Search Results (50)

An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management. Full article

With the expansion of oil and gas exploration and development to complex oil and gas resource areas such as deep and ultra-deep formation onshore and offshore, the kick is one of the high drilling risks, and timely and accurate early kick detection is increasingly important. Based on the kick generation mechanism, kick characterization parameters are preliminarily selected. According to the characteristics of the data and previous research progress, Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Long Short-term Memory Neural Network (LSTM) are established using experimental data from Memorial University of Newfoundland. The test results show that the accuracy of the SVM-linear model was 0.968, and the missing alarm and the false alarm rate only was 0.06 and 0.11. Additionally, through the analysis of the kick response time, the lag time of the SVM-linear model was 1.3 s, and the comprehensive equivalent time was 23.13 s, which showed the best performance. The different effects of the model after data transformation are analyzed, the mechanism of the best effect of the SVM model is analyzed, and the changes in the effect of other models including RF are further revealed. The proposed early-warning model warns in advance in historical well logging data, which is expected to provide a fast, efficient, and accurate gas kick warning model for drilling sites. Full article

This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. First, sparse kernel principal component analysis (SKPCA) is used to accomplish the feature decoupling of multi-source monitoring data, and its optimal dimensionality reduction performance is verified using long-term and short-term neural networks (LSTMs). Second, an innovative TCN–Transformer hybrid architecture is proposed. The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R² of 0.980988. Finally, a dynamic confidence interval is established using the locally weighted kernel density estimation (LWD-KDE) method with an optimized bandwidth, and an unsupervised early warning mechanism for the risk of gas concentration fluctuations in coal mines is constructed. The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm. Full article

Accurate prediction of coal spontaneous combustion levels is crucial for preventing and controlling spontaneous combustion in goaf areas. To address the ambiguity in classification standards of coal spontaneous combustion warning levels, 21 groups of coal samples from different mining areas were subjected to experiments with programmed temperatures, generating a database of 336 sets of temperatures and data on indicator gas concentrations. An unsupervised learning approach combining t-distributed Stochastic Neighbor Embedding (t-SNE) and k-means clustering was proposed to perform dimensionality reduction and clustering of high-dimensional data features. The clustering results of the original data were compared with Principal Component Analysis (PCA) and Stochastic Neighbor Embedding (SNE) methods to determine coal spontaneous combustion warning levels. The indicator gases and warning levels were input into a trained Support Vector Classifier (SVC) to establish a classification model for coal spontaneous combustion warning levels in goaf areas. The results showed that the maximum Maximal Information Coefficients (MICs) between temperature and CO and O₂ concentrations were 0.95 and 0.81, respectively, indicating strong nonlinear relationships between indicator gases and warning levels. The t-SNE method effectively extracted nonlinear mapping relationships between the indicator gas features, while the k-means clustering categorized coal spontaneous combustion data using distance as a similarity measure. By combining the t-SNE and k-means methods for accurate dimensionality reduction and clustering of goaf spontaneous combustion data, the warning levels were classified into six categories: safe, low risk, moderate risk, high risk, severe risk, and extremely severe risk. The application in the Longgu mine demonstrated that the SVC method could accurately classify spontaneous combustion warning levels in field goaf areas and implement corresponding response measures based on different warning levels, providing a valuable reference for spontaneous combustion prevention in goaf areas. Full article

