TY  - JOUR
AU  - Mitrović, Tatjana
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Lazović, Saša
PY  - 2020
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4513
AB  - In this paper, the results of decolourisation of Reactive Orange 16 (RO 16), Reactive Blue 19 (RB 19) and Direct Red 28 (DR 28) textile dyes in aqueous solution by plasma needle are presented. Treatment time, feed gas flow rate (1, 4 and 8 dm(3)min(-1)) and gas composition (Ar, Ar/O-2) were optimized to achieve the best performance of the plasma treatment. An artificial neural network (ANN) was used for the prediction of parameters relevant for the decolourisation outcome. It was found that more than 95 % decolourisation could be achieved for all three dyes after plasma treatment, although the decolourisation of DR 28 was much slower than those of the other two dyes, which could be explained by the complexity of its molecular structure. It was concluded that the oxidation was very dependent on all three mentioned parameters. The ANN predicted the treatment time as the crucial factor for decolourisation performance of RO 16 and DR 28, while the Ar flow rate was the most relevant for RB 19 decolourisation. The obtained results suggest that the plasma needle is a promising tool for the oxidation of organic pollutants and that an ANN could be used for optimization of the treatment parameters to achieve high removal rates.
PB  - Srpsko hemijsko društvo, Beograd
T2  - Journal of the Serbian Chemical Society
T1  - ANN prediction of the efficiency of the decolourisation of organic dyes in wastewater by plasma needle
EP  - 844
IS  - 6
SP  - 831
VL  - 85
DO  - 10.2298/JSC191004002M
ER  - 

@article{
author = "Mitrović, Tatjana and Ristić, Mirjana and Perić-Grujić, Aleksandra and Lazović, Saša",
year = "2020",
abstract = "In this paper, the results of decolourisation of Reactive Orange 16 (RO 16), Reactive Blue 19 (RB 19) and Direct Red 28 (DR 28) textile dyes in aqueous solution by plasma needle are presented. Treatment time, feed gas flow rate (1, 4 and 8 dm(3)min(-1)) and gas composition (Ar, Ar/O-2) were optimized to achieve the best performance of the plasma treatment. An artificial neural network (ANN) was used for the prediction of parameters relevant for the decolourisation outcome. It was found that more than 95 % decolourisation could be achieved for all three dyes after plasma treatment, although the decolourisation of DR 28 was much slower than those of the other two dyes, which could be explained by the complexity of its molecular structure. It was concluded that the oxidation was very dependent on all three mentioned parameters. The ANN predicted the treatment time as the crucial factor for decolourisation performance of RO 16 and DR 28, while the Ar flow rate was the most relevant for RB 19 decolourisation. The obtained results suggest that the plasma needle is a promising tool for the oxidation of organic pollutants and that an ANN could be used for optimization of the treatment parameters to achieve high removal rates.",
publisher = "Srpsko hemijsko društvo, Beograd",
journal = "Journal of the Serbian Chemical Society",
title = "ANN prediction of the efficiency of the decolourisation of organic dyes in wastewater by plasma needle",
pages = "844-831",
number = "6",
volume = "85",
doi = "10.2298/JSC191004002M"
}

Mitrović, T., Ristić, M., Perić-Grujić, A.,& Lazović, S.. (2020). ANN prediction of the efficiency of the decolourisation of organic dyes in wastewater by plasma needle. in Journal of the Serbian Chemical Society
Srpsko hemijsko društvo, Beograd., 85(6), 831-844.
https://doi.org/10.2298/JSC191004002M

Mitrović T, Ristić M, Perić-Grujić A, Lazović S. ANN prediction of the efficiency of the decolourisation of organic dyes in wastewater by plasma needle. in Journal of the Serbian Chemical Society. 2020;85(6):831-844.
doi:10.2298/JSC191004002M .

Mitrović, Tatjana, Ristić, Mirjana, Perić-Grujić, Aleksandra, Lazović, Saša, "ANN prediction of the efficiency of the decolourisation of organic dyes in wastewater by plasma needle" in Journal of the Serbian Chemical Society, 85, no. 6 (2020):831-844,
https://doi.org/10.2298/JSC191004002M . .

