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

Abstract

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...

Review of 30 Years of Research Using the Chemical Percolation Devolatilization Model

“…A double Gaussian activation energy model was used in conjunction with the CPD model by de Caprariis et al A pyrolysis kinetic preprocessor (PKP) was developed to use CPD, FLASHCHAIN, or FG–DVC to develop first- or second-order rate models . The PKP code was also used to compare pyrolysis predictions between these three different coal pyrolysis models that use lattice statistics. , A similar code (C3M) was developed by Van Essendelft et al to determine rate constants for simple models using complex pyrolysis and char oxidation models, including the CPD model. Zhao et al used the CPD model to develop rate constants for a two-competing-step pyrolysis model, while Richter et al used the CPD model to determine rate constants for both a two-step and n th order pyrolysis rate.…”

“…141 The PKP code was also used to compare pyrolysis predictions between these three different coal pyrolysis models that use lattice statistics. 150,151 A similar code (C3M) was developed by Van Essendelft et al 152 to determine rate constants for simple models using complex pyrolysis and char oxidation models, including the CPD model. Zhao et al 153 used the CPD model to develop rate constants for a two-competing-step pyrolysis model, while Richter et al 154 used the CPD model to determine rate constants for both a two-step and nth order pyrolysis rate.…”

Multifluid Modeling of Coal Pyrolysis in a Downer Reactor

“…A CFD simulation of pulverized coal combustion with a new coal devolatilization model, called the TDP model, has been carried out in a combustion furnace . A model based on carbonaceous chemistry for computational modeling (C3M) database has been developed and implemented into Eulerian–Eulerian CFD simulation of coal gasification and pyrolysis. − …”

CPFD Simulation of Hydrodynamics, Heat Transfer, and Reactions in a Downer Reactor for Coal Pyrolysis with Binary Particles

“…The pyrolysis reaction heat of Q reaction = −425.0 kJ/kg was obtained from experimental measurement. The reaction rate is written by where T p is the particle temperature, Mw vm is the molecular weight of volatile, X vm is the mass fraction of volatile, and X * is the minimum mass fraction of volatile in the coal. θ coal is the solid holdup of coal particles.…”