TY  - JOUR
AU  - Rajaković-Ognjanović, Vladana
AU  - Lekić, Branislava
AU  - Živojinović, Dragana Z.
AU  - Rajaković, Ljubinka V.
PY  - 2014
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/620
AB  - Analysis and monitoring of arsenic is still a challenging analytical task. Due to its complex behaviour (different forms of arsenic that can be present depending on pH and oxidation states of arsenic) as well as demanding analytical procedures and instrumental tools for control of arsenic concentration in drinking water which is set to 10 mu g L-1, there are still some open questions and issues when arsenic is the scientific topic. In this paper the idea was to use a multivariate statistical approach to identify the key variables and their relation to high arsenic concentration in surface waters of Serbia. The main idea was to identify and connect the key water quality parameters with arsenic concentration and to suggest adequate treatment technologies for water purification and arsenic removal. The data set for multivariate statistical approach were water quality parameters of surface water samples from Serbia. The artificial neural network (ANN) was applied for data analysis. After applying ANN the results showed strong relation between arsenic concentration and P-tot, SO42-, COD, carbonate, N-org, DO, and SiO2 content. What could be concluded from the obtained results is that high concentration of organic matter, proportional to nutrients (nitrogen and phosphorus), silica (SiO2) and dissolved oxygen highly correlates with the dissolved arsenic which implies that the most adequate technology for the water treatment could be precipitation, which in general includes coagulation. What remains unquestioned and needs to be performed is arsenic speciation analysis.
T2  - Environmental Engineering and Management Journal
T1  - Challenging analytical task: analysis and monitoring of arsenic species in water
EP  - 2282
IS  - 9
SP  - 2275
VL  - 13
DO  - 10.30638/eemj.2014.254
ER  - 

@article{
author = "Rajaković-Ognjanović, Vladana and Lekić, Branislava and Živojinović, Dragana Z. and Rajaković, Ljubinka V.",
year = "2014",
abstract = "Analysis and monitoring of arsenic is still a challenging analytical task. Due to its complex behaviour (different forms of arsenic that can be present depending on pH and oxidation states of arsenic) as well as demanding analytical procedures and instrumental tools for control of arsenic concentration in drinking water which is set to 10 mu g L-1, there are still some open questions and issues when arsenic is the scientific topic. In this paper the idea was to use a multivariate statistical approach to identify the key variables and their relation to high arsenic concentration in surface waters of Serbia. The main idea was to identify and connect the key water quality parameters with arsenic concentration and to suggest adequate treatment technologies for water purification and arsenic removal. The data set for multivariate statistical approach were water quality parameters of surface water samples from Serbia. The artificial neural network (ANN) was applied for data analysis. After applying ANN the results showed strong relation between arsenic concentration and P-tot, SO42-, COD, carbonate, N-org, DO, and SiO2 content. What could be concluded from the obtained results is that high concentration of organic matter, proportional to nutrients (nitrogen and phosphorus), silica (SiO2) and dissolved oxygen highly correlates with the dissolved arsenic which implies that the most adequate technology for the water treatment could be precipitation, which in general includes coagulation. What remains unquestioned and needs to be performed is arsenic speciation analysis.",
journal = "Environmental Engineering and Management Journal",
title = "Challenging analytical task: analysis and monitoring of arsenic species in water",
pages = "2282-2275",
number = "9",
volume = "13",
doi = "10.30638/eemj.2014.254"
}

Rajaković-Ognjanović, V., Lekić, B., Živojinović, D. Z.,& Rajaković, L. V.. (2014). Challenging analytical task: analysis and monitoring of arsenic species in water. in Environmental Engineering and Management Journal, 13(9), 2275-2282.
https://doi.org/10.30638/eemj.2014.254

Rajaković-Ognjanović V, Lekić B, Živojinović DZ, Rajaković LV. Challenging analytical task: analysis and monitoring of arsenic species in water. in Environmental Engineering and Management Journal. 2014;13(9):2275-2282.
doi:10.30638/eemj.2014.254 .

Rajaković-Ognjanović, Vladana, Lekić, Branislava, Živojinović, Dragana Z., Rajaković, Ljubinka V., "Challenging analytical task: analysis and monitoring of arsenic species in water" in Environmental Engineering and Management Journal, 13, no. 9 (2014):2275-2282,
https://doi.org/10.30638/eemj.2014.254 . .

