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The energy performance and efficiency of Pelton turbine units is the focus of attention in engineering. The countertorque of the bucket during the operation of the Pelton turbine will affect the above indicators of the unit. However, there is still a lack of systematic research on the generation mechanism of countertorque and control strategies. Numerical simulation and experimental research were conducted on a Pelton turbine. It was found that the countertorque of the bucket is generated during the initial contact stage between the jet and the suction surface of the bucket. At this time, the jet acts on the tip of the suction surface of the bucket, generating a force in the opposite direction of the bucket's rotation, resulting in the generation of countertorque. A geometric optimization method is proposed, which suppresses the countertorque by controlling the inclination angle of the bucket. As the inclination angle decreases, the countertorque, pressure, circumferential force, and entropy production rate of the bucket are effectively controlled. Compared to the original scheme, the optimal scheme reduces the maximum countertorque by 49.33% and improves the unit efficiency by 0.57%. [ABSTRACT FROM AUTHOR]

This research proposes a novel Trombe wall system using direct absorption solar collector based on phase change slurry (DPTW). The dynamic coupling model of the DPTW system is established, and its accuracy is verified. The indoor temperature of the room under heating conditions in January is simulated. Simulation results indicate that the DPTW system has higher indoor thermal comfort. The indoor personnel activity area temperature was within the range of 18.5–24 °C for 148.4 h, which was 167% longer than that of the traditional Trombe wall system (TW). The indoor thermal comfort index PMV is in the range of −0.5∼0.5 for 119.2 h, an increase of 121% compared with the TW system. In addition, the DPTW system reduces the heat load in winter, which is 39% less than that of the TW system in January. The proportion of solar energy utilization is higher, and the solar fraction of the DPTW system can be increased by 45% at lower temperatures. According to the performance of January, the DPTW system is more robust. Therefore, the TW system can be substituted by the DPTW system because the DPTW system can better maintain the indoor temperature and reduce heating energy consumption. [ABSTRACT FROM AUTHOR]

Cool paint (CP) is effective to decrease cooling energy consumption, but it brings heating penalty. Phase change material (PCM) can alleviate the heating penalty caused by CP. In this work, eight cities with different climates are analyzed. According to cooling/heating energy consumption, these cities are divided into different categories. Compared with CP roof, adding PCM on the CP has little effect for further reducing the cooling energy use or peak load. CP increases heating energy consumption, but does not aggravate the peak heating load range. The higher the proportion of heating energy consumption increased by CP, the more PCM amount needs to compensate for the heating penalty. When increasing PCM thickness, the peak heating load decreases first and then unchanged. For the total energy consumption, CP + PCM is always superior to CP. Applying CP alone is only preferable in cities whose heating energy consumption ratio does not exceed 0.66. In other cities, applying 10–20 mm PCM on the basis of CP can realize positive energy saving benefits, which broadens the applicable climate for CP. This work provides valuable guides for the application of CP alone, and the optimization effect of PCM on CP under different climates. [ABSTRACT FROM AUTHOR]

The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications and integrating collaborative computing techniques into edge computing. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.); therefore, we propose a cost-based collaborative computation offloading model. In this model, when a task requests these devices' assistance in computing, it needs to pay the corresponding calculation cost; and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers. [ABSTRACT FROM AUTHOR]

Indoor overheating in high window-wall ratio (WWR) buildings has drawn widespread attention, but there is limited research on it during winter. Similarly, the application of radiative cooling (RC) technology on high-WWR facades and the effectiveness of combining RC with phase change materials (PCMs) need exploration. In Guangzhou, typical rooms in commercial buildings were studied, revealing that high-WWR buildings experience greater indoor overheating in winter compared to summer. For a 100 % WWR, the total equivalent energy consumption (EEC) is 71.389 kWh · m − 2 in winters, 7.8 % higher than summer. Using RC glass with high solar (diffuse) reflectivity (0.65) on south-facing windows reduced EEC by over 70 %. RC glass also provided significant cooling capacity during unoccupied periods, which is sufficient to meet daytime cooling needs. In the case of a 100 % WWR, the proportion of available cooling capacity (37.5 %) during unoccupied period exceeded daytime cooling EEC (24.9 %). Therefore, PCMs were adopted to store this cooling capacity and transfer it for release during the occupied period, which bring an additional improvement in energy-saving performance by 4.7 %. The combination of PCMs and RC technology further achieves building energy efficiency. This study offers insights for addressing winter indoor overheating in high-WWR buildings. [ABSTRACT FROM AUTHOR]

The spent automobile catalysts (SAC) is the major secondary source of palladium and the production of SAC is increasing rapidly over years. The price of palladium keeps rising over the years, which demonstrates its preciousness and urgent industrial demand. Recovering palladium from the spent automobile catalysts benefits a lot from economic and environmental protection aspects. This review aims to provide some new considerations of recovering palladium from the spent automotive catalysts by summarizing and discussing both hydrometallurgical and pyrometallurgical methods. The processes of pretreatment, leaching/extraction, and separation/recovery of palladium from the spent catalysts are introduced, and related reaction mechanisms and process flows are given, especially detailed for hydrometallurgical methods. Hydrometallurgical methods such as chloride leaching with oxidants possess a high selectivity of palladium and low consumption of energy, and are cost-effective and flexible for different volume feeds compared with pyrometallurgical methods. The recovery ratios of palladium and other platinum-group metals should be the focus of competition since their prices have been rapidly increased over the years, and hence more efficient extractants with high selectivity of palladium even in the complexed leachate should be proposed in the future. [ABSTRACT FROM AUTHOR]

In an open office environment with high occupant density and random occupancy patterns, excessive energy consumption is often observed because mechanical ventilation (MV) is usually designed and operated assuming a full-occupancy-based ventilation rate (VR). Considering the duo challenges of post-pandemic and climate change, this study proposes a real-time monitoring and optimization approach for operating portable air cleaners (PACs) to assist and reduce the energy consumption of the MV while maintaining the minimal VR and improving indoor air quality (IAQ). The approach was as follows. First, by assuming four VRs and 36 different emission sources, numerical simulations were conducted and validated based on an actual open office on non-uniform concentrations of a surrogate for indoor air pollutants (IAPs) throughout the breathing zone. Second, the pre- and post-purified IAP distributions were obtained using a limited number of sensors and trained by two artificial neural networks. Third, the real-time optimization of PACs' on/off operation and placements was accomplished by the particle swarm optimization algorithm to balance energy output and improve IAQ simultaneously. Results showed that by deploying four sensors, the predictions of post-purifying concentrations were in acceptable accuracy (i.e., CV-RMSE <2.0%) within 30–40s. Using at most three PACs resulted in a significant decline in local IAP concentration levels. Meanwhile, an average reduction of total energy consumption of MV and PACs was 34.6% compared to using MV to reach the same levels. Overall, this study supports the use of PACs to assist MV in achieving energy efficiency and good IAQ. • Proposed a method to quickly optimize air cleaners' on/off operation and placements. • Considered pre- and post-purified and non-uniform concentrations of a IAQ surrogate. • To reduce the total ventilation-related energy consumption while improving IAQ. • Observed an average energy reduction of 34.6% compared to using mechanical ventilation. [ABSTRACT FROM AUTHOR]

