| dc.contributor | | en-US |
| dc.creator | Kokya, Taher Ahmadzadeh | |
| dc.creator | Mehrdadi, Naser | |
| dc.creator | Ardestani, Mojtaba | |
| dc.creator | Baghvand, Akbar | |
| dc.creator | Kazemi, Arash | |
| dc.creator | Kalhori, Aram A. M. | |
| dc.date | 2017-05-23 | |
| dc.date.accessioned | 2019-04-16T16:42:49Z | |
| dc.date.available | 2019-04-16T16:42:49Z | |
| dc.identifier | https://www.jcchems.com/index.php/JCCHEMS/article/view/73 | |
| dc.identifier.uri | http://revistaschilenas.uchile.cl/handle/2250/42380 | |
| dc.description | UV inactivity and fluorescence irradiance of various organic substances are the major drawbacks for a wide applicability of UV based TOC assessment models, especially in drinking water utilities and environmental fields. The adoption of an intelligent model is the key factor to access a reliable and effective detection. The accurate training of the artificial neural network model and backward elimination of less significant parameters, conferred more predictive properties to TOC detection. This led to an efficient optimal TOC detection model based on turbidity, UV254 absorbance and true color. The validation of model performance was investigated through application of untrained scenarios. The outcome of the validation analysis showed a correlation coefficient of 0.87 and root mean square error of 0.48 while the training performance of the model showed 0.95 and 0.33 respectively. The results indicated that the trained ANN model was efficiently capable for TOC detection in water resources based on the main drivers. | en-US |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Sociedad Chilena de Química | en-US |
| dc.relation | https://www.jcchems.com/index.php/JCCHEMS/article/view/73/64 | |
| dc.rights | Copyright (c) 2017 Taher Ahmadzadeh Kokya, Naser Mehrdadi, Mojtaba Ardestani, Akbar Baghvand, Arash Kazemi, Aram A. M. Kalhori | en-US |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/4.0 | en-US |
| dc.source | Journal of the Chilean Chemical Society; Vol 61, No 3 (2016): Journal of the Chilean Chemical Society | en-US |
| dc.source | 0717-9707 | |
| dc.subject | Total Organic Carbon; Modeling; Artificial Neural Network; UV254; Color; Turbidity | en-US |
| dc.title | INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON | en-US |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | | en-US |
