Browsing by Author "Metodiev V."

Now showing 1 - 4 of 4
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    ANALYSIS OF MEASURED PM2.5 CONCENTRATIONS AND METHOD FOR DETERMINING THEIR ORIGIN
    (2023-01-01) Ilieva N.; Metodiev V.
    The significance of atmospheric Particulate Matter (PM) size as a determining factor for their source is of utmost importance. The analysis of PM2.5/PM10 ratios serves as a crucial indicator for particle origin. This study utilizes data obtained from measurements of PM2.5 and PM10 concentrations at the “Kamenitsa” automatic measuring station, located in the Kamenitsa district of Plovdiv, Bulgaria. The data covers a period of 6 years. A statistical approach for identifying the source of particulate matter in the air has been investigated and implemented. The findings of the applied method indicate that during winter, the primary source of particles is anthropogenic in nature.
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    EXPONENTIAL MOVING AVERAGE FOR AIR POLLUTION DATA: ASSESSING ITS ROLE IN PM10 MONITORING ACCURACY
    (2025-11-02) Stoyanova K.; Metodiev V.; Lavrova S.
    Accurate estimation of particulate matter (PM10) concentrations is critical for assessing air quality and mitigating public health risks. Traditional monitoring data processing methods, such as simple moving averages (MA), often struggle to capture rapid fluctuations in pollutant levels due to their uniform weighting of historical data, potentially compromising real - time decision - making. This study evaluates the efficiency of the Exponential Moving Average (EMA) algorithm, which prioritizes recent observations through exponential weighting, to improve PM10 concentration estimates. Using data from urban air quality monitoring stations, EMA was applied across varying time windows and compared against conventional MA approaches. Performance was assessed against ground - truth measurements. Results demonstrated that EMA significantly reduced estimation errors. The algorithm exhibited enhanced responsiveness to abrupt PM10 spikes, attributed to its dynamic weighting mechanism. Sensitivity analysis revealed that optimal smoothing factors depended on the selected time window, balancing noise reduction and trend detection. These findings underscore EMA’s potential as a robust tool for air pollution monitoring data analyses, offering superior adaptability to temporal variability. Implementation of EMA in regulatory and public health frameworks could enhance early warning systems and pollution control strategies. Future research should explore integrating EMA with machine learning models and low - cost sensor networks to further optimize real - time air quality management.
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    High - Temperature Carbothermic Reduction of Coal Beneficiation Waste
    (2025-01-01) Metodiev V.
    This study investigates the high - temperature carbothermic reduction of coal beneficiation waste (CBW) as a sustainable approach to resource recovery and waste valorisation. Addressing the environmental burden of CBW accumulation, the inherent carbon content of the waste is utilized as a reductant to transform residual metal oxides into valuable metallurgical products. Experiments were conducted to evaluate the influence of temperature, holding time, reductant quantity, and additives on the reduction process. Mathematical models describing the degree of carbothermic reduction and the reaction rate were developed. Results demonstrate the technical feasibility of carbothermic reduction for converting CBW into reusable materials, offering the dual benefits of waste mitigation and resource circularity. This approach aligns with circular economy principles, providing a scalable solution to minimize landfill disposal and promote sustainability within coal - dependent industries. The study highlights the potential for industrial implementation, emphasizing the economic and environmental advantages of metallurgical reprocessing of these mineral wastes.
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    Investigation of elastomers ratio influence in the composites for truck tires treads production
    (2022-01-01) Malinova P.; Ilieva N.; Metodiev V.
    An experimental design of model tread compounds for truck tires treads was carried out to test the possibilities for optimizing the ratio between the combination of elastomers (butadiene styrene rubber, isoprene rubber and butadiene rubber) used in their compositions. The experiment was designed using a Scheffe simplex lattice. The target functions, object of the optimization problem, are the main characteristics of the obtained vulcanizates: abrasion, stress at 100 % elongation, stress at 300 % elongation, tensile strength, elongation at break, residual elongation and Shore A hardness. The optimization was performed in the barycentric factor space with limits of variation of all factors from 0 to 1. Since the seven target functions obtain their extreme values at different points, a generalized criterion for optimality is formulated using the desirability function. The composition of the rubber compound at which this function receives its maximum has been established.