1. Microwave Heating Potential of Aggregates in Bituminous Mixtures: Role of Aggregate Minerology, Dielectric Loss Factor, and Power Level.
- Author
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Noojilla, Satya Lakshmi Aparna and Kusam, Sudhakar Reddy
- Subjects
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DIELECTRIC loss , *DIELECTRIC properties , *ENERGY levels (Quantum mechanics) , *THERMOGRAPHY , *FLUORESCENCE spectroscopy - Abstract
The heating potential of microwave-heated bituminous mixtures depends on the heating characteristics of aggregates. This study focused on understanding the effect of mineral, chemical, and dielectric properties of aggregates on the rate of heating of aggregates, with special emphasis on the effect of the power level of microwave energy. Properties of the aggregates collected from 18 different sources were evaluated using X-ray diffraction, X-ray fluorescence spectroscopy, and a microwave vector network analyzer. Heating studies were conducted on the aggregates using a microwave oven at four different power levels (330, 525, 630, and 700 W). Thermal images, captured using a forward-looking infrared (FLIR) thermal camera, were used to obtain the heating characteristics, such as heating time, rate and nonuniformity of heating. The rate of heating of aggregates (0.212°C/s−1.007°C/s), nonuniformity of heating (0.059°C–0.218°C), and time of heating to 100°C (40–520 s) varied widely with the source of aggregate. In general, the heating rate and the degree of nonuniformity of the aggregates increased with increase in microwave power. Bulk mineralogy and chemical composition of aggregates significantly influenced the dielectric response of aggregates (adjusted R-squared values of 0.75 and 0.70, respectively) and the sensitivity of heating parameters to microwave power (adjusted R-squared values of 0.83 and 0.92, respectively). Two different predictive models were developed for estimation of heating rate of aggregates with (1) power, density, and dielectric loss factor, and (2) power, density, and aggregate chemical indices as independent variables. The adjusted R-squared values of these two predictive models were 0.84 and 0.92, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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