With the ratification of the Paris Agreement at the Paris Climate Conference, reducing carbon emissions has become a global interest. Coal is one of the main industries causing carbon emissions; thus, quantifying carbon emissions from coal mining is an important step in reducing these emissions. Firstly, based on the life cycle idea, in this paper, we define the Carbon Emission Boundary of the fully mechanized coal mining method. Secondly, the carbon emission accounting model (B-R model) of fully mechanized coal mining is established, which includes the total amount of carbon emissions and the carbon emissions of each mining link during the mining process. The Fifth-II mining area of the Jinda Coal Mine in Tengzhou City is taken as an example. We collect the relevant data on carbon emissions in the mining process of the Jinda Coal Mine, and the B-R model is used to obtain the carbon emissions in the mining process of this mining area. Finally, the feasibility of the B-R model is further verified according to the international authoritative carbon emission IPCC calculation method and the China Coal Production Enterprises Greenhouse Gas Emissions Accounting Methodology and Reporting Guide. The results show that the B-R model in this paper is feasible and that the greatest amount of carbon emissions arises from the coal breaking link and coal transportation, which provides a basis for other coal mines to calculate carbon emissions. The B-R model lays a foundation for coal mines to formulate a carbon emission reduction system.

Effectively avoiding coal mine safety accidents has always been an important issue in the process of coal mining. In order to predict mine pressure hazard and reduce the occurrence of mine safety accidents, this paper innovatively combines logistic regression and mine pressure hazard prediction to establish a mine pressure hazard prediction model. By standardizing the data, the model improves the reliability of the mine pressure data and reduces the interference of the prediction effect of random errors. Based on the batch gradient descent algorithm and the Adagrad optimization algorithm, the prediction model is solved innovatively, which greatly improves the calculation speed and prediction accuracy of the model. Accuracy rate, precision rate, recall rate, and F1-score were selected as the evaluation indices to evaluate the prediction effect of the Adagrad optimization algorithm to solve the logistic regression model for mine pressure hazard. Compared with the existing classification algorithms, such as SVM and decision tree, the Adagrad optimization algorithm has the highest four indices when solving the logistic regression prediction model, and it takes the least time to predict. The results show that the model can efficiently predict mine pressure hazard. Finally, C Coal Mine was selected as the example for analysis. The prediction function was added to the mine pressure monitoring interface design. The practical application effect is similar to the theoretical verification. The establishment of this model provides a reliable guarantee for the secure and efficient production of coal mines and provides helpful research for the prediction of mine pressure.

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

The fleet operation model in which electric vehicles coexist with traditional vehicles is becoming increasingly popular. Because electric vehicles have certain disadvantages and usage limitations, the multi-center management of the distribution of mixed fleets is very complex. There is no research on the multi-center mixed vehicle routing problem based on the integration of reverse logistics and dynamic energy consumption. In response to this challenge, this study proposes a solution to the multi-center mixed vehicle routing problem considering dynamic energy consumption and integrated reverse logistics. Specifically, three studies were carried out: (1) Considering the influencing factors of the operating cost system of the mixed fleet, a system dynamics model was constructed. (2) On the basis of considering delaying the aging of electric vehicle batteries, a new charging station insertion strategy was designed. (3) Based on a novel charging station insertion strategy, a fast non-dominated sorting multi-objective genetic algorithm with an elite strategy was designed to solve this problem. We designed 15 groups of examples to prove the effectiveness of the model and algorithm. The experimental results show that 46.67% of the cases have more than 60% customer satisfaction. The average expenditure cost of 15 groups of cases is CNY 2018.33, which can improve the average customer satisfaction by 22.94%. This method helps companies to formulate transportation plans according to the actual situation, including providing a cost model that considers multiple influencing factors and improving the average customer satisfaction while reducing the total cost expenditure. We believe that the results of this research can provide methods and ideas for logistics companies with multiple distribution centers to formulate large-scale distribution plans.

The sustainable development of mines has been the focus of attention in recent years. In China, there are a large number of untreated mined-out areas, and a series of disasters caused by the instability of the goaf will bring heavy blows to people’s safety and financial resources. Filling treatment will lead to increasing costs and decreasing profits, which will seriously reduce the motivation of mining enterprises and even lead to a moral hazard. Therefore, the analysis of the economic benefits of goaf control plays a vital role in the sustainable construction and long-term development of mines. This paper proposed the mined-out area treatment economic model. The proposed method employs the guiding philosophy of the newsboy model to create a mathematical economy model that provides the basis for a goaf management mode for mines. The following research results were obtained: (1) The economic model of the mined-out area backfilling treatment is constructed, which is classified as three different modes. (2) Combined with mathematical derivation and simulation, the influence of relevant variable parameters on each type of filling mode is discussed. (3) Various types of goaf filling treatment mode are compared with a non-filling scheme (benchmark mode), to provide theoretical support to help mining enterprises choose appropriate filling schemes. The results show that the economic model of mined-out area management provides the optimal mode for mined-out area filling, and the balance of tailings and ultra-high-water filling material procurement is realized, resulting in maximum profits. In this paper, we explain how the use of economic thinking has an important impact on the sustainable development of safety goaf management.