The transport of gaseous components through porous media could be described according to the well-known Fick model and its modifications. It is also known that Fick’s law is not suitable for predicting the fluxes in multicomponent gas mixtures, excluding binary mixtures. This model is still frequently used in chemical engineering because of its simplicity. Unfortunately, besides the Fick’s model there is no generally accepted model for mass transport through porous media (membranes, catalysts etc.). Numerous studies on transport through porous media reveal that Dusty Gas Model (DGM) is superior in its ability to predict fluxes in multicomponent mixtures. Its wider application is limited by more complicated calculation procedures comparing to Fick’s model. It should be noted that there were efforts to simplify DGM in order to obtain satisfactory accurate results. In this paper linearized DGM, as the simplest form of DGM, is tested under conditions of zero system pressure drop, small pressure drop, and different temperatures. Published experimental data are used in testing the accuracy of the linearized procedure. It is shown that this simplified procedure is accurate enough compared to the standard more complicated calculations.

AIM To examine caries risk using the Cariogram model, interactive PC program for caries risk evaluation in 12-year-old children and to correlate caries risk in children of different socioeconomic backgrounds. MATERIAL AND METHODS 109, Sarajevo 12-year-olds in three groups based on socioeconomic background. Baseline data on general health condition, diet frequency and use of fluoride were obtained. DMFT and plaque scores were calculated. Saliva analyses included lactobacillus and mutans streptococci levels in saliva, saliva secretion and buffer capacity. Scores were entered into the Cariogram model and risk was calculated for each child. RESULTS Most 12-year-old children have a medium risk of caries, with a 59.4% chance of avoiding future caries. In an average caries risk profile of children from Sarajevo the dominant sector is diet, with 12.5% risk; bacteria sector (plaque and mutans streptococci level) 10.8% risk; susceptibility (fluoride, saliva secretion and buffering capacity) 9.7% risk; circumstances (caries experience and medical history) 7.4% risk. Caries risk profiles showed that there are differences in the socioeconomic status of children with significantly greater risk in children with poor living conditions who also have the most unfavourable caries risk profiles. CONCLUSIONS The Cariogram model can successfully determine caries risk profiles for 12-year-old children of different socioeconomic status and can be used in developing preventive strategies for reducing caries risk in children.