This paper designed a diffuser-augmented flapping wing device. Based on the orthogonal experimental design and numerical method, it is aimed to simulate the impact of structural parameters on the power coefficient and energy harvesting efficiency and research the effect and prioritize of structural practices on flapping wing performance. The results show that the energy harvesting efficiency can be improved by 53.7% by selecting appropriate parameters. The performance of the power coefficient is sensitive to the variation of flange height and diffuse angle, and the efficiency is sensitive to the variation of inlet spacing and flange height. Besides, the energy efficiency of the diffuser-augmented flapping wing will be improved under unsteady upstream velocity. • A diffuser-augmented flapping wing device was designed. • The energy harvesting efficiency of the flapping wing increases significantly. • The low-pressure zone at the outlet of the diffuser shroud can enhance the kinetic energy of the internal fluid. [ABSTRACT FROM AUTHOR]

MXenes have recently been recognized as potential materials based on their unique physical and chemical characteristics. The widely growing family of MXenes is rapidly expanding their application domains since their first usage as energy materials was reported in 2011. The inherent chemical nature, high hydrophilicity, and robust electrochemistry regard MXenes as a promising avenue for environment-remediation technologies such as adsorption, membrane separation, photocatalysis and the electrocatalytic sensor designed for pollutant detection. As the performance of MXenes in these technologies is on a continuous path to improvement, this review intends to cumulatively discuss the diversity and chemical abilities of MXenes and their hybrid composites in the fields mentioned above with a focus on MXenes improving surface-characteristics. The review is expected to promote the diversity of MXenes and their hybrid configuration for advanced technologies widely applied for environmental remediation. [ABSTRACT FROM AUTHOR]

China Energy's National Institute of Clean-and-Low-Carbon Energy (NICE) is developing a Power Plant Smart Management (PPSM) platform that employs digital-twin technology to undertake techno-economic modelling analysis on China Energy's existing coal-fired power-plant units and explore cost-effective solutions to improve those plant units' thermal efficiencies and operating performance. This paper presents a case study of PPSM on a 320-MWe coal-fired thermal power-plant unit, demonstrating how the digital-twin technology was employed to explore and analyse optimization solutions. Various optimization solutions and their cost-effectiveness were assessed using the digital-twin-modelling analysis; the results indicated the optimization solutions are expected to improve the plant unit's operating efficiency and reduce its current electricity-generation coal consumption by up to 3.5 g/kWh standard coal equivalent (sce), worth annual fuel-cost savings of approximately 4 million RMB for a single unit or 8 million RMB for the two identical 320-MWe units that the power plant currently operates. The digital twin was also employed to assess the power-plant unit's operating economics during both summer and winter. In summer, when the unit operates in electricity-generation-only mode, the unit's operating thermal efficiency could drop by up to 6% points following the grid demand of load changes from 100% maximum continuous rating (MCR) down to 30%MCR, resulting in an ~ 45 RMB/MWh increase of electricity-generation cost. In winter, when the unit operates in combined heat and power (CHP) cogeneration mode, for the same boiler load, the CHP operation increases the plant unit's operating profit with increasing district-heating duty, although the relative profit gain from the CHP cogeneration could start to decrease when the district-heating steam-extraction flow increases to a certain point that varies depending on the market prices of heat and electricity, while the fuel cost was found to be equivalent to ~50% of the unit's total CHP income cogenerated from its electricity and district heat outputs. [ABSTRACT FROM AUTHOR]

Rapid development of agriculture mechanization and the agro–industry in China (considered as a large agricultural country) has led to a substantial increase in energy consumption and CO 2 emissions. Majority of existing studies usually explore the driving forces of the sector's CO 2 emissions using the averaging method. However, data distribution of economic variables is often non–normal, with the tail having hidden important information. According to the average annual CO 2 emissions, this paper divides China's 30 provinces into six quantile grades, and uses the quantile regression method to investigate the driving forces of CO 2 emissions under high, medium, and low emission levels. The results show that the effects of economic growth on CO 2 emissions in the upper 90th and 75th–90th quantile provinces are higher than in the 50th–75th, 25th–50th, 10th–25th and lower 10th quantile provinces due to the differences in fixed–asset investment and agricultural processing. The impact of energy efficiency in the upper 90th, 75th–90th, and 50th–75th quantile provinces are stronger than those in the 25th–50th, 10th–25th, and lower 10th quantile provinces because of the huge difference in R&D funding and R&D personnel investments. The effect of urbanization in the higher 90th quantile provinces is higher than in the other quantile provinces, owing to the differences in the level of agricultural mechanization and human capital accumulation. Similarly, financial capacity has the largest impact on CO 2 emissions in the upper 90th quantile provinces in all quantile provinces. However, the impact of industrialization in the upper 90th quantile provinces is lower than in other quantile provinces. Thus, the heterogeneous effects of the driving forces should be taken into consideration when discussing CO 2 emissions reduction in China's agriculture sector. [ABSTRACT FROM AUTHOR]

Both phase change material (PCM) and cool paint (CP) are effective passive strategies to improve thermal comfort and decrease energy consumption. In this work, eight typical cities were selected to represent different climates: five in China and three in Europe. According to the cooling/heating energy consumption, the cities were divided into different categories. PCM and CP + PCM were applied to the roof, and the phase transition temperature and thickness of PCM were analyzed. In cities with lower heating energy consumption ratio, PCM was inferior to CP + PCM. In other cities with relatively higher ratio, PCM was superior to CP + PCM. When PCM was coupled with CP, the optimal phase change temperature may change, and the influence of the phase change temperature was further weakened. With increasing PCM thickness, the energy consumption constantly decreased, but the peak load range decreased first and then remained unchanged. There was the minimum PCM thickness and the corresponding optimal phase transition temperature to reach the lowest peak load range, which could be used to select more energy-saving and economical PCM. This work provided valuable information for the design, preparation and optimization of PCM and the combination of PCM and CP according to corresponding energy consumption characteristics. • Eight cities are classified according to energy consumption characteristics. • Long-term analysis of PCM and the combination of PCM and CP is obtained. • Phase transition temperature and thickness of PCM is analyzed and optimized. • Energy consumption and load for cooling and heating is considered respectively. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2 ) is one of the main sources of global warming, rising sea levels, and frequent outbreaks of extreme weather. China is now one of the largest energy consumer and CO 2 emitters in the world. As one of China's economic centers, Shanghai city has a perfect industrial system with large industrial scale. The industrial sector is an energy– and emission–intensive industry, which contributes the significant part of CO 2 emissions in Shanghai city. Therefore, an in–depth investigation of the main driving forces of CO 2 emissions in the industrial sector is essential to reduce CO 2 emissions in the city. This study uses Vector Autoregressive model to analyze the main factors causing the increase in CO 2 emissions in the industrial sector. The results show that economic growth leads to an increase in CO 2 emissions in the short run, but is conducive to reducing CO 2 emissions in the long run, due to the differences in fixed–asset investment and export trade. Energy consumption structure leads to a growing CO 2 emissions in the short term, and is beneficial to mitigate CO 2 emissions in the long term, owing to the gradual optimization of energy consumption structure. However, urbanization helps to reduce CO 2 emissions in the short term, but leads to an increase in CO 2 emissions in the long term, because of urban real estate and infrastructure construction investments as well as vehicle use. Energy efficiency leads to an increase in CO 2 emissions both in the short and long run since the scale effect exceeds the technical effect. Industrial structure produces a positive effect in the short run, but the impact is gradually narrowing in the long run. [ABSTRACT FROM AUTHOR]

