Problems of energy security, diversification of energy sources, and improvement of technologies (including alternatives) for obtaining motor fuels have become a priority of science and practice today. Many scientists devote their scientific research to the problems of obtaining effective brands of alternative (reformulated) motor fuels. Our scientific school also deals with the problems of the rational use of traditional and alternative motor fuels.This article focused on advances in motor fuel synthesis using natural, associated, or biogas. Different raw materials are used for GTL technology: biomass, natural and associated petroleum gases. Modern approaches to feed gas purification, development of Gas-to-Liquid-technology based on Fischer–Tropsch synthesis, and liquid hydrocarbon mixture reforming are considered.Biological gas is produced in the process of decomposition of waste (manure, straw, grain, sawdust waste), sludge, and organic household waste by cellulosic anaerobic organisms with the participation of methane fermentation bacteria. When 1 tonne of organic matter decomposes, 250 to 500–600 cubic meters of biogas is produced. Experts of the Bioenergy Association of Ukraine estimate the volume of its production at 7.8 billion cubic meters per year. This is 25% of the total consumption of natural gas in Ukraine. This is a significant raw material potential for obtaining liquid hydrocarbons for components of motor fuels.We believe that the potential for gas-to-liquid synthetic motor fuels is associated with shale and coalfield gases (e.g. mine methane), methane hydrate, and biogas from biomass and household waste gases.
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
Global awareness on energy consumption and the environmental impacts of fossil fuels boost actions and create more supportive policies towards sustainable energy systems, in the last energy outlook, by the International Energy Agency, it was forecasted totals of 3600 GW from 2016 to 2040 of global deployment of renewables sources (RES), covering 37% of the power generation. While the Natural Gas overtake the coal demand in the energy mix, growing around 50%, manly by more efficiency system and the use of LNG for long-distance gas trades. The energy infrastructure will be more integrated, deploying decentralized and Hybrid Energy Networks (HEN).This transformation on the energy mix leads to new challenges for the energy system, related to the uncertainty and variability of RES, such as: Balancing flexibility, it means having sufficient resources to accommodate when variable production increase and load levels fall (or vice versa). And Efficiency in traditional fired plants, the often turn on and off or modify their output levels to accommodate changes in variable demand, can result in a decrease in efficiency, particularly from thermal stresses on equipment. This paper focus in the possibility to offer balancing resources from the LNG regasification, while ensure an efficient system.In order to asses this issue, using the energy Hub concept a model of a distributed HEN was developed. The HEN consist in a Waste to Energy plant (W2E), a more sustainable case of Combine Heat and Power (CHP) coupled with a LNG cold recovery regasification. To guarantee a most efficiency operation, the HEN was optimized to minimized the Exergy efficiency, additionally, the system is constrained by meeting Supply with variable demand, putting on evidence the sources of balancing flexibility. The case study show, the coupled system increases in overall exergy efficiency from 25% to 35% compared to uncoupled system; it brings additional energy between 1.75 and 3 MW, and it meets variable demand in the most exergy efficient with power from LNG reducing inputs of other energy carriers. All this indicated that LNG cold recovery in regasification coupled other energy systems is as promising tool to support the transition towards sustainable energy systems.
