No 5 (2025)

The article considers methodological issues related to the assessment of the potential and further use of low-potential heat sources. A definition of these sources is given. Based on the thermodynamic approach, the temperature boundaries of the low-potential heatsources for various types of heat carriers are determined. A special feature of the approach is the connection between the upper boundary of the low-potential heat source and the average temperature level of heat consumers for obtaining electrical energy and thermal energy. Five main groups of low-potential heat utilization technologies are identified. These include recirculation of heat carrier flows, heat recovery and regeneration, the use of heat transformers, the generation of electrical energy before transferring heat to the consumer, as well as obtaining cold based on low-potential heat in absorption refrigeration units. The most promising areas of low-potential heat source use are considered, and the corresponding consumers are given. It is noted that they cover almost all sectors of the Russian economy — energy, industry, housing and communal services, agriculture. It is shown that the currently available technical solutions are focused on the entire temperature range of low-potential heat sources and are able to meet the needs of a wide range of heat consumers.

This paper presents a review of the current state and development directions of digital twin (DT) and artificial intelligence (AI) technologies, with a focus on their application in energy systems at oil and gas industry facilities. The architectural foundations of DTs are examined, including the six-level model and its classification by levels and application domains. Key AI methods used in conjunction with DTs are discussed, such as machine learning for load and renewable generation forecasting, reinforcement learning for control optimization under uncertainty, and generative AI for decision support and scenario modeling. Applied solutions are considered in detail, including predictive maintenance of equipment, optimization of associated petroleumgas utilization, and the development of intelligent energy management systems. Current challenges and issues are identified, including data processing infrastructure selection, cybersecurity, and economic and legal regulation. Prospects for DT implementation are outlined, taking into account international and Russian environmental requirements, in particular the target of utilizing at least 95% of associated petroleum gas by 2027. Examples of domestic and international developments and studies demonstrate that DT and AI technologies have significant potential for addressing tasks such as equipment monitoring, load forecasting, production process optimization, and automated energy system control in the oil and gas sector.