China currently is the largest emitter of carbon dioxide (CO 2 ) in the world. Moreover, total energy consumption and CO 2 emissions in China will continue to increase due to the rapid advance of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO 2 emissions at the moment or in the future. However, most of the existing literature analyzes the impact of the high–tech industry on emission mitigation from an aggregate perspective. Few studies have focused on regional differences in China. Based on 1999–2015 panel data of China's 30 provinces, this study uses the STIRPAT model to explore the influence of the high–tech industry on CO 2 emission reduction in China from a regional perspective. The results show that the high–tech industry is beneficial to reduce CO 2 emissions. Moreover, the impact intensity of the high–tech industry in the eastern region is higher than those in the central and western regions due to significant differences in R&D funding, R&D personnel investments and high–tech purchase expenditure. The study's findings not only contribute to the existing literature, but also worthy of adequate attention from China's policy makers. [ABSTRACT FROM AUTHOR]

China is currently the world's largest carbon dioxide (CO 2 ) emitter. Moreover, total energy consumption and CO 2 emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO 2 emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO 2 emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Based on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO 2 emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO 2 emissions in the three regions should be given adequate attention in developing effective abatement policies. [ABSTRACT FROM AUTHOR]

ABSTRACT The surface wave propagation along a multi-coil wireless power transfer system is investigated with the motivation of extending power transfer range. First, the power transfer efficiencies of coil arrays with different numbers of repeaters are simulated and measured. Then the wave propagation along the array is investigated and its associated wave characteristics are extracted using a super resolution algorithm. A backward traveling surface wave is found to be the dominant propagation mechanism, and its decay constant decreases as the number of repeater increases. An empirical formula correlating the power transfer efficiency with the surface wave decay constant is proposed. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2261-2265, 2016 [ABSTRACT FROM AUTHOR]

Identifying the drivers of carbon dioxide emissions in the manufacturing industry is vital for developing effective environmental policies. This study adopts provincial panel data from 2000 to 2013 and uses nonparametric additive regression models to analyze the drivers of CO 2 emissions in the industry. The results show that the nonlinear effect of economic growth on CO 2 emissions supports the Environmental Kuznets Curve (EKC) hypothesis. Energy structure has an inverted “U-shape” effect owing to massive coal consumption in the early stages and the optimization of energy structure in the later stage. The inverted “U-shaped” impact of industrialization may be due to the priority development of heavy industry in the early stages and the optimization of industrial structure in the later stages. The impact of urbanization also exhibits an inverted “U-shaped” pattern because of mass consumption of steel and cement products in the early stages and the advancement in clean energy technologies at the later stages. However, specific energy consumption has a positive “U-shaped” impact because of the difference in the speed of technological progress at different times. Thus, the differential effects of these indicators at different times should be taken into consideration when discussing reduction of CO 2 emissions in China's manufacturing industry. [ABSTRACT FROM AUTHOR]

It is often difficult to determine the effects of policies at a sectoral level in the overlapping domains of pollution control and energy conservation, making it difficult to consider co-benefits of energy conservation and air pollution reduction and tensions. In China, the industry sector contributes much more carbon and pollution emissions than other sectors. In this paper, Baoshan District (BSD), one of the most important industrial zones in the Shanghai metropolis, is used as a case study to illustrate the situation and to provide information to assess the effectiveness of environmental sustainable development policies in industries. The policies resulting from China's Eleventh Five-year Plan (FYP) are considered at the level of BSD. In particular, the result of implementing local policies of air pollution control and energy are analysed at BSD. Three indicator systems are adopted for quantitatively and qualitatively evaluating energy saving and air pollution reduction achieved by technical, structural and management measures in industrial enterprises of BSD. Analysis made in the paper shows that the co-benefits of pollution cutting and energy conservation have been achieved at certain scopes and degrees in the industry sector in BSD, e.g. SO 2 and Particulate Matter (PM) were reduced by 35.1% and 7.7% respectively, and the energy density was cut by 26.7%. Due to existing barriers of the lack of co-planning and co-operations in the process of designing and implementing the policies, and the overlooking the leakage of emissions in the whole project area, the overall co-benefits cannot be achieved effectively in BSD. Recommendations are made for future detailed studies of this or similar districts to develop an indicator system of co-benefits to help demonstrate the advantages of joint planning and policies for co-benefits. [ABSTRACT FROM AUTHOR]

Pollution emissions have become a matter of public concern in recent years. However, the vast majority of existing researches on PM 2.5 pollution are from the natural science perspective, and few studies have been conducted from an economic point of view. This paper adopts provincial panel data from 2001 to 2012 and panel data models to analyze the key driving forces of PM 2.5 emissions at the regional level in China. The results show that economic growth is a decisive factor of PM 2.5 emissions. The impacts of urbanization on PM 2.5 emissions vary across regions and decrease continuously from the central region to the western and eastern regions. The effects of private cars and coal consumption on PM 2.5 emissions in the eastern region are greater than that in the central and western regions because of significant differences in R&D investment, private car ownership and total coal consumption. Energy efficiency improvement has greater potential to mitigate PM 2.5 emissions in the central and western regions than that in the eastern region due to its low level of technology. Hence, in order to effectively reduce emissions, the Chinese government should consider all these factors as well as regional heterogeneity in developing appropriate mitigation policies. [ABSTRACT FROM AUTHOR]

Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO 2 ) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Urbanization also has significant effect on CO 2 emissions because of mass urban infrastructure and real estate construction. Economic growth has more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the iron and steel industry. [ABSTRACT FROM AUTHOR]

