{"title":"利用 MLP 人工神经网络模型对褐铁矿球团的干燥过程进行预测研究","authors":"Yunpeng Wang, Xiaolei Zhou","doi":"10.1016/j.powtec.2024.120026","DOIUrl":null,"url":null,"abstract":"<div><p>Due to the decline in high-grade iron ore production, the utilization of low-grade iron ore, such as limonite, has become necessary. Limonite contains a significant amount of bound water, which requires a drying process prior to use. Excessive heat stress caused by the evaporation of bound and free water during the drying of limonite pellets can lead to pellet disintegration and adversely affect gas-solid reactions. In recent years, artificial neural network (ANN) has been developing continuously in the fields of modeling and intelligent control, and has been widely used. Many predecessors used artificial neural network model to study the drying process of natural organic matter, and analyzed the factors affecting the drying rate of organic matter. In this study, we employed big data analysis, specifically Multilayer Perceptron (MLP) artificial neural networks, to analyze the drying process of limonite pellets and successfully established a predictive drying model applicable to limonite pellets. The MLP artificial neural network demonstrated excellent fitting between predicted and experimental values, with a maxi-mum R2 value of 0.999. The artificial neural network for drying developed in this study provides technical guidance for industrial material drying, reduces the workload of manual measurements, and minimizes energy consumption.</p></div>","PeriodicalId":407,"journal":{"name":"Powder Technology","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive study of drying process for limonite pellets using MLP artificial neural network model\",\"authors\":\"Yunpeng Wang, Xiaolei Zhou\",\"doi\":\"10.1016/j.powtec.2024.120026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Due to the decline in high-grade iron ore production, the utilization of low-grade iron ore, such as limonite, has become necessary. Limonite contains a significant amount of bound water, which requires a drying process prior to use. Excessive heat stress caused by the evaporation of bound and free water during the drying of limonite pellets can lead to pellet disintegration and adversely affect gas-solid reactions. In recent years, artificial neural network (ANN) has been developing continuously in the fields of modeling and intelligent control, and has been widely used. Many predecessors used artificial neural network model to study the drying process of natural organic matter, and analyzed the factors affecting the drying rate of organic matter. In this study, we employed big data analysis, specifically Multilayer Perceptron (MLP) artificial neural networks, to analyze the drying process of limonite pellets and successfully established a predictive drying model applicable to limonite pellets. The MLP artificial neural network demonstrated excellent fitting between predicted and experimental values, with a maxi-mum R2 value of 0.999. The artificial neural network for drying developed in this study provides technical guidance for industrial material drying, reduces the workload of manual measurements, and minimizes energy consumption.</p></div>\",\"PeriodicalId\":407,\"journal\":{\"name\":\"Powder Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Powder Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0032591024006703\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Powder Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0032591024006703","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Predictive study of drying process for limonite pellets using MLP artificial neural network model
Due to the decline in high-grade iron ore production, the utilization of low-grade iron ore, such as limonite, has become necessary. Limonite contains a significant amount of bound water, which requires a drying process prior to use. Excessive heat stress caused by the evaporation of bound and free water during the drying of limonite pellets can lead to pellet disintegration and adversely affect gas-solid reactions. In recent years, artificial neural network (ANN) has been developing continuously in the fields of modeling and intelligent control, and has been widely used. Many predecessors used artificial neural network model to study the drying process of natural organic matter, and analyzed the factors affecting the drying rate of organic matter. In this study, we employed big data analysis, specifically Multilayer Perceptron (MLP) artificial neural networks, to analyze the drying process of limonite pellets and successfully established a predictive drying model applicable to limonite pellets. The MLP artificial neural network demonstrated excellent fitting between predicted and experimental values, with a maxi-mum R2 value of 0.999. The artificial neural network for drying developed in this study provides technical guidance for industrial material drying, reduces the workload of manual measurements, and minimizes energy consumption.
期刊介绍:
Powder Technology is an International Journal on the Science and Technology of Wet and Dry Particulate Systems. Powder Technology publishes papers on all aspects of the formation of particles and their characterisation and on the study of systems containing particulate solids. No limitation is imposed on the size of the particles, which may range from nanometre scale, as in pigments or aerosols, to that of mined or quarried materials. The following list of topics is not intended to be comprehensive, but rather to indicate typical subjects which fall within the scope of the journal's interests:
Formation and synthesis of particles by precipitation and other methods.
Modification of particles by agglomeration, coating, comminution and attrition.
Characterisation of the size, shape, surface area, pore structure and strength of particles and agglomerates (including the origins and effects of inter particle forces).
Packing, failure, flow and permeability of assemblies of particles.
Particle-particle interactions and suspension rheology.
Handling and processing operations such as slurry flow, fluidization, pneumatic conveying.
Interactions between particles and their environment, including delivery of particulate products to the body.
Applications of particle technology in production of pharmaceuticals, chemicals, foods, pigments, structural, and functional materials and in environmental and energy related matters.
For materials-oriented contributions we are looking for articles revealing the effect of particle/powder characteristics (size, morphology and composition, in that order) on material performance or functionality and, ideally, comparison to any industrial standard.