Search results

The purpose of this paper is to design and develop an IR and sprinkler based embedded controller operated robotic arm for automatic dust removal system to mitigate the dust effect on the solar panel surface, since dust accumulation normally affected by real weather conditions is one of the serious concern for the deterioration of photovoltaic (PV) system output.

The system is a wet cleaning device which provides a cheap silicon rubber-based wiping operation controlled by the pulse width modulation-operated motors of robotic arm. The IEEE 1149.1-compliant mixed signal-embedded platform of C8051F226DK is involved to command the complete system.

A prototype of 30 W_P system is capable of producing an inspiring average value of 11.26 per cent in energy increase, 13.63 per cent in PV module efficiency and 85.20 per cent in performance ratio of the system after 73 days of cleaning in summer season. In addition, a total of 1,617.93 W power; 1,0516.55 Wh energy; and 350.55 KWh/KW_P final yield was found during the entire cleaning period.

A novel technique of the implementation of IR sensor and sprinkler in dust mitigation is proposed in this paper. The IR sensor is used as a versatile object which can manage the robotic arm setting and control the automatic switching between cleaning and charging, as well as identify the thermal condition of solar panel for overheating.

Automated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.

This study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.

This study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.

This study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

The purpose of this paper is to present site planning and design (SPD) relevant variables and items in practice for practitioners to better understand and implement SPD in green building projects.

The research methods include questionnaire survey and case studies in the context of China. A questionnaire survey was adopted to identify the importance of 13 variables and the corresponding 38 items in SPD of green residential buildings. Three green residential projects including one in Hong Kong and two in Mainland China were selected to investigate the SPD considerations in practice and to discuss the necessary improvement.

The results show that 12 out of the 13 variables of SPD in green buildings are involved in the three case projects to some extent, thereby underscore the importance of these variables. The potential improvement in real-life SPD of green buildings is also discussed such as adopting design-build and integrated project delivery methods and preserving and protecting cultural characteristics on site.

The research findings may serve as a reference for practitioners to better conduct SPD in green building projects.

Autonomous mobile cleaning robots are widely used to clean solar panels because of their flexibility and high efficiency. However, gravity is a challenge for cleaning robots on inclined solar panels, and robots have problems such as high working power and short battery life. This paper aims to develop a following robot to improve the working time and efficiency of the cleaning robot.

The mechanical structure of the robot is designed so that it can carry a large-capacity battery and continuously power the cleaning robot. The robot determines its position and orientation relative to the edge of solar panel by using optoelectronic sensors. Based on the following distance, the robot changes its state between moving and waiting to ensure that supply cable will not drag.

Prototype following robot test results show that the following robot can stably follow the cleaning robot and supply continuous power to cleaning robot. The linear error of the following robot moving along the solar panel is less than 0.3 m, and the following distance between the robot and the cleaning robot is in 0.5–1.5 m.

The working time of cleaning robots and working efficiency is improved by using following robot, thereby reducing the labor intensity of workers and saving the labor costs of cleaning.

The design of the following robot is innovative. Following robot works with the existing cleaning robots to make up for shortcomings of the existing cleaning system. It provides a more feasible and practical solution for using robots to clean solar panels.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

The purpose of this paper is to analyze the phenomenon of makeup effect using numerical simulation and model experiments on seven different natural smoke extraction patterns of tall space. Airflow distribution and heat accumulation phenomenon in different cases are compared. The natural smoke exhaust system for tall spaces has many advantages, including low cost, no power and low maintenance cost. It is more advantageous than the mechanical type of exhaust. However, the internal air distribution is complicated since the large span spatial character. Effective and correct verification method is very important for the analysis of flow fields in tall spaces.

This study used fire dynamics simulator (FDS) software to simulate the fire scene. The model experiments are conducted to determine if the numerical simulation results are reasonable. A single-mirror Schlieren system, including an 838 (H) × 736 mm (W) square concave mirror, as well as the focal length of 3,100 mm was adopted to record the dynamic flow of hot gas. Six smokeless candles were burned in a 1/12.5 model in experiments to record the distribution of inflow, accumulation and outflow of airflow in the space. In addition, the thermocouple lines were mounted in the model for temperature measurement.

The results of numerical simulation and model experiments have proved that makeup air has a significant effect on the effectiveness of a natural smoke vent system. Larger areas of smoke vents will produce more heat accumulation phenomenon. In this study, the air inlet and vent installed on the same side have a better heat removal effect. Moreover, Schlieren photography technique is proved to be an accurate measurement method to record the dynamic flow of hot air immediately, directly and accurately. The dynamic flow behavior of hot gas in the model has been visualized in this paper.

At present, there is no examination method other than checking the smoke vent area to validate the effectiveness of a natural smoke vent system in Taiwan, as well as no requirements regarding the makeup inlet. The effect of makeup air in generating the effective push-pull phenomenon of airflow has been analyzed. In addition, the post-combustion hot gas distributions were visualized by using Schlieren photography technology in the model space, compared with the FDS simulation result and thermocouple recorded temperature. A verification method in the model experiments is established to determine if the numerical simulation results are reasonable.