Environmental regulations are an important means for government managers to manage the environment. The motivation of this article is to investigate the influence mechanism of incentive and mandatory environmental regulations on energy efficiency and carbon dioxide (CO 2) emissions in the logistics industry. The quantile regression can estimate the comprehensive effect of explanatory variables on dependent variables, including maximum, minimum, and median. Based on the panel data of China's 30 provinces from 2005 to 2019, this paper adopts quantile regression to simulate the impact of environmental regulations on CO 2 emissions and energy efficiency. The empirical results obtained are as follows: (1) incentive environmental regulations make a greater contribution to CO 2 emission reduction in Ningxia, Qinghai, and Hainan provinces, due to their more aggressive levy of pollution fees. (2) Mandatory environmental regulations contribute the most to CO 2 emission reductions in the 25th-50th percentile provinces, since these provinces issue more environmental decrees. (3) Incentive environmental regulations produce a greater influence on the energy efficiency in the 50th-75th, 75th-90th and upper 90th percentile groups, due to their greater R&D investment. (4) Mandatory environmental regulations have a greater impact on energy efficiency in Xinjiang, Heilongjiang, and Yunnan provinces. The findings can provide empirical support for the government to formulate effective environmental policies. • Incentive environmental regulations have a greater impact on CO 2 emissions in Ningxia, Qinghai, and Hainan. • Mandatory environmental regulations exert a greater effect on CO 2 emissions in the 25th–50th quantile group. • Incentive environmental regulations produce a greater impact on energy efficiency in the high quantile groups. [ABSTRACT FROM AUTHOR]

China has promised that by 2030, its carbon intensity will be cut down by about 60%. The purpose of this article is to investigate the main influencing factors of the heavy industry's carbon intensity. Different from the existing related literature, this article uses a semiparametric model to empirically analyze the carbon intensity of China's heavy industry. The main results are as follows: (1) the relationship between spatial agglomeration and carbon intensity shows a non-linear pattern, mainly caused by the cross-regional flow of production factors. (2) The impact of technological progress on carbon intensity also displays significant non-linear characteristics, mainly due to the staged differences in research and development investment. The following three factors are linearly related to carbon intensity. (1) Energy consumption structure has a more significant linear impact on the carbon intensity in the central and eastern regions, due to the heavy coal consumption of the heavy industry in these two regions. (2) The linear impact of environmental regulations on the carbon intensity in the eastern region is more significant, owing to the region's considerable investments in environmental governance. (3) The industrial structure has the most significant linear impact on the carbon intensity in the eastern region, since the scale of the tertiary industry in this region is larger. The conclusions can provide empirical support for local government managers to formulate differentiated energy policies. • The impact of environmental regulations on carbon intensity in the eastern region is even greater. • The nexus between spatial agglomeration and carbon intensity shows a non-linear pattern. • Energy structure has a greater impact on carbon intensity in the central and eastern regions. [ABSTRACT FROM AUTHOR]

This paper has analyzed the structure of Struts2 and then proposed a new method to use AJAX in Struts2 with the help of JSON data type and jQuery. The new method can make developing AJAX application in Struts2 more flexible and efficient. The cases in the end have proved the effectiveness of this new method. [ABSTRACT FROM AUTHOR]

Developing distributed energy resources can help reduce carbon dioxide emissions and control environmental pollution. Investigating the main driving factors of distributed energy resources can provide empirical support for government departments to formulate relevant energy policies. Different from traditional linear models, the semiparametric regression model has data-driven characteristics and can reveal possible nonlinear relationships between economic variables. Based on 2005–2017 panel data, this article uses the semiparametric regression model to investigate distributed energy resources in China. Estimated results show that technological progress has the largest impact on the distributed energy resources in the western region, due to the difference in R&D expenditures and patented technology. Foreign oil dependence produces a greater effect on the distributed energy resources in the eastern region, because it imports the most oil. The impact of energy subsidies in the central and western regions is greater, because their financial subsidies for renewable energy and natural gas consumption have grown faster. Industrial structure has an inverted N -shaped nonlinear impact in the eastern region, but exerts a positive U-shaped impact in the central and western regions. In addition, distributed energy resources have significant geographic differences. In the future, we should take the spatial element into consideration when exploring distributed energy resources. • Technological progress has a larger effect on distributed energy resources in the western region. • Foreign oil dependence produces a greater impact in the eastern region. • The influence of industrial structure exhibits significant nonlinear characteristics. [ABSTRACT FROM AUTHOR]

Propulsion system electrification revolution has been undergoing in the automotive industry. The electrified propulsion system improves energy efficiency and reduces the dependence on fossil fuel. However, the batteries of electric vehicles experience degradation process during vehicle operation. Research considering both battery degradation and energy consumption in battery/ultracapacitor electric vehicles is still lacking. This study proposes a Q -learning-based strategy to minimize battery degradation and energy consumption. Besides Q -learning, two rule-based energy management methods are also proposed and optimized using Particle Swarm Optimization algorithm. A vehicle propulsion system model is first presented, where the severity factor battery degradation model is considered and experimentally validated with the help of Genetic Algorithm. In the results analysis, Q -learning is first explained with the optimal policy map after learning. Then, the result from a vehicle without ultracapacitor is used as the baseline, which is compared with the results from the vehicle with ultracapacitor using Q-learning, and two rule-based methods as the energy management strategies. At the learning and validation driving cycles, the results indicate that the Q -learning strategy slows down the battery degradation by 13–20% and increases the vehicle range by 1.5–2% compared with the baseline vehicle without ultracapacitor. • Q -learning is proposed to actively determines the engagement of ultracapacitor. • Two heuristic strategies are proposed and optimized by Particle Swarm Optimization. • Battery aging model identification is described and conducted using Genetic Algorithm. • Results from Q -learning are extensively analyzed and explained. • Q -learning reduces battery degradation by 20% and extends vehicle range by 2%. [ABSTRACT FROM AUTHOR]

Most of the existing related studies use linear methods to investigate distributed energy resources, ignoring the objectively existing nonlinear relationships. This article makes improvement and uses a nonparametric additive regression model to explore China's distributed energy resources. Estimated results show that technological progress exerts an inverted "U-shaped" nonlinear effect on distributed energy resources, due to the phased changes in R&D expenditures. Energy consumption structure also has an inverted U-shaped impact, which main reason is the changes in natural gas and renewable energy consumption. On the contrary, international oil prices have a positive U-shaped impact on distributed energy resources, because international oil prices are gradually increasing over time. Foreign oil dependence has an N-shaped nonlinear impact, which is mainly due to the changes in oil imports at different stages. The relationship between urbanization and distributed energy resources also presents an N-shaped pattern. This is mainly due to the periodic changes in the growth rate of the number of residents using natural gas. The research results can provide empirical support for the government to formulate targeted distributed energy policies at different stages. Image 1 • Technological progress has an inverted U-shaped effect on distributed energy. • International oil prices have a U-shaped impact on distributed energy resources. • Foreign oil dependence has an N-shaped nonlinear impact distributed energy. [ABSTRACT FROM AUTHOR]

