促进剂对气固流化床动力学的影响——统计与人工神经网络方法

Y.K. Mohanty , K.C. Biswal , G.K. Roy , B.P. Mohanty
{"title":"促进剂对气固流化床动力学的影响——统计与人工神经网络方法","authors":"Y.K. Mohanty ,&nbsp;K.C. Biswal ,&nbsp;G.K. Roy ,&nbsp;B.P. Mohanty","doi":"10.1016/j.cpart.2007.09.002","DOIUrl":null,"url":null,"abstract":"<div><p>In this study, a bubbling fluidized bed column, 99<!--> <!-->mm in inside diameter and 960<!--> <!-->mm in height, was used to investigate the effect of rod and disc promoters on fluctuation and expansion ratios. Factorial design (statistical approach) and artificial neural network (ANN) models were developed to predict the fluctuation and expansion ratios in this gas–solid fluidized bed with varying gas flow rates, bed heights, particle sizes and densities. The fluctuation and expansion predicted using these statistical and ANN models, for beds with and without promoters, were found to agree well with corresponding experiments. The statistical model was found to be superior to the ANN model due to its ability to take into account both individual and interactive effects. The rod promoters were found to be more effective in reducing bed fluctuation, and in increasing bed expansion at high gas mass velocities.</p></div>","PeriodicalId":100239,"journal":{"name":"China Particuology","volume":"5 6","pages":"Pages 401-407"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cpart.2007.09.002","citationCount":"5","resultStr":"{\"title\":\"Effect of promoters on dynamics of gas–solid fluidized bed—Statistical and ANN approaches\",\"authors\":\"Y.K. Mohanty ,&nbsp;K.C. Biswal ,&nbsp;G.K. Roy ,&nbsp;B.P. Mohanty\",\"doi\":\"10.1016/j.cpart.2007.09.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this study, a bubbling fluidized bed column, 99<!--> <!-->mm in inside diameter and 960<!--> <!-->mm in height, was used to investigate the effect of rod and disc promoters on fluctuation and expansion ratios. Factorial design (statistical approach) and artificial neural network (ANN) models were developed to predict the fluctuation and expansion ratios in this gas–solid fluidized bed with varying gas flow rates, bed heights, particle sizes and densities. The fluctuation and expansion predicted using these statistical and ANN models, for beds with and without promoters, were found to agree well with corresponding experiments. The statistical model was found to be superior to the ANN model due to its ability to take into account both individual and interactive effects. The rod promoters were found to be more effective in reducing bed fluctuation, and in increasing bed expansion at high gas mass velocities.</p></div>\",\"PeriodicalId\":100239,\"journal\":{\"name\":\"China Particuology\",\"volume\":\"5 6\",\"pages\":\"Pages 401-407\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.cpart.2007.09.002\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"China Particuology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1672251507001261\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Particuology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1672251507001261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

本文采用内径99 mm、高960 mm的鼓泡流化床塔,研究了棒式和盘式促进剂对波动比和膨胀比的影响。利用析因设计(统计方法)和人工神经网络(ANN)模型,预测了不同气体流速、床高、颗粒大小和密度下气固流化床的波动和膨胀率。用统计模型和人工神经网络模型预测的有启动子和没有启动子的床的波动和膨胀与相应的实验结果吻合得很好。统计模型被发现优于人工神经网络模型,因为它能够考虑到个体和相互作用的影响。在高气体质量速度下,杆状促进剂在减小床层波动和增加床层膨胀方面更为有效。
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
Effect of promoters on dynamics of gas–solid fluidized bed—Statistical and ANN approaches

In this study, a bubbling fluidized bed column, 99 mm in inside diameter and 960 mm in height, was used to investigate the effect of rod and disc promoters on fluctuation and expansion ratios. Factorial design (statistical approach) and artificial neural network (ANN) models were developed to predict the fluctuation and expansion ratios in this gas–solid fluidized bed with varying gas flow rates, bed heights, particle sizes and densities. The fluctuation and expansion predicted using these statistical and ANN models, for beds with and without promoters, were found to agree well with corresponding experiments. The statistical model was found to be superior to the ANN model due to its ability to take into account both individual and interactive effects. The rod promoters were found to be more effective in reducing bed fluctuation, and in increasing bed expansion at high gas mass velocities.

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
自引率
0.00%
发文量
0
期刊最新文献
Editorial Board Effect of promoters on dynamics of gas–solid fluidized bed—Statistical and ANN approaches Effects of fibrous fillers on friction and wear properties of polytetrafluoroethylene composites under dry or wet conditions Investigation on the mechanical properties of sintered porous copper compacts Large-scale distribution of elements in Chinese aerosol
×
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