945 resultados para Allan, James, d. 1810.


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Bibliography: p. 30.

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Translation of Les phenomènes de la physique.

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Vol. 2 has imprint: Ann Arbor, Institute for Social Research, University of Michigan.

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Includes bibliographical references and index.

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In a previous paper, Hoornaert et al. (Powder Technol. 96 (1998); 116-128) presented data from granulation experiments performed in a 50 L Lodige high shear mixer. In this study that same data was simulated with a population balance model. Based on an analysis of the experimental data, the granulation process was divided into three separate stages: nucleation, induction, and coalescence growth. These three stages were then simulated separately, with promising results. it is possible to derive a kernel that fit both the induction and the coalescence growth stage. Modeling the nucleation stage proved to be more challenging due to the complex mechanism of nucleus formation. From this work some recommendations are made for the improvement of this type of model.

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Nucleation is the first step in granulation where the powder and liquid first contact. Two types of nucleation in wet granulation processes are proposed. Drop controlled nucleation, where one drop forms one nucleus, occurs when drops hitting the powder surface do not overlap (low spray flux Psi(a)) and the drop must wet quickly into the bed (short drop penetration time t(p)). If either criterion is not met, powder mixing characteristics will dominate (mechanical dispersion regime). Granulation experiments were performed with lactose powder, water, PEG200, and 7% HPC solution in a 6 L and a 25 L mixer granulator. Size distributions were measured as the drop penetration time and spray flux were varied. At short penetration times, decreasing Psi(a) caused the nuclei distribution to become narrower. When drop penetration time was high, the nuclei size distribution was broad independent of changes in dimensionless spray flux. Nucleation regime maps were plotted for each set of experiments in each mixer as a function of the dimensionless distribution width delta. The nucleation regime map demonstrates the interaction between drop penetration time and spray flux in nucleation. The narrowest distribution consistently occurred at low spray flux and low penetration time, proving the existence of the drop controlled regime. The nucleation regime map provides a rational basis for design and scale-up of nucleation and wetting in wet granulation.

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Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.