Experimental Investigation of a Novel Reaction in Reduction of Sulfates by Natural Gas as a Reducing Agent
In a pilot plant scale of a fluidized bed reactor, a
reduction reaction of sodium sulfate by natural gas has been
investigated. Natural gas is applied in this study as a reductant. Feed
density, feed mass flow rate, natural gas and air flow rate
(independent parameters)and temperature of bed and CO
concentration in inlet and outlet of reactor (dependent parameters)
were monitored and recorded at steady state. The residence time was
adjusted close to value of traditional reaction [1]. An artificial neural
network (ANN) was established to study dependency of yield and
carbon gradient on operating parameters. Resultant 97% accuracy of
applied ANN is a good prove that natural gas can be used as a
reducing agent. Predicted ANN model for relation between other
sources carbon gradient (accuracy 74%) indicates there is not a
meaningful relation between other sources carbon variation and
reduction process which means carbon in granule does not have
significant effect on the reaction yield.
[1] J. H. Cameron, T.M. Grace, "Kinetic study of sulfate reduction with
carbon," Ind. Eng. Fundam. Vol. 22, 1983, pp.486-494.
[2] J. H. Cameron, T.M. Grace, "Sulfate reduction with carbon is strongly
influenced by bed surface temperature," tappi. Vol.65, no. 7, 1982,
pp.84-87.
[3] J. D. Brile, "Sulfate to sulfide reaction mechanism in pyritic material",
MS Thesis , the Ohio state university, 1993.
[4] E. Humeres, R. Moreira, M. B. Peruch, "Reduction of SO on different
carbons," Carbon. Vol. 40, 2002, pp.751-760.
[5] P. Pre, M. Hemati, B. Marchand, "Study on natural gas combustion in
fluidized beds: modelling and experimental validation," Chem. Eng. Sci.
Vol. 53, 1998, pp.2871-2883.
[6] W. Chen, M. Lin, T. Leu, S. Du, "An evaluation of hydrogen production
from the perspective of using blast furnace gas and coke oven gas as
feedstocks," Int. J. Hydrogen Energy. Vol. 36, 2011, pp.11727-11737.
[7] G. Guan, C. Fushimi, M. Ishizuka, Y. Nakamura et. al, "Flow behaviors
in the downer of a large-scale triple-bed combined circulating fluidized
bed system with high solids mass fluxes," Chem. Eng. Sci. Vol. 66,
2011, pp. 4212-4220.
[8] J. Chen, X. Lu, "Progress of petroleum coke combusting in circulating
fluidized bed boilersÔÇöA review and future perspectives," Resources,
Conservation and Recycling, Vol. 49, 2007, pp.203-216.
[9] S. Heukelman, D. Groot, "Fluidized bed roasting of micro-pelletized
zinc concentrate: Part II-Particle entrainment and residence time, The
Journal of The Southern African Institute of Mining and Metallurgy,"
Vol. 11, 2011, pp.767-772.
[10] D. Kunii, O, Levenspiel, Fluidization Engineering, 2nd edition.
Butterworth-Heinemann, Boston, 1991.
[11] J. Werther, "Measurement techniques in fluidized beds," Powder
Technology, Vol. 102,1999, pp.15-36.
[12] V. Jiradiloka, D.Gidaspow, S. Damronglerd,W. J. Kovesc, R. Mostofi,
"Kinetic theory based CFD simulation of turbulent fluidization of FCC
particles in a riser," Chem. Eng. Sci. Vol. 61,2006, pp.5544-5559.
[13] G. A. Ulrich, L Krumholz, J. M. Suflita, ÔÇÿA rapid and simple method for
estimating sulfate reduction activity and quantifying inorganic sulfides,-
Applied and Environmental Microbiology, 1997, pp. 1627-1630.
[14] Bas, D., Dudak, F.C. and Boyac─▒, I.H., "Modeling and optimization III:
Reaction rate estimation using artificial neural network (ANN) without a
kinetic model". Journal of Food Engineering, vol. 79.2007a pp.622-628.
[15] Bas, D., Dudak, F.C. and Boyac─▒, I.H., "Modeling and optimization IV:
Investigation of reaction kinetics and kinetic constants using a program
in which artificial neural network (ANN) was integrated. Journal of
Food Engineering," vol.79. 2007b. pp.1152-1158.
[16] Istadia, I. and Aminb, N.A.S., "Modelling and optimization of catalytic-
dielectric barrier discharge plasma reactor for methane and carbon
dioxide conversion using hybrid artificial neural networkÔÇögenetic
algorithm technique," Chem. Engineering Sci. vol.62, 2007 pp.6568-
658.
[17] Bishop, C.M., 1995. Neural Networks for Pattern Recognition.
CLARENDON PRESS, Oxford.
[18] Duda, R.O., Hart, P.E. and Stork, D.G., 2000. Pattern Classification.
Wiley India Pvt. Ltd.1.
[19] Kuncheva, L.L., 2004. Combining Pattern Classifiers, Methods and
Algorithms. A Wiley-Interscience publication, Hoboken, 350 pp.
[1] J. H. Cameron, T.M. Grace, "Kinetic study of sulfate reduction with
carbon," Ind. Eng. Fundam. Vol. 22, 1983, pp.486-494.
