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The flue-wall deformation during the service life of carbon anode baking furnaces has a substantial impact on the carbon anode quality (i.e., thermal, electrical, and mechanical properties) used in the reduction cell of aluminum production. Deformation of the furnace flue-walls, which is one critical mode of furnace aging, leads among others to inhomogeneous baking of the anodes and consequently to a deterioration of the resulting anode quality. This paper focuses on the development of a 3D multi-physics computational model, which is able to take into account a large number of physical phenomena and parameters that play a role in the baking process while considering different levels of the flue-wall deformation. In fact, this 3D model takes into account the thermo-hydro-mechanical coupling due to coupled fluid flow and transient heat transfer, packing coke load and the thermal expansion, and enable us to analyze the influence of these parameters on the resistance to deflection of the flue-walls, and ultimately improved baking process and furnace geometry can be proposed The developed model can predict the anode temperature distribution, creation of hot spot and anode overbaking in certain area as a function of the flue wall deformation mode. By developing this tool, we can effectively predict the deformable flue wall reliability under varying operating conditions, and provide useful insights on enhancing the long-term structural integrity through furnace retrofitting or design adjustment.

For many decades, state estimation (SE) has been a critical technology for energy management systems utilized by power system operators. Over time, it has become a mature technology that provides an accurate representation of system state under fairly stable and well understood system operation. The integration of variable energy resources (VERs) such as wind and solar generation, however, introduces new fast frequency dynamics and uncertainties into the system. Furthermore, such renewable energy is often integrated into the distribution system thus requiring real-time monitoring all the way to the periphery of the power grid topology and not just the (central) transmission system. The conventional solution is two fold: solve the SE problem (1) at a faster rate in accordance with the newly added VER dynamics and (2) for the entire power grid topology including the transmission and distribution systems. Such an approach results in exponentially growing problem sets which need to be solver at faster rates. This work seeks to address these two simultaneous requirements and builds upon two recent SE methods which incorporate event-triggering such that the state estimator is only called in the case of considerable novelty in the evolution of the system state. The first method incorporates only event-triggering while the second adds the concept of tracking. Both SE methods are demonstrated on the standard IEEE 14-bus system and the results are observed for a specific bus for two difference scenarios: (1) a spike in the wind power injection and (2) ramp events with higher variability. Relative to traditional state estimation, the numerical case studies showed that the proposed methods can result in computational time reductions of 90%. These results were supported by a theoretical discussion of the computational complexity of three SE techniques. The work concludes that the proposed SE techniques demonstrate practical improvements to the computational complexity of classical state estimation. In such a way, state estimation can continue to support the necessary control actions to mitigate the imbalances resulting from the uncertainties in renewables.

The concentration of furanic compounds in transformer's oil can be an effective measurement towards assessing the aging state of oil impregnated paper in the transformer. The rate of change of the concentration of furan content is vital for assessing the rate of deterioration of cellulose insulation and its severity. This promotes furan content as effective parameter in transformer oil for transformer condition assessment and accordingly asset management. In this paper the correlation between oil parameters and furan content is studied using artificial neural networks (ANN). A neural network is used for predicting the furan content based on different combinations of input parameters that are known to be correlated to cellulose paper degradation of the transformer. These input parameters are carbon monoxide (CO), carbon dioxide (CO2), water content, acidity, and break down voltage (BDV). Results on real data of forty transformers show that the proposed model is capable of predicting the furan content with an average accuracy of 90%. Consequently, this proposed model improves the efficiency of oil chemical tests and dissolved gas analysis (DGA) and their abilities to assess the condition of transformer solid insulation.

The sudden increase in the concentration of carbon dioxide (CO2) in the atmosphere due to the high dependency on fossil products has created the need for an urgent solution to mitigate this challenge. Global warming, which is a direct result of excessive CO2 emissions into the atmosphere, is one major issue that the world is trying to curb, especially in the 21st Century where most energy generation mediums operate using fossil products. This investigation considered a number of materials ideal for the capturing of CO2 in the post-combustion process. The application of aqueous ammonia, amine solutions, ionic liquids, and activated carbons is thoroughly discussed. Notable challenges are impeding their advancement, which are clearly expatiated in the report. Some merits and demerits of these technologies are also presented. Future research directions for each of these technologies are also analyzed and explained in detail. Furthermore, the impact of post-combustion CO2 capture on the circular economy is also presented.

Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.

