采用FTIR光谱法对煤炭和煤灰中的进行了新的多模型估计
Arya Vinod1, Anup Krishna Prasad2,3, Sameeksha Mishra1
1Photogeology and Image Processing Laboratory, Department of Applied Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India.
Scientific reports
|June 14, 2024
概括
精确监测煤炭和煤灰中的对于效率和环境保护至关重要. 福利埃变换红外光谱法 (FTIR) 与机器学习相结合,提供了一种快速可靠的方法来量化低水平.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
背景情况:
- 煤炭和煤灰中的监测对于优化发电厂效率,降低成本和最大限度地减少环境影响至关重要.
- 像X射线光 (XRF) 这样的传统方法在检测低度方面面临挑战,原因是信号强度较弱.
- 福利埃变换红外光谱 (FTIR) 呈现出一种经济高效且快速的替代方案,尽管其在这些矩阵中用于量化的应用尚未得到充分探索.
研究的目的:
- 研究FTIR光谱学与机器学习模型相结合的潜力,以准确量化煤炭和煤灰中的含量.
- 开发和验证用于快速检测的多模型方法,解决现有方法的局限性.
- 建立一个可靠的近实时分析工具,用于评估煤炭和煤灰中的含量.
主要方法:
- 利用FTIR光谱法捕获与特定功能群和矿物质相关的中红外吸收峰值强度.
- 开发和应用机器学习回归模型,包括断片线性回归 (PLR),部分最小平方回归 (PLSR),随机森林 (RF) 和支持向量回归 (SVR).
- 实施了结合PLR,PLSR和RF的多模型估计策略,以提高量化准确度.
主要成果:
- 多模型FTIR方法在量化煤炭中的 (R=0.836) 和煤灰 (R=0.803) 中表现出强的表现.
- 实现了低误差指标,包括煤炭的0.735 ppm和煤灰的0.676 ppm的根平均平方误差 (RMSE).
- 开发的模型为快速和近乎实时的测量提供了可靠的方法,并有可能适用于其他自然样本.
结论:
- 结合多模型机器学习方法 (PLR,PLSR,RF) 的FTIR光谱是一种可行且有效的工具,用于快速量化煤炭和煤灰中的.
- 这种方法克服了低水平检测传统技术的局限性,提供了更高的效率和准确性.
- 这些发现表明,这种技术在土壤和页岩等类似的自然基质中对分析具有更广泛的适用性.
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