Browsing by Author "Kamyshatskyi, Oleksandr"

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Integration of Digital Technologies and Environmentally Sustainable Solutions for Optimizing Hydrocarbon Transportation
(IOP Publishing Ltd, 2026) Rastsvietaiev, Valerii; Kamyshatskyi, Oleksandr; Petrenko, Vitalii O.; Dmytruk, Oleh; Vashchenko, Iryna
ENG: The transportation of hydrocarbons faces significant challenges in efficiency, environmental impact, and safety, necessitating innovative solutions to align with global sustainability goals. This study proposes a novel framework integrating digital technologies – digital twins, artificial intelligence, and Internet of Things – with sustainable practices, including energy-efficient pipeline designs and carbon capture technologies, to optimize hydrocarbon transportation. Through a case study on a regional pipeline network, the framework demonstrated substantial improvements, achieving up to 25% reduction in energy consumption, 24% decrease in CO2 emissions, and 60% lower safety incident risks compared to traditional methods. The methodology combines real-time monitoring, predictive analytics, and eco-friendly engineering to enhance operational performance and environmental stewardship. Results indicate the framework’s feasibility and scalability, though challenges such as high initial costs and integration barriers require strategic solutions. The findings offer practical recommendations for industry adoption and policy support, contributing to a sustainable energy transition. Future research directions include advanced artificial intelligence algorithms, next-generation materials, and broader applications in energy transport.
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.