PCDDs &amp; PCDFs from the MSW incinerator

Horaguchi, Mitsuhiro; Ogawa, Hiroshi; Ose, K.; Tomisawa, S.; Matsuura, Shigeharu

doi:10.1016/0045-6535(89)90462-1

Cited by 11 publications

(3 citation statements)

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“…The flue gas contains not only conventional inorganic pollutants such as NO X , but also organic micropollutants such as dioxins and chlorinated compounds. The concentration of organic pol lutants could be reduced considerably by exhaust gas treatment processes or so-called end-of-pipe technologies such as bag filters for dust collection, removal of acidic gases through lime addition, or catalyst reactors ( − ). In a report by Kawamoto (), when the concentration of chlorobenzenes at the inlet of a bag filter was 8.7 μg m -3 N , the outlet concentration was reduced to 5.3 μg m -3 N by keeping the temperature at 150 °C.…”

Section: Introductionmentioning

confidence: 99%

Fundamental Adsorption Characteristics of Carbonaceous Adsorbents for 1,2,3,4-Tetrachlorobenzene in a Model Gas of an Incineration Plant

Inoue

Kawamoto

2005

Environ. Sci. Technol.

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Carbonaceous adsorbents such as activated carbon have been used to reduce the emission of organic pollutants from incineration plants. However, with this method, the amount and type of adsorbent to be used are based only on empirical results, which may lead to overuse of the adsorbents. The fundamental adsorption characteristics of several kinds of activated carbon, activated coke, and carbide wood were examined using 1 ,2,3,4-tetrachlorobenzene as an adsorbate. The removal performance and various equilibrium adsorption characteristics of these adsorbents were analyzed using laboratory-scale adsorption equipment. The equilibrium adsorption amount increased by a factor of 1.9-3.2 at 150 degrees C compared with that at 190 degrees C. The effect of the moisture content on adsorption capacity was relatively small in comparison with that of the temperature. The micropore volume for pore diameters of 2 nm or less was the most important factor governing the adsorption capacity for all adsorbents. Activated carbon showed superior adsorption ability compared to activated coke and carbide wood, although all adsorbents were sufficient for practical use.

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Section: Introductionmentioning

confidence: 99%

Fundamental Adsorption Characteristics of Carbonaceous Adsorbents for 1,2,3,4-Tetrachlorobenzene in a Model Gas of an Incineration Plant

Inoue

Kawamoto

2005

Environ. Sci. Technol.

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“…The emissions of a full range of polychlorinated dibenzo-pdioxins and furans (PCDDs/PCDFs) have become the most controversial issue in many incineration projects. However, earlier focus was placed upon the evaluation of the PCDD/ PCDF formation and emission control technologies (Hasselriis 1986(Hasselriis , 1987Horaguchi et al 1989; Kilgroe & Bma 1990;Hiraoka et al 1992). Recent advances, based on several emission tests for various types of municipal incinerators, have resulted in an intensive debate regarding the significance of new combustion criteria, public regulations, and health risk assessments (Environment Canada 1985, 1986, 1988, 1991Beychok 1987;Neulicht 1987;Schindler 1987;Hasberg 1989;Miles 1989;Kelly 1990;Ontario Environment 1990).…”

Section: Introductionmentioning

confidence: 99%

Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling

Chang

Chen

2000

Waste Manag Res

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The potential emissions of PCDDs/PCDFs from municipal incinerators have received wide attention in the last decade. Concerns were frequently addressed in the scienti®c community with regard to the aspects of health risk assessment, combustion criteria, and the public regulations. Without accurate prediction of PCDD/PCDF emissions, however, reasonable assessment of the health risk and essential appraisal of the combustion criteria or public regulations cannot be achieved. Previous prediction techniques for PCDD/PCDF emissions were limited by the linear models based on a least-square-based analytical framework, such that the inherent non-linear features cannot be explored via advanced system identi®cation techniques. Recent development of genetic algorithms and neural network models has resulted in a dramatic growth of the use of non-linear structure for optimization and prediction analyses. Such approaches with the inherent thinking of arti®cial intelligence were found useful in this study for the identi®cation of non-linear structure in relation to the PCDD/PCDF emissions from municipal incinerators. Examples were drawn from the emission test of PCDDs/ PCDFs through the¯ue gas discharge from several municipal incinerators in both Europe and North America. Although the neural network model may exhibit better predictive results based on the performance indexes of percentage error and mean square error, model structure cannot be directly identi®ed and expressed for illustrating the possible chemical mechanism with respect to the PCDD/PCDF emissions. However, the tree-structured genetic algorithms, or so-called genetic programming, can rapidly screen out those applicable non-linear models as well as identify the optimal system parameters simultaneously in a highly complex system based on a small set of samples.

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“…The emissions of a full range of polychlorinated dibenzo‐p‐dioxins and furans (PCDDs/PCDFs) have become the most controversial issue in many incineration projects. However, earlier focus was placed upon the evaluation of the PCDD/PCDF formation and emission control technologies ( Hasselriis 1986, 1987; Horaguchi et al . 1989 ; Kilgroe & Brna 1990; Hiraoka et al .…”

Section: Introductionmentioning

confidence: 99%

Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling

Chang

Chen

2000

Waste Man Res

View full text Add to dashboard Cite

The potential emissions of PCDDs/PCDFs from municipal incinerators have received wide attention in the last decade. Concerns were frequently addressed in the scientific community with regard to the aspects of health risk assessment, combustion criteria, and the public regulations. Without accurate prediction of PCDD/PCDF emissions, however, reasonable assessment of the health risk and essential appraisal of the combustion criteria or public regulations cannot be achieved. Previous prediction techniques for PCDD/PCDF emissions were limited by the linear models based on a least‐square‐based analytical framework, such that the inherent non‐linear features cannot be explored via advanced system identification techniques. Recent development of genetic algorithms and neural network models has resulted in a dramatic growth of the use of non‐linear structure for optimization and prediction analyses. Such approaches with the inherent thinking of artificial intelligence were found useful in this study for the identification of non‐linear structure in relation to the PCDD/PCDF emissions from municipal incinerators. Examples were drawn from the emission test of PCDDs/PCDFs through the flue gas discharge from several municipal incinerators in both Europe and North America. Although the neural network model may exhibit better predictive results based on the performance indexes of percentage error and mean square error, model structure cannot be directly identified and expressed for illustrating the possible chemical mechanism with respect to the PCDD/PCDF emissions. However, the tree‐structured genetic algorithms, or so‐called genetic programming, can rapidly screen out those applicable non‐linear models as well as identify the optimal system parameters simultaneously in a highly complex system based on a small set of samples.

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PCDDs & PCDFs from the MSW incinerator

Cited by 11 publications

References 1 publication

Fundamental Adsorption Characteristics of Carbonaceous Adsorbents for 1,2,3,4-Tetrachlorobenzene in a Model Gas of an Incineration Plant

Fundamental Adsorption Characteristics of Carbonaceous Adsorbents for 1,2,3,4-Tetrachlorobenzene in a Model Gas of an Incineration Plant

Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling

Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling

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