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed data, leading to inaccurate predictions. This study proposes a DT modeling framework for foundation pits, which is used to simulate, predict, and control the risks associated with the entire excavation process. Consequently, based on the DT modeling framework, a DT foundation pit model (DTFPM) was established using modeling and updating algorithms. This study summarizes and identifies the key modeling parameters of foundation pits. A parametric modeling algorithm based on ABAQUS (v2020) was developed to drive the excavation pit modeling process within seconds. Furthermore, an inverse analysis optimization algorithm based on genetic algorithms (GA) and real-time observed deformation was employed to update the elastic modulus of the soil. The algorithm supports parallel computing and can converge within 10 generations. The prediction error of the model after inverse analysis can be reduced to within 10%. Finally, the authors applied DTFPM to establish an intelligent monitoring system. The focus is on real-time and predictive warnings based on the monitoring deformation of the current construction step and the updated model. This study analyzes a Beijing project case to verify the effectiveness of the system, demonstrating the practical application of the proposed method. The results showed that the DTFPM could accurately simulate the deformation behavior of the foundation pit. The system could provide more timely and accurate safety warnings. The proposed method can potentially contribute to the intelligent construction of foundation pits in the future, both theoretically and practically. Full article

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article

The safety of power batteries in the automotive industry is of paramount importance and cannot be emphasized enough. As lithium-ion battery technology continues to evolve, the energy density of these batteries increases, thereby amplifying the potential risks linked to battery failures. This study explores pivotal safety challenges within the electric vehicle sector, with a particular focus on thermal runaway and gas emissions originating from lithium-ion batteries. We offer a non-dispersive infrared (NDIR) gas sensor designed to efficiently monitor battery emissions. Notably, carbon dioxide (CO₂) gas sensors are emphasized for their ability to enhance early-warning systems, facilitating the timely detection of potential issues and, in turn, improving the overall safety standards of electric vehicles. In this study, we introduce a novel CO₂ gas sensor based on the advanced pyroelectric single-crystal lead niobium magnesium titanate (PMNT), which exhibits exceptionally high pyroelectric properties compared to commercially available materials, such as lithium tantalate single crystals and lead zirconate titanate ceramics. The specific detection rate of PMNT single-crystal pyroelectric infrared detectors is more than four times higher than lithium tantalate single-crystal infrared detectors. The PMNT single-crystal NDIR gas detector is used to monitor thermal runaway in lithium-ion batteries, enabling the rapid and highly accurate detection of gases released by the battery. This research offers an in-depth exploration of real-time monitoring for power battery safety, utilizing the cutting-edge pyroelectric single-crystal gas sensor. Beyond providing valuable insights, the study also presents practical recommendations for mitigating the risks of thermal runaway in lithium-ion batteries, with a particular emphasis on the development of effective warning systems. Full article

In recent years, casing deformation has become a key factor affecting the scale and efficiency of shale gas development. Consequently, a fast and efficient integrated prevention, control, and treatment technology for casing deformation is of great significance in terms of both theory and application. This paper combines a geological mechanics analysis and multi-cluster fracture propagation to investigate the risk evaluation, early warning and identification, and warning and identification technology relating to casing deformation and its application. It proposes a method for the dynamic and static evaluation of casing deformation risk levels and types, and establishes an index system incorporating stress, fracture, time, and space factors. This four-factor evaluation method is in greater alignment with field conditions. It also proposes a method for the early warning and identification of casing deformation based on fracture monitoring and an operation curve, and clarifies the dominant engineering factors around casing deformation. According to the findings, the total fluid volume per stage has a greater impact on casing deformation than a high pump rate. The prevention and control of casing deformation should preferably be realized by optimizing the fracturing parameters. Moreover, the paper reviews existing technologies for treating casing deformation, several of which are defined as major technologies: small-diameter bridge plug staged fracturing and small-size gun perforation, and long-stage multi-cluster asynchronous fracture initiation and composite temporary plugging and diversion. The study results provide support for a significant reduction in the casing deformation rate during fracturing, improving the effective stimulation degree in the casing deformation section in shale gas wells in the southern Sichuan Basin. These results could serve as references for subsequent research. Full article

