content of flotation froth. Initially, the collected flotation froth video is preprocessed to extract the feature dataset of flotation froth images. Subsequently, a hybrid CNN-LSTM network architecture is constructed. Convolutional neural networks are employed to extract image features, while long short-term memory networks capture time series information, enabling the prediction of ash content. Experimental results indicate that the prediction accuracy on the training set achieves an R value of 0.9958, mean squared error (MSE) of 0.0012, root mean square error (RMSE) of 0.0346, and mean absolute error (MAE) of 0.0251. On the test set, the prediction accuracy attains an R value of 0.9726, MSE of 0.0028, RMSE of 0.0530, and MAE of 0.0415. The proposed model effectively extracts flotation froth features and accurately predicts ash content. This study provides a new approach for the intelligent control of the flotation process and holds broad application prospects.
期刊介绍:
Minerals (ISSN 2075-163X) is an international open access journal that covers the broad field of mineralogy, economic mineral resources, mineral exploration, innovative mining techniques and advances in mineral processing. It publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.