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您的检索式：作者名="Mikel Tellabide"
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| 1 | Elutriation, attrition and segregation in a conical spouted bed with a fountain confiner显示文摘This study examined elutriation,attrition,and segregation in a conical spouted bed with a fountain confiner and incorporating an open sided draft tube.Fine silica sand with a wide particle size distribution was employed as a model material,operating in both the batch and continuous modes.The use of a fountain confiner is crucial when operating with fine particles,because otherwise the bed rapidly exhibits significant entrainment.The extent of attrition was quantified using a tracing technique based on differently-coloured sand fractions as well as monitoring size distributions by sieving.Particle breakage was found to be the primary attrition mechanism,and the fountain confiner was determined to limit the elutriation of fine particles resulting from breakage.Consequently,only a small fraction of the finest particles were entrained from the bed.The incorporation of a confiner increased operational stability while reducing segregation,especially in the upper half of the bed where the majority of segregation typically occurs.Thus,the bed was perfectly mixed apart from very minimal segregation close to the wall and at the bottom of the contactor.These results provide a basis for the design and operation of larger scale equipment for the continuous drying of materials. | Aitor Pablos Roberto Aguado Jorge Vicente Mikel Tellabide Javier Bilbao Martin Olazar | 2020 | Particuology2020，18，4： | 2 |
| 2 | Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations显示文摘Pressure drop is an essential parameter in the operation of conical spouted beds(CSB)and depends on its geometric factors and materials used.Irregular materials,like biomass,are complex to treat and,unlike other gas–solid contact methods,CSB turn out to be a suitable technology for their treatment.Artificial neural networks were used in this study for the prediction of operating and peak pressure drops,and their performance has been compared with that of empirical correlations reported in the literature.Accordingly,a multi-layer perceptron network with backward propagation was used due to its ability to model non-linear multivariate systems.The fitting of the experimental data of both operating and peak pressure drop was significantly better than those reported in the literature,specifically in the case of the peak pressure drop,with R^(2) being 0.92.Therefore,artificial neural networks have been proven suitable for the prediction of pressure drop in CSB. | Juan F.Saldarriag Yuby Cruz Idoi Estiati Mikel Tellabide Martin Olazar | 2022 | Particuology2022，，11： | 0 |
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