A Reduced Order Model for the Design of Oxy-Coal Combustion Systems

Rowan, Steven L.; Gutierrez, Albio D.; Vargas, Jose Escobar

doi:10.1155/2015/943568

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Rowan et al 99 developed a reduced-order model of a boiler, reducing the need for complex computational fluid dynamics. The CPD model was used for pyrolysis.…”

Section: Energy and Fuelsmentioning

confidence: 99%

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

Fletcher

2019

Energy Fuels

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The chemical percolation devolatilization (CPD) model for coal pyrolysis was first published in 1989, and a completed version that included the vapor–liquid equilibrium model and cross-linking model was published in 1992. The CPD model was one of three pyrolysis models developed using a lattice model to account for the chemical structure of the coal and was directly based on solid-state 13C nuclear magnetic resonance (NMR) measurements of the coal structure. A correlation of coal structure parameters measured by NMR spectroscopy was performed to permit use of the CPD model to determine pyrolysis rates and yields of tars and light gases for any coal type. A separate nitrogen release model was also developed on the basis of the chemical structure. In the past 30 years, the CPD model or the concepts in the CPD model have been used to describe pyrolysis in many situations for many fuels. The CPD model has been incorporated directly into simulations of large coal combustors as well as detailed simulations of single-pyrolyzing or burning coal particles, which was the original intent. Some investigators added a more rigorous treatment of light gas release. Other investigators have used the CPD model to determine rate coefficients for simpler models for a given range of heating conditions. In addition, the concepts in the CPD model have been used to develop models for other solid fuels, including biomass, black liquor, oil shale, rigid foams, propellants, heavy oil, asphalt, and scrap tires. The CPD model has also been extended to low heating rates for underground coal thermal treatment and hydropyrolysis. This paper is a review of the development, improvement, and uses of the CPD model, along with extended uses of the concepts in the CPD model.

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“…Rowan et al 99 developed a reduced-order model of a boiler, reducing the need for complex computational fluid dynamics. The CPD model was used for pyrolysis.…”

Section: Energy and Fuelsmentioning

confidence: 99%

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

Fletcher

2019

Energy Fuels

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“…Concerning the combustion process, machine learning techniques applications have been used for over two decades [19]. For instance, a reduced order model based on CFD simulations results for oxy-coal combustion enabled the estimation of the average outlet temperature of the burnt gases for a given fuel and oxidant mass flow rates, and also to determine the inlet mass flow rate required to obtain the desired temperature [20]. In a different application, a non-intrusive reduced order model has been applied for an unstable flow using a proper orthogonal decomposition combined with a feed-forward neural network approach [21].…”

Section: Introductionmentioning

confidence: 99%

Reduced order model of diffusion flames based on multi-scale data from detailed CFD: the impact of preprocessing

Lopes Junqueira,

da Costa Ramos,

Figueira da Silva

2024

J Braz. Soc. Mech. Sci. Eng.

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Machine learning techniques, such as reduced order models (ROM), have demonstrated low cost when creating models of complex systems while aiming at the same accuracy as high fidelity models, such as Computational Fluid Dynamics (CFD). Here, ROM are created using data from CFD simulations of nonpremixed laminar flames with detailed chemistry and transport. The data obtained for variable fuel velocity are reduced using singular value decomposition (SVD) and then a genetic aggregation response surface algorithm is applied to predict the properties fields for an arbitrary velocity. This work analyzes the effect of different data preprocessing approaches on the ROM, i.e., (1) the properties treated as a uncoupled or as a coupled system, (2) normalization of different properties, and (3) the logarithm of the chemical species. ROM of Diffusion Flames: The Impact of preprocessing For all constructed ROM, the energy content of the reduction process and the reconstructed fields of the flame properties evidence the slow convergence of SVD modes for the uncoupled ROM, while a faster one is seen when the logarithm preprocessing is applied. Also, the learning is shown to be achieved with a smaller number of modes for two of the coupled ROM and the ROM using the logarithm. The reconstruction of the mass fraction fields is characterized by regions of negative values, underscoring that the baseline ROM methodology does not preserve the properties monotonicity, positivity and boundedness. The proposed logarithm preprocessing enables to overcome such problems and to accurately reproduce the original data.

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“…Concerning the combustion process, machine learning techniques applications have been used for over a decade, as highlighted in the reviews of (Kalogirou, 2003;Ananthkrishnan et al, 2005). Chakravarthy et al (2015) built a reduced order model using CFD simulations for oxy-coal combustion. Where was possible to quickly estimate the average outlet temperature of the burned gases for a given fuel and oxidant mass flow rates.…”

Section: Introductionmentioning

confidence: 99%

The influence of the learning data on the reduced order model of laminar non-premixed flames

Junqueira¹,

Silva²,

Ramos³

et al. 2021

Proceedings of the 26th International Congress of Mechanical Engineering

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Computational fluid dynamics (CFD) is often applied to the study of combustion, enabling to optimize the process and control the emission of pollutants. This numerical methodology enables the analysis of different flame properties, such as the components of velocity, temperature, and mass fractions of chemical species. However, reproducing the behavior observed in engineering problems requires a high computational cost associated with memory and simulation time. Reduced order model (ROM) is a machine learning technique that has been applied to several engineering applications, aiming to develop models for complex systems with reduced computational cost. In this way, a high-fidelity model of complex systems is created from available data to learn its behavior and its main characteristics. In this work, different ROMs are created using CFD simulation data. The CFD model solves the mass, species, energy, and momentum conservation equations for a methane/air laminar diffusion flame, stabilized on the Gülder burner. Chemistry is modeled using a 19-species skeletal chemical kinetic mechanism. The static reduced order model uses the singular value decomposition (SVD) algorithm to decompose the CFD data and obtain the system's modes. Then, genetic aggregation response surface interpolation is applied on the higher SVD modes, creating the static ROM. This work analyzes the effect of different data preprocessing approaches on the ROM. The first analysis is the impact of reducing the number of learning data points, showing that this decrease does not directly impact the energy of the SVD modes, but, in the reconstruction field is possible to notice a degradation of the reconstruction. The second analysis is related to the effect of creating a ROM for each uncoupled flame property or treating the properties as a coupled system. The results of the coupled and uncoupled reduced order models are quite similar in terms of properties field reconstruction. However, in the energy analysis the coupled ROM converges rapidly, similarly to the uncoupled temperature ROM, while the uncoupled chemical species ROMs have a slower convergence.

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A Reduced Order Model for the Design of Oxy-Coal Combustion Systems

Cited by 5 publications

References 12 publications

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

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

Reduced order model of diffusion flames based on multi-scale data from detailed CFD: the impact of preprocessing

The influence of the learning data on the reduced order model of laminar non-premixed flames

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