TY  - JOUR
AU  - Adamović, Vladimir
AU  - Antanasijević, Davor Z.
AU  - Ćosović, Aleksandar
AU  - Ristić, Mirjana D.
AU  - Pocajt, Viktor V.
PY  - 2018
UR  - https://ritnms.itnms.ac.rs/handle/123456789/487
AB  - Although the use of municipal solid waste to generate energy can decrease dependency on fossil fuels and consequently reduces greenhouse gases emissions and areas that waste occupies, in many countries municipal solid waste is not recognized as a valuable resource and possible alternative fuel. The aim of this study is to develop a model for the prediction of primary energy production from municipal solid waste in the European countries and then to apply it to the Balkan countries in order to assess their potentials in that field. For this purpose, general regression neural network architecture was applied, and correlation and sensitivity analyses were used for optimisation of the model. The data for 16 countries from the European Union and Norway for the period 2006-2015 was used for the development of the model. The model with the best performance (coefficient of determination R-2 = 0.995 and the mean absolute percentage error MAPE = 7.757%) was applied to the data for the Balkan countries from 2006 to 2015. The obtained results indicate that there is a significant potential for utilization of municipal solid waste for energy production, which should lead to substantial savings of fossil fuels, primarily lignite which is the most common fossil fuel in the Balkans.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Waste Management
T1  - An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries
EP  - 968
SP  - 955
VL  - 78
DO  - 10.1016/j.wasman.2018.07.012
UR  - conv_832
ER  - 

@article{
author = "Adamović, Vladimir and Antanasijević, Davor Z. and Ćosović, Aleksandar and Ristić, Mirjana D. and Pocajt, Viktor V.",
year = "2018",
abstract = "Although the use of municipal solid waste to generate energy can decrease dependency on fossil fuels and consequently reduces greenhouse gases emissions and areas that waste occupies, in many countries municipal solid waste is not recognized as a valuable resource and possible alternative fuel. The aim of this study is to develop a model for the prediction of primary energy production from municipal solid waste in the European countries and then to apply it to the Balkan countries in order to assess their potentials in that field. For this purpose, general regression neural network architecture was applied, and correlation and sensitivity analyses were used for optimisation of the model. The data for 16 countries from the European Union and Norway for the period 2006-2015 was used for the development of the model. The model with the best performance (coefficient of determination R-2 = 0.995 and the mean absolute percentage error MAPE = 7.757%) was applied to the data for the Balkan countries from 2006 to 2015. The obtained results indicate that there is a significant potential for utilization of municipal solid waste for energy production, which should lead to substantial savings of fossil fuels, primarily lignite which is the most common fossil fuel in the Balkans.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Waste Management",
title = "An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries",
pages = "968-955",
volume = "78",
doi = "10.1016/j.wasman.2018.07.012",
url = "conv_832"
}

Adamović, V., Antanasijević, D. Z., Ćosović, A., Ristić, M. D.,& Pocajt, V. V.. (2018). An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries. in Waste Management
Pergamon-Elsevier Science Ltd, Oxford., 78, 955-968.
https://doi.org/10.1016/j.wasman.2018.07.012
conv_832

Adamović V, Antanasijević DZ, Ćosović A, Ristić MD, Pocajt VV. An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries. in Waste Management. 2018;78:955-968.
doi:10.1016/j.wasman.2018.07.012
conv_832 .

Adamović, Vladimir, Antanasijević, Davor Z., Ćosović, Aleksandar, Ristić, Mirjana D., Pocajt, Viktor V., "An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries" in Waste Management, 78 (2018):955-968,
https://doi.org/10.1016/j.wasman.2018.07.012 .,
conv_832 .

