人工神经网络辅助的热重量测量分析燃烧反应中的热降解:在多种有机样本上进行的一项研究
Haider Khan1, Symeon Savvopoulos1, Isam Janajreh1
1Mechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates; Center for Membranes and Advanced Water Technology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
Environmental research
|February 11, 2024
概括
这项研究开发了一个人工神经网络 (ANN) 模型,从热重力分析仪 (TGA) 数据中预测气化动力学和热力学参数. 该模型准确地估计了各种原料的激活能量等参数,有助于热化学转换研究.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 气化动力学和热力学参数是原料和过程依赖的.
- 从热重力分析仪 (TGA) 数据中精确的这些参数的数值建模对于优化气化过程至关重要.
- 现有的模型需要改进,以有效地整合TGA数据来预测气化行为.
研究的目的:
- 通过使用一种新的人工神经网络 (ANN) 方法,数值建模气化过程的动力学和热力学参数.
- 开发一种基于原料和运行条件准确预测参数的计算模型.
- 建立一个用于估计热化学转换特征的多功能工具.
主要方法:
- 在 MATLAB 中开发一个优化的 8-20-20-10-1 ANN 模型.
- 在80多个不同的样本上训练ANN模型,使用Levenberg-Marquardt算法.
- 使用热重力测量分析仪 (TGA) 数据,包括时间,温度,加热速率和元素组成,用于模型输入.
主要成果:
- 该ANN模型准确地预测了关键的动力学 (激活能量,预指数因子) 和热力学参数 (,,吉布斯自由能量,点火指数,沸温度).
- 实现了高预测准确性,平均平方误差 (MSE) 为6.185e-6和R2值>0.9996.
- 通过准确预测TGA曲线和激活能量来证明各种材料的多功能性,如糖醇,煤炭和木材.
结论:
- 开发的ANN模型提供了一种高度准确和通用的方法,用于从TGA数据中预测气化动力学和热力学参数.
- 该模型能够估计各种原料和条件的参数,使其成为热化学转换研究的宝贵工具.
- 这种方法增强了对气化过程的理解和计算建模,促进了高效的能量回收和材料转化.
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