Smart computing approach for design and scale-up of conical spouted beds with open-sided draft tubes

• 作者: M.Karimi B.Vaferi S.H.Hosseini M.Olazar S.Rashidi
• 来源期刊: 颗粒学报（英文版）
• 年，卷（期）: 2021,（2）
• 页码范围: 179-190
• 页数: 12页
• 语种: 英文

摘要

Open-sided draft tubes provide an optimal gas distribution through a cross flow pattern between the spout and the annulus in conical spouted beds.The design,optimization,control,and scale-up of the spouted beds require precise information on operating and peak pressure drops.In this study,a multi-layer perceptron(MLP)neural network was employed for accurate prediction of these hydrodynamic characteristics.A relatively huge number of experiments were accomplished and the most influen-tial dimensionless groups were extracted using the Buckingham-pi theorem.Then,the dimensionless groups were used for developing the MLP model for simultaneous estimation of operating and peak pressure drops.The iterative constructive technique confirmed that 4-14-2 is the best structure for the MLP model in terms of absolute average relative deviation(AARD％),mean square error(MSE),and regres-sion coefficient(R2).The developed MLP approach has an excellent capacity to predict the transformed operating(MSE = 0.00039,AARD％= 1.30,and R2 = 0.76099)and peak(MSE = 0.22933,AARD％= 11.88,and R2 = 0.89867)pressure drops.