Predicting Total Organic Carbon Removal Efficiency and Coagulation Dosage Using Artificial Neural Networks

2012 ◽  
Vol 29 (8) ◽  
pp. 743-750 ◽  
Author(s):  
Solitha Dharman ◽  
Viswanathan Chandramouli ◽  
Srinivasa Lingireddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Removal Efficiency ◽  
Carbon Removal ◽  
Total Organic Carbon Removal ◽  
Artificial Neural

Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale from the Spectral Gamma-Ray and Based on the Artificial Neural Networks

2021 ◽  
pp. 1-28
Author(s):  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin Elkatatny
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Linear Regression Analysis ◽  
Empirical Correlation ◽  
Training Data ◽  
Validation Data ◽  
Artificial Neural

Abstract Evaluation of the quality of unconventional hydrocarbon resources becomes a critical stage toward characterizing these resources, this evaluation requires evaluation of the total organic carbon (TOC). Generally, TOC is determined from laboratory experiments, however, it is hard to obtain a continuous profile for the TOC along the drilled formations using these experiments. Another way to evaluate the TOC is through the use of empirical correlation, the currently available correlations lack the accuracy especially when used in formations other than the ones used to develop these correlations. This study introduces an empirical equation for evaluation of the TOC in Devonian Duvernay shale from only gamma-ray and spectral gamma-ray logs of uranium, thorium, and potassium as well as a newly developed term that accounts for the TOC from the linear regression analysis. This new correlation was developed based on the artificial neural networks (ANN) algorithm which was learned on 750 datasets from Well-A. The developed correlation was tested and validated on 226 and 73 datasets from Well-B and Well-C, respectively. The results of this study indicated that for the training data, the TOC was predicted by the ANN with an AAPE of only 8.5%. Using the developed equation, the TOC was predicted with an AAPE of only 11.5% for the testing data. For the validation data, the developed equation overperformed the previous models in estimating the TOC with an AAPE of only 11.9%.

scholarly journals Total organic carbon (TOC) quantification using artificial neural networks: Improved prediction by leveraging XRF data

2021 ◽  
pp. 109302
Author(s):  
Septriandi A. Chan ◽  
Amjed M. Hassan ◽  
Muhammad Usman ◽  
John D. Humphrey ◽  
Yaser Alzayer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Artificial Neural

scholarly journals Degradation and mineralization of 4-tert-butylphenol in water using Fe-doped TiO2 catalysts

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ardak Makhatova ◽  
Gaukhar Ulykbanova ◽  
Shynggys Sadyk ◽  
Kali Sarsenbay ◽  
Timur Sh. Atabaev ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Photocatalytic Degradation ◽  
Total Organic Carbon ◽  
Uv Light ◽  
Electric Energy ◽  
Carbon Removal ◽  
Wet Impregnation ◽  
Total Organic Carbon Removal ◽  
Butyl Phenol ◽  
Pseudo Second Order

AbstractIn the present work, the photocatalytic degradation and mineralization of 4-tert-butylphenol in water was studied using Fe-doped TiO2 nanoparticles under UV light irradiation. Fe-doped TiO2 catalysts (0.5, 1, 2 and 4 wt.%) were prepared using wet impregnation and characterized via SEM/EDS, XRD, XRF and TEM, while their photocatalytic activity and stability was attended via total organic carbon, 4-tert-butyl phenol, acetic acid, formic acid and leached iron concentrations measurements. The effect of H2O2 addition was also examined. The 4% Fe/TiO2 demonstrated the highest photocatalytic efficiency in terms of total organic carbon removal (86%). The application of UV/H2O2 resulted in 31% total organic carbon removal and 100% 4-t-butylphenol conversion, however combining Fe/TiO2 catalysts with H2O2 under UV irradiation did not improve the photocatalytic performance. Increasing the content of iron on the catalyst from 0.5 to 4% considerably decreased the intermediates formed and increased the production of carbon dioxide. The photocatalytic degradation of 4-tert-butylphenol followed pseudo-second order kinetics. Leaching of iron was observed mainly in the case of 4% Fe/TiO2, but it was considered negligible taking into account the iron load on catalysts. The electric energy per order was found in the range of 28–147 kWh/m3/order and increased with increasing the iron content of the catalyst.

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