High energy-consuming heavy industry is one of the main sources of China's carbon dioxide (CO 2) emissions. Based on 2005–2017 panel data of China's 30 provinces, this paper uses a quantile regression model to investigate CO 2 emissions in the heavy industry. The empirical results show that economic growth exerts a stronger influence on the heavy industry's CO 2 emissions in the 25th-50th quantile provinces, due to the difference in the fixed asset investment and heavy industrial output. The impact of urbanization on CO 2 emissions in the 10th-25th quantile provinces is lower than that in other quantile provinces because these provinces have the least number of college graduates. Energy efficiency has a smaller impact on CO 2 emissions in the upper 90th quantile province, owing to the difference in R&D personnel investment and the number of patents granted. Similarly, environmental regulations have minimal impact on CO 2 emissions in the upper 90th quantile province, since the growth rate of industrial pollution treatment investment in these provinces is the lowest. However, the impact of energy consumption structure on CO 2 emissions in the 10th-25th and 25th-50th quantile provinces is the highest, because of the provincial differences in coal consumption. • Energy efficiency has a minimal promotion effect on CO 2 emission reduction in the upper 90th quantile province. • Economic growth has a greater pulling action on CO 2 emissions in the 25th-50th quantiles. • Urbanization helps reduce CO 2 emissions, and its contribution to emission reduction in the 10th-25th quantiles is minimal. [ABSTRACT FROM AUTHOR]

• Q -learning is integrated in vehicle model as the energy management strategy. • Parametric studies are conducted on four key factors in Q -learning design. • Selective learning experience improves the fuel economy. • Two states Q -learning show comparable performance with four states Q -learning. • Higher discretization does not always generate better results in tabular Q -learning. An efficient energy split among different source of energy has been a challenge for existing hybrid electric vehicle (HEV) supervisory control system. It requires an optimized energy use of internal combustion engine and electric source such as battery, fuel cell, ultracapacitor, etc. In recent years, Reinforcement Learning (RL) based energy management strategy (EMS) has emerged as one of the efficient control strategies. The effectiveness Reinforcement Learning method largely depends on optimized parameter selections. However, a thorough parametric study still lacks in this field. It is a fundamental step for efficient implementation of the RL-based EMS. Different from existing RL-based EMS literature, this study conducts a parametric study on several key factors during the RL-based EMS development, including: (1) state types and number of states, (2) states and action discretization, (3) exploration and exploitation, and (4) learning experience selection. The main results show that learning experience selection can effectively reduce the vehicle fuel consumption. The study of the states and action discretization show that the vehicle fuel consumption reduces as action discretization increases while increasing the states discretization is detrimental to the fuel consumption. Moreover, the increasing number of states improves fuel economy. With the help of the proposed parametric analysis, the RL-based EMS can be easily adapted to other power split problems in a HEV application. [ABSTRACT FROM AUTHOR]

The study is to investigate the effects of diesel–alcohol blends containing low ratio of alcohols on the performance and emissions of a diesel engine. Methanol, ethanol, iso‐propanol, and n‐butanol containing 5% by volume were blended with diesel fuel. Then, these blends and neat diesel fuel were tested in an electronic control high‐pressure common rail turbocharged direct injection (DI) diesel engine working at the speed of 2000 revolutions per minute (rpm) and 2800 rpm at five different loads without any modification in the original engine structure. The results of the blends tests were compared with the results of neat diesel fuel under the same condition. The results revealed that, with the increase of the carbon atom number of alcohol, peak combustion pressure of main injection, cumulative heat release, maximum combustion temperature and brake specific fuel consumption (BSFC) increased for the blends. The combustion duration for the blends became shorter than that of pure diesel. Diesel–alcohol blends containing low ratios of various alcohols can effectively reduce at least 29% of smoke emissions and under one condition the percentage can reach 81%. Additionally, the blends have a slight influence on the oxides of nitrogen (NOx) emissions. The emissions of carbon monoxide (CO) and unburnt hydrocarbon (THC) increased very slightly at low load, but they decreased distinctively at medium and high load. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13035, 2019 [ABSTRACT FROM AUTHOR]

• Novel biphasic solvent based on polyamine was developed for CO 2 capture. • Microcalorimetry strategy was used to directly measure the CO 2 absorption heat. • The energy consumption of this absorbent was 32.6 % lower than that of 5 M MEA. • This biphasic solvent has superior phase-splitting behavior. • The salting-out effect was the main reason for the phase-splitting behavior. The CO 2 phase change absorbents(CPCAs) formed by introducing phase-splitting agents have attracted increasing attention because of their great energy-saving potential. However, the volatilization problem of the phase-splitting agents and the high viscosity of the solution affect the application of CPCAs. In this work, a novel CPCAs based on 2-Methylpiperazine (2MPRZ)/H 2 O was established by introducing triethylene glycol monobutyl ether (TGBE) with a high boiling point as a phase-splitting agent, and the 2MPRZ/H 2 O/TGBE system exhibited low volatility and low viscosity. The CO 2 capture performance of the novel CPCAs with different ratios was investigated in this work. The best CO 2 absorption rate was achieved by 2.5 M 2MPRZ/H 2 O/TGBE. The viscosity of this system before and after the CO 2 absorption process was merely 7.68 mPa·s and 11.64 mPa·s, respectively, and no volatilization of the phase-splitting agent was observed simultaneously. A convenient and rapid titration method was employed to determine the distribution of the 2MPRZ products in the two phases, which showed that 98 % of the 2MPRZ products were located in the CO 2 -rich phase. In addition, the CO 2 -rich phase captured 99 % of the total CO 2 , indicating that this system has superior phase-splitting behaviors. The regeneration energy consumption of this biphasic absorbent was 2.59 GJ/t CO 2 , which was 32.6 % lower than that of traditional MEA. Furthermore, the salting-out effect was proved to be the cause of the phase-splitting behavior by 13C NMR analysis. [ABSTRACT FROM AUTHOR]

The adaptive coordination of trust services can provide highly dependable and personalized solutions for industrial requirements in the service-oriented industrial internet of things (IIoT) architecture to achieve efficient utilization of service resources. Although great progress has been made, trust service coordination still faces challenging problems such as trustless industry service, poor coordination, and quality of service (QoS) personalized demand. In this paper, we propose a QoS-driven and adaptive trust service coordination method to implement Pareto-efficient allocation of limited industrial service resources in the background of the IIoT. First, we established a Pareto-effective and adaptive industrial IoT trust service coordination model and introduced a blockchain-based adaptive trust evaluation mechanism to achieve trust evaluation of industrial services. Then, taking advantage of a large and complex search space for solution efficiency, we introduced and compared multi-objective gray-wolf algorithms with the particle swarm optimization (PSO) and dragonfly algorithms. The experimental results showed that by judging and blacklisting malicious raters quickly and accurately, our model can efficiently realize self-adaptive, personalized, and intelligent trust service coordination under the given constraints, improving not only the response time, but also the success rate in coordination. [ABSTRACT FROM AUTHOR]