As commercial cooking is known as a source that generates great concentrations of particulate matter (PM) emissions first accumulating in kitchens before spreading to dining areas, this study aims to explore how to improve restaurants’ efforts to reduce PM emissions by the application of attribution theory.

Data were obtained from restaurant managers operating their business in South Korea, considered to be qualified to provide accurate information regarding the survey questions. A scenario-based experimental approach was used to test the hypothesized relationships. Cognitive and emotional risk judgements were assessed for its potential interaction effects on the relationships between restaurant perceptions of PM source attributions, preventions attitudes and mitigation behavioral intentions.

Results revealed that perceptions of PM main sources were attributed to internal rather than external factors, which improved mitigation behavioral intentions. Such an effect was partially mediated through PM pollution prevention attitudes. Additionally, when applying external source attributions, PM mitigation behavioral intentions were improved by cognitive risk judgements, and PM prevention attitudes were enhanced by affective risk judgements.

Results assist restaurants to better understand their operations that may be emitting significant levels of PM, thereby encouraging them to set more ambitious and effective PM mitigation operational guidelines for their employees and diners.

This study provides a fundamental baseline of management perceptions regarding PM emissions related to restaurant mitigation behavioral intentions. Results are useful in designing appropriate communication strategies addressing restaurant PM pollution issues to improve internal restaurant practices regarding clean air quality.

Emission of the suspended particulate matter (SPM) from cement factories results in a wide range of negative health effects to its workers. It induces substantial cost incurred by them in the form of wage loss and mitigation cost. Therefore, the purpose of this paper is to estimate emission-induced cost of illness (COI) and the share of this cost that could be saved through limiting the current emission level at national safety standard.

COI approach which accounts workers’ wage loss and mitigation cost due to emission-induced illness was used in this study. A sample of 120 workers from three factories followed by 40 from each was randomly surveyed for collecting information on their health status and mitigation cost. It covered almost 10 percent of cement factory workers in the south-west region of Bangladesh. In addition, factory-specific emission data were also collected from the Department of Environment for addressing the study objective.

It revealed that the average level of SPM emission by these factories which was almost three times higher than the national safety standard induced 34.39 million Bangladeshi Taka (BDT) (USD0.46 million) as COI paid by workers yearly. It accounted around 28 percent of their annual income of which 64 percent worth BDT22.16 (USD0.30) million could be saved by meeting the standard annually.

This study provides insights into the essence of regulating cement industrialists toward meeting the national safety standard of emission.

While there is a rich body of risk management literature and while there have been valuable theoretical advancements on the specific impact of the COVID-19 pandemic on risks, this paper aims to posit that at least four more advancements are needed.

The co-author from Rolls Royce (RR) illustrates the risks experienced and risk management approaches taken in its manufacturing and supply chain operations both in the earlier stages of the pandemic as well as after the first year of the pandemic.

The COVID-19 pandemic offers a unique risk scenario that is beyond the scope of most existing risk management literature. The impact of the pandemic is very multi-faceted, not location specific but very global and experienced throughout the entire supply chain, across industries and over a much extended timeline with multiple time horizons. In manufacturing operations, there have been major instances of supply chain heroism in the first year of the pandemic and there is a lot more work ahead.

The authors' co-created paper enriches the perspective on COVID-19 research in manufacturing and supply chain operations by pointing at empirical opportunities, the need for more inter disciplinary research and the need to consider multiple time horizons.

The effects of dust exposure in buildings and its health and comfort consequences continue to concern occupants, particularly those who spend most of their time indoors. This study examines the influence of building opening characteristics on surface dust loading in indoor environments to determine the dust particles' impact on different opening configurations.

Indoor Harmattan dust surface loading data were collected from Maiduguri, Northeastern Nigeria, using model rooms with six different window configurations. A simple mathematical relationship was employed to assess surface dust loading characteristics in the model rooms. The study measured dust thrice between December and February for three days (72 h). The results were analyzed using descriptive statistics.

The results determined the highest average surface dust loading of 12.03 g/m2 in the room with awning windows at an indoor-to-outdoor (I/O) ratio of 0.7. In contrast, the experiment in the room with a closed window recorded the lowest average surface dust loading of 5.24 g/m2 at an I/O ratio of 0.30, which is infiltration. The outcomes further indicate that the average surface dust loading varies with the building opening type and position, as higher surface dust loadings were recorded in locations closer to the openings (doors and windows), reaffirming that the dominant source of the dust particles is outdoors. According to the study, dust incursion due to infiltration accounts for 30% of the outdoor surface loading.

Thus, Harmattan dust is a serious challenge to the health, productivity and hygiene of building occupants in the study area. The built-environment professionals must use the study's outcome to optimize building openings' designs (shape, size and form) for effective indoor dust control.