Bayesian estimation of parametric uncertainties, quantification and reduction using optimal design of experiments for CO2 adsorption on amine sorbents

Kalyanaraman, Jayashree; Fan, Yanfang; Labreche, Ying; Lively, Ryan P.; Kawajiri, Yoshiaki; Park, Jongwoo

doi:10.1016/j.compchemeng.2015.04.028

Cited by 29 publications

(19 citation statements)

References 53 publications

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“…Thus Kennedy and O'Hagan [19] provide a fully Bayesian framework to calibrate global model parameters θ, quantify model inadequacy (i.e., systematic bias), and arXiv:1912.06269v1 [math.OC] 12 Dec 2019 make probabilistic predictions. This is an extremely powerful and flexible approach that has been used in diverse fields such as computational fluid dynamics [23], electrochemical energy storage [24], and CO 2 capture [25], [26], [27], [28].…”

Section: Statisticians Kennedy and O'haganmentioning

confidence: 99%

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Learning and Optimization with Bayesian Hybrid Models

Eugene

Gao

Dowling

2020

2020 American Control Conference (ACC)

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Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.

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Section: Statisticians Kennedy and O'haganmentioning

confidence: 99%

“…( 18) and the data-driven Gaussian process Eqs. ( 19) - (25) to establish the Bayesian hybrid model Eq. (26).…”

Section: A Mathematical Modelsmentioning

confidence: 99%

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Learning and Optimization with Bayesian Hybrid Models

Eugene

Gao

Dowling

2020

2020 American Control Conference (ACC)

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“…Kalyanaraman, et al [35] . studied the uncertain parameters in adsorption of CO 2 on amine sorbents.…”

Section: Introductionmentioning

confidence: 99%

CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

2020

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When choosing experimental conditions, Bayesian statistical tools can predict the experimental choices which will yield the highest information gain. Experimental choices could be temperature, pressure, reaction time, number of measurements, reactor volume, etc.. Three example analyses are presented here, each using the software Chemical Kinetics Parameter Estimation and Uncertainty Quantification (CheKiPEUQ). Information gain is a measure of reduction of uncertainty in a model's parameters. The three chemical system examples presented each illustrate Bayesian Design of Experiments using information gain. In the first chemical example, temperature selection impacts the information gain for the free energy of reaction in a two‐component equilibrium reaction. In the second example, temperature and pressure are explored for a competitive adsorption Langmuir replacement reaction system. The third example is a catalytic membrane reactor which is a culmination of the previous examples. The catalytic membrane reactor has a complex and nonlinear response in the observables which is solved by numerical evaluation. In the three examples, the experimental conditions are treated as design variables for maximizing information gain.

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“…Bayesian inference is a powerful tool for updating model uncertainties from experimental observations. Bayesian inference has been applied to chemical kinetics − models and other domain sciences. − …”

Section: Introductionmentioning

confidence: 99%

Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference

et al. 2020

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Three potential adsorption sites within the Ag/SSZ-13 zeolite are compared for their ethylene and water adsorption capacity. Ethylene acts as a model hydrocarbon molecule in this work to stand in for vehicle exhaust, as Ag/SSZ-13 is a candidate material for trapping vehicle emissions during cold-start. Water is also present in vehicle exhaust and competes for adsorption sites with ethylene. The three active sites studied are the intended Ag ion-exchanged with an H in the zeolite framework, an H site (known as a Brønsted acid site) and Ag2O which may form as a nonzeolite adsorption site during the Ag ion-exchange synthesis. Density functional theory (DFT) calculations are conducted using the BEEF-vdw functional for up to two molecules of ethylene and/or water adsorbed per site. A microkinetic model parametrized by the DFT predicts the ethylene adsorption capacity for shifting ethylene feed gas concentrations at 100 °C in the presence of 6% water. Experimental observations are taken at matching conditions as microkinetic model simulations. The DFT energies and their uncertainties for each adsorption site are updated from experiments using a Bayesian statistical framework. The Ag ion and Ag2O sites adsorb more ethylene relative to the Brønsted acid site. The Ag ion and Brønsted acid sites adsorb more water relative to Ag2O.

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Bayesian estimation of parametric uncertainties, quantification and reduction using optimal design of experiments for CO2 adsorption on amine sorbents

Cited by 29 publications

References 53 publications

Learning and Optimization with Bayesian Hybrid Models

Learning and Optimization with Bayesian Hybrid Models

CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference

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