Entropy: Thermodynamics – Energy – Environment – Economy

Entropie - ISSN 2634-1476 - © ISTE Ltd

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The so-called “water vapor pump” cycle is defined by the selective recycling of the water vapor carried by the combustion products at the outlet of the thermal machine by exchange of mass and heat between the exiting combustion products and the incoming atmospheric air. With hydrogen fuel, this form of wet combustion is capable of very high energy and ecological performance. In this context, we present here the Hydrometric Combustion Diagram (HCD) of hydrogen and apply this tool to anticipate the energy performance of this new fuel whose PCS exceeds its PCI by 18%. These expectations also concern the case of gas turbines in the case of wet combustion which, moreover, are, a priori, highly consuming additional water. The formation of atmospheric water plumes, the "cost" of its elimination, the possible residual pollution due to NOx are also presented, this concerning the use of hydrogen fuel in all thermal combustion machines, including in fuel cells. All applications combined and in a cogeneration context, wet combustion, of which the so-called “water vapor pump” cycle is part, increases the dew point temperature of the combustion products by approximately 10°C and promotes useful energy recovery. approaching 100% of the higher calorific value of the fuel (100% of the PCS). What is to be emphasized with hydrogen fuel.

The exponential growth in the use of digital technology is forcing changes in the engineering of material and energy transformation processes, not only in terms of training, but also in terms of inventions of various origins, to revitalize this discipline, which is subject to strong environmental constraints. This near-breakthrough situation needs to be translated into greater creativity before we can achieve breakthrough or incremental innovations. To achieve this objective, it is sometimes useful to bring together two normally disjoint fields to generate ideas that are robust enough to be applied. This article discusses this method, based on biomimicry. Can taking advantage of nature’s ingenuity foster the emergence of anthropocentric inventions and thus accelerate innovation in the field of process engineering? This is the question posed by the authors in their quest to illustrate the opportunities and advantages of such an approach, as well as its current limitations.

A look at the curricula of many French engineering schools reveals that their global vision is reflected in educational categories geared towards theoretical, rational and deterministic generalist learning, with a focus on the main application targets. Links with industry are developed in large part through internships, which broaden the somewhat closed vision provided by their school. This situation has its roots in history, with the need to master mathematical models to design structures, weapons, bridges, factories, material and energy transformation processes, and so on. What we are witnessing is the decline of technological eras from coal to electricity to electronics, with an increasingly constrained environment and demands for ever more sophisticated devices, with ever shorter life spans, in a changing social context. The question posed in this reflection is to analyze whether the impact of the major trends we have just mentioned is likely to call into question, at least in part, the fundamentals of current training courses. What we are showing is the importance of rigorous concepts, which must nevertheless be extended in different ways to other fields, promoting creativity, imagination and agility to bring the engineer’s work closer to the social demand for new needs. For the time being, the authors see this as a flexible/adaptive approach that should encourage creative modes on the part of students, the mastery of doubt, interdisciplinarity and the management of complexity in the development of industrial processes.

The analysis of very irregular processes is often based on the search for a trend, an average curve representing the general course of the observed phenomenon. If each people is more or less capable of drawing such a curve freehand, in an intuitive way, its objective determination is very delicate. The method of moving averages being one of the most used in the search for trends, we propose here to study some properties of these moving averages (essentially mean and variance, or standard deviation). We then show that they can be used to detect characteristic observation windows, leading to structural trends of the analyzed signal. The notion of tau-average (trend obtained by a moving average with a variable window) is also re-examined.

The energy crisis, global warming, and rising energy consumption have positioned renewable energy as a priority from national and international planning perspectives. Not only to reach the goals of the renewable energy mix, but also as part of overall energy security strategy. Rising energy prices and supply concerns have made the need for energy changes tangible for society and have increased public awareness of renewable energy. To achieve its renewable energy targets, Ireland has placed a focus on the development of offshore wind energy projects, due to its massive potential in the region. Other regions have already commenced the deployment of large-scale offshore wind farms and the technology is now competitive with fossil fuels. This work presents a comparison of Geographic Information System (GIS) applications and Multi-Criteria Decision-Making (MCDM) methods applied in the process of multicriteria site selection for Floating Offshore Wind Farms (FOWF) and highlights current trends in FOWF site selection and characterisation. This work is an objective review of the methodologies applied by researchers and a discussion of their adequacy to find the answer to the research questions posed by industry. Furthermore, it outlines the limitations of the methods and comments on the chosen criteria in the context of reaching the researches objectives. It also highlights the suitability of the industry standards methods and best practices. Finally, the work attempts to map the next steps that shall be taken to improve the methodology for criteria selection.

The exponential growth in the number of papers published annually in the field of machine learning applications in energy systems presents a challenge to researchers seeking to conduct comprehensive and effective literature reviews. To address this issue, we took a systematic literature review approach with three distinct smaller case studies focusing on the application of machine learning in energy systems, namely 1. Machine learning in drilling, 2. Machine learning for rooftop solar energy potential quantification, and 3. Machine learning in district heating and cooling in the context of seasonal thermal energy storages. In each case, we employed a systematic literature review methodology. For topic one, we utilized an existing comprehensive review to generate further insights and information. For topics two and three, we used predefined search criteria to conduct relevant publications in a systematic and reproducible manner. We investigate the state of the art of the use of machine learning in these distinct areas of inquiry, thereby facilitating the identification of research gaps. Ultimately, we compare approaches and models utilized in each field, identified common best practices, and propose methods to address potential challenges. The instructions put together below fall into four categories.

Adiabatic compressed air energy storage (A-CAES) is a promising storage technology to face the challenges of high shares of renewable energies in an energy system by storing electric energy for periods of several hours up to weeks. In order to reduce the investment costs and increase the flexibility of the storage system, the so called KompEx LTA-CAES® was developed by Fraunhofer UMSICHT. This new A-CAES concept is using a combination of reversibly operable turbo- and piston machines (KompEx machines). Doing so, these modules can achieve wide CAS pressure ranges (corresponding to high exergy densities) and thus can be combined with any compressed air storage volume. To realize efficient and stable operation despite a wide pressure range, a suitable control strategy of both KompEx machines is required. This paper investigates the introduced A-CAES system by a dynamic simulation, focusing on the interaction and synergy between the reversibly operable turbo- and piston machines. Results indicate that the roundtrip efficiency of this system is expected to be at the low end (55,5%) of literature values for A-CAES (52–66% for low-temperature A-CAES), which is relatively high compared to published A-CAES systems considering similar pressure ranges.

High-temperature heat pump (HTHP) is a promising technology for decarbonization of process heating through electrification and energy efficiency. Exploiting the potentials requires a simultaneous optimization of the cycle layout and the working fluid. This paper proposes an efficient cascade HTHP and optimizes its thermodynamic performance. Using steam for high-temperature loop and use of alternative hydrocarbons for low-temperature loop are examined. On the application level, district heating is considered as a heat source and evaluated for different supply temperatures, including 80 °C, 70 °C and 40 °C. The results reveal that pentane with highest critical temperature among the suggested hydrocarbons, shows the best energy performance to be paired with steam in the proposed cascade HTHP system. However, concerning the hydrocarbon compressor volumetric heating capacity (VHC) and safety issues, butane is an excellent candidate. In addition, when the heat available in the main transmission lines of district heating unit is considered as the source cooled from 80 °C down 70 °C, the highest value of coefficient of performance (COP) is achieved as 2.74 for the sink condensation temperature of 160 °C.