Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge |
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| Affiliation: | 1. Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India;2. Management Systems Subject Group, Faculty of Business, Law and Politics, Hull University Business School, UK;1. Mechanical Department, University of Zaragoza EINA, Maria de Luna 3, 50018 Zaragoza, Spain;2. Department of Design and Manufacturing Engineering, University of Zaragoza EINA, Maria de Luna 3, 50018 Zaragoza, Spain;1. Institute of Thermal Engineering, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China;2. Beijing Key Laboratory of Flow and Heat Transfer of Phase Changing in Micro and Small Scale, Beijing, 100044, China;3. School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, M13 9PL, UK;1. State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), School of Environmental Sciences and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;1. Servicio de Cardiología, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain;2. Instituto de Investigación Biomédica de Bellvitge (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain |
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| Abstract: | The back-propagation (BP) and generalized regression neural network models (GRNN) were investigated to predict the thin layer drying behavior in municipal sewage sludge during hot air forced convection. The accuracy of the BP model to predict the moisture content of the sewage sludge thin layer during hot air forced convective drying was far higher than that of the GRNN model. The GRNN models could automatically determine the best smoothing parameters, which were 0.6 and 0.3 for predicting the moisture content and average temperature, respectively. The model type for predicting the average temperature of the sewage sludge thin layer was selected for different sample groups by comparing their MSE values or R2 values. The GRNN model was suitable for predicting the average temperature corresponding to the sample groups at hot air velocity of 0.6 m/s, and drying temperatures of 100 °C, 160 °C; hot air velocity of 1.4 m/s, and drying temperatures of 130 °C, 140 °C; hot air velocity of 2.0 m/s, and drying temperatures of 150 °C, 160 °C. The average temperature for the other sample groups was best predicted by the BP model. |
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| Keywords: | Neural network Sewage sludge Thin-layer drying Forced convection Moisture content Temperature |
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