To alleviate the shortage of natural gas supply, the in-situ conversion of coal to natural gas is more beneficial for advancing the clean and efficient use of energy. Since in-situ coal gas contains complex components, such as H₂, CH₄, and CO, their leakage poses a serious risk to human life and property. Currently, the area of consequence of the harm caused by a leak in a gathering pipeline transporting in-situ coal gas has not been clarified. Therefore, this paper adopted the method of numerical simulation to pre-study the concentration distribution of each component and determined that the main components of concern are CO and H₂ components. Afterward, the diffusion law of in-situ coal gas is analyzed and studied under different working conditions, such as wind speed, temperature, pipe diameter, leakage direction, and leakage aperture ratio. The results indicate that when a pipeline leak occurs, the CO component has the largest influence range. With increasing wind speed, the warning boundary of CO rapidly expands downwind, then gradually diminishes, reaching a peak value of 231.62 m at 7 m/s. The range of influence of the leaked gas is inversely proportional to temperature and directly proportional to pipe diameter and leakage aperture ratio. When the gas leaks laterally, the diffusion early warning boundary value of each component is maximal. Among them, the leakage aperture ratio has a significant impact on the concentration distribution of in-situ coal gas, whereas the effect of temperature is relatively minor. This study contributes to an understanding of the leakage and diffusion characteristics of in-situ coal gas-gathering pipelines. Full article

As the preferred technology in the current energy storage field, lithium-ion batteries cannot completely eliminate the occurrence of thermal runaway (TR) accidents. It is of significant importance to employ real-time monitoring and warning methods to perceive the battery’s safety status promptly and address potential safety hazards. Currently, the monitoring and warning of lithium-ion battery TR heavily rely on the judgment of single parameters, leading to a high false alarm rate. The application of multi-parameter early warning methods based on data fusion remains underutilized. To address this issue, the evaluation of lithium-ion battery safety status was conducted using the cloud model to characterize fuzziness and Dempster–Shafer (DS) evidence theory for evidence fusion, comprehensively assessing the TR risk level. The research determined warning threshold ranges and risk levels by monitoring voltage, temperature, and gas indicators during lithium-ion battery overcharge TR experiments. Subsequently, a multi-parameter fusion approach combining cloud model and DS evidence theory was utilized to confirm the risk status of the battery at any given moment. This method takes into account the fuzziness and uncertainty among multiple parameters, enabling an objective assessment of the TR risk level of lithium-ion batteries. Full article

Background: The respiratory adjusted shock index (RASI) is a risk score whose usefulness in patients with sepsis and trauma has previously been reported. However, its relevance in elderly emergency patients with medical diseases is yet to be clarified. This study assessed the usefulness of the RASI, which can be evaluated without requiring special equipment, to provide objective and rapid emergency responses. Methods: In this retrospective study, we recruited patients with medical diseases, aged 65 years or older, who were transported to the emergency room from Tokushima Prefectural Kaifu Hospital and underwent arterial blood gas testing from 1 January 2022 to 31 December 2023. We investigated the association of the RASI with other indices, including the lactate level, National Early Warning Score 2 (NEWS2), Shock Index (SI), Sequential Organ Failure Assessment (SOFA) score, quick SOFA (qSOFA) score, and systemic inflammatory response syndrome (SIRS). Results: In this study, we included 260 patients (mean age, 86 years), of whom 234 were admitted to the hospital; 27 and 49 patients died within 7 and 30 days of admission, respectively. The RASI was positively correlated with the lactate level, NEWS2, SI, and increase in the SOFA score (p < 0.001). The RASI was higher in patients with a SIRS or qSOFA score ≥ 2 than in those without (p < 0.001). It predicted death within 7 and 30 days of admission with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.73–0.87), sensitivity of 96.3%, and specificity of 53.6% when the cutoff value was set to 1.58 and with an AUC of 0.73 (95% CI: 0.66–0.81), sensitivity of 69.4%, and specificity of 70.6% when the cutoff value was set to 1.83, respectively. Conclusions: The RASI is a simple indicator that can be used for predicting in-hospital outcomes in elderly emergency patients with medical diseases. Larger prospective studies based on this study are needed. Full article

