Uncertainty quantification via bayesian inference using sequential monte carlo methods for CO₂ adsorption process

Abstract

This work presents the uncertainty quantification, which includes parametric inference along with uncertainty propagation, for CO2 adsorption in a hollow fiber sorbent, a complex dynamic chemical process. Parametric inference via Bayesian approach is performed using Sequential Monte Carlo, a completely parallel algorithm, and the predictions are obtained by propagating the posterior distribution through the model. The presence of residual variability in the observed data and model inadequacy often present a significant challenge in performing the parametric inference. In this work, residual variability in the observed data is handled by three different approaches: (a) by performing inference with isolated data sets, (b) by increasing the uncertainty in model parameters, and finally, (c) by using a model discrepancy term to account for the uncertainty. The pros and cons of each of the three approaches are illustrated along with the predicted distributions of CO2 breakthrough capacity for a scaled‐up process. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3352–3368, 2016

A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacy

“…[2][3][4][5][6]. 31,32 The use of a single dispersion parameter should therefore be reserved to cases where a single uncertainty source is strongly dominant:…”

“…46 This has been done in various ways: uncertainty scaling, 10,11,13 embedded stochastic models 30,46 and hierarchical models. 9 Parameters and their enlarged uncertainty are then transferable, but this approach is not without drawbacks: 32,49 the transfer of parameter uncertainty to MPU is governed by the functional shape (with respect to the control variables) of the model sensitivity coefficients. This leads to confidence bands with a model-specific shape, not necessarily representative of the actual model errors.…”

Global sensitivity analysis of n-butanol ignition delay times to thermodynamics class and rate rule parameters

“…A variance decomposition approach is applied to infer the contribution of each uncertain parameter on the variability of τ ign . The first and total order sensitivities are obtained from GSA where the first order sensitivity indices show the direct contribution of the individual or group of parameters while the total sensitivity indices, T i , where i ∈ [1][2][3][4][5][6][7][8][9][10], account additionally for the mixed interactions among the uncertain parameters. Fig.…”

“…Motivated by this goal, several studies have focused on uncertainty quantification particularly [4][5][6][7] to assess sensitivities and to accelerate Bayesian inference studies [8][9][10][11][12][13][14][15]. Most of the work in the literature [16][17][18] addresses the uncertainty in Arrhenius rate parameters, typically characterized in terms of uncertainty factors (UFs) of pre-exponential parameters.…”

Influence of water vapor and acid gases on CO2 adsorption using N,N-dimethylethylenediamine decorated Cu-BTC

“…Capturing CO 2 from flue gas is extremely challenging due to the high flow rates, the low partial pressure of CO 2 and the need to consider the separation costs [5]. Adsorption CO 2 with solid media is considered to be a more energy efficient technology for CO 2 capture than traditional absorption method with alkaline solvents [6,7].…”