energy utilization
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Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 612
Arkadiusz Dyjakon ◽  
Łukasz Sobol ◽  
Tomasz Noszczyk ◽  
Jakub Mitręga

A large portion of food loss and waste (FSL) is comprised of seeds and stones. Exotic fruits such as mangoes, lychees and avocados, in which the seeds account for a significant part of the weight and volume of the entire product, are most affected by this problem. The seeds contain a large quantity of polyphenols and essential nutrients, which makes them a good material for extraction. However, conventional extraction techniques are considered time-consuming, and therefore significantly limit their use on an industrial scale. An alternative method of managing the seeds may be their energy utilization. In this study, torrefaction was proposed as a method for the valorization of exotic fruit seeds (mango, lychee, avocado). Thus, the influence of torrefaction temperature (200–300 °C) on the physical-chemical properties of substrates was investigated. The obtained results revealed that, in relation to the unprocessed raw materials, the torreficates are characterized by improved hydrophobic properties (all materials are classified as extremely hydrophobic), higher heating value (at 300 °C the values increased from 17,789 to 24,842 kJ∙kg−1 for mango, from 18,582 to 26,513 kJ∙kg−1 for avocado, and from 18,584 to 25,241 kJ∙kg−1 for lychee), higher fixed carbon content (which changed from 7.87–15.38% to 20.74–32.47%), and significant mass loss, by 50–60%. However, as a side effect of thermal treatment, an increase in ash content (approx. 2–3 times but still less than in coal) was observed. Therefore, the torreficates may be competitive with coal. The possibility of using residues from the food processing sector as a substrate for energy purposes is important from the point of view of environment protection and is a part of the functioning of the circular economy.

2022 ◽  
Vol 14 (2) ◽  
pp. 856
Qianqian Yang ◽  
Yishao Shi ◽  
Liangliang Zhou

Industrial centralization is an important policy choice in the industrial economy era. The purpose of this paper is to evaluate the overall performance and the influential effects of the industrial centralization strategy in the suburbs of Shanghai. The results show that (1) the strategy of industrial concentration in the suburbs of Shanghai effectively promoted economic growth; (2) on different spatial scales, there are visible differences in the impact of industrial concentration on the performance of industrial land; (3) industrial concentration has significantly improved industrial energy utilization efficiency; and (4) industrial concentration has narrowed the gap of economic development among the suburbs, but it has not resulted in a corresponding narrowing of the urban-rural gap. The main recommendations are to pay more attention to the high-end and centralization of urban industries in the central city, promote the interactive development of manufacturing and service industries as well as the integrated development of industry and city, moderately control the scale and speed of industrial suburbanization and residential suburbanization, promote the transformation of the traditional industrial land into “industry + R&D + business and office + exhibition” and further narrow the income gap between and within regions.

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 218
SaravanaKumar Venkatesan ◽  
Jonghyun Lim ◽  
Hoon Ko ◽  
Yongyun Cho

Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.

2022 ◽  
Vol 14 (2) ◽  
pp. 727
Yingzi Jiang ◽  
Arul Prakash Raji ◽  
Vijayanandh Raja ◽  
Fuzhang Wang ◽  
Hussein A. Z. AL-bonsrulah ◽  

Hydropower is a superior energy extraction approach, which has been made to work based on renewable energy sources. In the generation of hydropower, Gravitational Vortex Hydropower (GVHP) plays a predominant contributor role because of its free turbulence-relayed energy utilization concept and flexible as well as compact size. Owing to the huge contribution of GVHP in the hydropower sector, multi-objective-based investigations have emerged. However, there is still insufficient literature available for the technology to precede optimum turbine blade design. Two important categories are involved in these multidisciplinary investigations, in which the first phase, a numerical investigation has been done using ANSYS to identify the location of maximum tangential velocity in a conical basin with different notch angles, conical angles, basin shapes, anddiameters. In this second phase, the focal aim is to carry out the numerical investigation on Gravitation Vortex Turbine Blades (GVTB) for the different geometry in order to get the optimum power output with a high structural lifetime through HSI (Hydro–Structural Interaction) computation. The entire conceptual designs of this SGVHP and its hydro-rotors are modeled with the help of CATIA. ANSYS Fluent is a CFD (Computational Fluid Dynamics) numerical tool, which is primarily used in this paper for all the hydrodynamic analyses. Finally, the standard analytical approaches are used for the comparative determinations of thrust production by hydro-rotors, power extraction by hydro-rotors, and propulsive efficiency for the selection process of best hydro-rotors. HSI analyses are additionally carried out and thereby the suitable lightweight material is picked.

2022 ◽  
Vol 9 ◽  
Wei Xu ◽  
Xiaoyin Tang ◽  
Luyao Cheng ◽  
Ying Dong ◽  
Yuping Zhang ◽  

The Xi’an Depression of the Weihe Basin, located in the transition zone where the North China, Qinling and Yangtze plates collide with each other, is an important area of geothermal energy utilization in China. Studies of heat flow and thermal sources are of great significance to the exploration and development of geothermal resources in this area. In this paper, six temperature logs boreholes, and 14 thermal conductivity samples have been used to study the geothermal gradient and terrestrial heat flow in the area. The results show that the geothermal gradients of Xi’an Depression range from 20.8 C/km to 49.1 C/km, with an average of 31.7 ± 5.0 C/km. The calculated heat flow is 59.4–88.6 mW/m2, and the average value is 71.0 ± 6.3 mW/m2, which indicates a high thermal background in the area. The high anomalous zones are near the Lintong-Chang’an Fault zone in the southeast, the Weihe Fault in the north, and the Fenghe Fault in the central Xi’an Depression. These deep and large faults not only control the formation of the Xi’an Depression but also provide an important channel for the circulation of groundwater and affect the characteristics of the shallow geothermal distribution. The temperature of the Moho in the Xi’an Depression ranges from 600 to 700°C, and the thermal lithosphere thickness is about 90–100 km. The characteristics of lithospheric thermal structure in Xi’an Depression indicate that the higher thermal background in the study area is related to lithospheric extension and thinning and asthenosphere thermal material upwelling.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 409
Bin Yang ◽  
Huangcheng Yao ◽  
Faming Wang

Because of rapid urbanization, traffic problems, and other factors, underground spaces have been used more in the twenty-first century. Large underground spaces are required for underground city, metro, tunnel, mine, industrial and agricultural engineering, and civil air defense engineering. Underground spaces with varying thermal, ventilation, and lighting environments can face problems of comfort, health, and safety. High temperatures, high humidity, difficulty in flue gas emission, harmful microorganisms, radon, and physical and psychological problems are examples of issues. Air quality control technologies for underground spaces, such as ventilation, dehumidification, natural energy utilization, smoke extraction, and ventilation resistance reduction, are discussed. Ventilation for smoke-proofing/evacuation is also extensively addressed.

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