Radon exhalation characteristics after pyrolysis of long flame coal.

Coal pyrolysis is a important method for classifying and utilizing coal resources and contributes to enhanced comprehensive resource utilization. However, In high-temperature areas such as coal pyrolysis, there is an abnormal phenomenon release of radioactive gas radon, understanding the relationship between temperature and radon exhalation characteristics, as well as the underlying mechanisms, holds great importance for assessing radon pollution in mining areas. After coal undergoes pyrolysis under high temperature conditions, its material composition, pore structure, water content, and other properties have changed. The pyrolysis products in different atmosphere environments have differences, and the characteristics of radon emission are also different. To address this, the present study conducted coal pyrolysis experiments in both aerobic and anaerobic environments, using long flame coal sourced from Yulin, China. The radon release concentration of the pyrolysis products was measured. The research findings indicate that during pyrolysis at elevated temperatures, the ratio of coal mass loss is constantly increasing. High temperatures promote the development of pores and fissures, and significant changes in coal properties at temperature thresholds (300 °C and 500 °C). The specific surface area, pore volume, and fracture ratio all display substantial increases, and the amplitude of change is greater under aerobic conditions. The fractal dimension of total pores and macropores shows continuous growth, while the specific surface area, pore volume, and fracture ratio exhibit a strong negative correlation with the radon emission rate of pyrolysis products. The expansion and penetration of pores and cracks, along with the release of a substantial amount of pyrolysis gas, accelerate the transformation, migration, and exhalation of radon, resulting in a negative correlation between the heat treatment temperature and the radon release rate of pyrolysis products. Under aerobic conditions, the radon release rate of pyrolysis products decreases more significantly.

Response of thermal conductivity of loess after high temperature in northern Shaanxi burnt rock area, China.

Spontaneous combustion of coal seams can produce a high temperature of about 800 ℃, which greatly changes the thermal conductivity of the overlying loess layer. The thermal conductivity of loess plays an important role in ecological restoration design and the calculation of roadbed and slope stability. In this study, loess in northern Shaanxi, China was taken as the research object to measure the mass-loss rate and heat conduction parameters of loess specimens after high temperature. The test results show that, between 23 and 900 °C, with temperature increasing, the mass-loss rate is reduced. And the heat conduction coefficient (λ), specific heat capacity (c), and thermal diffusion coefficient (α) decreased by 48.9%, 23.1%, and 35.6%. This is due to the air thermal resistance effect caused by the increase of pores and cracks in loess specimens after high temperature.

Bioenergy generation and nitrogen removal in a novel ecological-microbial fuel cell.

A novel ecological-microbial fuel cell (E-MFC) was constructed based on the mutualistic symbiosis relationship among wetland plants Ipomoea aquatic, benthic fauna Tubifex tubifex (T. tubifex) and microorganisms. The maximum power densities of sediment MFC (S-MFC), wetland plant MFC (WP-MFC) and E-MFC were 6.80 mW/m2, 10.60 mW/m2 and 15.59 mW/m2, respectively. Ipomoea aquatic roots secreted organic matter as electricigens' fuel for electricity generation, while T. tubifex decomposed decaying leaves and roots into soluble organic matter and plant nutrients, forming a co-dependent and mutually beneficial system, which was conducive to bioelectricity production. The E-MFC obtained the highest nitrogen removal, and the removal efficiencies of NH4+-N and NO3--N were 90.4% and 96.5%, respectively. Hydraulic retention time (HRT), cathodic aeration and T. tubifex abundance had significant effects on E-MFC power generation. The performeance boost of E-MFC was closely related to anodic microbial community change caused by the introduction of T. tubifex.

Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms.

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling.

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.