Diagnostic analysis of a small-scale incinerator by the Garson index

Jeng Chung Chen, Wei Hsin Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The formation of PCDD/Fs (dioxins/furans) due to incomplete combustion in solid waste incinerators has caused tremendous public concern. Consequently, more stringent standards for combustion and emission control have been implemented in order to mitigate the formation of these substances. This change in regulations will inevitably result in shutting down many small-scale incinerators because of the expense incurred in retrofitting such systems. Yet there is still an acute need for building small-scale incinerators for the purposes of disease control, environmental sanitation, and financial savings in rural areas and remote communities. For this reason, it is still worthwhile to pursue an optimal management strategy for small-scale incinerators. Through using the Garson index derived by a neural networks model, we can identify which operating factor is the one most influential to combustion status. Research findings clearly indicate that supplementing the auxiliary fuel via an on/off control unit is not an ideal method of maintaining a stable combustion evidenced by its relatively lower Garson index. Therefore the control of the auxiliary fuel system must be properly upgraded in order to improve its handling of the combustion unit. The results also show that the amount of waste in batch-charging and the lowest temperature of the primary chamber during the previous feeding are critical operating factors in this type of incinerator; controlling the charging amount per each feed around 30 kg is optimal for mitigating the variance of combustion status in the small-scale incinerator.

Original languageEnglish
Pages (from-to)4560-4570
Number of pages11
JournalInformation sciences
Volume178
Issue number23
DOIs
Publication statusPublished - 2008 Dec 1

Fingerprint

Refuse incinerators
Combustion
Diagnostics
Charging (furnace)
Disease control
Sanitation
Fuel systems
Unit
Retrofitting
Emission control
Solid wastes
Neural Network Model
Acute
Batch
Neural networks

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this

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abstract = "The formation of PCDD/Fs (dioxins/furans) due to incomplete combustion in solid waste incinerators has caused tremendous public concern. Consequently, more stringent standards for combustion and emission control have been implemented in order to mitigate the formation of these substances. This change in regulations will inevitably result in shutting down many small-scale incinerators because of the expense incurred in retrofitting such systems. Yet there is still an acute need for building small-scale incinerators for the purposes of disease control, environmental sanitation, and financial savings in rural areas and remote communities. For this reason, it is still worthwhile to pursue an optimal management strategy for small-scale incinerators. Through using the Garson index derived by a neural networks model, we can identify which operating factor is the one most influential to combustion status. Research findings clearly indicate that supplementing the auxiliary fuel via an on/off control unit is not an ideal method of maintaining a stable combustion evidenced by its relatively lower Garson index. Therefore the control of the auxiliary fuel system must be properly upgraded in order to improve its handling of the combustion unit. The results also show that the amount of waste in batch-charging and the lowest temperature of the primary chamber during the previous feeding are critical operating factors in this type of incinerator; controlling the charging amount per each feed around 30 kg is optimal for mitigating the variance of combustion status in the small-scale incinerator.",
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Diagnostic analysis of a small-scale incinerator by the Garson index. / Chen, Jeng Chung; Chen, Wei Hsin.

In: Information sciences, Vol. 178, No. 23, 01.12.2008, p. 4560-4570.

Research output: Contribution to journalArticle

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