Analysis of the effect of intrinsic sludge properties on sludge drying characteristics from both sludge composition and type scales.

Convective drying is an effective method for reducing the moisture content of the sludge. Fewer studies have discussed the effect of sludge physicochemical properties on drying compared to air parameters. Eleven types of sludge were collected, and ultimate analysis, proximate analysis, and heat value analysis were performed. Meanwhile, the maximum drying rate (umax) of sludge convection drying at 70 °C was determined. The results showed that the cumulative variance contribution of the two extracted principal components (PCs) was 92.5 %. Then, a regression model of umax was developed based on the extracted PCs. The coefficient of determination of this model was 0.788, and the difference was statistically significant, with a negative correlation between umax and PC2. Further, the principal component score plot enabled the traceability of the integrated sludge, and based on this classification results, the drying characteristics of various types of sludge were discussed, and a high correlation (R2 = 0.9590) between the initial moisture content of sludge and umax was found. Mathematical models between sludge physicochemical properties and drying characteristics can be effectively developed from both sludge composition and type scales. This exploration deepened the knowledge of sludge drying and facilitates the prediction of drying rate.

Research on the evolutionary control of unsafe behavior of construction personnel based on multi-field coupled-homogeneous analysis model.

Unsafe behavior among construction personnel poses significant risks in petroleum engineering construction projects. This study addresses this issue through the application of a multi-field coupled homogeneous analysis model. By conducting case analyses of petroleum engineering construction accidents and utilizing the WSR methodology, the influencing factors of unsafe behaviors among construction personnel are systematically categorized into organizational system factors, equipment management factors, and construction personnel factors. Subsequently, employing Risk coupling theory, the study delves into the analysis of these influencing factors, discussing their coupling mechanisms and classifications, and utilizing the N-K model to elucidate the coupling effect among them. Furthermore, a novel approach integrating coupling analysis and multi-agent modeling is employed to establish an evolutionary model of construction personnel's unsafe behavior. The findings reveal that a two-factor control method, concurrently reinforcing equipment and construction personnel management, significantly mitigates unsafe behavior. This study provides valuable insights into the evolution of unsafe behavior among construction personnel and offers a robust theoretical framework for targeted interventions. Significantly, it bears practical implications for guiding safety management practices within petroleum engineering construction enterprises. By effectively controlling unsafe behaviors and implementing targeted safety interventions, it contributes to fostering sustainable development within the petroleum engineering construction industry.

Identification of mine water sources using a multi-dimensional ion-causative nonlinear algorithmic model.

Based on the nonlinear algorithmic theory, the R-SVM water source discrimination model and prediction method were established by using the piper qualitatively to compare the differences between the ionic components and R-type factor approximation indicator input dimensions. Taking the mine water samples of Zhaogezhuang Coal Mine as an example, according to the chemical composition analysis of the water samples from different monitoring points, six indexes of Na+, Ca2+, Mg2+, Cl-, SO42- and HCO3- were selected as the discrimination factors. According to the water characteristics of each aquifer and the actual needs of discrimination, the water inrush sources in the mining area were divided into four categories: The goaf water is class I, Ordovician carbonate is class II, Sandstone fracture water from the 13 coal system is class III, and Sandstone fracture water from the 12 coal system is class IV. Taking 56 typical water inrush samples as training samples, 11 groups for prediction samples, establish the input index as typical ion content, output as water source type, using SPSS statistics and MATLAB to realize the R-SVM water source discriminant analysis model, automatically establishing the mapping relationship between the water quality indexes and the evaluation standards, which can achieve the purpose of rapid and accurate discrimination of the water sample data. The results showed that the accuracy of the R-SVM model classification was 90.90% in the verification of the water source discrimination example of Zhaogezhuang mine and the coupled model has high accuracy, good applicability and discriminant ability, and has certain guiding significance for the prevention and control of water damage and the related field work.

Study of roof water inrush forecasting based on EM-FAHP two-factor model.

To solve the problem of predicting and evaluating coal mine roof water inrush accidents, based on the background of Hongliu coal mine, the relevant weight values of the main control factors of water inrush were determined based on the water-richness index method, combined with the entropy method and FAHP two-factor method. The grid processing function of GIS and the kriging interpolation method are used to draw thematic maps of the main controlling factors, and the weight values of the water permeability indicators are coupled into the GIS. The FLAC3D numerical simulation software is used to analyze the water-conducting fracture zone after the mining of the Hongliu Coal Seam numerical simulation of development and water pressure distribution changes. The results show that the permeability hazard zoning map obtained by the EM-FAHP two-factor model is in line with the results of the damage height and the increased water pressure zone obtained by the numerical simulation.