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.
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Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
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Natural gas extraction from the Groningen gas fields in the Netherlands used to be a non-controversial activity, but became highly contested over the past few years. In addition to a political mandate to commercially operate the Groningen gas fields, NAM needs approval from local residents and society at large. In this study, we analyse how NAM attempted to maintain its social license
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