Most parameter estimation is based upon the assumption of normally distributed errors using least squares and the confidence intervals are computed from the sensitivities and the statistics of the residuals. For nonlinear problems, the assumption of a normal distribution of the parameters may not be valid. Determining the probability density distribution can be difficult, particularly when there is more than one parameter to be estimated or there is uncertainty about other parameters. An alternative approach is Bayesian inference, but the numerical computations can be expensive. Markov Chain Monte Carlo (MCMC) may alleviate some of the expense. The paper describes the application of MCMC to estimate the mass flow rate, the heat transfer coefficient, and the specific heat of a packed bed regenerator.

Volume Subject Area:
Heat Transfer
Keywords:
Packed Beds, Convective Heat Transfer, Bayesian Inference, Markov Chain Monte Carlo, Sensitivity, Parameter Estimation, Variability, Uncertainty
Topics:
Chain, Parameter estimation, Uncertainty
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