TY  - JOUR
AU  - Mitrović, Tatjana
AU  - Tomić, Nataša M.
AU  - Đukić-Vuković, Aleksandra
AU  - Dohcević-Mitrović, Zorana
AU  - Lazović, Saša
PY  - 2020
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4532
AB  - Purpose Every advanced oxidation process (AOP) has its limitations in water purification. Novel designs with simultaneous application of different AOPs can offer better solutions for cleaner water. Methods We have comparatively studied two advanced oxidation processes (AOPs) on decolourisation of Reactive Orange 16 (RO 16) azo dye pollutant from water: gas plasma treatment by low power atmospheric pressure plasma using novel plasma needle configuration, and semiconductor heterogeneous photocatalysis using titanium dioxide (TiO2) nanopowders. Additionally, simultaneous application of two advanced oxidation processes on azo dye decolourisation was studied. Results It was found that plasma treatment is very efficient system for the dye removal even for low flow rates (1 slm) of the Ar as feed gas. The presence of 10% of O-2 in Ar flow intensified dye oxidation process and shortened required time for total decolourisation. When plasma and catalyst were simultaneously applied, TiO2 was activated with a few Watts plasma source as well as 300 W UV lamp source. The synergic effect of two AOPs was more pronounced for higher feed gas flow rates, resulting in improved decolourisation efficiency. Conclusion Plasma needle can efficiently remove Reactive Orange 16 azo dye from water with a power consumption of only few Watts. With the addition of TiO2 the removal efficiency is significantly improved. [GRAPHICS] .
PB  - Springer, Dordrecht
T2  - Waste and Biomass Valorization
T1  - Atmospheric Plasma Supported by TiO2 Catalyst for Decolourisation of Reactive Orange 16 Dye in Water
EP  - 6854
IS  - 12
SP  - 6841
VL  - 11
DO  - 10.1007/s12649-019-00928-y
ER  - 

@article{
author = "Mitrović, Tatjana and Tomić, Nataša M. and Đukić-Vuković, Aleksandra and Dohcević-Mitrović, Zorana and Lazović, Saša",
year = "2020",
abstract = "Purpose Every advanced oxidation process (AOP) has its limitations in water purification. Novel designs with simultaneous application of different AOPs can offer better solutions for cleaner water. Methods We have comparatively studied two advanced oxidation processes (AOPs) on decolourisation of Reactive Orange 16 (RO 16) azo dye pollutant from water: gas plasma treatment by low power atmospheric pressure plasma using novel plasma needle configuration, and semiconductor heterogeneous photocatalysis using titanium dioxide (TiO2) nanopowders. Additionally, simultaneous application of two advanced oxidation processes on azo dye decolourisation was studied. Results It was found that plasma treatment is very efficient system for the dye removal even for low flow rates (1 slm) of the Ar as feed gas. The presence of 10% of O-2 in Ar flow intensified dye oxidation process and shortened required time for total decolourisation. When plasma and catalyst were simultaneously applied, TiO2 was activated with a few Watts plasma source as well as 300 W UV lamp source. The synergic effect of two AOPs was more pronounced for higher feed gas flow rates, resulting in improved decolourisation efficiency. Conclusion Plasma needle can efficiently remove Reactive Orange 16 azo dye from water with a power consumption of only few Watts. With the addition of TiO2 the removal efficiency is significantly improved. [GRAPHICS] .",
publisher = "Springer, Dordrecht",
journal = "Waste and Biomass Valorization",
title = "Atmospheric Plasma Supported by TiO2 Catalyst for Decolourisation of Reactive Orange 16 Dye in Water",
pages = "6854-6841",
number = "12",
volume = "11",
doi = "10.1007/s12649-019-00928-y"
}

Mitrović, T., Tomić, N. M., Đukić-Vuković, A., Dohcević-Mitrović, Z.,& Lazović, S.. (2020). Atmospheric Plasma Supported by TiO2 Catalyst for Decolourisation of Reactive Orange 16 Dye in Water. in Waste and Biomass Valorization
Springer, Dordrecht., 11(12), 6841-6854.
https://doi.org/10.1007/s12649-019-00928-y

Mitrović T, Tomić NM, Đukić-Vuković A, Dohcević-Mitrović Z, Lazović S. Atmospheric Plasma Supported by TiO2 Catalyst for Decolourisation of Reactive Orange 16 Dye in Water. in Waste and Biomass Valorization. 2020;11(12):6841-6854.
doi:10.1007/s12649-019-00928-y .

Mitrović, Tatjana, Tomić, Nataša M., Đukić-Vuković, Aleksandra, Dohcević-Mitrović, Zorana, Lazović, Saša, "Atmospheric Plasma Supported by TiO2 Catalyst for Decolourisation of Reactive Orange 16 Dye in Water" in Waste and Biomass Valorization, 11, no. 12 (2020):6841-6854,
https://doi.org/10.1007/s12649-019-00928-y . .