TY  - JOUR
AU  - Živojinović, Dragana Z.
AU  - Rajaković-Ognjanović, Vladana
AU  - Onjia, Antonije
AU  - Rajaković, Ljubinka V.
PY  - 2013
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/523
AB  - In this study, a multivariate statistical approach was used to identify the key variables responsible for process water quality in a power plant. The ion species that could cause corrosion in one of the major thermal power plants (TPP) in Serbia were monitored. A suppressed ion chromatographic (IC) method for the determination of the target anions and cations at trace levels was applied. In addition, some metals important for corrosion, i.e., copper and iron, were also analysed by the graphite furnace atomic absorption spectrophotometric (GFAAS) method. The control parameters, i.e., pH, dissolved oxygen and silica, were measured on-line. The analysis of a series of representative samples from the TPP Nikola Tesla, collected in different plant operation modes, was performed. Every day laboratory and on-line analysis provides a large number of data in relation to the quality of water in the water-steam cycle (WSC) which should be evaluated and processed. The goal of this investigation was to apply multivariate statistical techniques and choose the most applicable technique for this case. Factor analysis (FA), especially principal component analysis (PCA) and cluster analysis (CA) were investigated. These methods were applied for the evaluation of the spatial/temporal variations of process water and for the estimation of 13 quality parameters which were monitored at 11 locations in the WSC in different working conditions during a twelve month period. It was concluded that PCA was the most useful method for identifying functional relations between the elements. After data reduction, four main factors controlling the variability were identified. Hierarchical cluster analysis (HCA) was applied for sample differentiation according to the sample location and working mode of the TPP. On the basis of this research, the new design of an optimal monitoring strategy for future analysis was proposed with a reduced number of measured parameters and with reduced frequency of their measurements.
T2  - Central European Journal of Chemistry
T1  - Spatial variations in the distribution of trace ionic impurities in the water-steam cycle in a thermal power plant based on a multivariate statistical approach
EP  - 1470
IS  - 9
SP  - 1456
VL  - 11
DO  - 10.2478/s11532-013-0286-4
ER  - 

@article{
author = "Živojinović, Dragana Z. and Rajaković-Ognjanović, Vladana and Onjia, Antonije and Rajaković, Ljubinka V.",
year = "2013",
abstract = "In this study, a multivariate statistical approach was used to identify the key variables responsible for process water quality in a power plant. The ion species that could cause corrosion in one of the major thermal power plants (TPP) in Serbia were monitored. A suppressed ion chromatographic (IC) method for the determination of the target anions and cations at trace levels was applied. In addition, some metals important for corrosion, i.e., copper and iron, were also analysed by the graphite furnace atomic absorption spectrophotometric (GFAAS) method. The control parameters, i.e., pH, dissolved oxygen and silica, were measured on-line. The analysis of a series of representative samples from the TPP Nikola Tesla, collected in different plant operation modes, was performed. Every day laboratory and on-line analysis provides a large number of data in relation to the quality of water in the water-steam cycle (WSC) which should be evaluated and processed. The goal of this investigation was to apply multivariate statistical techniques and choose the most applicable technique for this case. Factor analysis (FA), especially principal component analysis (PCA) and cluster analysis (CA) were investigated. These methods were applied for the evaluation of the spatial/temporal variations of process water and for the estimation of 13 quality parameters which were monitored at 11 locations in the WSC in different working conditions during a twelve month period. It was concluded that PCA was the most useful method for identifying functional relations between the elements. After data reduction, four main factors controlling the variability were identified. Hierarchical cluster analysis (HCA) was applied for sample differentiation according to the sample location and working mode of the TPP. On the basis of this research, the new design of an optimal monitoring strategy for future analysis was proposed with a reduced number of measured parameters and with reduced frequency of their measurements.",
journal = "Central European Journal of Chemistry",
title = "Spatial variations in the distribution of trace ionic impurities in the water-steam cycle in a thermal power plant based on a multivariate statistical approach",
pages = "1470-1456",
number = "9",
volume = "11",
doi = "10.2478/s11532-013-0286-4"
}

Živojinović, D. Z., Rajaković-Ognjanović, V., Onjia, A.,& Rajaković, L. V.. (2013). Spatial variations in the distribution of trace ionic impurities in the water-steam cycle in a thermal power plant based on a multivariate statistical approach. in Central European Journal of Chemistry, 11(9), 1456-1470.
https://doi.org/10.2478/s11532-013-0286-4

Živojinović DZ, Rajaković-Ognjanović V, Onjia A, Rajaković LV. Spatial variations in the distribution of trace ionic impurities in the water-steam cycle in a thermal power plant based on a multivariate statistical approach. in Central European Journal of Chemistry. 2013;11(9):1456-1470.
doi:10.2478/s11532-013-0286-4 .

