Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks
Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks
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DOI：
10.1016/j.fuel.2018.06.089
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发表时间：
2018-12-01
期刊：
影响因子：
7.4
通讯作者：
Farooq, Wasif
中科院分区：
文献类型：
--
作者：
Naqvi, Salman Raza;Tariq, Rumaisa;Farooq, Wasif
高灰污泥的热解是一种有效的污泥资源化方法，也是一种很有前途的污泥资源化途径。这项工作的主要目的是建立知识的热解机理，动力学，热重分析高灰分（44.6%）污水污泥使用无模型的方法和结果验证人工神经网络（ANN）。在5、10和20 ℃/min下的TG-DTG曲线表明，热解区分为三个区。动力学模型的E值范围分别为：Friedman（10.6-306.2 kJ/mol）、FWO（45.6-231.7 kJ/mol）、KAS（41.4-232.1 kJ/mol）和Popescu（44.1-241.1 kJ/mol）。用OFW、KAS和Popescu方法预测的Δ H和Δ G值分别为41-236 kJ/mol和53-304 kJ/mol，符合较好。Δ S值为负值表示该过程的非自发性。采用2 * 5 * 1结构的人工神经网络模型对高灰污泥的热分解进行预测，预测值与实验值吻合较好（R-2 >= 0.999），更接近于1。总体而言，该研究反映了神经网络模型的意义，可以作为一个有效的拟合模型的热重实验数据。
Pyrolysis of high-ash sewage sludge (HASS) is a considered as an effective method and a promising way for energy production from solid waste of wastewater treatment facilities. The main purpose of this work is to build knowledge on pyrolysis mechanisms, kinetics, thermos-gravimetric analysis of high-ash (44.6%) sewage sludge using model-free methods & results validation with artificial neural network (ANN). TG-DTG curves at 5,10 and 20 degrees C/min showed the pyrolysis zone was divided into three zone. In kinetics, E values of models ranges are; Friedman (10.6-306.2 kJ/mol), FWO (45.6-231.7 kJ/mol), KAS (41.4-232.1 kJ/mol) and Popescu (44.1-241.1 kJ/mol) respectively. Delta H and Delta G values predicted by OFW, KAS and Popescu method are in good agreement and ranged from (41-236 kJ/mol) and 53-304 kJ/mol, respectively. Negative value of Delta S showed the non-spontaneity of the process. An artificial neural network (ANN) model of 2 * 5 * 1 architecture was employed to predict the thermal decomposition of high-ash sewage sludge, showed a good agreement between the experimental values and predicted values (R-2 >= 0.999) are much closer to 1. Overall, the study reflected the significance of ANN model that could be used as an effective fit model to the thermogravimetric experimental data.