TY  - JOUR
AU  - Tomić, Aleksandra
AU  - Pomeroy, Brett
AU  - Todić, Branislav
AU  - Likozar, Blaž
AU  - Nikačević, Nikola
PY  - 2024-07
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/7452
AB  - The implementation of the liquid organic hydrogen carrier (LOHC) technology for efficient energy storage requires the development of a reliable kinetic model for both hydrogenation and dehydrogenation processes. In this research study, the catalytic hydrocarbon saturation for a dibenzyltoluene (DBT) mixture solution, containing dibenzylbenzene (DBB), dibenzylethylbenzene (DBEB) and impurities has been performed in the presence of Ru/Al2O3 particles. The influence of different reaction conditions, such as temperature, pressure, initial reactant concentration, catalyst amount and stirring speed has been examined. A measurement-based system micro-kinetics, based on the Langmuir–Hinshelwood mechanism with dissociative H2 surface adsorption, has been derived. H2 thermodynamic solubility equilibrium was defined through Henry's law. The adsorbing, desorption and reactivity of inert solvent molecules was not considered to be relevant. The mass transfer resistance over 1000 rpm stirring speed was negligible. Relative- and mean squared error of representation were 40.9% and 1.00×10−4, respectively. Expressions gave an excellent data prediction for the profile period trends with a relatively accurate estimation of H2 intermediates' rate selectivity, H2-covered area approximation and pathway rate-determining steps. Due to the lack of commercially available standard chemical compounds for quantitative analysis techniques, a novel experiment-based numerical calibration method was developed. Mean field (micro)kinetics represent an advancement in the mesoscale mechanistic understanding of physical interface phenomena. This also enables catalysis structure–activity relationships, unlocking the methodology for new LOHC reaching beyond traditional, such as ammonia, methanol and formate, which do not release H2 alone. Integrated multiscale simulations could include fluidics later on.
PB  - Elsevier Ltd.
T2  - Applied Energy
T1  - Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier
SP  - 123262
VL  - 365
DO  - 10.1016/j.apenergy.2024.123262
ER  - 

@article{
author = "Tomić, Aleksandra and Pomeroy, Brett and Todić, Branislav and Likozar, Blaž and Nikačević, Nikola",
year = "2024-07",
abstract = "The implementation of the liquid organic hydrogen carrier (LOHC) technology for efficient energy storage requires the development of a reliable kinetic model for both hydrogenation and dehydrogenation processes. In this research study, the catalytic hydrocarbon saturation for a dibenzyltoluene (DBT) mixture solution, containing dibenzylbenzene (DBB), dibenzylethylbenzene (DBEB) and impurities has been performed in the presence of Ru/Al2O3 particles. The influence of different reaction conditions, such as temperature, pressure, initial reactant concentration, catalyst amount and stirring speed has been examined. A measurement-based system micro-kinetics, based on the Langmuir–Hinshelwood mechanism with dissociative H2 surface adsorption, has been derived. H2 thermodynamic solubility equilibrium was defined through Henry's law. The adsorbing, desorption and reactivity of inert solvent molecules was not considered to be relevant. The mass transfer resistance over 1000 rpm stirring speed was negligible. Relative- and mean squared error of representation were 40.9% and 1.00×10−4, respectively. Expressions gave an excellent data prediction for the profile period trends with a relatively accurate estimation of H2 intermediates' rate selectivity, H2-covered area approximation and pathway rate-determining steps. Due to the lack of commercially available standard chemical compounds for quantitative analysis techniques, a novel experiment-based numerical calibration method was developed. Mean field (micro)kinetics represent an advancement in the mesoscale mechanistic understanding of physical interface phenomena. This also enables catalysis structure–activity relationships, unlocking the methodology for new LOHC reaching beyond traditional, such as ammonia, methanol and formate, which do not release H2 alone. Integrated multiscale simulations could include fluidics later on.",
publisher = "Elsevier Ltd.",
journal = "Applied Energy",
title = "Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier",
pages = "123262",
volume = "365",
doi = "10.1016/j.apenergy.2024.123262"
}

Tomić, A., Pomeroy, B., Todić, B., Likozar, B.,& Nikačević, N.. (2024-07). Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier. in Applied Energy
Elsevier Ltd.., 365, 123262.
https://doi.org/10.1016/j.apenergy.2024.123262

Tomić A, Pomeroy B, Todić B, Likozar B, Nikačević N. Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier. in Applied Energy. 2024;365:123262.
doi:10.1016/j.apenergy.2024.123262 .

Tomić, Aleksandra, Pomeroy, Brett, Todić, Branislav, Likozar, Blaž, Nikačević, Nikola, "Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier" in Applied Energy, 365 (2024-07):123262,
https://doi.org/10.1016/j.apenergy.2024.123262 . .

