Adequate assessment of the uncertainties is becoming an integral part of the engineering design. This study is based on several efforts that are underway to investigate the uncertainty quantification (UQ) methods that are applicable to multiphase flows. Although particular emphasis has been given on identifying, characterizing and managing uncertainties in computational fluid dynamics (CFD) simulations, experimental data used in the validation of the CFD model is as critical. Hence, the goal of this paper is to demonstrate application of non-intrusive Bayesian uncertainty quantification methodology in multiphase (gas-solid) flows with experimental data as part of our research efforts to determine the most suited approach for UQ. For this purpose, a bench scale fluidized bed gasifier experiment from a prior study is used to demonstrate the applicability of Bayesian techniques on sparse data. Global sensitivity analysis performed as part of the UQ study shows that among the three operating factors, steam to oxygen ratio has the most influence on syngas composition in the gasifier. An analysis for forward propagation of uncertainties shows that an increase in steam to oxygen ratio leads to an increase in H-2 mole fraction and a decrease in CO mole fraction. These findings are in agreement with the reference experimental study. Another contribution in addition to the UQ analysis is the optimization-based approach employed to identify next best set of additional experimental samples. Hence, the surrogate models constructed as part of the UQ analysis is employed to improve the information gain and make incremental recommendation, should the possibility to add more experiments arise. The insight gained from this study has been extensively used in the follow-up UQ study with the CFD modeling of the-same gasifier configuration. (C) 2017 Elsevier B.V. All rights reserved.