A lightweight convolutional neural network model for quantitative analysis of phosphate ore slurry based on laser-induced breakdown spectroscopy

2021 ◽  
Author(s):  
Hai yan Dong ◽  
Lanxiang Sun ◽  
Lifeng Qi ◽  
Hai bin Yu ◽  
Peng Zeng
Keyword(s):  
Neural Network ◽  
Control Parameter ◽  
Phosphorus Content ◽  
Laser Induced Breakdown Spectroscopy ◽  
Flotation Process ◽  
Phosphate Ore ◽  
Stream Monitoring ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Control Stability

The phosphorus content is an important control parameter in the flotation process of phosphate ore slurry. The real-time and on-stream monitoring of the phosphorus content can improve the control stability...

Energetic materials identification by laser-induced breakdown spectroscopy combined with artificial neural network

2017 ◽  
Vol 56 (12) ◽  
pp. 3372 ◽  
Author(s):  
Amir Hossein Farhadian ◽  
Masoud Kavosh Tehrani ◽  
Mohammad Hossein Keshavarz ◽  
Seyyed Mohammad Reza Darbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energetic Materials ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Artificial Neural

Quantitative analysis of the major components of coal ash using laser induced breakdown spectroscopy coupled with a wavelet neural network (WNN)

2016 ◽  
Vol 8 (7) ◽  
pp. 1674-1680 ◽  
Author(s):  
Jiao Wei ◽  
Juan Dong ◽  
Tianlong Zhang ◽  
Zhanmei Wang ◽  
Hua Li
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Coal Ash ◽  
Wavelet Neural Network ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

A laser induced breakdown spectroscopy (LIBS) technique combined with a wavelet neural network (WNN) was proposed for the quantitative analysis of the major components of coal ash.

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