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从非食用油脂种子热解的二元混合物中进行能量优化:使用热重分析仪进行动力学三参数分析和通过人工神经网络进行预测建模。

Energy optimization from a binary mixture of non-edible oilseeds pyrolysis: Kinetic triplets analysis using Thermogravimetric Analyser and prediction modeling by Artificial Neural Network.

机构信息

Department of Energy Engineering, Central University of Jharkhand, Ranchi, 835205, India.

Department of Nanotechnology, Central University of Jharkhand, Ranchi, 835205, India.

出版信息

J Environ Manage. 2021 Nov 1;297:113253. doi: 10.1016/j.jenvman.2021.113253. Epub 2021 Jul 17.


DOI:10.1016/j.jenvman.2021.113253
PMID:34284329
Abstract

Pyrolysis kinetics and thermodynamic parameters of two non-edible seeds, Pongamia pinnata (PP) and Sapindus emarginatus (SE), and their blend in the ratio of 1:1 (PS) were studied using the thermogravimetric analyzer. Kinetic triplets were determined using both model-free [Starink (STR), Friedman (FRM), Iterative Kissinger-Akahira-Sunose (IT-KAS), Iterative Ozawa-Flynn-Wall (IT-OFW), Vyazovkin (VYZ), and Master plot (MP)] and model fitting Coats-Redfern (CR) methods at three different heating rates 10, 30 and 50 °C/min. Activation energies were 192.66, 179.44, and 163.25 kJ/mol for PP, SE, and PS, respectively. It was found that the blend of the two-biomass (PS) showed promising results with lower activation energy compared to the individual biomass. Thermodynamic parameters (ΔG, ΔS, and ΔH) were obtained using the model-free isoconversional method. The three hidden layers of complex neuron topology are well fitted to the experimental DTG curves by artificial neural network (ANN). The study confirmed that the heating rate had a significant impact on the kinetics and thermodynamic parameters. The reaction mechanism was also in consonance with the experimental data. The study suggests that the PP and SE seeds can be an appropriate feed for pyrolysis, and their blend (PS) can be a viable alternative in optimizing the entire process.

摘要

采用热重分析仪研究了两种非食用种子,麻疯树(PP)和无患子(SE)及其 1:1 混合物(PS)的热解动力学和热力学参数。使用无模型法[Starink(STR)、Friedman(FRM)、迭代 Kissinger-Akahira-Sunose(IT-KAS)、迭代 Ozawa-Flynn-Wall(IT-OFW)、Vyazovkin(VYZ)和 Master plot(MP)]和模型拟合 Coats-Redfern(CR)方法,在三个不同的加热速率 10、30 和 50°C/min 下确定了动力学三参数。PP、SE 和 PS 的活化能分别为 192.66、179.44 和 163.25 kJ/mol。结果发现,与单独的生物质相比,两种生物质(PS)的混合物表现出有希望的结果,具有较低的活化能。使用无模型等转化率法获得热力学参数(ΔG、ΔS 和 ΔH)。人工神经网络(ANN)很好地拟合了复杂神经元拓扑的三个隐藏层与实验 DTG 曲线。研究证实,加热速率对动力学和热力学参数有显著影响。反应机制也与实验数据一致。该研究表明,PP 和 SE 种子可以作为热解的合适原料,其混合物(PS)可以作为优化整个过程的可行替代品。

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Energy optimization from a binary mixture of non-edible oilseeds pyrolysis: Kinetic triplets analysis using Thermogravimetric Analyser and prediction modeling by Artificial Neural Network.

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