Distributed case-based reasoning

Plaza, E., Arcos, J.L., Martin, F. (1997). Cooperative case-based reasoning. In Distributed artificial intelligence meets machine learning (pp. 180-220). Berlin Springer (LAA7 7227). 

A very limited effect is found on temperature the maximum difference, arising close to the canister (point B4) and for the low permeability, is due to the variation of thermal conductivity induced by the changes in saturation. An illustration is given on Figure 7 for points B4 and B6, for the base case. From these results, it appears that, in the present analysis, the temperature distribution can be reasonably predicted by a single uncoupled thermal analysis. 

It is emphasized that the delta L law does not apply when similar crystals are given preferential treatment based on size. It fails also when surface defects or dislocations significantly alter the growth rate of a crystal face. Nevertheless, it is a reasonably accurate generahzation for a surprising number of industrial cases. When it is, it is important because it simphfies the mathematical treatment in modeling real crystallizers and is useful in predicting crystal-size distribution in many types of industrial crystallization equipment. 

It is important to keep in mind that demands occur on the time line. If a reasonable minimal time interval is chosen, it is in many cases justified to consider the demands in these intervals as independent and identically distributed random variables. This means for example that the demand per week is the iterated convolution of the daily demand. A large customer base is a good indicator of independent random demand in different time intervals. 

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