In a deregulated electricity market, optimal hydropower operation should be achieved through informed decisions to facilitate the delivery of energy production in forward markets and energy purchase level from other power producers within real-time markets. This study develops a stochastic programming model that considers the influence of uncertain streamflow on hydropower energy production and the effect of variable spot energy prices on the cost of energy purchase (energy shortfall). The proposed model is able to handle uncertainties expressed by both a probability distribution and discretized scenarios. Conflicting decisions are resolved by maximizing the expected value of net revenue, which jointly considers benefit and cost terms under uncertainty. Methodologies are verified using a case study of the Three Gorges cascade hydropower system. The results demonstrate that optimal operation policies are derived based upon systematic evaluations on the benefit and cost terms that are affected by multiple uncertainties. Moreover, near-optimal operation policy under the case of inaccurate spot price forecasts is also analyzed. The results also show that a proper policy for guiding hydropower operation seeks the best compromise between energy production and energy purchase levels, which explores their nonlinear tradeoffs over different time periods. [ABSTRACT FROM AUTHOR]

An energy efficient method called deformation and holding process (DHP) in two phase region, which applied for eliminating the harmful Laves eutectic (LE) phase of INCONEL 718 (IN718) was proposed based on semi-solid forming technology. To verify its feasibility, the effects of solid-liquid fraction and isothermal holding on formation of LE and the microstructure of the deformed alloys were investigated experimentally. LE can be eliminated and the microstructure is refined when deformation and holding were carried out at the end of solidification. The fraction of the LE decreases from about 10.8% in the as-cast specimens to 2.53% in the deformed specimens (0.1/s 50% compression at 1700 °C following 2 min isothermal holding process). [ABSTRACT FROM AUTHOR]

China is currently the largest carbon dioxide (CO 2 ) emitter in the world, and the iron and steel industry is the main contributor to the growth in CO 2 emissions. Most of the existing studies use the average estimation method to investigate the main drivers of CO 2 emissions in the iron and steel industry. However, the data distribution of economic variables is often not normal distribution, and the tail of the data hidden important information. In order to provide a realistic basis for emission reduction in this industry, this study uses the quantile regression model to explore the driving forces of CO 2 emissions under high, medium and low emission levels. The results show that the effect of economic growth on CO 2 emissions in the upper 90th quantile provinces is stronger than those in other quantile provinces due to the differences in fixed–asset investment and automobile production. However, the impact of energy efficiency in the upper 90th quantile provinces is lower than those in other quantile provinces. The influence of industrialization in the lower 10th quantile provinces is stronger than those in other quantile provinces. The influence intensity of energy structure has a similar story owing to the differences in coal consumption. Therefore, policymakers should focus on the heterogeneous effects of driving forces on CO 2 emissions in different quantiles during the process of carbon reductions. [ABSTRACT FROM AUTHOR]

A regional integrated energy system (RIES) guarantees the fulfillment of a diversified load demand of users by coordinating all types of energy equipment and is a two-layer optimal scheduling method that takes into consideration the flexibility of the characteristics of the combined heat and power (CHP) system is proposed in this paper. First, the thermoelectric output characteristics of hydrogen fuel cell (HFC) and CHP units are analyzed to establish an energy equipment model that takes into consideration the variability of the operating conditions. Subsequently, a two-layer optimal scheduling model of the RIES is established, where the upper layer takes into consideration the operating costs, carbon emission penalties, and renewable energy consumption capacity to determine the strategy for allocating the energy flow, and the lower layer considers the energy efficiency as the optimization objective to determine the output modes of the HFC and CHP units. Finally, an adaptive closed-loop control strategy is used to avoid the continuous startup and shutdown of the CHP unit. The simulation results show that the proposed method can effectively reduce operating costs and carbon emissions, improve energy efficiency and renewable energy consumption, and promote the efficient utilization of integrated energy resources. • A variable operating condition model of a RIES was established. • A scheduling strategy based on flexible characteristics of CHP system was proposed. • An adaptive closed-loop control method was used to coordinate the operating states. • The optimized operation of regional integrated energy system was realized. • Results were implemented to verify the effectiveness of the scheduling method. [ABSTRACT FROM AUTHOR]

To improve the comprehensive utilization efficiency of energy, a multi-objective optimization control strategy applied to the energy hub (EH) within the system is proposed to address the electrical and thermal load distribution of the integrated energy system (IES) and the low-carbon economic operation. First, a model of the electrical and thermal energy outputs is established based on the characteristics of the IES network and the multidimensional "load parameter" evolution law. Moreover, a distributed control strategy is proposed that utilizes the information interaction between neighboring EHs to accurately share the electrical and thermal loads, which reduces the communication burden of the system while avoiding overloads. Furthermore, to coordinate the optimal operation of the devices within the hub, based on the energy conversion characteristics of the EH, a multi-objective optimization model is proposed that considers low-carbon and economic aspects to realize efficient energy use. Simulation results show that the proposed strategy effectively improves the robustness of the system while realizing proportional load distribution and low-carbon economic operation. Under the same load, when focusing on system economics, the operating cost is 2.182/¥ lower than when focusing on low-carbon systems, but carbon emission is 1.6753/kg CO 2 higher. [Display omitted] • A novel energy hub management for integrated energy systems was proposed. • A multi-objective optimization control strategy was proposed. • The proposed strategy could be accurately shared electrical and thermal loads. • Optimized operation of devices within the Energy hub was achieved. • Simulation results were implemented to verify the effectiveness of the strategy. [ABSTRACT FROM AUTHOR]

Abstract: With the final aim of reducing the energy consumption and increase the methane production at Swedish pulp and paper mills, the methane potential of 62 wastewater effluents from 10 processes at seven pulp and/or paper mills (A–G) was determined in anaerobic batch digestion assays. This mapping is a first step towards an energy efficient and more sustainable utilization of the effluents by anaerobic digestion, and will be followed up by tests in lab-scale and pilot-scale reactors. Five of the mills produce kraft pulp (KP), one thermo-mechanical pulp (TMP), two chemical thermo-mechanical pulp (CTMP) and two neutral sulfite semi-chemical (NSSC) pulp. Both elementary and total chlorine free (ECF and TCF, respectively) bleaching processes were included. The effluents included material from wood rooms, cooking and oxygen delignification, bleaching (often both acid- and alkali effluents), drying and paper/board machinery as well as total effluents before and after sedimentation. The results from the screening showed a large variation in methane yields (percent of theoretical methane potential assuming 940NmL CH4 per g TOC) among the effluents. For the KP-mills, methane yields above 50% were obtained for the cooking effluents from mills D and F, paper machine wastewater from mill D, condensate streams from mills B, E and F and the composite pre-sedimentation effluent from mill D. The acidic ECF-effluents were shown to be the most toxic to the AD-flora and also seemed to have a negative effect on the yields of composite effluents downstream while three of the alkaline ECF-bleaching effluents gave positive methane yields. ECF bleaching streams gave higher methane yields when hardwood was processed. All TCF-bleaching effluents at the KP mills gave similar degradation patterns with final yields of 10–15% of the theoretical methane potential for four of the five effluents. The composite effluents from the two NSSC-processes gave methane yields of 60% of the theoretical potential. The TMP mill (A) gave the best average yield with all six effluents ranging 40–65% of the theoretical potential. The three samples from the CTMP process at mill B showed potentials around 40% while three of the six effluents at mill G (CTMP) yielded 45–50%. [Copyright &y& Elsevier]

