• Energy Research
• 11. Sustainability close
• 12. Responsible consumption close
• 4. Education close
• KR close
• Energies close

Biochar is the product of the pyrolysis of organic materials in a reduced state. In recent years, biochar has received attention due to its applicability to organic waste management, thereby leading to active research on biochar. However, there have been few studies using food waste. In particular, the most significant difference between food waste and other organic waste is the high salinity of food waste. Therefore, in this paper, we compare the chemical characteristics of biochar produced using food waste containing low- and high-concentration salt and biochar flushed with water to remove the concentrated salt. In addition, we clarify the salt component behavior of biochar. Peak analysis of XRD confirms that it is difficult to find salt crystals in flushed char since salt remains in the form of crystals when salty food waste is pyrolyzed washed away after water flushing. In addition, the Cl content significantly decreased to 1–2% after flushing, similar to that of Cl content in the standard, non-salted food waste char. On the other hand, a significant amount of Na was found in pyrolyzed char even after flushing resulting from a phenomenon in which salt is dissolved in water while flushing and Na ions are adsorbed. FT-IR analysis showed that salt in waste affects the binding of aromatic carbons to compounds in the pyrolysis process. The NMR spectroscopy demonstrated that the aromatic carbon content, which indicates the stability of biochar, is not influenced by the salt content and increases with increasing pyrolysis temperature.

Various problems often arise in high-rise buildings during the winter months due to the stack effect. In this study, the high-rise building of interest, located in South Korea, was experiencing constant loud noises in the winter due to the stack effect. Thus, we created a noise level reduction plan by creating a method for pressurizing the high-rise zones of the building according to outdoor conditions. To discover the appropriate pressurization operating modes, we applied a two-year commissioning process to the 50-story building of interest. The 1st- and 47th-floor elevator halls were identified to have the highest noise levels of all other floors. Prior to applying the reduction plan, the maximum noise level on the first floor with the HVAC system turned off was 85 dB(A) and with the HVAC system turned on it was 70 dB(A). Both values exceeded the criteria of 57 dB(A) for a lobby space of a commercial building. In the case of the 47th floor, the maximum noise level with the HVAC system turned off was 58.7 dB(A) and with the HVAC system turned off was 56.0 dB(A), despite the latter having increased airtightness performance and applying preliminary pressurization (i.e., HVAC operation mode 2). These values exceeded the criteria of 48 dB(A) for an elevator hall in a commercial building. Following this initial data, we determined to pressurize the high/mid-rise zones of the building according to the outdoor air temperature and wind velocity conditions, which we categorized into four types (i.e., HVAC operation mode 4). To this effect, the first-floor elevator hall’s maximum noise level was 56.6 dB(A), meeting the criteria, and the 47th-floor elevator hall’s maximum noise level was 49.5 dB(A), still exceeding the criteria but by an insignificant amount. Although the HVAC pressurization operation we utilized resulted in favorable results for the target building A, it may not be as effective in other new high-rise buildings, creating changes to the indoor air environment or to the energy costs in maintaining a building. However, for the purposes of resolving the stack effect, we believe that the commissioning process we took to optimize the HVAC operation that is presented here can be applied to other new and existing high-rise commercial buildings.

Heated glass can be applied to improve windows’ condensation resistance and indoor thermal comfort in buildings. Although this applied technology has advantages, there are still some concerns in practical applications, such as additional energy consumption and control issues. This study evaluates the effectiveness of a heated window heating (HWH) system in terms of thermal comfort and heating energy performance (HEP). The simulation-based analysis is performed to evaluate the effectiveness of the HWH using a residential building model and to compare it with radiant floor heating (RFH) and hybrid heating (HH) systems (i.e., combined HWH and RFH). This study also investigates the peak and cumulative heating loads using HWH systems with various scenarios of control methods and setpoint temperature. The predicted mean vote (PMV) is used as an indoor thermal comfort index. The ratio of cumulative thermal comfort time to the entire heating period is calculated. The results show that HWH and HH can reduce the heating load by up to 65.60% and 50.95%, respectively, compared to RFH. In addition, the times of thermal comfort can be increased by 12.55% and 6.98% with HWH and HH, respectively. However, considering the social practices of South Korea, HH is more suitable than HWH. Further investigations for HH show that a surface setpoint of 26 °C is proper, considering both heating demands and thermal comfort. In addition, the setpoint temperature should be determined considering HEP and the thermal comfort for HWH, and the optimal setpoint temperature was suggested under specific conditions.

Recently, building automation system (BAS) and building energy management system (BEMS) technologies have been applied to efficiently reduce the energy consumption of buildings. In addition, studies on utilizing large quantities of building data have been actively conducted using artificial intelligence and machine learning. However, the high cost and installation difficulties limit the use of measuring devices to sense the indoor environment of all buildings. Therefore, this study developed a comprehensive indoor environment sensor module with relatively inexpensive sensors to measure the indoor environment of a university building. In addition, an algorithm for predicting the load in real time through machine learning based on indoor environment measurement is proposed. When the reliability of the algorithm for predicting the number of occupants and load according to the indoor CO2 concentration was quantitatively assessed, the mean squared error (MSE), root mean square deviation (RMSD), and mean absolute error (MAE) were calculated to be 23.1, 4.8, and 2.5, respectively, indicating the high accuracy of the algorithm. Since the sensor used in this study is economical and can be easily applied to existing buildings, it is expected to be favorable for the dissemination of load prediction technology.

