Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations |
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| Institution: | 1. Department of Civil and Environmental Engineering, Universidad de los Andes, Carrera 1Este #19A-40, Bogota, Colombia;2. Department of Chemical Engineering, University of the Basque Country, Barrio Sarriena s/n, Leioa, Spain;1. State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, China;2. Guangdong Provincial Key Laboratory of Distributed Energy Systems, School of Chemical Engineering and Energy Technology, Dongguan University of Technology, Dongguan 523808, China;1. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China;2. Department of Chemical & Biochemical Engineering, Western University, London, Ontario N6A 3K7, Canada;3. Institute of Shaoxing, Tianjin University, Zhejiang 312300, China;1. Polatl? Science and Literature Faculty, Nanosan Laboratory, Ankara Hac? Bayram Veli University, Ankara 06900, Turkey;2. Advanced Technologies Application and Research Center, Hacettepe University, Ankara 06900, Turkey;3. Science and Literature Faculty, Biology Department, Marmara University, Istanbul 34722, Turkey;1. Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor, Malaysia;2. Centre for Sustainable Nanomaterials (CSNano), Ibnu Sina Institute for Scientific and Industrial Research (ISI-ISIR), Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor, Malaysia;3. Office of Deputy Vice Chancellor (Research and Innovation), Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor, Malaysia;1. School of Civil Engineering, Chongqing University, Chongqing 400045, China;2. School of Transportation, Southeast University, Nanjing 210096, China |
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| Abstract: | 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 R2 being 0.92. Therefore, artificial neural networks have been proven suitable for the prediction of pressure drop in CSB. |
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| Keywords: | Pressure drop Artificial neural networks Biomass Spouted bed |
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