To proactively respond to the national fourteenth Five-Year Plan policy, we will adhere to a comprehensive land and sea planning approach, working together to promote marine ecological protection, optimize geological space, and integrate the marine economy. This paper provides a comprehensive review of the sustainable development of marine geological hazards (MGHs), with a particular focus on submarine landslides, the marine environment, as well as the marine economy. First, the novelty of this study lies in its review and summary of the temporal and spatial distribution, systematic classification, inducible factors, and realistic characteristics of submarine landslides to enrich the theoretical concept. Moreover, the costs, risks, and impacts on the marine environment and economy of submarine engineering activities such as oil and gas fields, as well as metal ores, were systematically discussed. Combined with the current marine policy, an analysis was conducted on the environmental pollution and economic losses caused by submarine landslides. Herein, the key finding is that China and Mexico are viable candidates for the future large-scale offshore exploitation of oil, gas, nickel, cobalt, cuprum, manganese, and other mineral resources. Compared to land-based mining, deep-sea mining offers superior economic and environmental advantages. Finally, it is suggested that physical model tests and numerical simulation techniques are effective means for investigating the triggering mechanism of submarine landslides, their evolutionary movement process, and the impact on the submarine infrastructure. In the future, the establishment of a multi-level and multi-dimensional monitoring chain for submarine landslide disasters, as well as joint risk assessment, prediction, and early warning systems, can effectively mitigate the occurrence of submarine landslide disasters and promote the sustainable development of the marine environment and economy. Full article

With the widespread application of lithium-ion batteries (LIBs) energy storage stations in high-altitude areas, the impact of ambient pressure on battery thermal runaway (TR) behavior and venting flow characteristics have aroused wide research attention. This paper conducts a lateral heating experiment on 280 Ah lithium iron phosphate batteries (LFPs) and proposes a method for testing battery internal pressure using an embedded pressure sensor. This paper analyzes the battery characteristic temperature, internal pressure, chamber pressure, and gas components under different chamber pressures. The experiment is carried out in a N₂ atmosphere using a 1000 L insulated chamber. At 40 kPa, the battery experiences two instances of venting, with a corresponding peak in temperature on the battery’s side of 136.3 °C and 302.8 °C, and gas generation rates of 0.14 mol/s and 0.09 mol/s, respectively. The research results indicate that changes in chamber pressure significantly affect the center temperature of the battery side (T_s), the center temperature of the chamber (T_c), the opening time of the safety valve (t_open), the triggering time of TR (t_TR), the time difference (Δt), venting velocity, gas composition, and flammable limits. However, the internal pressure and gas content of the battery are apparently unaffected. Considering the TR characteristics mentioned above, a safety assessment method is proposed to evaluate the TR behavior and gas hazard of the battery. The results indicate that the risk at 40 kPa is much higher than the other three chamber pressures. This study provides theoretical references for the safe use and early warning of energy storage LIBs in high-altitude areas. Full article

This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires. Full article

To address the problems of inadequate training and low precision in prediction models with small-sample-size and incomplete data, a novel SALGAN-CatBoost-SSAGA framework is proposed in this paper. We utilize the standard K-nearest neighbor algorithm to interpolate missing values in incomplete data, and employ EllipticEnvelope to identify outliers. SALGAN, a generative adversarial network with a self-attention mechanism of label awareness, is utilized to generate virtual samples and increase the diversity of the training data for model training. To avoid local optima and improve the accuracy and stability of the standard CatBoost prediction model, an improved Sparrow Search Algorithm (SSA)–Genetic Algorithm (GA) combination is adopted to construct an effective CatBoost-SSAGA model for risk warning, in which the SSAGA is used for the global parameter optimization of CatBoost. A UCI heart disease dataset is used for heart disease risk prediction. The experimental results show the superiority of the proposed model in terms of accuracy, precision, recall, and F1-values, as well as the AUC. Full article