INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON

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...

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Autores principales:	KOKYA,TAHER AHMADZADEH, MEHRDADI,NASER, ARDESTANI,MOJTABA, BAGHVAND,AKBAR, KAZEMI,ARASH, KALHORI,ARAM A. M
Lenguaje:	English
Publicado:	Sociedad Chilena de Química 2016
Materias:	Total Organic Carbon Modeling Artificial Neural Network UV254 Color Turbidity
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072016000300010
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spelling	oai:scielo:S0717-970720160003000102017-06-14INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBONKOKYA,TAHER AHMADZADEHMEHRDADI,NASERARDESTANI,MOJTABABAGHVAND,AKBARKAZEMI,ARASHKALHORI,ARAM A. M Total Organic Carbon Modeling Artificial Neural Network UV254 Color Turbidity 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.info:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.61 n.3 20162016-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072016000300010en10.4067/S0717-97072016000300010
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Total Organic Carbon Modeling Artificial Neural Network UV254 Color Turbidity
spellingShingle	Total Organic Carbon Modeling Artificial Neural Network UV254 Color Turbidity KOKYA,TAHER AHMADZADEH MEHRDADI,NASER ARDESTANI,MOJTABA BAGHVAND,AKBAR KAZEMI,ARASH KALHORI,ARAM A. M INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON
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.
author	KOKYA,TAHER AHMADZADEH MEHRDADI,NASER ARDESTANI,MOJTABA BAGHVAND,AKBAR KAZEMI,ARASH KALHORI,ARAM A. M
author_facet	KOKYA,TAHER AHMADZADEH MEHRDADI,NASER ARDESTANI,MOJTABA BAGHVAND,AKBAR KAZEMI,ARASH KALHORI,ARAM A. M
author_sort	KOKYA,TAHER AHMADZADEH
title	INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON
title_short	INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON
title_full	INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON
title_fullStr	INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON
title_full_unstemmed	INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON
title_sort	intelligent multivariate model for the optical detection of total organic carbon
publisher	Sociedad Chilena de Química
publishDate	2016
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072016000300010
work_keys_str_mv	AT kokyataherahmadzadeh intelligentmultivariatemodelfortheopticaldetectionoftotalorganiccarbon AT mehrdadinaser intelligentmultivariatemodelfortheopticaldetectionoftotalorganiccarbon AT ardestanimojtaba intelligentmultivariatemodelfortheopticaldetectionoftotalorganiccarbon AT baghvandakbar intelligentmultivariatemodelfortheopticaldetectionoftotalorganiccarbon AT kazemiarash intelligentmultivariatemodelfortheopticaldetectionoftotalorganiccarbon AT kalhoriaramam intelligentmultivariatemodelfortheopticaldetectionoftotalorganiccarbon
_version_	1714200896256081920

INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON

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