Abstract One of the most significant current topics in the energy market is the decentralized operation of the power system wherein the entire discipline is constructed based on the consensus concept. In such a framework, an agreement among all effective participants in the market should be brought without the presence of an independent arbitrator. This paper proposes a secured energy market architecture based on peer-to-peer (P2P) idea to provide an appropriate platform based on the decentralized network and key aspects of energy market, including network architecture, interface communication, and network security. It is apparent that communication interfaces connecting the market participants is a fundamental property for achieving this purpose and that must be secured against the malicious attacks. In this regard, reaching the secured consensus is guaranteed by providing a new blockchain platform coincided by P2P energy market. The energy market is conducted by an effective Relaxed Consensus-Innovation (RCI) based algorithm with the aim of bringing the power/price exchanged among connecting participants in the form of P2P structure. In the proposed model, a microgrid and a smart grid are considered as the market participants, who tend to negotiate with each other in a way that follow their own benefits in the secured environment. The microgrid includes the wind turbine (WT), photovoltaic (PV), tidal turbine and storage unit to satisfy its demand and the smart grid is composed of distributed generations (DGs) and lines in the form of the IEEE 24-bus test system. In order to handle the uncertainty effects in the problem, a stochastic framework based on unscented transform (UT) is proposed in the P2P energy market. In order to assess and verify the fault-tolerant system ability against the cyber-attack, the fault data injection attack (FDIA) is modeled and applied to the P2P energy market in a blockchain platform. The simulation results approve the appropriate performance and applicable nature of the proposed concepts in this paper.

Abstract The Argon inert gas is used to dilute the intake air of a spark ignition engine to decrease nitrogen oxides and improve the performance of the engine. A research engine Ricardo E6 with variable compression was used in the present work. A special test rig has been designed and built to admit the gas to the intake air of the engine for up to 15% of the intake air. The system could admit the inert gas, oxygen and nitrogen gases at preset amounts. The variables studied included the engine speed, Argon to inlet air ratio, and air to fuel ratio. The results presented here included the combustion pressure, temperature, burned mass fraction, heat release rate, brake power, thermal efficiency, volumetric efficiency, exhaust temperature, brake specific fuel consumption and emissions of CO, CO2, NO and O2. It was found that the addition of Argon gas to the intake air of the gasoline engine causes the nitrogen oxide to reduce effectively and also it caused the brake power and thermal efficiency of the engine to increase. Mathematical program has been used to obtain the mixture properties and the heat release when the Argon gas is used.

In this study, a dynamic two-level framework is proposed to model investment incentives in a multi-carrier energy market from a strategic company’s point of view. Capacity payment and firm contract are assumed as investment incentives to encourage the strategic producer to invest in generation units. In addition, financial incentives to invest in combined heat and power (CHP) include tax rebate and loans. Strategic company’s behavior is considered as a two-level model so that, in the first level, the objective function is to maximize the profit of the strategic producer by participating in an energy hub market. The strategic producer can invest in transmission lines, generation units, CHP, and gas furnace. In the second level, the aim is to maximize a multi-carrier energy social welfare encompassing heat, gas, and electric energy. In this model, units invested by rival companies are modeled using possible scenarios. Electric energy loads in this energy hub system are envisaged to be elastic, while heat loads are assumed to be inelastic to the market price. On the other hand, gas loads are indirectly elastic to the price. Besides, in the proposed framework, DC power flow and an exact gas flow model with the linearized Weymouth equation are used. The proposed model is implemented on two case studies including 6-bus system, and an energy hub system encompassing 24-bus IEEE RTS power system and 10-node natural gas system.

The present age is characterized by a very complex economic relationship among finance, technology, social needs, etc., which can be summarized in the word “sustainability.” The sustainable consumption and production policies represent the keys to realize sustainable development. But, the analysis of the carbon footprint data points out that the present economies are still carbon-consumption production. The reduction of greenhouse gasses emissions is based on a shift from fossil to renewable and bio-based industrial raw materials, with a related reorganization of the chains of the energy and manufacturing sectors. But, this requirement implies technological choices based on a sustainable measurement of their impacts on the ecological and economical contexts. So, social and economic requirements must also be taken into account by the decision-makers. Bioeconomy can represent a possible approach to deal with the requirements of the present time. But, new needs emerge in relation to sustainability. So, sustainable policies require new indicators, in order to consider the link among economics, technologies, and social well-being. In this paper, an irreversible thermodynamic approach is developed, in order to introduce a thermoeconomic indicator, based on thermodynamic optimization methods, but also on socioeconomic and ecological evaluations. The entropy production rate is introduced in relation to the CO2emission flows from human activities, and it is related to the income index, in order to consider the economic and social equity. This approach is of interest of the researchers in the field of econophysics, thermoeconomy, economics, and bioeconomy.