Microstructure, corrosion and wear behavior of (AlCu)3.5CoCrNiFe and (AlCu)3.5CoCrNiTi high entropy alloy coatings prepared by laser cladding on AZ91 magnesium alloy

(AlCu)3.5CoCrNiFe and (AlCu)3.5CoCrNiTi high entropy alloy (HEA) coatings were prepared on AZ91 magnesium alloy by using laser cladding technology. The phase constitution, microstructure, microhardness, corrosion resistance and wear resistance of the prepared HEA coatings were investigated. Both the HEA coatings consisted of BCC and FCC phases. The microhardness of the (AlCu)3.5CoCrNiFe coating and (AlCu)3.5CoCrNiTi coating was 642.1 HV and 656.2 HV respectively, and both of them were about 9 times higher than that of the AZ91 substrate (71.9 HV). The addition of Ti element led to the formation of dense TiO2 passivation film, thus improving the corrosion resistance of the (AlCu)3.5CoCrNiTi coating. The wear loss of the (AlCu)3.5CoCrNiFe coating and (AlCu)3.5CoCrNiTi coating was 29% and 20% of that of the AZ91 substrate, respectively. The prepared HEA coatings could significantly improve the surface properties of the AZ91 alloy substrate, and the (AlCu)3.5CoCrNiTi coating showed better performance compared to the (AlCu)3.5CoCrNiFe coating

Research on Energy Saving and Environmental Protection Management Evaluation of Listed Companies in Energy Industry Based on Portfolio Weight Cloud Model

Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry comprehensively, this study puts forward the evaluation framework of “governance framework-implementation process-governance effectiveness” for ESEP management level. Based on the comprehensive collection and collating of related information reports (e.g., sustainable development reports) of listed energy companies from 2009 to 2018, the ESEP information was extracted, and the portfolio weight cloud model was used to evaluate the ESEP management status of listed energy companies in China. It is of great theoretical innovation and practical significance to promote the evolution of the economy from “green development” to “dark green development”. The results show that: (1) the number of SHEE information released by listed companies in the energy industry shows a steady increasing trend, but the release rate is low, and there are differentiation characteristics in different industries. (2) The ESEP management level of most listed companies in the energy industry is still at the low level, and only 17.19% (S = 65) of the sample companies are at the level of “IV level-acceptable” and “V level-claimable”. (3) In terms of governance framework-implementation process-governance effectiveness, B1-governance framework (Ex = 3.4451) and B2-implementation process (Ex = 2.9480) are relatively high, but B3-governance effectiveness (Ex = 2.0852) and B4-public welfare (Ex = 2.0556) are relatively low. The expectation of most ESEP evaluation indexes fluctuates between “III level-transition level” and “II Level-improvement level”. Finally, some suggestions are put forward to improve ESEP management levels

Prediction of Node Importance of Power System Based on ConvLSTM

In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the evaluation algorithm of node importance is calculated based on the power flow of the power grid, so the calculation results must be lagging behind, and it does not have the ability to provide early warning for the power grid to provide protection signals. Therefore, it is necessary to predict the importance of nodes in the power system. After using a reasonable prediction model to predict the importance of nodes, we can simulate the future state of power system operation and avoid accidents for the dispatching agency of the power grid company according to the prediction results. This paper proposes a prediction model based on convolutional long short-term memory (ConvLSTM) to predict the importance of nodes. This method has obvious advantages over the long short-term memory (LSTM) network. The convolution operation is used to replace the original full connection operation of the LSTM network, which not only utilizes the advantages of convolution to extract spatial features but also retains the ability of LSTM to process time-series features. The simulation results show that the prediction of node importance using the ConvLSTM network is much more accurate than LSTM. Using statistical indicators to compare and analyze the prediction results, it can be seen that ConvLSTM has higher prediction accuracy. Therefore, using the ConvLSTM model to predict node importance has certain significance for grid dispatching agencies to accurately simulate the future state of the power system and avoid risks in advance

Prediction of Node Importance of Power System Based on ConvLSTM

In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the evaluation algorithm of node importance is calculated based on the power flow of the power grid, so the calculation results must be lagging behind, and it does not have the ability to provide early warning for the power grid to provide protection signals. Therefore, it is necessary to predict the importance of nodes in the power system. After using a reasonable prediction model to predict the importance of nodes, we can simulate the future state of power system operation and avoid accidents for the dispatching agency of the power grid company according to the prediction results. This paper proposes a prediction model based on convolutional long short-term memory (ConvLSTM) to predict the importance of nodes. This method has obvious advantages over the long short-term memory (LSTM) network. The convolution operation is used to replace the original full connection operation of the LSTM network, which not only utilizes the advantages of convolution to extract spatial features but also retains the ability of LSTM to process time-series features. The simulation results show that the prediction of node importance using the ConvLSTM network is much more accurate than LSTM. Using statistical indicators to compare and analyze the prediction results, it can be seen that ConvLSTM has higher prediction accuracy. Therefore, using the ConvLSTM model to predict node importance has certain significance for grid dispatching agencies to accurately simulate the future state of the power system and avoid risks in advance

Research on Energy Saving and Environmental Protection Management Evaluation of Listed Companies in Energy Industry Based on Portfolio Weight Cloud Model

Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry comprehensively, this study puts forward the evaluation framework of “governance framework-implementation process-governance effectiveness” for ESEP management level. Based on the comprehensive collection and collating of related information reports (e.g., sustainable development reports) of listed energy companies from 2009 to 2018, the ESEP information was extracted, and the portfolio weight cloud model was used to evaluate the ESEP management status of listed energy companies in China. It is of great theoretical innovation and practical significance to promote the evolution of the economy from “green development” to “dark green development”. The results show that: (1) the number of SHEE information released by listed companies in the energy industry shows a steady increasing trend, but the release rate is low, and there are differentiation characteristics in different industries. (2) The ESEP management level of most listed companies in the energy industry is still at the low level, and only 17.19% (S = 65) of the sample companies are at the level of “IV level-acceptable” and “V level-claimable”. (3) In terms of governance framework-implementation process-governance effectiveness, B1-governance framework (Ex = 3.4451) and B2-implementation process (Ex = 2.9480) are relatively high, but B3-governance effectiveness (Ex = 2.0852) and B4-public welfare (Ex = 2.0556) are relatively low. The expectation of most ESEP evaluation indexes fluctuates between “III level-transition level” and “II Level-improvement level”. Finally, some suggestions are put forward to improve ESEP management levels

Dynamic evaluation method of urban green growth level in Anhui province: a comprehensive analysis of 16 cities’ panel data from 2013-2017

Considering that green growth development is an increasingly important environmental trend, this paper develops an urban green growth development index and applies it to Anhui Province in China and its 16 cities. Previously, such analyses have taken place mostly at the provincial level, and research on cities is relatively rare. To fill the gap, this paper constructs an urban green growth economy evaluation index based on economic technology, social development, ecological environment, and energy emissions. Using the vertical and horizontal pull-off method to comprehensively evaluate the green growth development levels of 16 cities in Anhui province from 2013 to 2017, the residual expectation coefficient is used to measure and analyze differences in the development levels. The results show that Hefei and Huangshan emit a medium-high level of carbon, and the other 14 cities belong in the high-carbon category. Furthermore, cluster analysis shows that the green growth development levels of the 16 cities fall into four groups. There is a wide disparity between the groups, and the differences between groups are significantly larger than the differences within groups