Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs

2016 ◽  
Vol 33 ◽  
pp. 687-702 ◽  
Author(s):  
Xian Shi ◽  
Jian Wang ◽  
Gang Liu ◽  
Liu Yang ◽  
Xinmin Ge ◽  
...  
Keyword(s):  
Neural Networks ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Extreme Learning Machine ◽  
Total Organic Carbon Content ◽  
Organic Carbon Content ◽  
Learning Machine ◽  
Organic Shale

scholarly journals Relationship between Color CIEL∗a∗b∗ and Total Organic Carbon in Compost

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Jhon Jairo Palechor-Tróchez ◽  
Luis Eduardo Ordoñez Santos ◽  
Hector Samuel Villada-Castillo
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Total Organic Carbon Content ◽  
Organic Carbon Content ◽  
Particle Sizes ◽  
Surface Color ◽  
Total Organic Carbon Concentration ◽  
Organic Carbon Concentration ◽  
Pearson Correlations

The CIEL∗a∗b∗ coordinates and the total organic carbon content in compost were correlated. Two particle sizes of 0.5 and 2 mm were obtained in the compost samples; the surface color was analyzed with a CIEL∗a∗b∗ colorimeter and the total organic carbon content by spectrophotometry at 588.9 nm. The results indicate that all chromaticity values were significantly affected (p<0.001) by particle size. Chromaticity values a∗, b∗, C∗, and h° showed significantly strong Pearson correlations (r>0.95). The coordinates a∗ (r=−0.992) and b∗ (r=0.968) have the potential to be used in estimating the total organic carbon concentration in the compost samples analyzed.

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