4.6 Article

Prediction of Size Distribution and Mass Concentration of Smoke Particles on Moisture Content and Combustion Period from Para Rubber Wood Burning

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APPLIED SCIENCES-BASEL
卷 11, 期 12, 页码 -

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MDPI
DOI: 10.3390/app11125649

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response surface; para rubber wood; MMAD; size distribution; prediction

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This study investigated the size distribution and total particle mass concentration of smoke particles from para rubber wood combustion in the RSS process. It found that the moisture content and combustion period of the wood have a direct effect on the temperature data above the fire base, as well as the TPMC and MMAD values. By using the response surface method and different samplers, the study confirmed that the second-degree model was more accurate in predicting TPMC and MMAD values based on moisture content in each combustion period.
The size distribution and total particle mass concentration (TPMC) of smoke particles from para rubber wood (Hevea brasiliensis) combustion in the ribbed smoked sheet (RSS) process were studied. In this experiment, temperature data values of para rubber wood combustion were recorded at 500 mm above the base of the fire by K-type thermocouples. The wood moisture content and wood combustion period were used to find and improve an equation of smoke particle size distribution (SPSD) and TPMC by the response surface method (RSM). An eight-stage Andersen air sampler and a high-volume sampler were used to measure and calculate SPSD and TPMC, respectively. Resulting data in this experiment showed that TPMC ranged from 3.12 to 77.42 mg/m(3). SPSD was single mode in which MMAD, mass median aerodynamic diameter, ranged from 0.64 to 1.27 microns for para wood with moisture content ranging from 31.5 to 89.7% dry weight basis. The combustion period and moisture content of para wood have a direct effect on the change of temperature data above the base of the fire and the TPMC and MMAD values. For predicting TPMC and MMAD values by the para wood moisture contents in each combustion period, the results found that the second-degree model was a better plot than the first-degree model, confirmed by higher values of the coefficient of determination (R-2).

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