Among green energy technologies, solar power is a promising technology in the net-zero era of the power industry. Conventional solar modules have decreasing efficiency weaknesses as the temperature rises, whereas photovoltaic thermal (PVT) systems do not remove heat from the module or lower the temperature. However, the profitability of PVT systems has been underestimated. The profitability of new convergence products, such as PVT technology, entering the market for the first time allows for the evaluation of the appropriate starting price and policy requirements to support the product’s market entrance. We used the contingent valuation method (CVM) to solve this problem. The survey derived the respondents’ mean willingness to pay (WTP) and the total amount of social benefits. Among these CVM models, the one-and-one-half-bound (OOHB) spike model was used to distinguish zero WTP. Based on a survey of 300 households, respondents were willing to pay an additional yearly average income tax of KRW 10,608 (USD 7.90), and the total social benefit of PVT technology was calculated to be KRW 145 billion (USD 108 million). This result shows that individuals evaluate the value of PVT as 3.69% of their monthly electricity bill and 2.8% of their monthly gas bill. Several additional factors that influence WTP were analyzed, indicating a high possibility of PVT adoption by individuals who have replaced or are planning to replace photovoltaic (PV) modules. This study is significant because it examines the economic value of PVT and proposes a focus group for the effective market entry of new carbon neutrality products.

In the cement industry, utilization of a sustainable binder that has a lower energy consumption and carbon dioxide (CO2) emission than Portland cement is becoming increasingly important. Air lime is a binder that hardens by absorbing CO2 from the atmosphere, and its raw material, hydrated lime, is manufactured at a lower temperature (around 900 °C) than cement (around 1450 °C). In this study, the amount and rate of CO2 uptake by air lime-based materials are quantitatively evaluated under ambient curing conditions of 20 °C, 60% relative humidity, and 0.04% CO2 concentration. In addition, the effects of the water-to-binder ratio (w/b) and silica fume addition on the material properties of the air lime mortar, such as strength, weight change, carbonation depth, and pore structure, are investigated. Unlike hydraulic materials, such as Portland cement, the air lime mortar did not set and harden under a sealed curing condition, however, once exposed to dry air, the mortar began to harden by absorbing CO2. During the first week, most of the internal water evaporated, thus, the mortar weight was greatly reduced. After that, however, both the weight and the compressive strength consistently increased for at least 180 days due to the carbonation reaction. Based on the 91-day properties, replacing 10% of hydrated lime with silica fume improved the compressive and flexural strengths by 27% and 13% respectively, whereas increasing the w/b from 0.4 to 0.6 decreased both strengths by 29% due to the increased volume of the capillary pores. The addition of silica fume and the change in the w/b had no significant impact on the amount of CO2 uptake, but these two factors were effective in accelerating the CO2 uptake rate before 28 days. Lastly, the air lime-based material was evaluated to be capable of recovering half of the emitted CO2 during the manufacture of hydrated lime within 3 months.

As the time spent by people indoors continues to significantly increase, much attention has been paid to indoor air quality. While many IAQ studies have been conducted through field measurements, the use of data-driven techniques such as machine learning has been increasingly used for the prediction of indoor air pollutants. For the present study, the concentrations of indoor air pollutants such as CO2, PM2.5, and VOCs in child daycare centers were predicted by using an artificial neural network model with three different training algorithms including Levenberg–Marquardt, Bayesian regularization, and Broyden–Fletcher–Goldfarb–Shanno quasi-Newton methods. For training and validation, data of indoor pollutants measured in child daycare facilities over a 1-month period were used. The results showed all the models produced a good performance for the prediction of indoor pollutants compared with the measured data. Among the models, the prediction by the LM model met the acceptable criteria of ASHRAE guideline 14 under all conditions. It was observed that the prediction performance decreased as the number of hidden layers increased. Moreover, the prediction performance was differed by the type of indoor pollutant. This was caused by patterns observed in the measured data. Considering the outcomes of the study, better prediction results can be obtained through the selection of suitable prediction models for time series data as well as the adjustment of training algorithms.

The aim of nuclear facility decommissioning is to make local settlements safe, sustainable and professedly acceptable. The challenges are the clean-up of the nuclear site and waste management. This means a definite promise in terms of safety and security, taking into account social and environmental accountability. There is an essential need to develop safe and efficient methods for nuclear decommissioning. Thus, chemical decontamination technology is of great significance to the decommissioning of nuclear energy facilities. In particular, chemical decontamination technology is applicable to the pipelines and internal loop. The iron-rich oxides, such as Fe3O4 or NiOFe2O3, of a nuclear power plant should have sound decontamination follow-through and should put through a very small amount of secondary waste. It is important to be able to detect and quantify hydrazine in decontamination situations with high sensitivity and selectivity. A colorimetric assay is a technique used to determine the concentration of colored compounds in a solution. However, detecting targeted species rapidly and easily, and with high sensitivity and specificity, is still challenging. Here, the catalytic reaction of oxidants in the p-dimethylaminobenzaldehyde and hydrazine reaction is elucidated. Oxidants can catalyze the reaction of hydrazine and p-dimethylaminobenzaldehyde to form an azine complex such as p-dimethylaminobenzaldazine, with high selectivity and sensitivity within 30 min at ambient temperatures. In the absence of an oxidant such as iron or hydrogen peroxide no detectable colorimetric change was observed by the reaction of p-dimethylaminobenzaldehyde and hydrazine unless an external oxidant was present in the system. In this study, we demonstrated a colorimetric method for the sensitive detection of hydrazine decomposition in the chemical decontamination process. Furthermore, the colorimetric response was easy to monitor with the unaided eye, without any sophisticated instrumentation. This method is thus suitable for on-site detection of contamination in a nuclear facility. In addition, this colorimetric method is convenient, non-invasive, free of complex equipment, and low-cost, making it possible to analyze hydrazine in industrial nuclear facilities. The proposed method was successfully applied to the determination of hydrazine decomposition in the nuclear decontamination process.