• Energy Research
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To meet international efforts to prevent climate change, the South Korean government is seeking to transform its main power source from coal to natural gas (NG), which emits less carbon dioxide (CO2) than coal. The government needs information about public preferences for increasing NG-fired generation. Therefore, the purpose of this paper is to provide the government with this information by gathering the data on public preferences for increasing NG-fired generation for reducing CO2 emissions, analyzing these data, and obtaining the implications from the analysis. To this end, a survey of 1000 people, after explaining the merits and demerits of NG-fired power, were asked to determine the proper ratio of NG-fired generation by choosing one of six examples: lower than 10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and higher than 50%. An interval data model was employed to deal with the data. The average value of the ratio was estimated to be 26.7% with statistical significance, which is greater than the actual value of 22.2% from 2017. Finally, whether and how much some variables such as education level, age, gender, and income level of the respondent affected their preferences for NG-fired generation were investigated.

This research aimed to examine the willingness to pay (WTP) of the young generation (the age group from 18 to 29) for the participatory solutions and actions on fine particulate matter (PM2.5) pollution control and abatement in Daegu, Korea, and Beijing, China. This study found out that Korean respondents and Chinese respondents shared a wide range of similarities, both in terms of socio-demographic characters like age, family size, and house/apartment ownership, as well as a number of perceptions related to environmental and social responsibilities. Around one-third of the Korean respondents and one-fifth of the Chinese respondents expressed negative WTP due to primary reasons such as lack of trust in the effectiveness of PM2.5 control measures (Korea) and limitation of budget (China). The mean estimated values of WTP with and without explanatory variables in Korea were merely slightly higher than those in China. The mean WTP without control variables in Korea was 11,882.97 KRW/month (10.61 USD/month) and the one in China was 65.09 CNY/month (9.48 USD/month). The mean WTP with explanatory variables for Daegu, Korea, was KRW 11,982.33 (USD 10.70) per person per month, and the one for Beijing, China, was CNY 64.84 (USD 9.40) per person per month. The annual total WTP for Daegu, Korea, was assessed around KRW 47572 million (USD 42.45 million), whereas the estimated total WTP for Beijing, China, was around CNY 3260.14 million (USD 474.94 million) per year. Based on the results and the findings, this study proposes to further strengthen the comprehensive cooperation between China and Korea in the field of air quality improvement, with a particular focus on PM2.5 control and abatement and cooperation in academic circles and among the general public.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Methanesulfonic acid (MSA) is used to recover silver (Ag) from solar cells by adding an oxidizing agent. It is possible to regenerate by substituting of H+ for Ag+, and thus it can be reused for additional reactions. However, MSA is highly hygroscopic, and as an oxidizing agent can easily decompose in the acidic environment during Ag extraction, leading to dilution due to the formation of H2O. This H2O in the MSA solution hinders the Ag extraction. In this study, we present a fractional distillation process for restoring the reactivity of reused MSA solutions by reducing the H2O content. Our results showed that the reactivity of the separated MSA was restored and Ag could be recovered from the solar cell.

The purpose of this paper is to recommend a break-even point for the capital investment cost for a Sodium-cooled Fast Reactor (SFR) when choosing between a Pyro-SFR nuclear fuel cycle (recycling option via Pyro-processing) and a direct disposal option. This is because the selection of an alternative cannot be justified without a guarantee of economic feasibility. The calculation of a break-even point is necessary because SFR capital investment cost makes up the largest share of the cost for electricity generation. In other words, the cost of capital investment is an important cost driver, and the one that exerts the greatest effect on Pyro-SFR nuclear fuel cycle economics. In the end, the break-even point of the SFR capital investment cost between the Pyro-SFR nuclear fuel cycle and the direct disposal was calculated to be 4284 US$/kWe. In other words, it is possible to claim that the potential for the economic viability of the Pyro-SFR nuclear fuel cycle is greater (compared to investing in direct disposal) when the SFR capital investment cost is 4284 US$/kWe or less. In addition, Pyro-SFR technology will enable sustainable nuclear power generation.

This study aimed to conduct a techno-economic performance and optimisation analysis of grid-connected PV, wind turbines, and battery packs for Syiah Kuala University, situated at the tip of Sumatra island in the tsunami-affected region. The simulation software Hybrid Optimisation Model for Electric Renewables (HOMER) was used to analyse and optimise the renewable energy required by the institution. The methodology began with the location specification, average electric load demand, daily radiation, clearness index, location daily temperature, and system architecture. The results revealed that the energy storage system was initially included in the simulation, but it was later removed in order to save money and optimise the share of renewable energy. Based on the optimisation results, two types of energy sources were chosen for the system, solar PV and wind turbine, which contributed 62% and 20%, respectively. Apart from the renewable energy faction, another reason for the system selection is cost of energy (CoE), which decreased to $0.0446/kWh from $0.060/kWh. In conclusion, the study found that by connecting solar PV and wind turbines to the local grid, this renewable energy system is able to contribute up to 82% of the electricity required. However, the obstacle to implementing renewable energy in Indonesia is the cheap electricity price that is mainly generated using cheap coal, which is abundantly available in the country.

Faults in electrical facilities may cause severe damages, such as the electrocution of maintenance personnel, which could be fatal, or a power outage. To detect electrical faults safely, electricians disconnect the power or use heavy equipment during the procedure, thereby interrupting the power supply and wasting time and money. Therefore, detecting faults with remote approaches has become important in the sustainable maintenance of electrical facilities. With technological advances, methodologies for machine diagnostics have evolved from manual procedures to vibration-based signal analysis. Although vibration-based prognostics have shown fine results, various limitations remain, such as the necessity of direct contact, inability to detect heat deterioration, contamination with noise signals, and high computation costs. For sustainable and reliable operation, an infrared thermal (IRT) image detection method is proposed in this work. The IRT image technique is used in various engineering fields for diagnosis because of its non-contact, safe, and highly reliable heat detection technology. To explore the possibility of using the IRT image-based fault detection approach, object detection algorithms (Faster R-CNN; Faster Region-based Convolutional Neural Network, YOLOv3; You Only Look Once version 3) are trained using 16,843 IRT images from power distribution facilities. A thermal camera expert from Korea Hydro & Nuclear Power Corporation (KHNP) takes pictures of the facilities regarding various conditions, such as the background of the image, surface status of the objects, and weather conditions. The detected objects are diagnosed through a thermal intensity area analysis (TIAA). The faster R-CNN approach shows better accuracy, with a 63.9% mean average precision (mAP) compared with a 49.4% mAP for YOLOv3. Hence, in this study, the Faster R-CNN model is selected for remote fault detection in electrical facilities.

Biomass co-firing in coal-fired power plants has been widely accepted to reduce the environmental burden. In this study, food waste (FW) and sewage sludge (SS), which are the main types of municipal organic waste, were selected as solid refuse fuel (SRF). To compensate for the limitations of FW and SS, a mixture of FW and SS with varying ratios was processed using pyrolysis and desalination. The fuel properties such as the calorific value, chlorine content, alkali and alkaline earth metallic species (AAEMs) content, and heavy metal content were determined. The calorific values of all biochars were greater than 12.6 MJ/kg, which satisfies the national threshold of Bio-SRF in Korea. Chlorine and AAEMs contents exhibited clear trends for the FW ratio and pyrolysis temperature. Increasing concentrations of heavy metals were observed with increasing SS ratio and pyrolysis temperature. These results provide important insights into the practical application of municipal waste-based biochar in coal-fired plants, as well as the influence of mixing ratio and pyrolysis temperature.