Abstract: In chemical production, the accuracy of pipeline fluid temperature control is very important for ensuring product quality, improving production efficiency and ensuring production safety. In this paper, the design and implementation of chemical pipeline fluid temperature control system based on boundary proportional-integral-derivative (PID) control algorithm are deeply discussed. Firstly, the principle of PID control algorithm and its advantages in the field of temperature control are expounded in detail. Secondly, according to the specific requirements of chemical pipeline fluid temperature control, the boundary PID control architecture is proposed and the key advantages are analyzed in detail. Finally, the effectiveness of the proposed control method is verified by simulation experiments. The results show that the system based on boundary PID control can respond quickly and achieve the goal of fluid temperature control, which provides reliable technical support for chemical production. Read More

Abstract: The second largest greenhouse gas, methane (CH4), is mainly emitted from the coal industry. In order to propose targeted emission reduction measures, it is urgent to establish a refined emission accounting method. Based on the whole life cycle theory and carbon emission characteristics of coal mines, the mining and mining mines are divided into four stages: geological exploration, coal mining, post-mining activities and abandoned mines. The “IPC-2019 Guidelines” and “GB/T 32151.11-2018” established a method for accounting methane emissions during the whole life cycle of Jingong coal mines. Taking the methane emission of Fangzhuang Coal Mine in Jiaozuo, Henan Province from 2010 to 2017 as an example, it is calculated that the methane emission of coal mining stage in the whole life cycle accounts for about 80%, which is consistent with the historical methane emission law of coal mines in China revealed by the evaluation results based on T3 method. The establishment of this method can provide a theoretical basis for proposing effective measures to reduce methane emission and greenhouse effect in coal mines. Read More

Abstract: Based on groundwater observation data from the Wuzhi area during 2000–2023, this study integrated the Mann–Kendall trend test, geostatistical analysis, and principal component analysis to investigate the spatiotemporal evolution and driving mechanisms of the groundwater flow field. The results show that during the study period, the groundwater depth generally increased, with a slightly larger rise in the dry season than in the wet season. The groundwater level exhibited a spatial pattern of being higher in the northwest and lower in the southeast, and declined continuously over time. Human activities were the main cause of anomalous changes in the groundwater flow field, with agricultural irrigation and groundwater extraction playing dominant roles. Spatially, groundwater depth decreased from the alluvial zone of the Yellow River and Qin River in the west to the Yellow River alluvial plain in the southeast. The reduction in groundwater storage was primarily due to increased extraction driven by rising irrigation water use, whose impact far exceeded that of natural factors. Sustainable management of agricultural water resources is essential for ensuring groundwater security in this region. Read More

Abstract: Coal seam floor water inrush is a major geological hazard restricting the safe production of coal mines, directly threatening personnel life and engineering property safety. Accurate prediction of its occurrence risk is of great engineering significance. To address the problems of insufficient generalization ability, easy missed judgments and misjudgments of traditional prediction models caused by scarce water inrush samples and unbalanced data categories in actual mining, this study proposes a prediction model (DBO-XGBoost) integrating the improved SMOTE algorithm and Dung Beetle Optimizer (DBO) with eXtreme Gradient Boosting (XGBoost). A total of 50 sets of water inrush case data from Ordovician limestone nationwide were collected, and 6 core characteristic indicators including water pressure and aquiclude thickness were selected. The improved adaptive SMOTE algorithm was used to balance the data categories, and the 8:2 training-test set split ratio was determined through ten-fold five-cross validation. The global optimization ability of DBO simulating the natural behavior of dung beetles was utilized to optimize the key hyperparameters of XGBoost, fully excavating the nonlinear coupling relationships among features. Comparative verification with 7 models such as XGBoost and PSO-XGBoost showed that the accuracy, precision, recall, and F1-score of the proposed model reached 0.88, 0.89, 0.88, and 0.87 respectively, with an AUC value of 0.928. Compared with the traditional XGBoost, the true positive rate increased by 13.3% and the false negative rate decreased by 40%. The model was applied to the first mining area of Dongda Coal Mine to realize the visual evaluation of water inrush risk. It exhibits excellent accuracy and stability under small sample and complex geological scenarios, providing reliable technical support for the prevention and control of coal mine water inrush disasters. Read More

Abstract: With the growing severity of global climate change, accelerating low-carbon transition and emission reduction has become urgent. A key research focus is how to identify the main mechanisms driving carbon emissions and to accurately predict the timing and pathway of future carbon peaking. This paper systematically reviews and synthesizes two core methodological strands, and examines their respective strengths and limitations. The results indicate that, in decomposition of emission drivers, combined models help overcome the constraints of single methods, while in carbon peaking prediction, machine learning models can improve forecasting accuracy. Nevertheless, both types of approaches still have considerable room for improvement. Building on the summary of existing methods and current applications, this study proposes suggestions and directions for future research. Read More

Abstract: Soil carbon emissions significantly influence the global carbon balance as a major component of the terrestrial carbon pool. Biochar, an emerging soil amendment, has garnered significant interest for its potential to enhance carbon sequestration, improve soil fertility, and mitigate greenhouse gas emissions. However, the impacts of biochar on soil CO2 and CH4 emissions are complex and context-dependent, varying with biochar properties, soil types, and environmental conditions. Integrating proposed mechanisms for biochar-induced soil greenhouse gas mitigation with statistical analyses of published literature, this review systematically examines the effects of biochar on CO2 and CH4 emissions and identifies future research priorities. Specific focus is given to biochar produced at pyrolysis temperatures of 500-600 °C, its application in flooded soils, and its effects within straw-return agricultural systems. Through critical analysis of existing literature, this review aims to offer insights to guide further research and practical applications in this field. Read More

Abstract: As China enters the stage of inventory-based development, the functional regeneration of urban villages is critical for sustainable urban planning. This study develops a ternary analytical framework—commercial-led, cultural-creative-led, and mixed-incremental—to evaluate regeneration strategies. Through a case study of Group 8, Huanghe Village in Changsha, we examine an innovative "cultural-social" hybrid model. Situated near major universities and ecologically sensitive waters, the project adopted a non-demolition organic renewal approach, transforming a degraded "training village" into the Houhu Art District. Results demonstrate that integrating cultural-creative industries with systematic ecological restoration and phased implementation effectively balances economic vitality, social equity, and cultural continuity. This research validates that "local wisdom"—the adaptive fusion of specialized renewal models—is essential for regenerating resource-dense urban villages. The findings offer a replicable template for organic renewal in similar contexts, bridging the gap between physical spatial updates and the preservation of social-spatial fabric. Read More