• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Zhang, Runyu (Zhang, Runyu.) | Tang, Jian (Tang, Jian.) (学者:汤健) | Xia, Heng (Xia, Heng.) | Pan, Xiaotong (Pan, Xiaotong.) | Yu, Wen (Yu, Wen.) | Qiao, Junfei (Qiao, Junfei.)

收录:

EI Scopus SCIE

摘要:

Carbon monoxide (CO) is a toxic gas emitted during municipal solid waste incineration (MSWI). Its emission prediction is conducive to pollutant reduction and optimized control of MSWI. The variables of MSWI exhibit redundant and interdependent correlations with CO emissions. Furthermore, the mapping relationship is difficult to characterize. Therefore, the work proposed a CO emission prediction method based on reduced depth features and long short-term memory (LSTM) optimization. The particle design for reduced depth feature and LSTM optimization was initially developed-incorporating an adaptive threshold range for feature selection based on the inherent characteristics of modeling data. Secondly, the nonlinear depth features were extracted using ultra-one-dimensional convolution and subsequently fed into an LSTM model for prediction construction. The hyperparameters of the convolutional layer and LSTM were updated based on the loss function. The generalization performance of the model was used as the fitness function of the optimization. Finally, the particle swarm optimization (PSO) was used to adaptively reduce depth features and model's hyperparameters. The rationality and effectiveness of the proposed method were validated using the benchmark dataset and CO dataset of MSWI. R2 of the testing datasets for RB and CO were 0.9097 +/- 3.64E-04 and 0.7636 +/- 3.19E-03, respectively, by repeating 30 times.

关键词:

Particle swarm optimization (PSO) Reduced depth features Long short-term memory (LSTM) Municipal solid waste incineration (MSWI) Emission concentration of carbon monoxide

作者机构:

  • [ 1 ] [Zhang, Runyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Xia, Heng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Pan, Xiaotong]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Zhang, Runyu]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 7 ] [Tang, Jian]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 8 ] [Xia, Heng]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 9 ] [Pan, Xiaotong]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 10 ] [Qiao, Junfei]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 11 ] [Yu, Wen]Natl Polytech Inst, Dept Control Automat CINVESTAV IPN, Mexico City 07360, Mexico

通讯作者信息:

  • 汤健

    [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Tang, Jian]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China

电子邮件地址:

查看成果更多字段

相关关键词:

来源 :

NEURAL COMPUTING & APPLICATIONS

ISSN: 0941-0643

年份: 2024

6 . 0 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 8

SCOPUS被引频次: 8

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

归属院系:

在线人数/总访问数:540/4931111
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司