利用深度神经网络预测燃煤发电厂的 SOx-NOx 排放量

IF 2.1 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Machines Pub Date : 2023-11-23 DOI:10.3390/machines11121042
Min-Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim, Jong-Ho Shin
{"title":"利用深度神经网络预测燃煤发电厂的 SOx-NOx 排放量","authors":"Min-Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim, Jong-Ho Shin","doi":"10.3390/machines11121042","DOIUrl":null,"url":null,"abstract":"Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"89 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network\",\"authors\":\"Min-Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim, Jong-Ho Shin\",\"doi\":\"10.3390/machines11121042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.\",\"PeriodicalId\":48519,\"journal\":{\"name\":\"Machines\",\"volume\":\"89 1\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machines\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/machines11121042\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machines","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/machines11121042","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

煤炭一直是发电厂最常用的能源,因为它相对便宜,而且容易获得。得益于这些优势,许多国家都在运营燃煤发电厂。然而,燃煤发电厂在燃烧煤炭的过程中会排放硫氧化物(SOx)和氮氧化物(NOx)等污染物,这些污染物被怀疑会对环境造成破坏,同时也对人体有害。因此,大多数国家都在加强对煤炭消费行业的监管。因此,燃煤发电厂也应遵守这些法规。本研究的重点是预测燃煤发电厂在燃烧过程中将优质煤和劣质煤混合时的有害排放物。硫氧化物和氮氧化物的排放受优质煤和劣质煤混合比例的影响,因此确定煤的混合比例非常重要。要确定煤的混合比例,前提是预测燃烧过程中 SOx 和 NOx 的排放量。为此,本文开发了一个深度神经网络(DNN)模型,该模型可以预测煤炭混合时与煤炭特性相关的 SOx 和 NOx 排放量。该模型使用燃煤发电厂的现场数据进行训练,其预测 SOx 和 NOx 的平均绝对百分比误差 (MAPE) 分别为 7.1% 和 5.68%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network
Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Machines
Machines Multiple-
CiteScore
3.00
自引率
26.90%
发文量
1012
审稿时长
11 weeks
期刊介绍: Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. 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. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.
期刊最新文献
Investigative Study of the Effect of Damping and Stiffness Nonlinearities on an Electromagnetic Energy Harvester at Low-Frequency Excitations Vibration Research on Centrifugal Loop Dryer Machines Used in Plastic Recycling Processes Novel Design of Variable Stiffness Pneumatic Flexible Shaft Coupling: Determining the Mathematical-Physical Model and Potential Benefits Considerations on the Dynamics of Biofidelic Sensors in the Assessment of Human–Robot Impacts Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1