  Prominent features of simulated moving bed (SMB) chromatography processes in
the downstream processing is based on the determination of operating
conditions. However, effects of different types of uncertainties have to be
studied and analysed whenever the triangle theory or numerical optimization
approaches are applied. In this study, a Bayesian inference based method is
introduced to consider the uncertainty of operating conditions on the
performance assessment, of a glucose-fructose SMB unit under linear condition.
A multiple chain Markov Chain Monte Carlo (MCMC) algorithm (i.e., Metropolis
algorithm with delayed rejection and adjusted Metropolis) is applied to
generate samples. The proposed method renders versatile information by
constructing from the MCMC samples, e.g., posterior distributions,
uncertainties, credible intervals of the operating conditions, and posterior
predictive check and Pareto fronts between each pair of the performance
indicators. Additionally, the MCMC samples can be mapped onto the
$(m_\text{II}, m_\text{III})$ and $(m_\text{IV}, m_\text{I})$ plains to show
the actually complete separation region under uncertainties. The proposed
method is a convenient tool to find both optimal values and uncertainties of
the operating conditions. Moreover, it is not limited to SMB processes under
the linear isotherm; and it should be more powerful in the nonlinear scenarios.
