从基于人工神经网络的近距离分析中估计了几种木质生物质的热量值
I Ketut Gary Devara1, Windy Ayu Lestari1, Uma Maheshwera Reddy Paturi2
1Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea.
Materials (Basel, Switzerland)
|July 30, 2025
概括
这项研究开发了一个人工神经网络 (ANN) 模型,使用近距离分析准确预测木材生物质的更高加热值 (HHV). 该ANN模型为可再生能源评估提供了一个强大的工具.
科学领域:
- 生物质能源研究生物质能源研究
- 材料科学中的人工智能
- 可再生能源预测建模的建模.
背景情况:
- 准确估计木材生物质更高的加热值 (HHV) 对于评估其作为可再生能源的潜力至关重要.
- 目前用于HHV预测的方法可能缺乏在各种生物质类型中准确性或通用性.
研究的目的:
- 开发和验证人工神经网络 (ANN) 模型,用于预测木质生物质的HHV.
- 使用近距离分析参数 (水分,挥发物质,灰,固定碳) 作为ANN模型的输入.
- 为实时HHV预测创建一个用户友好的工具.
主要方法:
- 来自Phyllis数据库的252个木材生物质样本的数据集被用于模型培训和测试.
- 探索各种ANN架构,确定最佳的4-11-11-11-11配置.
- 使用反向传播算法训练最好的ANN模型,并使用R平方,MAE和RMSE指标评估性能.
- 开发一个用于实际应用的图形用户界面 (GUI).
主要成果:
- 优化的ANN模型获得了0.967的高调整R平方值,表明了出色的预测准确性.
- 该模型显示了较低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE),证实了其可靠性.
- 基准测试显示,ANN模型在准确性和概括性方面表现优于26个现有的实证和统计模型.
结论:
- 开发的ANN模型为预测木材生物质HHV提供了一个高度准确和强大的方法.
- 基于GUI的工具可为各种木材类型提供可访问的实时HHV估计.
- 这种方法支持碳利用策略的进步和新能源存储材料的开发.
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