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 . .