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For example, if the feed is a saturated liquid, q = 1 and the slope of the q-line is infinite (drawn as a vertical line). As another example, if the feed is saturated vapor, q = 0 and the slope of the q-line is 0 (a horizontal line). [2] The typical McCabe–Thiele diagram in Figure 1 uses a q-line representing a partially vaporized feed.
This is a diagram exemplifying how the en:McCabe-Thiele method is used to determine the number of theoretical equilibrium stages required in a distillation unit. Licensing I, the copyright holder of this work, hereby publish it under the following license:
Fractionation at total reflux. The Fenske equation in continuous fractional distillation is an equation used for calculating the minimum number of theoretical plates required for the separation of a binary feed stream by a fractionation column that is being operated at total reflux (i.e., which means that no overhead product distillate is being withdrawn from the column).
The calculation process requires the availability of a great deal of vapor–liquid equilibrium data for the components present in the distillation feed, and the calculation procedure is very complex. [2] [3] In an industrial distillation column, the N t required to achieve a given separation also depends upon the amount of reflux used. Using ...
The equations for the use of the data retrieved from these tables are very simple. Q= heat gain, usually heat gain per unit time A= surface area. U= Overall heat transfer coefficient. CLTD= cooling load temperature difference SCL= solar cooling load factor CLF= cooling load factor SC= shading coefficient
The McCabe-Thiele diagram is not just for distillation. It is also used (and very pertinent) to solvent extraction, where two liquids contact each other to exchange solute species. Somebody that knows about the solvent extraction field needs to add this technology as an application of the M-T diagram, and create a link to the Wikipedia liquid ...
where σ ij represents the covariance of two variables x i and x j. The double sum is taken over all combinations of i and j, with the understanding that the covariance of a variable with itself is the variance of that variable, that is, σ ii = σ i 2. Also, the covariances are symmetric, so that σ ij = σ ji. Again, as was the case with the ...
Just a general form of the equation, a plot of the objective function, boundaries of the object variables and the coordinates of global minima are given herein. Test functions for single-objective optimization