Filtravimo grūdėtais filtrais procesų interpoliacinių kriterinių modelių interpretavimas

Abstract

Environmental protection problem is vital not only for Lithuania, but for all countries as well. The problem of effective dust collection is an urgent problem, because of an increasing number of dust exuding units and the intensity of other technological processes as well as of their power. The most dangerous particles for respiratory organs are from 0.01 to 1.0 mkm, because approximately 50 percent of them collect in human lungs. Workplace pollution by dust results in different diseases, wearing out of mechanisms and devices. Special attention must be paid to control methods and devices used at atmosphere pollution by fine dust particles. This knowledge enables a precise analysis of dust collection by different types of granular filters. As it was revealed before, the filtering process with progressive porous blinding is defined by the equation: f = f(w, de, H, t, ZH, ZK) It is purposeful to reduce the number of independent variables by a rational development of a measureless ratio with a clear physical sense, that corresponds with the essence of the studied process. It would help to reduce both experimental and analytical work and allow to apply the achieved results for similar processes. The next dependence was proposed for characterizing the filtering process: [...]=0 On the basis of partition factorial experiments planing matrices and pass throw coefficients were obtained. The next regression model was obtained as a result of following algorithm fulfilment: y = 1.773 -1.050 • x1 - 0.834 • x2 + +0.782 • x[...] + 0.525 • x[...] - 0.147 • x1 • x2 The model compliance verification was fulfilled (P = 0.05, f = N (m - 1) = 36 and l(o.o5;36) = 2.03). The obtained model was used for the 3-D graphic surface plotting after simple transformations. The obtained results could be applied for the granular filter parameters choice and their efficiency forecast as well as for a graphic interpretation of the filtering process using granular filters.