The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A Simple Method for Estimating f(E) and k0(E) in the Distributed Activation Energy Model
Abstract
A new method that is simpler and more accurate than the author's previous method is presented for estimating f(E) and k 0 (E) in the distributed activation energy model. It utilizes at least three sets of experimental data obtained at different heating rates, a . The Arrhenius plot of ln( a/T 2 ) vs 1 /T ( T: temperature) at the same conversion level allows determination of both E and k 0 at that level by introducing an approximation. f(E) and k 0 (E), unique to the reaction system of concern, can then be obtained by changing the conversion level. The validity and accuracy of this new method are shown through model calculations. The method does not require a priori assumptions for f(E) and k 0 (E) . The f(E) curves and the k 0 vs E relationships for the pyrolysis reactions of Argonne premium coals estimated by this method were successfully used to predict the weight loss curves at markedly different heating conditions.
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Cites
Kinetic Parameters from Thermogravimetric Data
COATS, A. W., & REDFERN, J. P. (1964) · Nature · doi:10.1038/201068a0 · Cited by 6,930
Coal devolatilization and hydrogastification
Anthony, D. B., & Howard, J. B. (1976) · AIChE Journal · doi:10.1002/aic.690220403 · Cited by 687
General model of coal devolatilization
Solomon, P. R., Hamblen, D. G., Carangelo, R. M., Serio, M. A., & Deshpande, G. V. (1988) · Energy & Fuels · doi:10.1021/ef00010a006 · Cited by 514
Chemical model of coal devolatilization using percolation lattice statistics
Grant, D. M., Pugmire, R. J., Fletcher, T. H., & Kerstein, A. R. (1989) · Energy & Fuels · doi:10.1021/ef00014a011 · Cited by 470
Braun, R. L., & Burnham, A. K. (1987) · Energy & Fuels · doi:10.1021/ef00002a003 · Cited by 378
Miura, K. (1995) · Energy & Fuels · doi:10.1021/ef00050a014 · Cited by 369
Self-citationA theory of the irreversible electrical resistance changes of metallic films evaporated in vacuum
Vand, V. (1943) · Proceedings of the Physical Society · doi:10.1088/0959-5309/55/3/308 · Cited by 312
Product Composition and Kinetics of Lignite Pyrolysis
Suuberg, E. M., Peters, W. A., & Howard, J. B. (1978) · Industrial & Engineering Chemistry Process Design and Development · doi:10.1021/i260065a008 · Cited by 266
Pyrolysis kinetics for lacustrine and marine source rocks by programmed micropyrolysis
Braun, R. L., Burnham, A. K., Reynolds, J. G., & Clarkson, J. E. (1991) · Energy & Fuels · doi:10.1021/ef00025a033 · Cited by 121
Pyrolysis of Argonne premium coals: activation energy distributions and related chemistry
Burnham, A. K., Oh, M. S., Crawford, R. W., & Samoun, A. M. (1989) · Energy & Fuels · doi:10.1021/ef00013a008 · Cited by 101
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Wang, S., Dai, G., Yang, H., & Luo, Z. (2017) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2017.05.004 · Cited by 2,595
A comprehensive review on the pyrolysis of lignocellulosic biomass
Dhyani, V., & Bhaskar, T. (2018) · Renewable Energy · doi:10.1016/j.renene.2017.04.035 · Cited by 1,545
White, J. E., Catallo, W. J., & Legendre, B. L. (2011) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2011.01.004 · Cited by 1,232
Mineral protection regulates long-term global preservation of natural organic carbon
Hemingway, J. D., Rothman, D. H., Grant, K. E., Rosengard, S. Z., Eglinton, T. I., Derry, L. A., & Galy, V. V. (2019) · Nature · doi:10.1038/s41586-019-1280-6 · Cited by 785
Reaction mechanisms and multi-scale modelling of lignocellulosic biomass pyrolysis
Anca-Couce, A. (2016) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2015.10.002 · Cited by 664
Global kinetic analysis of complex materials
Burnham, A. K., & Braun, R. L. (1999) · Energy & Fuels · doi:10.1021/ef9800765 · Cited by 585
Cai, J., Wu, W., & Liu, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.04.052 · Cited by 347
Kinetic studies on the pyrolysis of chitin and chitosan
Wanjun, T., Cunxin, W., & Donghua, C. (2005) · Polymer Degradation and Stability · doi:10.1016/j.polymdegradstab.2004.08.006 · Cited by 324
Wang, B., Xu, F., Zong, P., Zhang, J., Tian, Y., & Qiao, Y. (2019) · Renewable Energy · doi:10.1016/j.renene.2018.08.021 · Cited by 129
Correlations of kinetic parameters in biomass pyrolysis with solid residue yield and lignin content
Hashimoto, K., Hasegawa, I., Hayashi, J., & Mae, K. (2011) · Fuel · doi:10.1016/j.fuel.2010.08.023 · Cited by 54
Zhang, Y., He, Z. B., Xue, L., Chu, D. M., & Mu, J. (2016) · RSC Advances · doi:10.1039/c5ra18068f · Cited by 30
Chen, T., Wu, W., Wu, J., Cai, J., & Wu, J. (2016) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-016-5649-6 · Cited by 23
Metadata
- Authors
- Miura, K., & Maki, T.
- Venue
- Energy & Fuels
- Publisher
- American Chemical Society (ACS)
- Published
- 1998-07-11
- Volume
- 12
- Issue
- 5
- Pages
- 864-869
- ISSN
- 0887-0624 (ISSN-L)
- 1520-5029
- OpenAlex
- W2009093385
- Cited by
- 502
- References
- 19
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- closed
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