Browsing by Author "Simeonova T."

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    Composition, synthesis and properties of color architecture building foam glass obtained from waste packing glass
    (2013-05-21) Lakov L.; Toncheva K.; Staneva A.; Ilcheva Z.; Simeonova T.
    The foam glass is known mainly as an insulation material. It is less popular for the production of industrial colored foam glass. This material is for the application as coatings for building, architectural and artistic elements, especially in interior design. The synthesized compositions contain different coloring and foaming components. New foaming agents are characterized with dual function, both for foaming and coloring. By present study some environmental problems are solved for utilization of waste glasses to obtain a color foam glasses for use as heat and sound insulation. The obtained results are a part of a more general project for the development of technologies and special equipment for production of foam glasses.
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    Composition, synthesis and properties of insulation foam glass obtained from packing glass waste
    (2013-05-21) Lakov L.; Toncheva K.; Staneva A.; Simeonova T.; Ilcheva Z.
    So far, the starting compositions for obtaining foam glass have been developed based on glasses pre-synthesized for the purpose. From the point of view of such important issues as increasing the energy efficiency and utilization of municipal and industrial waste, the possibility for production of foam glass insulation out of packing glass waste is a very good alternative. The present study to suggest a decision for utilization of waste glass and serves as a basis for developing the technology for production of continuous strip of foam glass material. This technology will be implemented in vertical production installation, which is currently under construction.
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    Deep Learning-Driven Insights into Hardness and Electrical Conductivity of Low-Alloyed Copper Alloys
    (2025-12-01) Kolev M.; Javorova J.; Simeonova T.; Hadjitodorov Y.; Krastev B.
    Understanding the intricate relationship between composition, processing conditions, and material properties is essential for optimizing Cu-based alloys. Machine learning offers a powerful tool for decoding these complex interactions, enabling more efficient alloy design. This work introduces a comprehensive machine learning framework aimed at accurately predicting key properties such as hardness and electrical conductivity of low-alloyed Cu-based alloys. By integrating various input parameters, including chemical composition and thermo-mechanical processing parameters, the study develops and validates multiple machine learning models, including Multi-Layer Perceptron with Production-Aware Deep Architecture (MLP-PADA), Deep Feedforward Network with Multi-Regularization Framework (DFF-MRF), Feedforward Network with Self-Adaptive Optimization (FFN-SAO), and Feedforward Network with Materials Mapping (FFN-TMM). On a held-out test set, DFF-MRF achieved the best generalization (R2_test = 0.9066; RMSE_test = 5.3644), followed by MLP-PADA (R2_test = 0.8953; RMSE_test = 5.7080) and FFN-TMM (R2_test = 0.8914; RMSE_test = 5.8126), with FFN-SAO slightly lower (R2_test = 0.8709). Additionally, a computational performance analysis was conducted to evaluate inference time, memory usage, energy consumption, and batch scalability across all models. Feature importance analysis was conducted, revealing that aging temperature, Cr, and aging duration were the most influential factors for hardness. In contrast, aging duration, aging temperature, solution treatment temperature, and Cu played key roles in electrical conductivity. The results demonstrate the effectiveness of these advanced machine learning models in predicting critical material properties, offering insightful advancements for materials science research. This study introduces the first controlled, statistically validated, multi-model benchmark that integrates composition and thermo-mechanical processing with deployment-grade profiling for property prediction of low-alloyed Cu alloys.