On the heating rate and volatile yield for coal particles injected into fluidised bed combustors

Stubington, John F.; Sasongko, Dwiwahju

doi:10.1016/s0016-2361(98)00008-8

Cited by 18 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In accordance with previous analyses, reaction endother-Ž . micity Narayan and Antal, 1996;Di Blasi, 1996a and convective cooling, caused by the flow of volatiles products out Ž from the particle Di Blasi, 1998b;Stubington and Sasongko, . 1998;Di Blasi, 2000b , introduce significant delay in particle heating, so that the temperature remains almost constant Ž .…”

Section: Influences Of Particle Size and External Temperaturementioning

confidence: 99%

Modeling intra‐ and extra‐particle processes of wood fast pyrolysis

Blasi

2002

AIChE Journal

125

View full text Add to dashboard Cite

Section: Influences Of Particle Size and External Temperaturementioning

confidence: 99%

Modeling intra‐ and extra‐particle processes of wood fast pyrolysis

Blasi

2002

AIChE Journal

125

View full text Add to dashboard Cite

“…Stubington and Sasongko's [25] conclusion for coal pyrolysis performances at a heating rate from 2 to 150 K/s and coal particle sizes from 2 to 20 mm is that the volatile yield under fluidized bed combustor conditions is approximately the same as the volatile matter in the proximate analysis result. This infers that the present correlation Eq.…”

Section: Effects Of Temperature and Heating Ratementioning

confidence: 95%

New Correlations for Coal and Biomass Pyrolysis Performances with Coal-Biomass Type Number and Temperature

Bindar

2013

j.eng.technol.sci.

View full text Add to dashboard Cite

Abstract. The pyrolysis of coal and biomass is generally reported as the mass yield of released chemicals at various temperatures, pressures, heating rates and coal or biomass type. In this work, a new coal-biomass type number, N CT , is introduced. This number is constructed from the mass fractions of carbon, hydrogen, and oxygen in the ultimate analysis. This number is unique for each coal or biomass type. For 179 different species of coal and biomass from the literature, the volatile matter mass yield can be expressed by the second order polynomial function ln(N CT ). This unique correlation allows the effects of the temperature and heating rate on the volatile yield Y VY for coal and biomass to be empirically correlated as well. The correlation for the mass fraction of each chemical component in the released volatile matter correlation is obtained from the Y VY correlation. The weight factor for some of the components is constant for the variation of N CT , but not for others. The resulted volatile matter and yield correlations are limited to atmospheric pressure, very small particles (less than 0.212 mm) and interpreted for wire-mesh pyrolysis reactor conditions and a nitrogen gas environment.

show abstract

“…Char Dry Dry,Initial Char ash Char ash (9) In this model, the variables A tot , E a Tot , X Final , and Y i are all closed form surface expressions that were fit to the output of the pyrolysis model within C3M. These variables could be functions of heating rate, reactor temperature, reactor pressure, and particle diameter depending on what is chosen as application inputs to the UQ engine within C3M.…”

Section: Methodsmentioning

confidence: 99%

Advanced Chemistry Surrogate Model Development within C3M for CFD Modeling, Part 1: Methodology Development for Coal Pyrolysis

Essendelft

Nicoletti

et al. 2014

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

A generalized method was developed to implement complex chemistry from third-party computational coal chemistry tools within multiphase, reacting computational fluid dynamic (CFD) codes. This method involves generating response surfaces that represent the computational coal chemistry tool output and using them to build a comprehensive surrogate model which can be easily incorporated into a CFD code. In this first part of a series of work, the method was applied to coal pyrolysis of Powder River Basin coal using PC Coal Lab (PCCL) and Carbonaceous Chemistry for Computational Modeling (C3M) to generate the series of response surfaces which form the basis of the surrogate model that can be implemented in CFD. This method has the benefit that local environmental variables (such as heating rate, final pyrolysis temperature, local pressure, and particle diameter) can be incorporated as part of the chemistry model in CFD on a cell by cell, time step by time step basis which creates the potential to significantly increase the accuracy of and reduce uncertainty in modeling coal chemistry within CFD. As demonstrated with this surrogate modeling method, the modeler is alleviated of the responsibility to predetermine a heating rate and reactor temperature for pyrolysis. This particular part of the work focuses on the theory and development of the surrogate model method. The methods used to generate the response surfaces, build them into a coherent surrogate model, and estimate the thermochemical properties of pseudocomponents are discussed. Future work and publication will focus on verification within CFD, validation against experimental data, multidimensional surrogate model development, and application of the surrogate model methodology to other reaction chemistry. ■ INTRODUCTIONThe thermal pyrolysis of coal is among the most complex reaction systems in modern science. 1−8 The chemical/physical structure of coal is very complex and there is a large degree of variation within coals. Not only does coal significantly vary between ranks, it can have wide variations within the same coal seam. These variations and complexities make the task of accurately modeling coal pyrolysis difficult. Furthermore, coal pyrolysis has been shown to be quite sensitive to environmental factors such as heating rate, pressure, and ultimate temperature. 9−11 That said, coal pyrolysis remains an extremely important part of most coal-based energy conversion processes such as combustion and gasification. In recent times, several computer codes have been developed that attempt to predict bulk kinetics and gas composition during pyrolysis (e.g., CPD, FG-DVC, and PCCL). 1,7,8,12−35 These tools rely on large databases of coal experiments or structural information supplied by the user to generate kinetic expressions and gas yields. Most of the experimental information available was conducted on either wire grid experiments with a prescribed temperature profile or with a drop tube experiment. Within reason, these computational tools have proven to be useful...

show abstract

On the heating rate and volatile yield for coal particles injected into fluidised bed combustors

Cited by 18 publications

References 17 publications

Modeling intra‐ and extra‐particle processes of wood fast pyrolysis

Modeling intra‐ and extra‐particle processes of wood fast pyrolysis

New Correlations for Coal and Biomass Pyrolysis Performances with Coal-Biomass Type Number and Temperature

Advanced Chemistry Surrogate Model Development within C3M for CFD Modeling, Part 1: Methodology Development for Coal Pyrolysis

Contact Info

Product

Resources

About