The golden rule of process modelling is "garbage in = garbage out"
In the past, mass production of mathematical models and column design correlations was hindered by the extensive calculations involved. With the event of computers, this bottleneck has been eliminated. Flood gates have opened, and new mathematical models are pouring into the published literature at a record pace. Further growth in mathematical model production appears to be restricted only by the availability of persons willing to punch buttons on computer keyboards.
One would expect this state of art to be the heaven that column designers always dreamt of. Instead, it turned out to be the hell always feared. Few could keep up with the large influx of mass produced mathematical models. Little is known about the limitations of each new correlation or design method. Our prediction methods turned into black boxes: key in numbers, print out results. But how reliable are these results?
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As we head into the 21 century, the above type of anecdote is becomming ancient history. The blak box in the computer has taken over. Looking for correlation limitations today becomes like looking for a needle in an ever-growing haystack.
With the busy life style and the pressure to publish papers, the problem is becoming more acute. There are deadlines to meet, technical papers need to be produced, and there is no time to explore correlation limitations. Besides, who needs to look for limitations when a computerized regression analysis (performed, of course, by one of the best regression packages in the business) shows an excellent data fit? Does it really matter if a handful of points for systems above atm pressure? In real life, no one will know, unless the designer ends up with a column that does not work. And if the errors is on the conservative side, no one will ever find out, because the column will work.
Data collection is another neglected child of the late 20th century. There are so many data around that collecting them all (or even most of them) for the sake of deriving a correlation becomes painful, mundane, and an extremely unattractive exercise. Not to mention the labor involved in reading data off plots and in the arithmetic involved in ensuring that all the data points have been correctly entered. I challenge anyone to cite a more boring task than this. An economical way of dealing with the excess data problem is by using the "ignore it and hope it goes away" principle. It will suffice that the new correlation will fit a handful of data thrown at it. And if data from other sources do not agree, that just means there is something wrong with other data.
What hope has the designer who sits at the end of the rainbow and attempts to make use of the mathematical models and design correlations?
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Contrary to a popular belief, some distillation characteristics still cannot be satisfactorily predicted by correlation, regardless of the number of correlations available for their prediction. Data interpolation with the aid of an empirical procedure is probably the most reliable means of estimating these characteristics. ... Computers have provided distillation designers with speed, accuracy, and flexibility. Computers, however, still have a long way to go before - if ever - they are capable of replacing good engineering judgment.
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