Volume 65 (2018): Issue 3 (September 2018)

This paper presents the experimental determination of the dependence of emissivity of brass on surface roughness and temperature. The investigation was conducted using the infrared thermographic technique on brass alloy C27200 workpieces with different degrees of surface roughness, during the continuous cooling process. The results obtained showed that the emissivity of the chosen brass alloy increases with greater surface roughness and decreases during the cooling process, its value ranging from 0.07 to 0.19. It was concluded that surface roughness has a greater influence on the increase of the emissivity at higher temperatures, which can be seen in the three-dimensional infrared images. Multiple regression analysis confirmed a strong correlation between the examined parameters and the emissivity, and an original multiple regression model was determined.

This article presents preliminary model results of climate change impact on biogeochemical processes in soil. With the use of DNDC (DeNitrification-DeComposition) model, a simulation with climate data over seventy years period (1947-2016) from central part of Slovenia has been carried out. Amongst assessed sources of variability, time variability has been estimated to around 10% of the total annual nitrogen leaching. In some cases, a statistically significant downward trend was observed with a 5 kg reduction in nitrogen per hectare in seventy years period. This study represents the first quantitative assessment of nitrogen leaching variability due to precipitation and air temperature variability in three representative soil profiles in the central Slovenia. It offers a starting point for future regional research for the purpose of farming practice optimization, especially in catchment areas of major regional water resources in Slovenia.

Studies of structural and hydrogeomorphological units (HGU) that are indicators of groundwater occurrence were carried out across an area extent of more than 700 km² within the hard rock terrain of southwestern Nigeria. These studies integrated geological remote sensing techniques (RST) and geographical information system (GIS) methods to generate thematic maps that included elevation, drainage, lineaments and vegetation index for characterising the attributes of groundwater occurrence across the area. The results revealed that the lineament system is mainly rectilinear with major trends of NNW-SSE and NE-SW on the gneiss, NW-SE and NE-SW on porphyritic granite and NNE-SSW, NW-SE and E-W on migmatite. The discharge zones in the area are the lowland terrains underlain by gneiss and amphibolite. Similarly, variably directional discontinuities that are related to rock contacts are equally laden with groundwater. Conversely, the recharge areas are the high-lying terrains characterised by higher fracture density and underlain by porphyritic granite and migmatite. Additionally, there are evidences of groundwater seepage along the major river channels. Therefore, besides the rock structures, landform is another crucial factor that guides groundwater distribution in the study area.

The Ladislavci Field (oil and gas reservoirs) is located 40 km from the city of Osijek, Croatia. The oil reservoir is in structural-stratigraphic trap and Miocene rocks of the Vukovar formation (informally named as El, F1a and F1b). The shallower gas reservoir is of Pliocene age, i.e. part of the Osijek sandstones (informally named as B). The oil reservoirs consist of limestones, breccias and conglomerates, and gas is accumulated in sandstones. Using neural networks, it was possible to interpret applicability of neural algorithm in well log analyses, and using neural model, it was possible to predict reservoir without or with small number of log data. Neural networks are trained on the data from two wells (A and B), collected from the interval starting with border of Sarmatian/ Lower Pannonian (EL marker Rs7) to the well’s bottom. The inputs were data from spontaneous potential (SP) and resistivity (R16 and R64) logs. They were used for neural training and validation as well as for final prediction of lithological composition in the analysed field. The multilayer perceptron (MLP) network had been selected as the most appropriate.

The paper presents the results of processing low-grade phosphorites by microorganisms of activated sludge from the biochemical purification production unit of JSC “Navoiazot”. The obtained results on the leaching of rare and rare-earth elements into the liquid phase make it possible to separate them and thus enrich the phosphorites. Other options are the gravitational separation of the crushed calcite particles. In addition to this, there is a real possibility of creating complex organomineral fertilisers.