In a fluidized bed, air flow is used to fluidize solid material inside the container. Transient computations on fluidized beds are quite time consuming, and current models may fail in certain cases. One way to speed up the computations is to use steady simulations, but they require closure models, which need experimental parameters. On the other hand, particle image velocimetry (PIV) and volume fraction (VOF) measurements on industrial fluidized beds are very difficult to execute. In this study, a novel VOF computation method and a mass conservation-based least square method are proposed in order to decrease errors in the measurement. The methods performance was tested in various simulations, and experimentally in a case of a small-scale fluidized bed. The method was found to decrease error caused by noise and outliers, while the required time was of the same order as the time used in PIV computations. Our experiments on a small-scale cold model bed suggest that the proposed methods have better accuracy than the logarithmic model often used in the literature. (C) 2013 Elsevier Ltd. All rights reserved.