In the paper the sorption capacity of shungite rocks of the Koksu field (Kazakhstan) in relation to the oil from the Karazhanbas and Tengiz fields (Kazakhstan) were studied. Oil spills occurring during production, gathering, transportation, storage and refining, and repair work on wells are an urgent environmental problem. There are effective methods of soil purification, including particular interests addressed to sorption process. The aim of this research is to study oil sorption by shungite rocks of the Koksu deposit after mechanochemical activation. The mechanochemical activation of shungite rock samples was carried out in a planetary ball mill at different speeds of rotation and ratios of ball mass to the sample. The developed sorbents based on shungite rocks of the Koksu deposit were tested for cleaning samples of oil-contaminated soils and their sorption capacities under dynamic and static conditions were determined. For the sorption of oil, the sorbent based on shungite of shale grade (TS) after mechanochemical activation is recommended, which sorption capacity under dynamic conditions is 2.57-2.85 g/g. Sorption of oil from 10 % of oil contaminated soil samples with the sorbents based on shungite after mechanochemical activation showed sorption capacity of 0.44-0.45 g/g in 60 days under static conditions. The practical significance of the research lies in the prospects of using shungite rocks to clean up oil spills.
A promising direction for the development of passive radar monitoring stations is to improve their efficiency by increasing their speed of performance. For the digital spectral-correlation method for determining the delay of radio signals and direction finding, analytical expressions have been derived for a variance of the estimation of the delay in receiving a signal by radio channels and directions to the source of radio emission. A feature of the method reported in this study is the use of two-stage temporal and spatial spectral analysis of the mutual spectrum, a single-iteration correlation analysis. The duration of estimating the direction finding has been evaluated through the total number of multiplication operations with accumulation. The proposed method, while providing for a gain of 27 times in terms of performance speed, demonstrated a slight decrease in accuracy compared to the optimal one due to energy signal loss. The result of the simulation has established the dependences of the standard deviation in the direction finding and delay estimates on the signal-to-noise ratio, the type of spectral analysis window, and the size of the antenna base. The standard deviation of the direction-finding estimate depends on the signal-to-noise ratio and varies over the range of values [0.08; 0.034]° with a change in the signal/noise ratio [−10; 40] dB. As the signal/noise ratio increases, the error decreases in line with a hyperbolic dependence. The standard deviation of the delay estimate depends on the signal-to-noise ratio and varies similarly to the error of the directional estimate, and is in the range of values [18.176; 1.56] ns, which corresponds to an error of [0.637; 0.055] %. The error of direction-finding estimation, depending on the size of the antenna base, decreases in the exponent within [1.6; 0.03]° with an increase in the antenna base in the range from 200 to 7,500 m. The results reported here could be used for the parametric optimization of spectral-correlation radio direction finders at passive radar monitoring stations.
The paper discusses the characteristics of spatial electromagnetic noise generators, as well as the formation of a broadband noise signal. A number of well-known methods for assessing the quality of masking noise interference and the approaches used in them have been described. Approaches to the measurement of masking noise were also determined in assessing their quality. In conclusion, additional methods are proposed for assessing the quality of masking noises, such as searching for correlation of noise in different frequency sub-bands and using statistical and (or) graphical methods (tests) for randomness.
Using the mixture of carbonized rice husk and shungite from the Kazakhstan Koksu deposit and the experimentally determined oil sorption capacity from contaminated soil with oil originating in the Karazhanbas oil field, a set of Artificial Neural Network (ANN) models were built for sorption predictions. The ANN architecture design, training, validation and testing methodology were performed, and the sorption capacity prediction was evaluated. The ANN models were successfully trained for capturing the sorption capacity dependence on time and on a carbonized rice husk and shungite mixture ratio for the 10% and 15% oil-contaminated soil. The best trained ANNs revealed a very good prediction capability for the testing data subset, demonstrated by the high coefficient of the determination values of R2 = 0.998 and R2 = 0.981 and the mean absolute percentage errors ranging from 1.60% to 3.16%. Furthermore, the ANN sorption models proved their interpolation ability and utility for predicting the sorption capacity for any time moments in the investigated time interval of 60 days and for new values of the shungite and rice husk mixture ratios. The ANN developed models open opportunities for planning new experiments, maximizing the sorption performance and for the design of dedicated equipment.
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