TY  - JOUR
AU  - Šiljić-Tomić, Aleksandra
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2018
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3974
AB  - Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R-2 = 0.82), but it was not robust in extrapolation (R-2 = 0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.
PB  - Elsevier Science Bv, Amsterdam
T2  - Science of the Total Environment
T1  - A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis
EP  - 1046
SP  - 1038
VL  - 610
DO  - 10.1016/j.scitotenv.2017.08.192
ER  - 

@article{
author = "Šiljić-Tomić, Aleksandra and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2018",
abstract = "Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R-2 = 0.82), but it was not robust in extrapolation (R-2 = 0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Science of the Total Environment",
title = "A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis",
pages = "1046-1038",
volume = "610",
doi = "10.1016/j.scitotenv.2017.08.192"
}

Šiljić-Tomić, A., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2018). A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis. in Science of the Total Environment
Elsevier Science Bv, Amsterdam., 610, 1038-1046.
https://doi.org/10.1016/j.scitotenv.2017.08.192

Šiljić-Tomić A, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis. in Science of the Total Environment. 2018;610:1038-1046.
doi:10.1016/j.scitotenv.2017.08.192 .

Šiljić-Tomić, Aleksandra, Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis" in Science of the Total Environment, 610 (2018):1038-1046,
https://doi.org/10.1016/j.scitotenv.2017.08.192 . .

TY  - JOUR
AU  - Šiljić-Tomić, Aleksandra
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2018
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4028
AB  - This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO43-, which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R-2  gt = 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.
PB  - Springer Heidelberg, Heidelberg
T2  - Environmental Science and Pollution Research
T1  - Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction
EP  - 9370
IS  - 10
SP  - 9360
VL  - 25
DO  - 10.1007/s11356-018-1246-5
ER  - 

@article{
author = "Šiljić-Tomić, Aleksandra and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2018",
abstract = "This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO43-, which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R-2  gt = 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Environmental Science and Pollution Research",
title = "Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction",
pages = "9370-9360",
number = "10",
volume = "25",
doi = "10.1007/s11356-018-1246-5"
}

Šiljić-Tomić, A., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2018). Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction. in Environmental Science and Pollution Research
Springer Heidelberg, Heidelberg., 25(10), 9360-9370.
https://doi.org/10.1007/s11356-018-1246-5

Šiljić-Tomić A, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction. in Environmental Science and Pollution Research. 2018;25(10):9360-9370.
doi:10.1007/s11356-018-1246-5 .

Šiljić-Tomić, Aleksandra, Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction" in Environmental Science and Pollution Research, 25, no. 10 (2018):9360-9370,
https://doi.org/10.1007/s11356-018-1246-5 . .

TY  - JOUR
AU  - Šiljić-Tomić, Aleksandra
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2016
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3254
AB  - This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.
PB  - Springer, Dordrecht
T2  - Environmental Monitoring and Assessment
T1  - Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models
IS  - 5
VL  - 188
DO  - 10.1007/s10661-016-5308-1
ER  - 

@article{
author = "Šiljić-Tomić, Aleksandra and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2016",
abstract = "This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.",
publisher = "Springer, Dordrecht",
journal = "Environmental Monitoring and Assessment",
title = "Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models",
number = "5",
volume = "188",
doi = "10.1007/s10661-016-5308-1"
}

Šiljić-Tomić, A., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2016). Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models. in Environmental Monitoring and Assessment
Springer, Dordrecht., 188(5).
https://doi.org/10.1007/s10661-016-5308-1

Šiljić-Tomić A, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models. in Environmental Monitoring and Assessment. 2016;188(5).
doi:10.1007/s10661-016-5308-1 .

Šiljić-Tomić, Aleksandra, Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models" in Environmental Monitoring and Assessment, 188, no. 5 (2016),
https://doi.org/10.1007/s10661-016-5308-1 . .

TY  - JOUR
AU  - Šiljić-Tomić, Aleksandra
AU  - Antanasijević, Davor
AU  - Perić-Grujić, Aleksandra
AU  - Ristić, Mirjana
AU  - Pocajt, Viktor
PY  - 2016
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3368
PB  - Springer Heidelberg, Heidelberg
T2  - Environmental Science and Pollution Research
T1  - Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Siljic et al., Environ Sci Pollut Res (2015) 22
EP  - +
IS  - 4
SP  - 3978
VL  - 23
DO  - 10.1007/s11356-015-5978-1
ER  - 

@article{
author = "Šiljić-Tomić, Aleksandra and Antanasijević, Davor and Perić-Grujić, Aleksandra and Ristić, Mirjana and Pocajt, Viktor",
year = "2016",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Environmental Science and Pollution Research",
title = "Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Siljic et al., Environ Sci Pollut Res (2015) 22",
pages = "+-3978",
number = "4",
volume = "23",
doi = "10.1007/s11356-015-5978-1"
}

Šiljić-Tomić, A., Antanasijević, D., Perić-Grujić, A., Ristić, M.,& Pocajt, V.. (2016). Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Siljic et al., Environ Sci Pollut Res (2015) 22. in Environmental Science and Pollution Research
Springer Heidelberg, Heidelberg., 23(4), 3978-+.
https://doi.org/10.1007/s11356-015-5978-1

Šiljić-Tomić A, Antanasijević D, Perić-Grujić A, Ristić M, Pocajt V. Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Siljic et al., Environ Sci Pollut Res (2015) 22. in Environmental Science and Pollution Research. 2016;23(4):3978-+.
doi:10.1007/s11356-015-5978-1 .

Šiljić-Tomić, Aleksandra, Antanasijević, Davor, Perić-Grujić, Aleksandra, Ristić, Mirjana, Pocajt, Viktor, "Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Siljic et al., Environ Sci Pollut Res (2015) 22" in Environmental Science and Pollution Research, 23, no. 4 (2016):3978-+,
https://doi.org/10.1007/s11356-015-5978-1 . .