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Author:

Tang, Jian (Tang, Jian.) | Qiao, Junfei (Qiao, Junfei.) (Scholars:乔俊飞) | Gu, Ke (Gu, Ke.) (Scholars:顾锞) | Yan, Aijun (Yan, Aijun.) (Scholars:严爱军)

Indexed by:

EI Scopus

Abstract:

Municipal solid waste incineration (MSWI) becomes the most popular technique to enhance environment protection. This process produces one of the most toxic chemicals in the world, i.e., polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). The dioxin (DXN) production should be restricted rigidly by using operation optimization and control of MSWI process based on present industrial devices. However, it is difficult to realize the on-line real-time continuous measuring of DXN duo to the complexity formation mechanism and high-cost long-time off-line detection approach. In this paper, a soft measuring method based on virtual sample generation (VSG) is used to address this problem at the first time. A few numbers of true training samples are used to produce virtual training samples based on feasibility-based programming (FBP) model using selective ensemble kernel partial least squares (SENKPLS) and prior knowledge. Simulation result based on dataset in reference [31] for a HL MSWI process shows effectiveness of the proposed method. © 2017 IEEE.

Keyword:

Virtual addresses Organic pollutants Least squares approximations Municipal solid waste Waste incineration Sampling

Author Community:

  • [ 1 ] [Tang, Jian]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Qiao, Junfei]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Gu, Ke]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Yan, Aijun]Faculty of Information Technology, Beijing University of Technology, Beijing, China

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Source :

Year: 2017

Volume: 2017-January

Page: 7323-7328

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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