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首页> 外文期刊>Waste Management >Synergistic effect on co-pyrolysis of rice husk and sewage sludge by thermal behavior, kinetics, thermodynamic parameters and artificial neural network
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Synergistic effect on co-pyrolysis of rice husk and sewage sludge by thermal behavior, kinetics, thermodynamic parameters and artificial neural network

机译:热行为,动力学,热力学参数和人工神经网络对稻壳和污泥共热解的协同作用

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This study investigates the thermal decomposition, thermodynamic and kinetic behavior of rice-husk (R), sewage sludge (S) and their blends during co-pyrolysis using thermogravimetric analysis at a constant heating rate of 20 degrees C/min. Coats-Redfern integral method is applied to mass loss data by employing seventeen models of five major reaction mechanisms to calculate the kinetics and thermodynamic parameters. Two temperature regions: I (200-400 degrees C) and II (400-600 degrees C) are identified and best fitted with different models. Among all models, diffusion models show high activation energy with higher R-2(0.99) of rice husk (66.27-82.77 kJ/mol), sewage sludge (52.01-68.01 kJ/mal) and subsequent blends (45.10-65.81 kJ/mol) for region I and for rice husk (7.31-25.84 kJ/mol), sewage sludge (1.85-16.23 kJ/mol) and blends (4.95-16.32 kJ/mol) for region II, respectively. Thermodynamic parameters are calculated using kinetics data to assess the co-pyrolysis process enthalpy, Gibbs-free energy, and change in entropy. Artificial neural network (ANN) models are developed and employed on co-pyrolysis thermal decomposition data to study the reaction mechanism by calculating Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R-2). The co-pyrolysis results from a thermal behavior and kinetics perspective are promising and the process is viable to recover organic materials more efficiently. (C) 2018 Elsevier Ltd. All rights reserved.
机译:本研究使用热重分析法以20℃/ min的恒定加热速率研究了共热解过程中稻壳(R),污水污泥(S)及其混合物的热分解,热力学和动力学行为。通过使用五个主要反应机理的十七种模型来计算动力学和热力学参数,将Coats-Redfern积分方法应用于质量损失数据。确定了两个温度区域:I(200-400摄氏度)和II(400-600摄氏度),并最适合于不同的型号。在所有模型中,扩散模型显示出较高的活化能,且稻壳的R-2(0.99)(66.27-82.77 kJ / mol),污水污泥(52.01-68.01 kJ / mal)和后续混合物(45.10-65.81 kJ / mol)高。 )分别适用于区域I和区域II的稻壳(7.31-25.84 kJ / mol),污水污泥(1.85-16.23 kJ / mol)和混合物(4.95-16.32 kJ / mol)。使用动力学数据计算热力学参数,以评估共热解过程的焓,无吉布斯能量和熵变。开发了人工神经网络(ANN)模型,并将其用于共热解热分解数据,通过计算平均绝对误差(MAE),均方根误差(RMSE)和测定系数(R-2)来研究反应机理。从热行为和动力学的角度来看,共热解的结果是有前途的,并且该方法对于更有效地回收有机材料是可行的。 (C)2018 Elsevier Ltd.保留所有权利。

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