Artificial neural network models for biomass gasification in fluidized bed gasifiers |
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| Affiliation: | 1. Universitat Rovira i Virgili, Dept. Eng. Mecànica, Av. Països Catalans 26, 43007 Tarragona, Spain;2. Universidad Autónoma del Estado de Morelos, Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Av. Universidad No. 1001 Col. Chamilpa, 62209 Cuernavaca, Mexico;1. Carbolea Research Group, Chemical and Environmental Science Department, Bernal Institute, University of Limerick, Ireland;2. Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom;3. Department of Earth Science and Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom;1. Biomass Processing Lab, Centre of Biofuel and Biochemical Research, Department of Chemical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia;2. Department of Chemical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia;3. Department of Chemical Engineering, University of Gujrat, Gujrat Pakistan;4. Department of Chemical Engineering, NED University of Engineering & Technology, 75270, Karachi, Pakistan;5. Department of Sustainable and Renewable Energy Engineering, University of Sharjah, 27272 Sharjah, United Arab Emirates;6. Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Defense Road Lahore, Pakistan;7. Department of Chemical Engineering, School of Chemical and Materials Engineering National University of Sciences and Technology Islamabad Pakistan;1. Department of Electrical and Electronics Engineering, Izmir Katip Celebi University, Izmir, Turkey;2. Department of Environmental Engineering, Izmir Katip Celebi University, Izmir, Turkey;1. Department of Energy, Power Engineering and Ecology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, No. 5 Ivana Lu?i?a, 10002 Zagreb, Croatia;2. Department of Biosystems, Faculty of Bioscience Engineering, KU Leuven, No. 30 Kasteelpark Arenberg, 3001 Leuven, Belgium;3. Institute of Power Engineering, Faculty of Mechanical Science and Engineering, Technical University Dresden, No. 3b George-Bähr-Strasse, 01069 Dresden, Germany;4. Department of Mechanical Engineering, Faculty of Engineering Science, KU Leuven, No. 300 Celestijnenlaan, 3001 Leuven, Belgium;1. Department of Energy, Tezpur University, Tezpur, Assam, India;2. Department of Mechanical Engineering, Girijananda Choudhury Institute of Management and Technology, Tezpur, Assam, India;1. Instituto Universitario de Ingeniería Energética, Universitat Politècnica de València, València, Spain;2. Departamento de Estudios del Agua y de la Energía, Centro Universitario de Tonalá de la Universidad de Guadalajara, Tonalá, Mexico;3. Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, València, Spain |
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| Abstract: | Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine the producer gas composition (CO, CO2, H2, CH4) and gas yield. Published experimental data from other authors has been used to train the ANNs. The obtained results show that the percentage composition of the main four gas species in producer gas (CO, CO2, H2, CH4) and producer gas yield for a biomass fluidized bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published experimental data used R2 > 0.98. Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are important. |
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