We present a detailed microkinetic analysis of the Fischer–Tropsch synthesis on a Co/γ‐Al2O3 catalyst over the full range of syngas conversions. The experiments were performed in a Carberry spinning basket batch reactor at initial H2/CO ratios between 1.8 and 2.9, temperatures of 469 and 484 K, and initial pressures of 2 MPa. A reaction mechanism based on the H2‐assisted CO activation pathway, which comprises 128 elementary reactions with 85 free parameters, was proposed to explain the experimental results. Each of these elementary reactions belongs to one of the following reaction groups: adsorption–desorption, monomer formation, chain growth, hydrogenation–hydrogen abstraction, or water–gas shift. A two‐stage parameter estimation method, based on a quasi‐random global search followed by a gradient‐free local optimization, has been used to calculate the values of pre‐exponential factors and activation energies. The use of data obtained from batch experiments enabled an effective analysis of dominating reactions at different stages of syngas conversions.
We apply a Bayesian parameter estimation technique to a chemical kinetic mechanism for n-propylbenzene oxidation in a shock tube to propagate errors in experimental data to errors in Arrhenius parameters and predicted species concentrations. We find that, to apply the methodology successfully, conventional optimization is required as a preliminary step. This is carried out in two stages: First, a quasi-random global search using a Sobol low-discrepancy sequence is conducted, followed by a local optimization by means of a hybrid gradient-descent/Newton iteration method. The concentrations of 37 species at a variety of temperatures, pressures, and equivalence ratios are optimized against a total of 2378 experimental observations. We then apply the Bayesian methodology to study the influence of uncertainties in the experimental measurements on some of the Arrhenius parameters in the model as well as some of the predicted species concentrations. Markov chain Monte Carlo algorithms are employed to sample from the posterior probability densities, making use of polynomial surrogates of higher order fitted to the model responses. We conclude that the methodology provides a useful tool for the analysis of distributions of model parameters and responses, in particular their uncertainties and correlations. Limitations of the method are discussed. For example, we find that using second-order response surfaces and assuming
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