With the emerging issues of the climate change, the international society has formed international coordination and cooperation, such as the IPCC and the UNFCCC, permitting to share climate-related information and discuss about strategic solutions of climate mitigation. In this context, countries have agreed on reducing a certain amount of carbon dioxide through the shift from fossil fuel to renewable energy, transitioning conventional energy system to be cleaner and sustainable within their geographical boundaries. Knowing that international issues such as climate change require coordination problem-solving strategy to increase its impact and synergy effects, cities, countries and regions have formed urban cooperative networks and coordination to increase synergies, share technical knowledge and engender climate change mitigation and adaptation impacts. The following study aims to investigate the C2C (city-to-city) climate network effectiveness in the presence and absence of network elements including (i) a specific linkage and (ii) network externality elements. In addition, network characteristics of the identified international C2C will be assessed through the result of their (i) eigenvector centrality and (ii) connectivity degree to ultimately assess the relative importance of network characteristics of highly effective transnational C2C networks, along with presumable geographical implications.
In this study, we analyze experimental time series temperature data and infer the transient behaviors of the test reactor, as well as how it changes with reactor scaling. We show that the thermal mass of the reactor has a significant part to play in the reactor’s temporal response to changes, and demonstrate that in our design, it is possible to achieve a reasonable temporal response time at scale. Based on our analysis, we devise a series of start-up and cooling operation strategies that seek to optimize the time and feedstock consumption requirements. The insights learned in this study provide a basis for a more comprehensive study of the reactor transitional operations that can be encapsulated into an automated control system to minimize human intervention.
This paper presents a multi-dimensional taxonomy of levels of automation and reparation specifically adapted to Virtual Assistants (VAs) in the context of Human-Human-Interaction (HHI). Building from this framework, the main output of this study provides a method of calculation which helps to generate a trust rating by which this score can be used to optimise users’ engagement. This tool may be critical for the optimisation of energy management and consumption. Based on the research findings, the relevance of contextual events and dynamism in trust could be enhanced, such as trust formation as a dynamic process that starts before a user’s first contact with the system and continues long thereafter. Furthermore, following the continuously evolving of the system, factor-affecting trust during user interactions change together with the system and over time; thus, systems need to be able to adapt and evolve as well. Present work is being dedicated to further understanding of how contexts and its derivative unintended consequences affect trust in highly automated VAs in the area of energy consumption.
Gas wave refrigerator uses movement of pressure waves to realize refrigeration. When high pressure inlet gas contains condensable component, condensation happens in gas wave refrigerator. By means of numerical simulation and experiment, this paper investigates effects of condensation on performance of gas wave refrigerator. The results show that evaporation also exists in gas wave refrigerator. Latent heat released by condensation makes temperature of low temperature region rise. When the gas in high pressure inlet contains saturated water vapor, with the increase of pressure of high pressure inlet, the percentage of reduction of temperature drop firstly rises then drops. Results of experiment show that with the increase of high pressure inlet relative humidity, the temperature drop between high pressure inlet and low temperature outlet decreases. With higher pressure of high pressure inlet, change trend of temperature drop becomes gentle.
It is difficult to effectively control the vertical grinding process of raw materials due to its characteristics of strong coupling, non-linearity and large hysteresis. This paper proposes a vertical mill intelligent control system based on data mining to predict the operating conditions of the slag grinding system. Taken into consideration corresponding shortcomings of each algorithm, we combine several algorithms to propose a feature extraction method for analyzing operating conditions and determining the indicators that affect the operation. Next, we clustered the healthy operating conditions to get the distribution of health conditions, and based on this, established a healthy operating condition library. The operational data are compared with the reference conditions, and the prediction model is trained using the ARIMA algorithm to predict the trend of the corresponding indicators. To verify the effectiveness and practicability of the method, we developed a software system and applied it to the actual case analysis. It is concluded that the vibration of the control group is decreased by an average of 10%, and the average power consumption per ton is decreased by 6.05%. According to the total number of vertical mills of 350,000 tons, the average power consumption per ton is 43.5 degrees. Therefore, the total annual power consumption will be 1.5225 million kilowatt hours, which can save 921,100 kilowatt hours. According to the average industrial price of 1.5 yuan / kWh, the annual saving will be 1,381,700 yuan.
Effective messaging and evidence-based demonstration of implicit economic opportunities in transitioning to leaner 1.5℃ pathways can go a long way in creating consensus for massive social mobilization and committed government actions towards deeper decarbonization. In this study we assess the direct and indirect economic impacts of India’s Nationally Determined Commitments (NDCs) and beyond at the subnational level using an integrated macro-econometric dynamic simulation model: E3-India. An array of distinct economic trajectories associated with energy decarbonization and energy efficiency targets at national level and state level were simulated using the model. Results reveal that selective investments in ambitious climate mitigation policies will lead to overall economic growth for Indian economy. However, distributional impacts across states, especially those already identified as climate hotspots, will be heterogenous. These regions will therefore need effective policy interventions to manage the transition and ensure resilience in the face of climate change.
Solar collectors (SCs) and Photovoltaics (PVs) can intervene with tri-generation systems to form a poly-generation system. Many studies have accessed this intervention, however, these studies depended on the performance of these components as individual components not on a system basis. They haven’t dealt with the environmental and exergetic aspects of the whole system. Moreover, they haven’t dealt with optimal planning and scheduling of these systems. A methodology of real system level comparison is presented in contrary to component-level comparisons that are available in the open literature. This methodology depends on comparing an optimized Solar CCHP poly-generation system with side-by-side PVs and SCs, against an optimized CCHP (Combined heating, cooling and power) system. The comparison is under the constraints of maximizing a formulated combined efficiency that combines energy, economy, environment and exergy aspects. Results showed that the Solar-CCHP system has higher combined efficiency but with lower Net Present Value (NPV). Another novel contribution for determining the actual selling price of both sold CCHP-electricity and Solar electricity is presented. These results assured the importance of reducing the capital costs of solar energy systems to facilitate its deployment in future energy systems as they already prove their ability to increase overall combined efficiency of energy systems by decreasing the fuel used and emission produced.
The relative permeability curves are the key parameters of mechanics of fluid flowing through porous media multiphase flow, which was influenced by many factors. However, only few study on the correlativity between relative permeability curves and porous media parameters were conducted, which is unfavorable for actual application. The capillary pressure and the relative permeability curve testing experiments were measured simultaneously for the same core samples, which could be used for theoretical and mathematical statistics analysis. The fractal theoretical model was used to analyze the capillary pressure curve and the fractal dimensions could be obtained through regression. Theoretical analysis and mathematical statistics were used for analyze the correlation between the relative permeability parameters and the physical property parameters of the porous media. The most significant finding is that the better the physical property parameters are, the lower the irreducible water saturation and the residual oil saturation are, and the higher the wetting phase endpoint values are. But the correlation between the oil phase index and water phase index were not good enough with the physical property parameters, however satisfied with the fractal dimension. The parametric variation trend of the relative permeability curve could be not only used for the development effect improvement, but also the porous media parameters control for other engineering fields application.
Overview of hydrocarbon and other energy sources and their impact on global warming is given. It is shown that the developed countries, which represent 15% of the world’s population, should provide business plans and clean technologies for the developing countries which constitute 85% of the world’s population. Thus, greenhouse gas (GHG) emissions and global warming can be globally reduced. Furthermore, cleaner transportation, such as electric and hybrid vehicles need much more adaptation in order to be universally applicable, this as well as other green technologies that are environment-friendly.