Deep learning‐based tomographic imaging of ECT for characterizing particle distribution in circulating fluidized bed

电容层析成像 流量(数学) 卷积神经网络 粒子(生态学) 断层摄影术 人工智能 机械 材料科学 计算机科学 模拟 物理 电容 地质学 光学 海洋学 电极 量子力学
作者
Jian Li,Zheng Tang,Biao Zhang,Chuanlong Xu
出处
期刊:Aiche Journal [Wiley]
卷期号:69 (5) 被引量:5
标识
DOI:10.1002/aic.18055
摘要

Abstract The gas and solids in a circulating fluidized bed (CFB) are heterogeneously dispersed and a multiscale flow regime may form both in time and space. Accurate measurement of the fluidizing process is significant for investigating the multiscale gas–solid flow characteristics and the design, optimization, and control of CFBs in various applications. This article develops a deep learning‐based tomographic imaging of electrical capacitance tomography (ECT) to characterize the particle concentration distribution in a CFB. The deep tomographic imaging approach is realized through training a well‐designed convolutional neural network (CNN) with the numerically built dataset. Simulation results demonstrate that the average values of the relative image errors reconstructed by CNN in the test set are 0.1110 and 0.1114 for the 60 and 100 mm pipes, respectively, which are better than the average values of 0.1819 and 0.2519 by the Landweber algorithm. With the verification of the trained model based on the prepared data can image the unseen typical flow patterns better than Landweber, it is further used to investigate the particle flow characteristics of a lab‐scale CFB. Experimental results reveal that the developed deep tomographic imaging of ECT can successfully measure the fluidized particle distribution in both the 60 and 100 mm pipes, showing good prediction and generality of the designed CNN model. A flow regime transformation from “annular” flow to “core‐annular” flow and pneumatic conveying is observed under the tested conditions. Besides, the flow regime would be highly affected by the fluidized gas flow rate and the initial bed height.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
Jasper应助wch采纳,获得10
1秒前
科研通AI2S应助科研通管家采纳,获得10
1秒前
大模型应助科研通管家采纳,获得10
1秒前
星辰大海应助科研通管家采纳,获得10
1秒前
科研通AI2S应助科研通管家采纳,获得10
1秒前
小马甲应助科研通管家采纳,获得10
1秒前
orange9完成签到,获得积分10
2秒前
3秒前
快去睡觉发布了新的文献求助10
4秒前
顾矜应助wz采纳,获得10
5秒前
orange9发布了新的文献求助10
5秒前
6秒前
liuyiduo完成签到,获得积分10
6秒前
7秒前
Thi发布了新的文献求助20
7秒前
7秒前
8秒前
马克董发布了新的文献求助10
9秒前
yyy关闭了yyy文献求助
10秒前
Dave发布了新的文献求助10
10秒前
xiaoluuu发布了新的文献求助10
11秒前
科研剧中人完成签到,获得积分0
12秒前
狂野抽屉发布了新的文献求助10
13秒前
丘比特应助倪好采纳,获得10
14秒前
方远锋完成签到,获得积分10
14秒前
14秒前
15秒前
一切顺利发布了新的文献求助10
16秒前
英俊的铭应助快去睡觉采纳,获得10
18秒前
大方谷梦完成签到 ,获得积分10
19秒前
19秒前
20秒前
无花果应助xiaoluuu采纳,获得10
23秒前
超级的鞅发布了新的文献求助10
24秒前
⊙▽⊙完成签到,获得积分10
24秒前
研友_r8YKvn完成签到,获得积分10
25秒前
Lin完成签到,获得积分20
25秒前
可爱的函函应助清神安采纳,获得10
25秒前
曾经的冥幽完成签到,获得积分10
25秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Chen Hansheng: China’s Last Romantic Revolutionary 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi 400
Classics in Total Synthesis IV 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3148089
求助须知:如何正确求助?哪些是违规求助? 2799137
关于积分的说明 7833616
捐赠科研通 2456348
什么是DOI,文献DOI怎么找? 1307222
科研通“疑难数据库(出版商)”最低求助积分说明 628086
版权声明 601655