Application of uncertainty quantification methods for coal devolatilization kinetics in gasifier modeling

Gel, Aytekin; Chaudhari, Kiran; Turton, Richard; Nicoletti, P.

doi:10.1016/j.powtec.2014.01.024

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…The results of an extensive MC uncertainty analysis provided a better understanding of how uncertainties can influence algae conversion processes cost of algae derived biodiesel production, as well as how it can affect economic viability of the algal biorefineries. A direct Monte Carlo simulation based approach has been presented to quantify uncertainty involved in devolatilization kinetics of coal gasification processes [15]. The study also identified that gasifier temperature has a strong effect on product gas yield and the devolatilization step was a source of high uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

Pan¹,

Pandey

2016

Fuel Processing Technology

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In recent years, different non-linear regression techniques using neural networks and genetic programming have been applied for data-driven modelling of fluidized bed gasification processes. However, none of these methods explicitly take into account the uncertainty of the measurements and predictions. In this paper, a Bayesian approach based on Gaussian processes is used to address this issue. This method is used to predict the syngas yield production and the lower heating value (LHV) for municipal solid waste (MSW) gasification in a fluidized bed gasifier. The model parameters are calculated using the maximum a-posteriori (MAP) estimate and compared with the Markov Chain Monte Carlo (MCMC) method. The simulations demonstrate that the Bayesian methodology is a powerful technique for handling the uncertainties in the model and making probabilistic predictions based on experimental data. The method is generic in nature and can be extended to other types of fuels as well.

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Section: Introductionmentioning

confidence: 99%

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

Pan¹,

Pandey

2016

Fuel Processing Technology

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“…[7][8][9] A direct Monte-Carlo simulation-based approach has been recently applied for uncertainty analysis of coal gasification kinetics. 10 Excellent work on UQ of multiphase CFD models has been recently published. 11,12 In the existing literature, a systematic approach to UQ of physical properties models is still scarce.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification of property models: Methodology and its application to CO₂‐loaded aqueous MEA solutions

et al. 2015

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in Wiley Online Library (wileyonlinelibrary.com) Uncertainties in property models can significantly affect the results obtained from process simulations. If these uncertainties are not quantified, optimal plant designs based on such models can be misleading. With this incentive, a systematic, generalized uncertainty quantification (UQ) methodology for property models is developed. Starting with prior beliefs about parametric uncertainties, a Bayesian method is used to derive informed posteriors using the experimental data. To reduce the computational expense, surrogate response surface models are developed. For downselecting the parameter space, a sensitivity matrix-based approach is developed. The methodology is then deployed to the property models for an MEA-CO 2 -H 2 O system. The UQ analysis is found to provide interesting information about uncertainties in the parameter space. The sensitivity matrix approach is also found to be a valuable tool for reducing computational expense. Finally, the effect of the estimated parametric uncertainty on CO 2 absorption and monoethanolamine (MEA) regeneration is analyzed.

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“…The work of Whiting (1996) provides an overview of UQ and simple examples of strategies for quantification of thermodynamic model uncertainties involving Monte Carlo simulation, regression analysis, and optimization. A Monte Carlo approach has been applied to study the sources of uncertainty in reaction kinetics of a coal devolatilization process (Gel et al, 2014). Multiple studies on UQ of multiphase computational fluid dynamics (CFD) of fluidized beds have been performed (Gel et al, 2013;Lane et al, 2014).…”

Section: Physical Property Modeling With Uncertainty Quantificationmentioning

confidence: 99%

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Morgan¹

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in Prof. Bhattacharyya's Advanced Process and Energy Systems Engineering Group, both past and current. I have enjoyed the opportunity to get to know many of you, and to have the opportunity to discuss research work and share ideas with many of you. I maintain fond memories of all of the adventures we have had outside of the work setting, particularly on travels for AIChE conferences. I would especially like to acknowledge the honor of working directly with Anderson Soares Chinen on the development of the MEA solvent model and various other projects; without his contributions, most of the work contained herein would not be possible. Similarly, I would like to thank Ben Omell for his very valuable guidance throughout the development of these process models, and for providing the results for the validation of our model with TCM data. There are many collaborators that I have been privileged to work with, and who have contributed much to the work described in this document. I would like to thank the National Carbon Capture Center (NCCC) and all of the involved staff for providing data for the 2014 and 2017 test campaigns for the MEA solvent system. I would like to thank Charles Tong from Lawrence Livermore National Laboratory for providing much assistance and advice for the uncertainty quantification phase of this work, particularly with respect to use of the PSUADE software, and for his patience while I learned to use these tools. I have also enjoyed the opportunity to collaborate with Sham Bhat and Christine Anderson-Cook from Los Alamos National vi Laboratory on the design of experiments for the 2017 NCCC test campaign, and acknowledge their contributions to the success of this project. I would like to thank Bill Buschle from the University of Edinburgh for his work on the measurement uncertainty in the solvent at NCCC, which has yielded results that have greatly improved the comparison between our MEA model and the pilot plant data. I would like to thank Prof. Gary Rochelle from the University of Texas at Austin for sharing the "Phoenix Model", which was used as a precursor to the model developed in this work, and members of his research team, specifically Brent Sherman, for being very helpful as I was starting out in the solvent modeling work. I would like to thank all involved in the high viscosity solvent model project for the opportunity to be involved with this collaborative effort. I am grateful for the CCSI and CCSI 2 research programs, and for the leadership of David Miller and Michael Matuszewski, that have made these collaborations possible. While pursuing a Ph.D. at West Virginia University, I have been privileged to be able to supplement my research with excellent coursework from within the Department of Chemical and Biomedical Engineering (CBE) and elsewhere. From CBE, I would like to acknowledge the following professors for offering excellent courses that have contributed to my research and professional development in some way: Prof. Charter Stinespring (thermodynamics and statistical and molecular t...

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Application of uncertainty quantification methods for coal devolatilization kinetics in gasifier modeling

Cited by 11 publications

References 29 publications

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

Uncertainty quantification of property models: Methodology and its application to CO₂‐loaded aqueous MEA solutions

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

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Application of uncertainty quantification methods for coal devolatilization kinetics in gasifier modeling

Cited by 11 publications

References 29 publications

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

Uncertainty quantification of property models: Methodology and its application to CO2‐loaded aqueous MEA solutions

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

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Uncertainty quantification of property models: Methodology and its application to CO₂‐loaded aqueous MEA solutions