TY  - JOUR
AU  - Adamović, Vladimir
AU  - Antanasijević, Davor Z.
AU  - Ristić, Mirjana D.
AU  - Perić-Grujić, Aleksandra A.
AU  - Pocajt, Viktor V.
PY  - 2018
UR  - https://ritnms.itnms.ac.rs/handle/123456789/482
AB  - This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual quantities of hazardous chemical and healthcare waste at the national level. Hazardous waste is being generated from many different sources and therefore it is not possible to conduct accurate predictions of the total amount of hazardous waste using traditional methodologies. Since they represent about 40% of the total hazardous waste in the European Union, chemical and healthcare waste were specifically selected for this research. Broadly available social, economic, industrial and sustainability indicators were used as input variables and the optimal sets were selected using correlation analysis and sensitivity analysis. The obtained values of coefficients of determination for the final models were 0.999 for the prediction of chemical hazardous waste and 0.975 for the prediction of healthcare and biological hazardous waste. The predicting capabilities of the models for both types of waste are high, since there were no predictions with errors greater than 25%. Also, results of this research demonstrate that the human development index can replace gross domestic product and in this context even represent a better indicator of socio-economic conditions at the national level.
PB  - Springer, New York
T2  - Journal of Material Cycles and Waste Management
T1  - An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level
EP  - 1750
IS  - 3
SP  - 1736
VL  - 20
DO  - 10.1007/s10163-018-0741-6
UR  - conv_825
ER  - 

@article{
author = "Adamović, Vladimir and Antanasijević, Davor Z. and Ristić, Mirjana D. and Perić-Grujić, Aleksandra A. and Pocajt, Viktor V.",
year = "2018",
abstract = "This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual quantities of hazardous chemical and healthcare waste at the national level. Hazardous waste is being generated from many different sources and therefore it is not possible to conduct accurate predictions of the total amount of hazardous waste using traditional methodologies. Since they represent about 40% of the total hazardous waste in the European Union, chemical and healthcare waste were specifically selected for this research. Broadly available social, economic, industrial and sustainability indicators were used as input variables and the optimal sets were selected using correlation analysis and sensitivity analysis. The obtained values of coefficients of determination for the final models were 0.999 for the prediction of chemical hazardous waste and 0.975 for the prediction of healthcare and biological hazardous waste. The predicting capabilities of the models for both types of waste are high, since there were no predictions with errors greater than 25%. Also, results of this research demonstrate that the human development index can replace gross domestic product and in this context even represent a better indicator of socio-economic conditions at the national level.",
publisher = "Springer, New York",
journal = "Journal of Material Cycles and Waste Management",
title = "An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level",
pages = "1750-1736",
number = "3",
volume = "20",
doi = "10.1007/s10163-018-0741-6",
url = "conv_825"
}

Adamović, V., Antanasijević, D. Z., Ristić, M. D., Perić-Grujić, A. A.,& Pocajt, V. V.. (2018). An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. in Journal of Material Cycles and Waste Management
Springer, New York., 20(3), 1736-1750.
https://doi.org/10.1007/s10163-018-0741-6
conv_825

Adamović V, Antanasijević DZ, Ristić MD, Perić-Grujić AA, Pocajt VV. An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. in Journal of Material Cycles and Waste Management. 2018;20(3):1736-1750.
doi:10.1007/s10163-018-0741-6
conv_825 .

Adamović, Vladimir, Antanasijević, Davor Z., Ristić, Mirjana D., Perić-Grujić, Aleksandra A., Pocajt, Viktor V., "An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level" in Journal of Material Cycles and Waste Management, 20, no. 3 (2018):1736-1750,
https://doi.org/10.1007/s10163-018-0741-6 .,
conv_825 .

TY  - JOUR
AU  - Milojković, Jelena
AU  - Stojanović, Mirjana
AU  - Mihajlović, Marija
AU  - Lopičić, Zorica
AU  - Petrović, Marija
AU  - Šoštarić, Tatjana
AU  - Ristić, Mirjana D.
PY  - 2014
UR  - https://ritnms.itnms.ac.rs/handle/123456789/330
AB  - Aquatic weed Myriophyllum spicatum L. is one of the most invasive water plants known. In many countries, it is usually harvested and landfilled, where aerobic and anaerobic decomposition takes place. In this research, the kinetic, equilibrium, and desorption studies of biosorption of Pb(II), Cu(II), Cd(II), Ni(II), and Zn(II) ions onto compost of M. spicatum were investigated in batch experiments. Biosorbent was characterized by scaning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). SEM analysis showed that ion exchange between divalent cations Ca(II) and selected metals takes place. The results of FTIR exposed that carbonyl, carboxyl, hydroxyl, and phenyl groups are main binding sites for those heavy metal ions. The rate of adsorption of the five heavy metals was fast, which achieved equilibrium in 40 min, and followed the pseudo-second-order model well. Langmuir, Freundlich, and Sips equilibrium adsorption models were studied, and Sips isotherm gave the best fit for experimental data. Desorption by 0.1 M HNO3 did not fully recover the metals sorbed onto the compost, indicating that reusing this material as biosorbent is not possible. Furthermore, the use of spent biosorbent as a soil fertilizer is proposed.
PB  - Springer International Publishing Ag, Cham
T2  - Water Air and Soil Pollution
T1  - Compost of Aquatic Weed Myriophyllum spicatum as Low-Cost Biosorbent for Selected Heavy Metal Ions
IS  - 4
VL  - 225
DO  - 10.1007/s11270-014-1927-8
UR  - conv_690
ER  - 

