Browsing by Author "Perić-Grujić, Aleksandra"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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 − .