Experimental Assessment, Model Validation, and Uncertainty Quantification of a Pilot-Scale Gasifier

Sahraei, M. Hossein; Duchesne, Marc; Hughes, Robin W.; Ricardez‐Sandoval, Luis A.

doi:10.1021/acs.iecr.6b00692

Cited by 15 publications

(20 citation statements)

References 43 publications

(52 reference statements)

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“…Many sources of uncertainty can exist in a model, such as boundary conditions, modeling assumptions, or model parameters . The quantification of every single source of uncertainty present in a model is impractical, and generally, uncertainty in a few model parameters only is quantified and propagated to the model predictions. − In the case of biomass pyrolysis, although a detailed three-dimensional model has recently been developed, a one-dimensional (1D) transport model coupled with a lumped chemical kinetic scheme is most commonly used to simulate particle-scale biomass pyrolysis experiments, ,− and different sources of uncertainty can be present in these 1D particle-scale models. In this study, we focus only on the uncertainty in the transport model parameters of woody biomass and neglect the other sources of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Several UQ studies exist in the literature for different types of problems, such as combustion, fluidized bed, and coke gasifier, yet, in our knowledge, no UQ study has been performed for biomass pyrolysis. For a detailed overview of various UQ methods, readers are referred to the review articles by Najm and Wang and Sheen .…”

Section: Introductionmentioning

confidence: 99%

“…For a detailed overview of various UQ methods, readers are referred to the review articles by Najm and Wang and Sheen . In this work, a nonintrusive UQ approach − is employed, where ensemble evaluations of the model are performed to propagate the uncertainty through the model. This brute force approach requires thousands of simulations to get the statistics of the output and would become computationally intractable if the original model is used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

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On the Validation of a One-Dimensional Biomass Pyrolysis Model Using Uncertainty Quantification

Goyal

Pepiot

2018

ACS Sustainable Chem. Eng.

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Predictive modeling tools have the potential to accelerate the development and deployment of biomass thermochemical conversion. Considerable progress has been made in the modeling of biomass pyrolysis at the particle level, where chemical kinetics and transport processes are coupled. However, rigorous validation of the corresponding models is challenging because of the considerable uncertainty in the values of several biomass properties. Toward this end, we use the principles of uncertainty quantification (UQ) for a rigorous analysis of the validity of a commonly used one-dimensional wood pyrolysis model. Uncertainty in the modeling parameters of the transport processes is propagated to the simulation results of the pyrolysis model. The model predictions are compared with several detailed experimental measurements for pyrolysis of wood particles. The results show that the uncertainty in the model predictions account for some of the discrepancies with the experimental measurements, especially for the particle temperature profiles and the gas phase species production rates. Experimental targets are identified whose predictions cannot be improved by an accurate knowledge of the transport model parameters and require further improvements in the chemical kinetics model. The use of a systematic optimization technique is also demonstrated to choose the optimal values of uncertain model parameters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Validation of a One-Dimensional Biomass Pyrolysis Model Using Uncertainty Quantification

Goyal

Pepiot

2018

ACS Sustainable Chem. Eng.

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“…Sahraei et al proposed a ROM for an entrained flow gasification unit located at CanmetENERGY in Ottawa, Canada [5,6,8,9]. Their reactor network model consists of three plug-flow reactors (PFR) and two continuous-stirred tank reactors (CSTR).…”

Section: Introductionmentioning

confidence: 99%

“…As a result of not simulating the complete multiphase dynamics of the system, to accurately represent the gasifier, a ROM requires a posteriori knowledge of the geometry of the solid flow inside the reactor, of which the solid jet half-angle has a significant impact. Sahraei et al proposed a ROM for an entrained flow gasification unit located at CanmetENERGY in Ottawa, Canada [5,6,8,9]. Their reactor network model consists of three plug-flow reactors (PFR) and two continuous-stirred tank reactors (CSTR).…”

Section: Introductionmentioning

confidence: 99%

Dry Fuel Jet Half-Angle Measurements and Correlation for an Entrained Flow Gasifier

et al. 2018

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Reduced order models (ROMs) are increasingly applied to entrained flow gasification development due to reduced computational requirements relative to computational fluid dynamics (CFD) models. However, they require greater a posteriori knowledge of the reactor physics. A significant parameter influencing ROM outputs is the jet half-angle of the solid fuel and oxidant mixture in the gasifier. Thus, it is important to understand the geometry of the jet in the gasifier, and how it is dependent on operating parameters, such as solid and carrier gas flow rates. In this work, an existing model for jet half-angles, which considers the ratio of surrounding gas density to jet core density, is extended to a dry solids jet with impinging gas. The model is fitted to experimental jet half-angles. The jet half-angle of a non-reactive flow was measured using laser-sheet imaging for solid fluxes in the range of 460–880 kg/m2·s and carrier gas fluxes in the range of 43–90 kg/m2·s at the transport line outlet. Jet half-angles ranged from 5.6° to 11.3°, increasing with lower solid/gas loading ratios. CFD simulations of two reactive conditions, with solid and gas fluxes similar to experiments, were used to test the applicability of the proposed jet half-angle model.

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Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

2023

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A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

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Experimental Assessment, Model Validation, and Uncertainty Quantification of a Pilot-Scale Gasifier

Cited by 15 publications

References 43 publications

On the Validation of a One-Dimensional Biomass Pyrolysis Model Using Uncertainty Quantification

On the Validation of a One-Dimensional Biomass Pyrolysis Model Using Uncertainty Quantification

Dry Fuel Jet Half-Angle Measurements and Correlation for an Entrained Flow Gasifier

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

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