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This paper aims to develop a Mini-Tribometer for in-situ observation of subsurface.

To observe the change of the microstructure during wear in real time, an in-situ observation mini-tribometer was developed according to the requirements of the basic frictional experiments and carried out the verification experiments.

The subsurface images and the tribological data obtained from the mini-tribometer clearly show that the graphite in the matrix moves to the surface and takes part in lubrication mainly in the form of extrusion and peeling off, and the migration of graphite in the copper-based composite to the frictional interface to act as lubricant and to result in the decrease of the friction coefficient. The experimental results of the developed tribometer are accurate, which can provide important references for further research on the wear mechanism of materials.

The developed in-situ observation mini-tribometer can be used to observe the dynamic wear mechanism of the frictional pairs, which is very important for optimization of material design and tribological performances.

The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted from wrong decision.

A new method of ranking the development priority of heavy oil reservoir is present, in which the neural network is applied for the first time to acquire reservoir parameters' weights through training samples and the genetic algorithm is used to optimize the joint weighs of neurons in case that neural network falling into local minimum. Additionally, the paper establishes subordinate function of every parameter. Eventually, comprehensive evaluation values of all heavy oil reservoirs are obtained.

The method can ensure the veracity and creditability of the parameters' weights, avoid the randomicity brought by experts.

Accessibility of the data of many heavy oil reservoirs is the main limitation.

A very useful and new method for the decision makers of heavy oil reservoirs development.

The new approach of ranking the development priority of heavy oil reservoir based on the neural network and the genetic algorithm. The paper is aimed at the leaders who manage the development of heavy oil reservoirs.

This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.

This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.

The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.

This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.

The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.

The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.

Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.

The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.

This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.