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The Energy Information Administration (EIA) of the U.S. Department of Energy has projected net electricity generation through 2040 for ten selected countries and the world. These ten countries including China and the U.S. are projected to produce 70% of the world electricity generation. Some of the past projections by EIA have been rated too conservative. Using a simple experience curve, we produce alternative projections for comparison. Our projections are significantly higher for eight out of the ten countries. Utilities operating in these eight countries may need to reexamine their plan for future net electricity generation.

Abstract Hydrogen is considered a potential game changer for world energy systems and a solution to climate change concerns, as it generates zero waste and it is suited for power generation and transportation. Despite its several advantages, there are significant technical challenges in deploying a stable hydrogen economy including improving its process efficiencies, lowering production costs, maintaining cost-effective transmission and distribution, and exploiting inexpensive and sustainable feedstocks. In this context, a detailed study was conducted to analyze the production sources, technologies, storage and transport systems, and global potential exportable feedstocks to produce hydrogen. A comprehensive analysis of current hydrogen production technologies with their energy efficiencies and hydrogen selling prices was reported in this study. Various hydrogen production technologies with their capital investments and CO2 emissions were also presented. Potential feedstocks for hydrogen production were identified and analyzed through a product space model, which characterizes a network of global exportable products based on their similarities and productive knowledge. It was established that the hydrogen production feedstocks and sources currently used are primarily available in six countries: the United States of America, France, Russia, Sweden, the Netherlands, and Spain. Broadly, the results revealed that the United States of America and Russia shared the highest hydrogen feedstock exports, indicating a higher probability of hydrogen production in these countries. Except for Russia, all the studied countries fell in the most desired quadrant, indicating that they can move in all product space directions to exploit unexplored hydrogen feedstocks for better sustainable economic growth.

Large-size woody biomass is a valuable renewable resource to replace fossil fuels in biorefinery processes. The preprocessing of wood chips and briquettes is challenging to manage, especially in an industrial setting, as it generates a significant amount of dust and noise and occasionally causes unexpected accidents. As a result, a substantial amount of resources, energy, labor, and space are needed. The thermochemical conversion behavior of large-size woody biomass was studied to reduce energy consumption for chipping. Large-size wood was 1.5 m in length, 0.1 m in breadth, and stacked 90 cm in height. This strategy has many benefits, including increased effectiveness and reduced CO2 emissions. The target of this paper presents the thermochemical process, and large-size wood was chosen because it provides high-quality product gas while reducing the preprocessing fuel cost. This review examines the benefits of thermochemical conversion technologies for assessing the likelihood of carbon neutrality.

Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

This study introduces a novel methodology to calculate the carbon dioxide (CO2) emission reduction related to residual emissions, calculating the CO2 emission reduction through a 2 MW (40 tCO2/day) carbon capture and utilization (CCU) plant installed at a 500 MW coal-fired power plant in operation, to evaluate the accuracy, maintainability, and reliability of the quantified reduction. By applying the developed methodology to calculate the CO2 emission reduction, the established amount of CO2 reduction in the mineral carbonation was evaluated through recorded measurement and monitoring data of the 2 MW CCU plant at the operating coal-fired plant. To validate the reduction, the accuracy, reproducibility, consistency, and maintainability of the reduction should be secured, and based on these qualifications, it is necessary to evaluate the contribution rate of nationally determined contributions (NDCs) in each country. This fundamental study establishes the concept of CCU CO2 reduction and quantifies the reduction to obtain the validation of each country for the reduction. The established concept of the CCU in this study can also be applied to other CCU systems to calculate the reduction, thereby providing an opportunity for CCU technology to contribute to the NDCs in each country and invigorate the technology.

Growing concerns over the use of limited fossil fuels and their negative impacts on the ecological niches have facilitated the exploration of alternative routes. The use of conventional plastic material also negatively impacts the environment. One such green alternative is polyhydroxyalkanoates, which are biodegradable, biocompatible, and environmentally friendly. Recently, researchers have focused on the utilization of waste gases particularly those belonging to C1 sources derived directly from industries and anthropogenic activities, such as carbon dioxide, methane, and methanol as the substrate for polyhydroxyalkanoates production. Consequently, several microorganisms have been exploited to utilize waste gases for their growth and biopolymer accumulation. Methylotrophs such as Methylobacterium organophilum produced highest amount of PHA up to 88% using CH4 as the sole carbon source and 52–56% with CH3OH. On the other hand Cupriavidus necator, produced 71–81% of PHA by utilizing CO and CO2 as a substrate. The present review shows the potential of waste gas valorization as a promising solution for the sustainable production of polyhydroxyalkanoates. Key bottlenecks towards the usage of gaseous substrates obstructing their realization on a large scale and the possible technological solutions were also highlighted. Several strategies for PHA production using C1 gases through fermentation and metabolic engineering approaches are discussed. Microbes such as autotrophs, acetogens, and methanotrophs can produce PHA from CO2, CO, and CH4. Therefore, this article presents a vision of C1 gas into bioplastics are prospective strategies with promising potential application, and aspects related to the sustainability of the system.

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

Abstract In case of a core melt-down accident in a light water nuclear reactor, hydrogen is produced during reactor core degradation and released into the reactor building. This subsequently creates a combustion hazard. A local ignition of the combustible mixture may generate standing flames or initially slow propagating flames. Depending on geometry, mixture composition and turbulence level, the flame can accelerate or be quenched after a certain distance. The loads generated by the combustion process (increase of the containment atmosphere pressure and temperature) may threaten the integrity of the containment building and of internal walls and equipment. Turbulent deflagration flames may generate high pressure pulses, temperature peaks, shock waves and large pressure gradients which could severely damage specific containment components, internal walls and/or safety equipment. The evaluation of such loads requires validated codes which can be used with a high level of confidence. Currently, turbulence and steam effect on flame acceleration, flame deceleration and flame quenching mechanisms are not well reproduced by combustion models usually implemented in safety tools and further model enhancement and validation are still needed. For this purpose, two hydrogen deflagration benchmark exercises have been organised in the framework of the SARNET network. The first benchmark was focused on turbulence effect on flame propagation. For this purpose, three tests performed in the ENACCEF facility were considered. They concern vertical flame propagation in an initially homogenous mixture with 13 vol.% hydrogen content and different geometrical configurations. Three blockage ratios of 0, 0.33 and 0.6 were considered to generate different levels of turbulence. The second benchmark objective was the investigation of the diluting effect on flame propagation. Thus, three tests performed in the ENACCEF facility using the same blockage ratio of 0.63 and three different initial gas compositions (with 10, 20 and 30 vol.% diluents) have been considered. Since ENACCEF runs at ambient temperature, a surrogate to steam was used consisting of a mixture of 0.6He + 0.4CO 2 on molar basis. This paper aims to present the benchmarks conclusions regarding the ability of LP and CFD combustion models to predict the effect of turbulence and diluent on flame propagation.