[Display omitted] • k app of isoproturon and chlorotoluron reacting with SO 4 •− and HO• were determined. • QSAR model implied the great effect of ring substituents in phenylureas on UV/PS behaviors. • Effects of PS dosages, UV intensity, pH and water matrixes (ions and NOM) were examined. • The UV/PS destruction pathways of phenylureas were proposed by MS analysis. • Compared with UV and UV/H 2 O 2 , UV/PS was superior in controlling the DBPs formation. The degradation performance of selected phenylurea herbicides (isoproturon and chlorotoluron) by UV/persulfate (PS) as well as toxicity assessments associated with disinfection by-products (DBPs) in subsequent oxidation were comparatively studied. The elimination of herbicides followed pseudo first-order kinetics well. Then effects of UV intensity, pH, PS dosage and water matrix (background ions and Nature organic matter (NOM)) were investigated detailedly. Increased k obs values were observed at higher PS dosages and greater UV intensity, but the degradation was basically resistant to pH variation. k obs could be inhibited by coexistent ions and NOM. Radical scavenging study revealed the more important role of sulfate radical (SR, SO 4 •−) than hydroxyl radical (HR, HO•). k app of HO• and SO 4 •− towards isoproturon and chlortoluron were determined respectively. The results also indicated that the relative reactivity of alkyl- or halogen- substituted phenylurea herbicides with HO• and SO 4 •− can be correlated with Hammett constants as the molecular descriptor. Then a quantitative structure-activity relationship was established to predict the structure-dependent reactivity of phenylurea herbicides by UV/PS concerning substituent effects. The energy demand of UV/PS system was evaluated. The degradation intermediates by direct UV, UV/H 2 O 2 as well as UV/PS were comparatively identified and the related destruction pathways were postulated. Compared with UV and UV/H 2 O 2 , UV/PS was superior in controlling the DBPs generation in post-disinfection of these herbicides. The data supported that SR-advanced oxidation processes (AOPs) had more advantages in removal efficiency and toxicity control than HR-AOPs. This study provided some theoretical bases for the UV/PS degradation behaviors of structurally similar phenylurea herbicides with regard to different substituents on the aromatic ring. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded 'warm start' method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications. • Expert knowledge is encoded in a decision tree to warm-start reinforcement learning. • The expert knowledge encode process allows flexible neural network architecture (FlexNet). • The proposed FlexNet reduces learning time by 60–80 % compared with existing method. • The proposed FlexNet reduces energy consumption compared with baseline methods. [ABSTRACT FROM AUTHOR]

The interconnectedness between water, energy, and carbon emissions has emerged as a significant topic in mitigating water scarcity, energy usage, and carbon emissions. The search for alternative water sources and low-energy alternative methods has been necessitated especially for drinking water treatment plants (DWTPs) operations. The study investigates the inherent relationships between water, energy, and carbon emissions in DWTP operations using the systematic review method. A total of 93 bibliographic records were analyzed. Data shows that research trends have increased in recent years, peaking in 2011 and 2021, with the US, Spain, and Saudi Arabia making significant advances in the research domain. It was noted that the optimal energy consumption and carbon emissions during water treatment depend on water volume, water characteristics, fuel type, and treatment requirements. Reverse osmosis and desalination are characterized as the most energy-intensive processes that emit large amounts of carbon compared to conventional methods. Pumping systems were identified as the most energy-intensive unit of DWTPs, and optimization could save up to 20% of future energy use. Despite the growing volume of literature, the study found that there is a lack of standardized principles for regulating energy usage and carbon emissions. Thus, research focusing on sustainable management practices, renewable energy sources, improving energy efficiency, and equipment optimization is essential. The research further identified gaps, potential research areas, and highlighted the need for further investigation in specific areas. The study aims to fill in the gaps in existing knowledge and provide direction for future research. [Display omitted] • Climate change impacts water availability, quality, and treatment requirements. • Conventional treatment methods are low-energy processes compared to advanced methods. • Desalination and reverse osmosis are energy-intensive water treatment processes. • Future DWTP pumping system optimization could save 20% of energy use. • Carbon accounting regulations and policies are lacking in most developing countries. [ABSTRACT FROM AUTHOR]

• Composite flocculant-electroosmosis combined with segmented solidification treatment of high-water-content slurry. • The drainage volume of the composite flocculant-electroosmotic combined method is increased by an average of 73.76% • Innovatively proposing segmented curing using the distribution law of water content. • The turbidity of electroosmotic drainage of composite flocculants is lower than 50 NTU. To address the challenges related to high drainage efficiency, energy consumption, and turbidity in traditional electroosmosis methods, particularly for mud with ultra-high water content and high clay content, this study proposes a novel research approach: the combined flocculant-electroosmosis combined with segmented solidification treatment of high-water-content slurry.This study explores the mechanism of action and drainage effect of the composite flocculant-electroosmotic method using a self-developed one-dimensional electroosmosis test device. The test results show that there is an optimal ratio of organic flocculant (anionic polyacrylamide,APAM) and inorganic flocculant (polymeric aluminum chloride,PAC,and anhydrous calcium chloride,CaCl 2) combined with the composite flocculant.Compared with the traditional single flocculant-electroosmosis method, the drainage volume of the composite flocculant-electroosmotic combined method is increased by an average of 73.76%. Although the total electroosmotic energy consumption and anode corrosion of the composite flocculant group have increased, the average electroosmotic energy consumption coefficient and anode corrosion coefficient are higher, indicating that the energy consumption coefficient is better than that of the traditional electroosmotic group. Innovatively, segmental solidification was carried out based on the anode corrosion cracks and sudden changes in water content after the completion of electro-osmosis, which yielded excellent reinforcement effects and met the requirements of external transportation and reuse. [ABSTRACT FROM AUTHOR]

