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Abstract Rice husk (RH), an agricultural waste, is abundantly available in rice producing countries like China, India, Bangladesh, Brazil, US, Cambodia, Vietnam, Myanmar, and South East Asia. Despite the massive amount of annual production worldwide, so far RHs have been recycled only for low-value applications. In recent years, many rice mills in rice producing countries have started using RH for the energy production for mill operations as well as household lighting in rural regions. Burning of RHs produces the rice husk ash (RHA). The disposal in landfills or open fields can be problematic and may cause a serious environmental and human health related problems due to the low bulk density of RHA. Several ways are being thought of for disposing RHA by making its commercial use. The amorphous silica forms the main component (83–90%) of RHA. The amorphous silica rich RHA has wide range of applications. High-value applications and current research investigations such as the use of RHA in manufacturing of silica gels, silicon chip, synthesis of activated carbon and silica, production of light weight construction materials and insulation, catalysts, zeolites, ingredients for lithium ion batteries, graphene, energy storage/capacitor, carbon capture, and in drug delivery vehicles are presented. Use of RHA in potential future applications is also discussed. It is suggested that the amorphous silica rich RHA could become a potential resource of low cost precursor for the production of value-added silica based materials for practical applications.

A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics.

A condensation experiment in the presence of non-condensable gas in a vertical tube of the passive containment cooling system of the CP-1300 is performed. The experimental results show that the heat transfer coefficients (HTCs) increase as the inlet air mass fraction decreases and the inlet saturated steam temperature decreases. However the dependence of the inlet mixture Reynolds number on the HTC is small for the operating range. An empirical correlation is developed, and its predictions are compared with experimental data to show good agreement with the standard deviation of 22.3%. The experimental HTCs are also compared with the predictions from the default and the alternative models used in RELAP5/MOD3.2. The experimental apparatus is modeled with two wall-film condensation models in RELAP5/MOD3.2 and the present model, and simulations are performed for several subtests to be compared with the experimental results. Overall, the simulation results show that the default model of RELAP5/MOD3.2 underpredicts the HTCs, and the alternative model over-predicts them, while the present model predicts them well throughout the condensing tube.

Abstract In this paper, the fabrication and characterization of a flexible thermoelectric generator (TEG) is presented. Bismuth telluride powder (Bi/Te) is utilized as p- and n-type thermoelectric materials, and carbon nanotubes (CNTs) are used to enhance electrical conductivity. Both the CNTs and bismuth telluride (Bi/Te) powder are mixed with a solution of polydimethyl siloxane (PDMS) as a precursor. The same PDMS used for the hybrid thermoelectric materials is also used as the substrate, making the TEG flexible and increasing its stability. The Seebeck coefficients of the fabricated p- and n-type thermoelectric materials are 143 and -174 μV/K, respectively. The output voltage of the fabricated device is 920 mV and the generated power is 570 μW/cm 2 with a temperature difference of 60 °C. The fabricated TEG maintains its performance level during bending reliability tests on a curvature with a radius as small as 5 mm, and after more than 1000 repetitions of bending on a curvature with a radius of 20 mm.

This study analyzes the changes in energy consumption of the Korean manufacturing sector using the index decomposition analysis (IDA) method. To capture the production effect based on actual physical activities, we applied the activity revaluation (AR) approach in the analysis. We also developed energy consumption data in terms of primary energy supply to consider conversion loss in the energy sector to avoid any distortions in the intensity effect. The analysis covers every manufacturing subsector in Korea over the period between 2006 and 2018. Combining two distinctive approaches from the previous literature, the AR approach and primary energy-based analysis gives us helpful findings for a climate policy. First, the overall activity effect estimated from the physical output indicator is lower than that from the monetary output indicator. The monetary indicator shows that the share of energy-intensive industries decreases, whereas the physical indicator shows the opposite. Second, in terms of energy efficiency, the intensity effect is estimated as an increasing factor of energy use, whereas inversed results are shown when we use the monetary indicator. Lastly, unlike the previous studies, the AR approach results indicate that Korean manufacturing sectors have been shifting toward an energy-intensive, so it is hard to anticipate positive intensity effects, which means decreasing energy consumption factor, for a while. These results support why analyzing the driving forces of energy consumption through the AR approach and primary energy base is highly recommended.

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

A new bicarbonate-based microalgae cultivation system coupled with an electrochemical CO2absorption process to improve carbon utilization efficiency.

Direct mineral carbonation (MC) is used to mitigate carbon dioxide (CO2) emissions. This method has the great advantages of reducing the amount of industrial residues and creating valuable materials by incorporating CO2. Waste gypsum, industrial waste including flue gas desulfurization (FGD) gypsum (25.27–53.40 wt% of CaO), and phosphogypsum (30.50–39.06 wt% of CaO) can be used for direct MC (conversion rate up to 96%). Mineral carbonation converts waste gypsum into calcium carbonate (CaCO3), which can be recycled during desulfurization. Furthermore, ammonium sulfate ((NH4)2SO4), which is used as a fertilizer, can be prepared as a by-product when the carbonation reaction is performed using ammonia (NH3) as a base. In this study, recent progress in the carbonation kinetics and preparation of CaCO3 using FGD gypsum and phosphogypsum with NH3 was investigated. Temperature, CO2 partial pressure, CO2 flow rate, and NH3 concentration were reviewed as factors affecting carbonation kinetics and efficiency. The factors influencing the polymorphs of the prepared CaCO3 were also reviewed and summarized. A state-of-the-art bench-scale plant study was also proposed. In addition, economic feasibility was investigated based on a bench-scale study to analyze the future applicability of this technology.