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RESEARCH PRODUCT
A probabilistic approach to radiant field modeling in dense particulate systems
Antonio BusciglioFrancesca ScargialiOrlando Mario AlfanoAlberto Brucatosubject
Work (thermodynamics)EngineeringField (physics)General Chemical EngineeringSettore ING-IND/25 - Impianti ChimiciMonte Carlo methodINGENIERÍAS Y TECNOLOGÍAS02 engineering and technology010402 general chemistry01 natural sciencesIndustrial and Manufacturing EngineeringMONTE CARLO SIMULATIONRADIANT FIELD MODELINGChemical Engineering (all)Statistical physicsSimulationMonte Carlo simulationDENSE PARTICULATE SYSTEMPlane (geometry)business.industryApplied MathematicsChemistry (all)Probabilistic logicStatistical modelDense particulate systemGeneral Chemistry021001 nanoscience & nanotechnology0104 chemical sciencesIngeniería QuímicaApplied MathematicPHOTO-BIOREACTORSOtras Ingeniería QuímicaPhoto-bioreactorClosed-form expression0210 nano-technologyFocus (optics)businessPHOTO-CATALYSISPhoto-catalysiRadiant field modelingdescription
Radiant field distribution is an important modeling issue in many systems of practical interest, such as photo-bioreactors for algae growth and heterogeneous photo-catalytic reactors for water detoxification.In this work, a simple radiant field model suitable for dispersed systems showing particle size distributions, is proposed for both dilute and dense two-phase systems. Its main features are: (i) only physical, independently assessable parameters are involved and (ii) its simplicity allows a closed form solution, which makes it suitable for inclusion in a complete photo-reactor model, where also kinetic and fluid dynamic sub-models play a role. A similar model can be derived by making use of concepts developed in the realm of stereology. The resulting equation is similar, yet not identical, to that obtained with the probabilistic approach, due to the fact that in stereology the front plane, or the focus plane, may well cut through particles, a circumstance excluded both in the probabilistic model and in actual photoreactors.The two models are compared with pseudo-experimental data obtained by means of Monte Carlo simulations, and the probabilistic model is found to give rise to the best agreement. Fil: Busciglio, A.. Universidad de Bologna; Italia Fil: Alfano, Orlando Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Scargiali, F.. Università Degli Studi Di Palermo; Fil: Brucato, A.. Università Degli Studi Di Palermo;
year | journal | country | edition | language |
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2016-03-01 |