As the primary inorganic by-product species of ClO 2 , chlorite is believed to have negative toxicological effects on human health and therefrom greatly limits the wide application of ClO 2 in water treatment. The synergistic trimethoprim (TMP) removal concerning degradation efficiency, energy consumption and disinfection by-products (DBPs) formation in the UV activated chlorite process accompanied by the simultaneously elimination of chlorite was comprehensively evaluated. UV/chlorite integrated process removed TMP far more rapidly than UV (1.52%) or chlorite (3.20%) alone due to the endogenous radicals (Cl•, ClO• and •OH), the contributing proportions of which were 31.96%, 19.20% and 44.12%. The second-order rate constants of TMP reaction with Cl•, ClO• and •OH were determined to be 1.75 × 1010, 1.30 × 109 and 8.66 × 109 M−1 s−1. The effects of main water parameters including chlorite dosage, UV intensity, pH as well as water matrixes (nature organic matter, Cl− and HCO 3 −) were examined. k obs obeyed the order as UV/Cl 2 >UV/H 2 O 2 ≈UV/chlorite>UV, and the cost ranking via electrical energy per order (EE/O, kWh m−3 order−1) parameter was UV/chlorite (3.7034) > UV/H 2 O 2 (1.1625) >UV/Cl 2 (0.1631). The operational scenarios can be optimized to achieve the maximum removal efficiencies and the minimum energy costs. The destruction mechanisms of TMP were proposed by LC-ESI-MS analysis. The overall weighted toxicity in subsequent disinfection was assessed as UV/Cl 2 >UV/chlorite > UV, the values of which in post-chlorination were 6.2947, 2.5806 and 1.6267, respectively. Owing to the vital roles of reactive chlorine species (RCS), UV/chlorite displayed far higher TMP degradation efficiency than UV, and concurrently presented much less toxicity than UV/Cl 2. In an effort to determine the viability of the promising combination technology, this study was devoted to reduce and reuse chlorite and synchronously realize the contaminants degradation efficiently. [Display omitted] • The efficiency/cost/toxicity tradeoff for TMP removal by UV/chlorite was comprehensively evaluated. • The roles and reactivities of relating radicals as.•OH, Cl• and ClO• were distinguished. • Effects of chlorite dosages, UV intensity, pH and water matrixes on k obs and EE/O were explored. • The UV/chlorite destruction mechanisms of TMP were proposed by LC-ESI-MS analysis. • UV/chlorite displayed much less toxicity than UV/Cl 2 due to the less reactivity of ClO.• in system. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) has a huge impact on the energy efficiency of hybrid electric vehicles (HEVs). Recently, fast-growing number of studies have applied different deep reinforcement learning (DRL) based EMS for HEVs. However, a unified performance review benchmark is lacking for most popular DRL algorithms. In this study, 13 popular DRL algorithms are applied as HEV EMSs. The reward performance, computation cost, and learning convergence of different DRL algorithms are discussed. In addition, HEV environments are modified to fit both discrete and continuous action spaces. The results show that the stability of agent during the learning process of continuous action space is more stable than discrete action space. In the continuous action space, SAC has the highest reward, and PPO has the lowest time cost. In discrete action space, DQN has the lowest time cost, and FQF has the highest reward. The comparison among SAC, FQF, rule-based, and equivalent consumption minimization strategies (ECMS) shows that DRL EMSs run the engine more efficiently, thus saving fuel consumption. The fuel consumption of FQF is 10.26% and 5.34% less than Rule-based and ECMS, respectively. The contribution of this paper will speed up the application of DRL algorithms in the HEV EMS application. • A propulsion model is designed for a Hybrid Electric Vehicle in python and the entire code is shared in Github. • 13 popular DRL algorithms are introduced and both discrete and continuous action space algorithms are considered. • Key measures, including convergence reward/episode/time and final test result, are compared. • The best DRL algorithms of each action space type are compared with baseline EMS. [ABSTRACT FROM AUTHOR]

Hybrid electric air-ground vehicles (HEAGVs) are deemed as promising transportations due to their great versatility, mobility, and environmental values. Capable of ground-driving, vertical-take-off-and-land, and near-ground flight, HEAGVs are competent to pass poor road conditions such as broken bridges or cliffs. As a crucial part of the HEAGV development, size of the power sources is hard to design owing to different characteristics of air-driving and ground-driving. Large power sources would result in increased fuel consumption and accelerating battery degradation, while small ones might lead to insufficient power supply. Thus, optimal sizing of the power sources is needed at the functional level of a HEAGV design. Moreover, at the performance level, the control strategy should be taken into account simultaneously, which significantly affects the sizing process. However, the intertwined optimal sizing and control strategy of HEAGV becomes difficult to address due to the expanded design space. Motivated by this, an efficient co-optimization strategy is presented for the studied HEAGV, intended to simultaneously find optimal sizing of the battery pack and turbine-generator pack and optimal logic threshold control parameters. The initial mass, fuel consumption, and battery degradation are chosen as optimization objectives to formulate an objective function. Then, a novel enhanced hypotrochoid spiral optimization algorithm (EHSOA) is proposed to address the intricated co-optimization problem. In this algorithm, an enhanced bi-considering mechanism is firstly proposed to avoid the optimization process being trapped in local optima. The co-optimization is implemented under an air-ground driving cycle. Results show that, compared to the initial design, the proposed strategy reduces initial mass, fuel consumption, and battery degradation by 5.08%, 26.10%, and 2.08%, respectively. Finally, the proposed EHSOA is demonstrated to be more qualified to solve the intricated co-optimization problem in comparison to other optimization algorithms. The proposed co-optimization strategy might provide theoretical insights for future HEAGV powertrain designs. • HEAGV powertrain is optimized at both functional level and performance level. • Optimization is solved using an enhanced hypotrochoid spiral optimization algorithm. • Efficiency of the algorithm is improved due to candidate and gravity adjustment mechanisms. • The proposed strategy is demonstrated more efficient for HEAGV powertrain designs. [ABSTRACT FROM AUTHOR]

For radiant heating system, less concern has been given to the fact that supply water temperatures are determined according to local conditions, and it is unclear how surface emissivity of the system affects indoor thermal comfort. Therefore, the application of radiant heating system with fixed water supply temperature in two different types of buildings is analyzed. The transient simulation is verified experimentally and the results show that when the radiator area is determined, adjusting water supply temperature has a great effect on thermal comfort. Changing the basic water temperature by 5 °C can reduce the thermal comfort by 20%. Therefore, it is more reasonable to determine the water supply temperature first and then design other parameters, which can improve thermal comfort and reduce unnecessary energy consumption. The radiator surface emissivity changes thermal comfort mainly by affecting the response speed. The higher the emissivity, the better the thermal comfort. However, if only considering the acceptable thermal comfort zone, changing the emissivity will have little effect on thermal comfort. The time proportion of acceptable thermal comfort zone is only reduced by 5% when emissivity changes from 0.95 to 0.5. Therefore, users can choose radiator surface materials more flexibly according to their needs. • Parameters of radiant heating system is studied with fixed water supply temperature. • Laying radiant panels partially can reduce the thermal stratification near the floor. • Design radiator parameters according to water supply temperature is more reasonable. • Radiator emissivity affects thermal comfort by changing response speed of the system. • Changing radiator emissivity has little effect on the acceptable thermal comfort zone. [ABSTRACT FROM AUTHOR]