Browsing by Author "Pavlychenko, Artem"

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Using Machine Learning to Model Mechanical Processes in Mining: Theory, Practice, and Legal Considerations
(Engineered Science Publisher LLC, Knoxville, USA, 2025) Ratov, Boranbay; Pavlychenko, Artem; Kirin, Roman; Pashchenko, Oleksandr; Khomenko, Volodymyr L.; Tileuberdi, Nurbol; Kamyshatskyi, Oleksandr; Sieriebriak, Stanislav; Seidaliyev, Askar; Muratova, Samal
ENG: Artificial intelligence (AI) technologies, though critical for economic development, also pose risks of unpredictable outcomes and loss of control. Thus, a legal framework is necessary to regulate their use. International and state oversight is required to establish clear rules of conduct for all parties involved in AI relations, ensuring these technologies remain human-oriented and secure. In geological studies, AI can enhance the accuracy of predictions, such as improving the understanding of rock behavior during drilling. Machine learning methods, including linear regression and gradient boosting, have proven effective in predicting the mechanical properties of rocks, which helps optimize drilling operations and minimize risks like equipment damage. However, models must be fine-tuned to account for more complex dependencies, such as mineralogical characteristics. Despite the effectiveness of AI, challenges remain, including the need for high-quality data and the potential for overfitting in some methods. Incorporating AI studies into the geological code is crucial for effectively managing these technologies. By enhancing transparency, security, and accountability in AI systems, governments can mitigate risks while fostering innovation. In geology, AI’s potential for reducing drilling costs and improving safety, as well as its application to other areas like mining and construction, will drive significant advancements in scientific and industrial fields.
Utilizing Fuel and Energy SectorWaste as Thermal Insulation Materials for Technical Buildings
(MDPI, Basel, Switzerland, 2024) Pavlychenko, Artem; Sala, Dariusz; Pyzalski, Michal; Dybrin, Serhii; Antoniuk, Olena; Dychkovskyi, Roman
ENG: The growing demand for sustainable construction materials has prompted intensive research into the potential reuse of waste from the fuel and energy sector as effective thermal insulation materials. This study examines the feasibility of utilizing ash–slag mixtures, fly ash, and aluminosilicates as insulation materials for technical buildings. These materials were selected due to their availability and potential to improve energy efficiency in construction. Practical tests were carried out to determine the thermal conductivity coefficients of various samples, which were produced using different cement mixtures as binders to ensure adequate structural strength. The results demonstrated that the use of industrial waste-derived materials not only provides satisfactory thermal insulation properties but also contributes to environmental sustainability by reducing the challenges associated with the disposal of industrial by-products. The study highlights the crucial role of cement as a binder, enhancing the mechanical strength and durability of the insulation samples. The integration of ash–slag mixtures, fly ash, and aluminosilicates into the construction sector may foster the adoption of more environmentally friendly building practices, thereby supporting a circular economy and mitigating the environmental impact of construction activities. The study showed that the lowest thermal conductivity coefficient (0.24 W/m·K) was achieved for mixtures containing fly ash and cement, while the highest value (0.30 W/m·K) was recorded in samples incorporating aluminosilicates. The obtained results confirm the effectiveness of fly ash as a cost-efficient additive that improves the thermal insulation properties of the material.