Browsing by Author "Mitrović, Tatjana"

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    ANN prediction of the decolourisation efficiency of the organic dyes in wastewater by plasma needle
    Mitrović, Tatjana; Ristić, Mirjana; Perić-Grujić, Aleksandra; Lazović, Saša
    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 dm3 min-1) and gas composition (Ar, Ar/O2) 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.
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    Atmospheric Plasma Supported by TiO2 Catalyst for Decolourisation of Reactive Orange 16 Dye in Water
    Mitrović, Tatjana; Tomić, Nataša; Đukić-Vuković, Aleksandra; Dohčević-Mitrović, Zorana; Lazović, Saša
    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 O2 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.
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    Non-thermal plasma needle as an effective tool in dimethoate removal from water
    Mitrović, Tatjana; Lazović, Saša; Nastasijević, Branislav; Pašti, Igor A.; Vasić, Vesna; Lazarević-Pašti, Tamara
    Intensive use of pesticides requires innovative approaches for their removal from the environment. Here we report the method for degradation of dimethoate in water using non-thermal plasma needle and analyze kinetics of dimethoate removal and possible degradation pathways. The effects of dimethoate initial concentration, plasma treatment time, Argon flow rate and the presence of radical promoters on the effectiveness of proposed method are evaluated. With argon flow rate of 0.5 slm (standard litres per minute) 1 × 10−4 M dimethoate can be removed within 30 min of treatment. Using UPLC analysis it was confirmed that one of the decomposition products is dimethoate oxo-analogue omethoate, which is in fact more toxic than dimethoate. However, the overall toxicity of contaminated water was reduced upon the treatment. The addition of H2O2 as a free radical promoter enhances dimethoate removal, while K2S2O8 results with selective conversion to omethoate. Using mass spectrometry in combination with the theoretical calculations, possible degradation pathways were proposed. The feasibility of the proposed method for dimethoate degradation in real water samples is confirmed. The proposed method is demonstrated as a highly effective approach for dimethoate removal without significant accumulation of undesirable toxic products and secondary waste.
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    Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia)
    Mitrović, Tatjana; Antanasijević, Davor; Lazović, Saša; Perić-Grujić, Aleksandra; Ristić, Mirjana
    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 cluster 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 < 10%): simultaneous multi-target model for pH, alkalinity, conductivity, hardness, dissolved oxygen, HCO 3 − , SO 4 2− and Ca, single-output multi-target models for temperature and Cl − , simultaneous single-target models for Mg and CO 2 , single output single target models for NO 3 − .