@article{
author = "Milojković, Jelena and Stojanović, Mirjana and Mihajlović, Marija and Lopičić, Zorica and Petrović, Marija and Šoštarić, Tatjana and Ristić, Mirjana D.",
year = "2014",
abstract = "Aquatic weed Myriophyllum spicatum L. is one of the most invasive water plants known. In many countries, it is usually harvested and landfilled, where aerobic and anaerobic decomposition takes place. In this research, the kinetic, equilibrium, and desorption studies of biosorption of Pb(II), Cu(II), Cd(II), Ni(II), and Zn(II) ions onto compost of M. spicatum were investigated in batch experiments. Biosorbent was characterized by scaning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). SEM analysis showed that ion exchange between divalent cations Ca(II) and selected metals takes place. The results of FTIR exposed that carbonyl, carboxyl, hydroxyl, and phenyl groups are main binding sites for those heavy metal ions. The rate of adsorption of the five heavy metals was fast, which achieved equilibrium in 40 min, and followed the pseudo-second-order model well. Langmuir, Freundlich, and Sips equilibrium adsorption models were studied, and Sips isotherm gave the best fit for experimental data. Desorption by 0.1 M HNO3 did not fully recover the metals sorbed onto the compost, indicating that reusing this material as biosorbent is not possible. Furthermore, the use of spent biosorbent as a soil fertilizer is proposed.",
publisher = "Springer International Publishing Ag, Cham",
journal = "Water Air and Soil Pollution",
title = "Compost of Aquatic Weed Myriophyllum spicatum as Low-Cost Biosorbent for Selected Heavy Metal Ions",
number = "4",
volume = "225",
doi = "10.1007/s11270-014-1927-8",
url = "conv_690"
}

Milojković, J., Stojanović, M., Mihajlović, M., Lopičić, Z., Petrović, M., Šoštarić, T.,& Ristić, M. D.. (2014). Compost of Aquatic Weed Myriophyllum spicatum as Low-Cost Biosorbent for Selected Heavy Metal Ions. in Water Air and Soil Pollution
Springer International Publishing Ag, Cham., 225(4).
https://doi.org/10.1007/s11270-014-1927-8
conv_690

Milojković J, Stojanović M, Mihajlović M, Lopičić Z, Petrović M, Šoštarić T, Ristić MD. Compost of Aquatic Weed Myriophyllum spicatum as Low-Cost Biosorbent for Selected Heavy Metal Ions. in Water Air and Soil Pollution. 2014;225(4).
doi:10.1007/s11270-014-1927-8
conv_690 .

Milojković, Jelena, Stojanović, Mirjana, Mihajlović, Marija, Lopičić, Zorica, Petrović, Marija, Šoštarić, Tatjana, Ristić, Mirjana D., "Compost of Aquatic Weed Myriophyllum spicatum as Low-Cost Biosorbent for Selected Heavy Metal Ions" in Water Air and Soil Pollution, 225, no. 4 (2014),
https://doi.org/10.1007/s11270-014-1927-8 .,
conv_690 .

