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Two typical particulate process examples, a continuous crystallizer and a batch protein crystallizer, modeled by population balance models (PBMs), are considered.
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It holds, for example, a continuous series of lectures in which Zeitzeugen, witnesses to the events, take part.
An example, a continuous stirred tank reactor, is analyzed with respect to convexity of the velocity set, stability etc.
We describe a method to extract a structured reaction network model, represented as a bipartite multigraph, for example, a continuous Petri net (CPN), from a system of Ordinary Differential Equations (ODEs).
In such a case, zeroth-order reactions can be used to simulate, for example, a continuous influx of chemicals or a creation process.
For example, a continuous alphabetical sequence of 'K-L-M-N' for SI-SI-SO-SO trials would have meant a decision on the letters 'L-M-M-N'.
Two typical examples of a continuous t-norm are (a*b =ab) and (a*b =min a,b)).
The recent mainstream introductions of absorbent glass mat batteries (AGM), enhanced flooded batteries (EFBs) and battery monitoring sensors (BMS) are obvious examples of a continuous improvement.
Two typical examples of a continuous t-conorm are a ⋄ b = min ( a + b, 1 ) and a ⋄ b = max ( a, b ).
The examples of a Continuous Stirred Tank Reactor and an adsorption process illustrate how the network structure and the choice of variables ease the reusability of submodels and localize the changes in models to some network elements.
Examples of a continuous t-norm are Lukasievicz t-norm, that is, a ∗ L b = max { a + b − 1, 0 }, product t-norm, that is, a ∗ P b = a b and minimum t-norm, that is, a ∗ M b = min { a, b }.
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