{"title":"Operation Parameters Optimization Method of Coal Flow Transportation Equipment Based on Convolutional Neural Network","authors":"Xueqi Yang, Xinqin Gao, Haiyang Zheng","doi":"10.1007/s42461-024-01031-0","DOIUrl":null,"url":null,"abstract":"<p>Mine coal flow transportation has some typical features of long-distance and complex environments. The transportation equipment usually adopts the mode of constant speed, which makes a large amount of energy waste. To solve these problems, the characteristics of the coal flow transportation system are analyzed. Based on a principal component analysis-convolutional neural network (PCA-CNN), the operation parameters optimization method of coal flow transportation equipment is proposed. Taking the transport time, transport cost, and equipment utilization of belt conveyors and other equipment as the optimization objectives, the multi-objective functions are established, and the operation parameters such as transport speed, transport distance, and equipment start-up time are optimized. The PCA and the CNN are respectively used to determine the weight of each objective function and iteratively train the practical production data samples under multiple constraints. The fully connected layer of CNN is constructed by the Lagrange multiplier method. The optimal production mode and operation parameters of the coal flow transportation equipment are obtained, satisfying the multi-objective functions and constraints. Finally, the practical engineering case is simulated by Plant Simulation, and the operation parameters of the coal flow transportation equipment are compared before and after optimization. The research results show that the objective function of each experiment is optimized to some degree. Furthermore, comprising other common algorithms, the advantages and effectiveness of the based-CNN operation parameters optimization method are verified. These have an important guiding significance for energy-saving and efficient coal flow transportation equipment operation.</p>","PeriodicalId":18588,"journal":{"name":"Mining, Metallurgy & Exploration","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mining, Metallurgy & Exploration","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42461-024-01031-0","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Mine coal flow transportation has some typical features of long-distance and complex environments. The transportation equipment usually adopts the mode of constant speed, which makes a large amount of energy waste. To solve these problems, the characteristics of the coal flow transportation system are analyzed. Based on a principal component analysis-convolutional neural network (PCA-CNN), the operation parameters optimization method of coal flow transportation equipment is proposed. Taking the transport time, transport cost, and equipment utilization of belt conveyors and other equipment as the optimization objectives, the multi-objective functions are established, and the operation parameters such as transport speed, transport distance, and equipment start-up time are optimized. The PCA and the CNN are respectively used to determine the weight of each objective function and iteratively train the practical production data samples under multiple constraints. The fully connected layer of CNN is constructed by the Lagrange multiplier method. The optimal production mode and operation parameters of the coal flow transportation equipment are obtained, satisfying the multi-objective functions and constraints. Finally, the practical engineering case is simulated by Plant Simulation, and the operation parameters of the coal flow transportation equipment are compared before and after optimization. The research results show that the objective function of each experiment is optimized to some degree. Furthermore, comprising other common algorithms, the advantages and effectiveness of the based-CNN operation parameters optimization method are verified. These have an important guiding significance for energy-saving and efficient coal flow transportation equipment operation.
期刊介绍:
The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society.
The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.