[2] J. H. Cameron, T.M. Grace, "Sulfate reduction with carbon is strongly
influenced by bed surface temperature," tappi. Vol.65, no. 7, 1982,
pp.84-87.
[3] J. D. Brile, "Sulfate to sulfide reaction mechanism in pyritic material",
MS Thesis , the Ohio state university, 1993.
[4] E. Humeres, R. Moreira, M. B. Peruch, "Reduction of SO on different
carbons," Carbon. Vol. 40, 2002, pp.751-760.
[5] P. Pre, M. Hemati, B. Marchand, "Study on natural gas combustion in
fluidized beds: modelling and experimental validation," Chem. Eng. Sci.
Vol. 53, 1998, pp.2871-2883.
[6] W. Chen, M. Lin, T. Leu, S. Du, "An evaluation of hydrogen production
from the perspective of using blast furnace gas and coke oven gas as
feedstocks," Int. J. Hydrogen Energy. Vol. 36, 2011, pp.11727-11737.
[7] G. Guan, C. Fushimi, M. Ishizuka, Y. Nakamura et. al, "Flow behaviors
in the downer of a large-scale triple-bed combined circulating fluidized
bed system with high solids mass fluxes," Chem. Eng. Sci. Vol. 66,
2011, pp. 4212-4220.
[8] J. Chen, X. Lu, "Progress of petroleum coke combusting in circulating
fluidized bed boilersÔÇöA review and future perspectives," Resources,
Conservation and Recycling, Vol. 49, 2007, pp.203-216.
[9] S. Heukelman, D. Groot, "Fluidized bed roasting of micro-pelletized
zinc concentrate: Part II-Particle entrainment and residence time, The
Journal of The Southern African Institute of Mining and Metallurgy,"
Vol. 11, 2011, pp.767-772.
[10] D. Kunii, O, Levenspiel, Fluidization Engineering, 2nd edition.
Butterworth-Heinemann, Boston, 1991.
[11] J. Werther, "Measurement techniques in fluidized beds," Powder
Technology, Vol. 102,1999, pp.15-36.
[12] V. Jiradiloka, D.Gidaspow, S. Damronglerd,W. J. Kovesc, R. Mostofi,
"Kinetic theory based CFD simulation of turbulent fluidization of FCC
particles in a riser," Chem. Eng. Sci. Vol. 61,2006, pp.5544-5559.
[13] G. A. Ulrich, L Krumholz, J. M. Suflita, ÔÇÿA rapid and simple method for
estimating sulfate reduction activity and quantifying inorganic sulfides,-
Applied and Environmental Microbiology, 1997, pp. 1627-1630.
[14] Bas, D., Dudak, F.C. and Boyac─▒, I.H., "Modeling and optimization III:
Reaction rate estimation using artificial neural network (ANN) without a
kinetic model". Journal of Food Engineering, vol. 79.2007a pp.622-628.
[15] Bas, D., Dudak, F.C. and Boyac─▒, I.H., "Modeling and optimization IV:
Investigation of reaction kinetics and kinetic constants using a program
in which artificial neural network (ANN) was integrated. Journal of
Food Engineering," vol.79. 2007b. pp.1152-1158.
[16] Istadia, I. and Aminb, N.A.S., "Modelling and optimization of catalytic-
dielectric barrier discharge plasma reactor for methane and carbon
dioxide conversion using hybrid artificial neural networkÔÇögenetic
algorithm technique," Chem. Engineering Sci. vol.62, 2007 pp.6568-
658.
[17] Bishop, C.M., 1995. Neural Networks for Pattern Recognition.
CLARENDON PRESS, Oxford.
[18] Duda, R.O., Hart, P.E. and Stork, D.G., 2000. Pattern Classification.
Wiley India Pvt. Ltd.1.
[19] Kuncheva, L.L., 2004. Combining Pattern Classifiers, Methods and
Algorithms. A Wiley-Interscience publication, Hoboken, 350 pp.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:60095", author = "Ali Ghiaseddin and Akram Nemati", title = "Experimental Investigation of a Novel Reaction in Reduction of Sulfates by Natural Gas as a Reducing Agent", abstract = "In a pilot plant scale of a fluidized bed reactor, a
reduction reaction of sodium sulfate by natural gas has been
investigated. Natural gas is applied in this study as a reductant. Feed
density, feed mass flow rate, natural gas and air flow rate
(independent parameters)and temperature of bed and CO
concentration in inlet and outlet of reactor (dependent parameters)
were monitored and recorded at steady state. The residence time was
adjusted close to value of traditional reaction [1]. An artificial neural
network (ANN) was established to study dependency of yield and
carbon gradient on operating parameters. Resultant 97% accuracy of
applied ANN is a good prove that natural gas can be used as a
reducing agent. Predicted ANN model for relation between other
sources carbon gradient (accuracy 74%) indicates there is not a
meaningful relation between other sources carbon variation and
reduction process which means carbon in granule does not have
significant effect on the reaction yield.", keywords = "reduction by natural gas, fluidized bed, sulfate,
sulfide, artificial neural network", volume = "6", number = "5", pages = "472-5", }
