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使用拉丁式超立方体采样和基于人工神经网络的方法高效高加热值估计.

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  • 1Department of Electrical Engineering, University Institute of Technology, HPU, Shimla, India.

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概括
此摘要是机器生成的。

本研究引入了一种结合人工神经网络 (ANN) 和拉丁超立方样本 (LHS) 的新方法,以准确估计城市固体废物 (MSW) 的高加热值 (HHV). 该研究确定碳是废物管理中HHV预测中最有影响力的元素.

关键词:
人工神经网络的人工神经网络具有高加热值的高加热值.拉丁语超立方体采样采样城市固体废物城市固体废物突触重量是一种突触重量.最终分析 最终分析

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科学领域:

  • 环境工程 环境工程
  • 恢复能源 恢复能源
  • 废物管理 废物管理

背景情况:

  • 准确估计城市固体废物 (MSW) 的高加热值 (HHV) 对于最大限度地利用废物转化能源 (WTE) 系统的能量回收至关重要.
  • 在MSW的元素组成的不确定性对准确的HHV预测提出了挑战.

研究的目的:

  • 开发和验证一个可靠的方法来预测MSW的HHV.
  • 确定影响MSW HHV的关键元素参数.
  • 提高WTE系统操作的准确性和可靠性.

主要方法:

  • 整合人工神经网络 (ANN) 模型与拉丁超立方样本 (LHS) 进行不确定性处理.
  • 使用元素组成 (碳,,,硫,氧) 作为ANN模型的输入参数.
  • 采用突触重量方法来确定参数显著性.

主要成果:

  • 拟议的ANN-LHS模型实现了2.18%的最小平均绝对百分比误差 (MAPE).
  • 统计绩效指标包括0.012的MSE,0.107的RMSE和0.767.7的R2.
  • 碳 (C) 被确定为影响MSWHV的最重要的参数.

结论:

  • 联合ANN-LHS方法为MSW HHV估计提供了准确,经济和强大的方法.
  • 这种方法通过解决组成的不确定性来加强MSW管理和WTE转换过程.
  • 这些发现支持提高废物转化能源应用的效率和有效性.