Pattern recognition methods for classification of soils based on their radionuclide content

Multivariate data analysis methods were used to recognize and classify soils of unknown geographic origin. A total of 103 soil samples were differentiated into classes, according to regions in Serbia and Montenegro from which they were collected. Their radionuclide (226Ra, 238U, 235U, 40K, 134Cs, 137Cs, 232Th and 7 Be) activities detected by gamma-ray spectrometry were then used as the inputs in different pattern recognition methods. The prediction ability of linear discriminant analysis (LDA), k-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA) and artificial neural network (ANN) were 82.8%, 88.6%, 60.0% and 92.1%, respectively.Physical chemistry 2006 : 8th international conference on fundamental and applied aspects of physical chemistry; Belgrade (Serbia); 26-29 September 200

Sorption of Ni2+ by different synthetic hydroxyapatite

Two hydroxyapatite (HAP) samples of different crystallinity were studied as a nickel immobilization matrix. Sorption isotherms were obtained by batch equilibration method, in the concentration range 1. 10-4 – 8. 10-3 mol/dm3 . Low crystalline sample has sorption capacity of 0.212 mmol/g, and due to its higher specific surface area and lower Ca/P ratio it was found to be better sorbent for Ni2+ than crystalline HAP (0.092 mmol/g).Physical chemistry 2004 : 7th international conference on fundamental and applied aspects of physical chemistry; Belgrade (Serbia); 21-23 September 200

Neural network prediction of the gas chromatographic separation of polycyclic aromatic hydrocarbons

This paper describes the application of artificial neural networks (ANNs) method to the modeling of 13 polycyclic aromatic hydrocarbons (PAHs)retentionsin temperature - programmed gas chromatography.The ANN method used resulted in relatively good agreement (RMStesting = 0.018) between the measured and the predicted retention times for 13 PAHs. Somewhat higher discrepancy in prediction was observed for the late – eluted PAHs at lower temperature ramps.Physical chemistry 2004 : 7th international conference on fundamental and applied aspects of physical chemistry; Belgrade (Serbia); 21-23 September 200

Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography

A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al, [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (3 %)

Design of an amino-functionalized chelating macroporous copolymer poly(GMA-co-EGDMA) for the sorption of Cu(II) ions

Polymer-based, highly porous nanocomposites with functionalized ligands attached to the core structure are extremely efficient in the detection, removal and recovery of metals through the process of sorption. Quantum-chemical models could be helpful for sorption process analyses. The sorption of Cu(II) ions by amino-functionalized chelating macroporous copolymers poly(GMA-co-EGDMA)-amine and sorption selectivity of the subject copolymers, ethylenediamine (en), diethylenetriamine (dien) and triethylenetetramine (trien), were successfully modelled by quantum chemical calculations. Considering the crystal structures from CSD and experimental conditions during the formation of metal complexes, the most frequent mononuclear complexes are those with the tetradentate teta ligand, while binuclear complexes are formed when the metal ion is in large excess. Although the en-copolymer was the most effective functionalized one, higher maximum sorption capacities (Qmax) were observed for the dien- and trien-copolymers, due to their abilities to form binuclear complexes. The enthalpy term has the greatest contribution to the total Gibbs energy change of reaction for the formation of mononuclear Cu(II) complexes (ΔGaq), while the solvation energy of the reaction has the greatest contribution in the formation of binuclear complexes. The results of the study indicate that small amines with the ability to form binuclear complex are the best choice for functionalization of the considered copolymer

Health Risk Assessment of Particulate Matter Emissions from Natural Gas and Fuel Oil Heating Plants Using Dispersion Modelling

A significant proportion of homes and apartments in Serbia are still reliant on central heating systems during winter months, with about fifty heating plants in operation. Common fuels used in these plants primarily include fossil fuels such as coal, fuel oil, and natural gas. Some of these fuels have a high sulfur content, leading to an increased concentration of sulfur dioxide and particulate matter in the atmosphere (Todorović et al, 2020; Todorović et al, 2021). This study compares and evaluates the environmental impact of the two heating boilers at the Valjevo city (Serbia) heating plant. The AERMOD air dispersion model was used for estimating the concentrations of the various pollutants (Kakosimos et al, 2011; Mokhtar et al, 2014; Shaikh et al, 2020). Onsite emission data were gathered separately for the two heating boilers at the facility fuelled by natural gas and fuel oil, respectively. A combination of topographical and historical meteorological data were used to set up a receptor grid that was exposed to the gas emission in a radius of 10 km. The environmental impact from the fuel oil boiler was shown to be significantly higher than that caused by the natural gas-fuelled boiler. The resulting distribution of pollutant gases and particles showed that the concentration gradient is less inclined towards the city centre and instead spreads eastwards into the surrounding villages. The data were used to evaluate carcinogenic and non-carcinogenic health risks. It was found that the health risk was acceptable for different averaging periods. However, further study is still required in order to properly assess the cumulative health risk generated by other surrounding industries

Inverse gas chromatography of chromia. Part II. Finite surface coverage

The interactions of n-hexane, benzene, chloroform, and tetrahydrofuran with dried (amorphous) chromia (I) and chromia heated at 1073 K (crystalline) (II), both obtained from a colloidal dispersion, and a commercially available chromia (III) were Studied by inverse gas chromatography (IGC) under Finite surface coverage conditions. The isotherms, in the temperature range 383-423 K, were used to estimate the surface area, the adsorption energy distribution, the isosteric heat of adsorption, and the spreading pressure on the surfaces of the solids. The uniformly reduced adsorption ability of the heated chromia was attributed to the dehydroxylation of the surface at the higher temperatures. Both solids showed an increased affinity toward chloroform molecules, as a result of strong acid-base interaction

Factorial design in isocratic high-performance liquid chromatography of phenolic compounds

A multifactor optimization strategy was utilized to predict the isocratic HPLC separation of nine phenols. The retention behavior was studied as a function of changing eluent (methanol - acetic acid) composition. The predicted and measured retentions; are in rather good agreement. To locate the optimum in the factor space, the normalized resolution product criterion was applied. In virtually every case, the resolution is limited by the separation of the 2-chlorophenol and 2,4-dinitrophenol pair

Removal of cationic dye from water by activated pine cones

Adsorption of a cationic phenothyazine dye methylene blueonto activated carbon prepared from pine cones was investigated with the variation in parameters of contact time, dye concentration and pH. The kinetic data were found to follow the pseudo-second-order kinetic modelclosely. The equilibrium data were best represented by the Langmuir isotherm with maximum adsorption capacity of 233.1 mg g-1. Adsorption was favored by using a higher solution pH. Textural analysis by nitrogen adsorption was used to determine specific surface area and pore structure of the obtained carbon. Boehm titrations revealed that carboxylic groups are present in the highest degree on the carbon surface. The results indicate that the presented method for activation of pine cones could yield activated carbon with significant porosity, developed surface reactivity and considerable adsorption affinity toward cationic dye methylene blue