TY  - JOUR
AU  - Milojković, Jelena
AU  - Mihajlović, Marija
AU  - Stojanović, Mirjana
AU  - Lopičić, Zorica
AU  - Petrović, Marija
AU  - Šoštarić, Tatjana
AU  - Ristić, Mirjana D.
PY  - 2014
UR  - https://ritnms.itnms.ac.rs/handle/123456789/327
AB  - BACKGROUND Lead is one of the frequent contaminants of industrial wastewater. Since it has been shown that aquatic plants can be used for the removal of heavy metals, herein Pb(II) biosorption by Myriophyllum spicatum and its compost were investigated. Effects of pH, ionic strength and contact time were analyzed using a batch experiment. Biomasses were characterized chemically and by Fourier transform infrared spectroscopy, scanning electron microscopy and X-ray diffraction techniques. RESULTS The adsorption process of both biosorbents followed a pseudo-second-order kinetic model. Compost exhibits better Pb(II) removal from solution (71%) compared with the plant (61%). Lead binding capacities for M. spicatum and its compost were 0.234 mmol g(-1) and 0.287 mmol g(-1) at pH 5.0, respectively. Lead binding takes place mainly through an ion exchange mechanism, but chemisorption via identified functional groups cannot be neglected. The Langmuir, Freundlich and Sips adsorption models for compost were applied. The Sips isotherm model gave the best fit with the equilibrium experimental data. The sorption process by compost was endothermic and spontaneous. CONCLUSION Aquatic weed compost as a low cost biosorbent with high biosorption capacity can potentially be used for the removal of lead from wastewaters.
PB  - Wiley, Hoboken
T2  - Journal of Chemical Technology and Biotechnology
T1  - Pb(II) removal from aqueous solution by Myriophyllum spicatum and its compost: equilibrium, kinetic and thermodynamic study
EP  - 670
IS  - 5
SP  - 662
VL  - 89
DO  - 10.1002/jctb.4184
UR  - conv_687
ER  - 

@article{
author = "Milojković, Jelena and Mihajlović, Marija and Stojanović, Mirjana and Lopičić, Zorica and Petrović, Marija and Šoštarić, Tatjana and Ristić, Mirjana D.",
year = "2014",
abstract = "BACKGROUND Lead is one of the frequent contaminants of industrial wastewater. Since it has been shown that aquatic plants can be used for the removal of heavy metals, herein Pb(II) biosorption by Myriophyllum spicatum and its compost were investigated. Effects of pH, ionic strength and contact time were analyzed using a batch experiment. Biomasses were characterized chemically and by Fourier transform infrared spectroscopy, scanning electron microscopy and X-ray diffraction techniques. RESULTS The adsorption process of both biosorbents followed a pseudo-second-order kinetic model. Compost exhibits better Pb(II) removal from solution (71%) compared with the plant (61%). Lead binding capacities for M. spicatum and its compost were 0.234 mmol g(-1) and 0.287 mmol g(-1) at pH 5.0, respectively. Lead binding takes place mainly through an ion exchange mechanism, but chemisorption via identified functional groups cannot be neglected. The Langmuir, Freundlich and Sips adsorption models for compost were applied. The Sips isotherm model gave the best fit with the equilibrium experimental data. The sorption process by compost was endothermic and spontaneous. CONCLUSION Aquatic weed compost as a low cost biosorbent with high biosorption capacity can potentially be used for the removal of lead from wastewaters.",
publisher = "Wiley, Hoboken",
journal = "Journal of Chemical Technology and Biotechnology",
title = "Pb(II) removal from aqueous solution by Myriophyllum spicatum and its compost: equilibrium, kinetic and thermodynamic study",
pages = "670-662",
number = "5",
volume = "89",
doi = "10.1002/jctb.4184",
url = "conv_687"
}

Milojković, J., Mihajlović, M., Stojanović, M., Lopičić, Z., Petrović, M., Šoštarić, T.,& Ristić, M. D.. (2014). Pb(II) removal from aqueous solution by Myriophyllum spicatum and its compost: equilibrium, kinetic and thermodynamic study. in Journal of Chemical Technology and Biotechnology
Wiley, Hoboken., 89(5), 662-670.
https://doi.org/10.1002/jctb.4184
conv_687

Milojković J, Mihajlović M, Stojanović M, Lopičić Z, Petrović M, Šoštarić T, Ristić MD. Pb(II) removal from aqueous solution by Myriophyllum spicatum and its compost: equilibrium, kinetic and thermodynamic study. in Journal of Chemical Technology and Biotechnology. 2014;89(5):662-670.
doi:10.1002/jctb.4184
conv_687 .

Milojković, Jelena, Mihajlović, Marija, Stojanović, Mirjana, Lopičić, Zorica, Petrović, Marija, Šoštarić, Tatjana, Ristić, Mirjana D., "Pb(II) removal from aqueous solution by Myriophyllum spicatum and its compost: equilibrium, kinetic and thermodynamic study" in Journal of Chemical Technology and Biotechnology, 89, no. 5 (2014):662-670,
https://doi.org/10.1002/jctb.4184 .,
conv_687 .