Rapid coal devolatilization as an equilibrium flash distillation

Niksa, Stephen

doi:10.1002/aic.690340509

Cited by 86 publications

(56 citation statements)

References 22 publications

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“…There are two main ways of modeling coal devolatilization: global models and network models. Network models, such as the chemical percolation devolatilization (CPD) model [2,3], FLASHCHAIN [4] model, and the FG-DVC model [5], have been shown to be very accurate in their predictions of devolatilization behavior, however, they are computationally complex [6]. The computational complexity of these network models directly impacts the amount of time required to run complex simulations [7], and hence some simulations use simple global devolatilization models instead of the complex models.…”

Section: Introductionmentioning

confidence: 99%

A comparison of simple global kinetic models for coal devolatilization with the CPD model

Richards

Fletcher

2016

Fuel

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Simulations of coal combustors and gasifiers generally cannot incorporate the complexities of advanced pyrolysis models, and hence there is interest in evaluating simpler models over ranges of temperature and heating rate that are applicable to the furnace of interest. In this paper, six different simple model forms are compared to predictions made by the Chemical Percolation Devolatilization (CPD) model. The model forms included three modified one-step models, a simple two-step model, and two new modified two-step models. These simple model forms were compared over a wide range of heating rates (5  10 3 K/s to 10 6 K/s) at final temperatures up to 1600 K. Comparisons were made of total volatiles yield as a function of temperature, as well as the ultimate volatiles yield.Advantages and disadvantages for each simple model form are discussed. A modified two-step model with distributed activation energies seems to give the best agreement with CPD model predictions (with the fewest tunable parameters).

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Section: Introductionmentioning

confidence: 99%

A comparison of simple global kinetic models for coal devolatilization with the CPD model

Richards

Fletcher

2016

Fuel

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“…Li and Eisermann both cite Hyman and Kay as the source of this information. 60 (35) If one were to assume that Hyman and Kay reported their units as calories per gram rather than Btu per pound, one obtains eq 34. Even if corrected, the data was taken for the condensed phase rather than the vapor phase and cannot be used in this case.…”

Section: Industrial and Engineering Chemistry Researchmentioning

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.

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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...

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“…The required reaction rate parameters were estimated from gas phase kinetics or treated as adjustable inputs to achieve agreement between model predictions and experimental data. Niksa andKerstein (1986, 1987) also assumed that coal structure can be modeled as a polymeric network. They described the devolatilization chemistry with four global chemical reactions: Formation of monomers by bridge dissociation Tar and char formation by monomer decomposition and recombination, respectively Gas formation from peripheral groups The model predicts very high conversion of the polymeric structure into monomers (up to 96%), requires a molecular weight for the monomer as high as 1,400 amu, and assigns a single molecular weight for the tar, equal to one-half the molecular weight of the monomer.…”

Section: Introductionmentioning

confidence: 99%

Rationalization for the molecular weight distributions of Coal pyrolysis liquids

1990

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A model for coal thermal depolymerization is described and used to predict molecular weight distributions (MWD) of primary coal pyrolysis liquids. The model assumes that rupture of kinetically similar bonds in the coal is the preferred pathway for release of oligomers which ultimately become tars or extractables. The analysis provides the liquids MWD as a probability function of finding different oligomers composed of whole integer numbers of monomers and it is in excellent qualitative agreement with measured MWDs of coal pyrolysis liquids. The predicted MWDs are of the form of the gamma distribution function (GDF), and its parameters are interpreted in terms of the postulated chemistry of coal thermal depolymerization. Consequently, successful empirical correlations of experimental data on coal pyrolysis liquids MWDs with the GDF are better understood. Thus a protocol is provided to predict MWDs of pyrolysis liquids from knowledge of coal molecular structure.

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Rapid coal devolatilization as an equilibrium flash distillation

Cited by 86 publications

References 22 publications

A comparison of simple global kinetic models for coal devolatilization with the CPD model

A comparison of simple global kinetic models for coal devolatilization with the CPD model

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

Rationalization for the molecular weight distributions of Coal pyrolysis liquids

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