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Dynamics of Markov Chain in deep bed filtration-theory and experiment
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Datum
2009
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Shaker Verlag
Zusammenfassung
Deep bed filtration is a special example of flow through porous media.It is an engineering operation in which the removal of suspended particulate and colloidal matter in a fluid stream is effected by passing the stream through porous media composed of granular substances.The main yardsticks of the dynamic behaviour of the operation are the filtrate quality history and also the pressure drop history required to maintain a uniform throughput.In this study a one-parameter stochastic model has been developed for the prediction of dynamic pressure drop in a deep bed filter.The model is based on a finite-state and discrete-time Markov chain method whereby the pressure drop in a deepbed filter can be estimated at discrete time intervals.This model is simpler than the stochastic birth and death models available in literature.The bed is assumed to pass through different states of porosity during the filtration and it is spatially lumped in each state.For pressure drop calculations,the Carman Kozeny equation is used in conjunction with the Payatakes-Tien-Turian model.The results obtained from theory agreed well with the plant data as well as with the available experimental data.