Živojinović, Dragana Z., Rajaković-Ognjanović, Vladana, Onjia, Antonije, Rajaković, Ljubinka V., "Spatial variations in the distribution of trace ionic impurities in the water-steam cycle in a thermal power plant based on a multivariate statistical approach" in Central European Journal of Chemistry, 11, no. 9 (2013):1456-1470,
https://doi.org/10.2478/s11532-013-0286-4 . .

TY  - JOUR
AU  - Rajaković-Ognjanović, Vladana
AU  - Živojinović, Dragana Z.
AU  - Grgur, Branimir N.
AU  - Rajaković, Ljubinka V.
PY  - 2011
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/410
AB  - A more effective chemical control in the water-steam cycle (WSC) of thermal power plants (TPP) is proposed in this paper. Minimization of corrosion effects by the production of ultra pure water and its strict control is the basis of all the investigated processes. The research involved the analysis of water samples in the WSC through key water quality parameters and by the most convenient analytical tools. The necessity for the stricter chemical control is demonstrated through a concrete example of the TPP Nikola Tesla, Serbia. After a thorough analysis of the chemical control system of the WSC, diagnostic and control parameters were chosen for continuous systematic measurements. Sodium and chloride ions were recognized as the ions which indicate the corrosion potential of the water and give insight into the proper production and maintenance of water within the WSC. Chemical transformations of crucial corrosion elements, iron and silica, were considered and related to their quantitative values.
T2  - Applied Thermal Engineering
T1  - Improvement of chemical control in the water-steam cycle of thermal power plants
EP  - 128
IS  - 1
SP  - 119
VL  - 31
DO  - 10.1016/j.applthermaleng.2010.08.028
ER  - 

@article{
author = "Rajaković-Ognjanović, Vladana and Živojinović, Dragana Z. and Grgur, Branimir N. and Rajaković, Ljubinka V.",
year = "2011",
abstract = "A more effective chemical control in the water-steam cycle (WSC) of thermal power plants (TPP) is proposed in this paper. Minimization of corrosion effects by the production of ultra pure water and its strict control is the basis of all the investigated processes. The research involved the analysis of water samples in the WSC through key water quality parameters and by the most convenient analytical tools. The necessity for the stricter chemical control is demonstrated through a concrete example of the TPP Nikola Tesla, Serbia. After a thorough analysis of the chemical control system of the WSC, diagnostic and control parameters were chosen for continuous systematic measurements. Sodium and chloride ions were recognized as the ions which indicate the corrosion potential of the water and give insight into the proper production and maintenance of water within the WSC. Chemical transformations of crucial corrosion elements, iron and silica, were considered and related to their quantitative values.",
journal = "Applied Thermal Engineering",
title = "Improvement of chemical control in the water-steam cycle of thermal power plants",
pages = "128-119",
number = "1",
volume = "31",
doi = "10.1016/j.applthermaleng.2010.08.028"
}

Rajaković-Ognjanović, V., Živojinović, D. Z., Grgur, B. N.,& Rajaković, L. V.. (2011). Improvement of chemical control in the water-steam cycle of thermal power plants. in Applied Thermal Engineering, 31(1), 119-128.
https://doi.org/10.1016/j.applthermaleng.2010.08.028

Rajaković-Ognjanović V, Živojinović DZ, Grgur BN, Rajaković LV. Improvement of chemical control in the water-steam cycle of thermal power plants. in Applied Thermal Engineering. 2011;31(1):119-128.
doi:10.1016/j.applthermaleng.2010.08.028 .

Rajaković-Ognjanović, Vladana, Živojinović, Dragana Z., Grgur, Branimir N., Rajaković, Ljubinka V., "Improvement of chemical control in the water-steam cycle of thermal power plants" in Applied Thermal Engineering, 31, no. 1 (2011):119-128,
https://doi.org/10.1016/j.applthermaleng.2010.08.028 . .