TY  - JOUR
AU  - Mitrović, Tatjana
AU  - Antanasijević, Davor
AU  - Lazović, Saša
AU  - Perić-Grujić, Aleksandra
AU  - Ristić, Mirjana
PY  - 2019
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4303
AB  - Rationalization of water quality monitoring stations nowadays is applied in many countries. In some cases, missing data from abandoned/inactive stations, spatial and temporal, could be very important, hence the use of artificial neural networks (ANNs) for virtual water quality monitoring at inactive monitoring sites was investigated. The aim was to develop single-output and simultaneous ANNs for the spatial interpolation of 18 water quality parameters at single- and multi-inactive monitoring sites on Danube River course through Serbia. Those different modeling approaches were considered in order to determine the most suitable combination of models. The variable selection and sensitivity analysis in the case of simultaneous models were performed using a modified procedure based on Monte Carlo Simulations (MCS). In general, the multi-target models tend to be more accurate than single target ones, while single output models outperform the simultaneous ones. Hence, for particular monitoring network and set of water quality parameters the optimal combination of models must be defined based on model's accuracy and computational effort needed. The MCS selection procedure has proved to be efficient only in the case of simultaneous multi-target model. MCS based analysis of input-output interactions has shown all significant interactions in the case of simultaneous single-target are grouped as a complex duster of interactions, where majority of inputs influence on several outputs. In the case multi-target model those interactions were portioned in five separate clusters, there majority of them mimic the input-output interactions that are present in single output models. The modeling strategy for study area was proposed on the basis of the performance of created models (mean average percentage error  lt  10%): simultaneous multi-target model for pH, alkalinity, conductivity, hardness, dissolved oxygen, HCO3-,SO42- and Ca, single-output multi-target models for temperature and Cl-, simultaneous single-target models for Mg and CO2, single output single target models for NO3-.
PB  - Elsevier Science Bv, Amsterdam
T2  - Science of the Total Environment
T1  - Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia)
EP  - 1009
SP  - 1000
VL  - 654
DO  - 10.1016/j.scitotenv.2018.11.189
ER  - 

@article{
author = "Mitrović, Tatjana and Antanasijević, Davor and Lazović, Saša and Perić-Grujić, Aleksandra and Ristić, Mirjana",
year = "2019",
abstract = "Rationalization of water quality monitoring stations nowadays is applied in many countries. In some cases, missing data from abandoned/inactive stations, spatial and temporal, could be very important, hence the use of artificial neural networks (ANNs) for virtual water quality monitoring at inactive monitoring sites was investigated. The aim was to develop single-output and simultaneous ANNs for the spatial interpolation of 18 water quality parameters at single- and multi-inactive monitoring sites on Danube River course through Serbia. Those different modeling approaches were considered in order to determine the most suitable combination of models. The variable selection and sensitivity analysis in the case of simultaneous models were performed using a modified procedure based on Monte Carlo Simulations (MCS). In general, the multi-target models tend to be more accurate than single target ones, while single output models outperform the simultaneous ones. Hence, for particular monitoring network and set of water quality parameters the optimal combination of models must be defined based on model's accuracy and computational effort needed. The MCS selection procedure has proved to be efficient only in the case of simultaneous multi-target model. MCS based analysis of input-output interactions has shown all significant interactions in the case of simultaneous single-target are grouped as a complex duster of interactions, where majority of inputs influence on several outputs. In the case multi-target model those interactions were portioned in five separate clusters, there majority of them mimic the input-output interactions that are present in single output models. The modeling strategy for study area was proposed on the basis of the performance of created models (mean average percentage error  lt  10%): simultaneous multi-target model for pH, alkalinity, conductivity, hardness, dissolved oxygen, HCO3-,SO42- and Ca, single-output multi-target models for temperature and Cl-, simultaneous single-target models for Mg and CO2, single output single target models for NO3-.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Science of the Total Environment",
title = "Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia)",
pages = "1009-1000",
volume = "654",
doi = "10.1016/j.scitotenv.2018.11.189"
}

Mitrović, T., Antanasijević, D., Lazović, S., Perić-Grujić, A.,& Ristić, M.. (2019). Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia). in Science of the Total Environment
Elsevier Science Bv, Amsterdam., 654, 1000-1009.
https://doi.org/10.1016/j.scitotenv.2018.11.189

Mitrović T, Antanasijević D, Lazović S, Perić-Grujić A, Ristić M. Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia). in Science of the Total Environment. 2019;654:1000-1009.
doi:10.1016/j.scitotenv.2018.11.189 .

Mitrović, Tatjana, Antanasijević, Davor, Lazović, Saša, Perić-Grujić, Aleksandra, Ristić, Mirjana, "Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia)" in Science of the Total Environment, 654 (2019):1000-1009,
https://doi.org/10.1016/j.scitotenv.2018.11.189 . .