Junwei Yan

Explosion accidents in aluminum processing pose a significant safety concern in China. Accurate hazard identification, explosion probabilistic assessment, and effective prevention are crucial for production safety. This study proposes an Identification–Assessment–Prevention (IAP) framework that integrates evidence-based hazard identification across multiple processes and factor types, Fault Tree–Bayesian Network (FT–BN) probabilistic assessment, basic-event importance analysis, causal-chain analysis, and propagation-node analysis. Compared with conventional FT–BN models, the proposed framework unifies the three methodological elements of identification, assessment, and prevention into an analytical system, broadens the evidence base and scope for hazard identification, and links key basic events (BEs), critical causation chains, and core propagation nodes to prevention measures. The framework was applied to aluminum processing, identifying 65 hazards across three processing stages and five factor types, and determining high-accident-probability processes and scenarios, key BEs, critical causation chains, and core propagation nodes. Based on these results, prioritized prevention measures were proposed. The proposed framework can assist decision-makers in improving safety management in aluminum processing.

In enclosed cold-aisle data-center rooms, hotspot temperatures within the cold aisles are directly related to server inlet air safety and air-conditioning operation performance. Therefore, high-accuracy temperature prediction is of great significance for thermal-risk warning and operational optimization. This study focuses on an enclosed cold-aisle data-center room in a large data center located in a hot-summer and warm-winter region. The maximum temperature of one selected cold aisle is taken as the prediction target, and a hybrid prediction model integrating TSception, Transformer, and TCN is proposed to characterize multi-scale local disturbances and cross-period temporal dependencies. The model inputs and historical time window are determined through cold–hot separation analysis, cold-aisle temperature-stability comparison, lag analysis between supply air temperature and cold-aisle temperature, and correlation analysis of candidate variables. The experimental results show that the proposed model achieves an MAE of 0.285 °C, an RMSE of 0.438 °C, an NRMSE of 4.76%, and a MAPE of 1.13% in predicting the cold-aisle temperature of the AB aisle, outperforming Persistence, BP, LSTM, XGBoost, and several ablation models. Further multi-aisle experiments demonstrate that the proposed model consistently maintained low prediction errors in independent prediction tasks across different cold aisles, indicating good applicability of the model architecture for characterizing temperature time series in multiple cold aisles within the same data-center room. The proposed method can provide a reference for hotspot-risk identification in enclosed cold aisles and operational optimization of precision air-conditioning systems.

Accurate electricity carbon emission factors are crucial for assessing overall social carbon emissions and achieving China’s “dual carbon” goals. This paper proposes a dynamic correction model that integrates lifecycle extension, power exchange networks, and multi-time-scale decomposition to address the limitations of static carbon emission factors. The model considers factors such as power generation structure, cross-regional transmission, clean energy proportion, line losses, and non-CO₂ greenhouse gas emissions, and achieves dynamic correction at quarterly and monthly scales, enhancing timeliness and regional adaptability. Results show that transmission losses, energy structure, and inter-provincial electricity exchange significantly impact carbon emission factors. For instance, in 2022, line losses in Xinjiang and Gansu raised the electricity carbon emission factor by over 0.06 kgCO₂/kWh. Monthly factors exhibit significant seasonal fluctuations, with some regions showing variations of up to 105% of the annual average. Areas rich in hydropower, such as Yunnan, Sichuan, and Qinghai, experience pronounced fluctuations, highly sensitive to changes in water volume, offering more accurate reflections of carbon emission changes during electricity consumption. This study presents a refined dynamic correction method for electricity carbon emission accounting, providing theoretical support for carbon emission policy development and performance evaluation.

Energy consumption forecasting offers a foundation for governmental agencies to establish energy consumption benchmarks for public institutions. Meanwhile, correlation analysis of institutional energy use provides clear guidance for building energy-efficient retrofits. This study employed five machine learning models to train and predict monthly energy consumption intensity data from 2020 to 2022 for three types of public institutions in China’s eastern coastal regions. A novel ensemble model was proposed and applied for energy consumption prediction. Additionally, the SHAP model was utilized to analyze the correlation between influencing factors and energy consumption data. Finally, the relationship between climatic factors and monthly energy consumption intensity was investigated. Results indicate that the ensemble model achieves higher predictive accuracy compared to other models, with regression metrics on the training set generally exceeding 0.9. Although XGBoost also demonstrated strong performance, it was less stable than the ensemble model. Energy intensity across different building types exhibited strong correlations with the number of energy users, floor area, electricity use, and water consumption. Linear analysis of temperature and energy consumption intensity revealed a directional linear relationship between the two for both medical and administrative buildings.

Optimizing airflow organization is essential for ensuring the energy-efficient and secure operation of data centers. To address common airflow distribution issues in air-cooled systems, such as uneven air supply and cooling capacity imbalance, this study investigates a bidirectional airflow data center room located in a hot-summer and warm-winter region. A computational fluid dynamics (CFD) model was developed based on field-measured data to analyze the airflow distribution characteristics and evaluate the existing thermal conditions. Three optimization strategies were systematically examined: (1) Installation of rack blanking panels, (2) cold aisle containment with varying degrees of closure, and (3) combined implementations of these measures. Performance evaluation was conducted using three thermal metrics: the Return Temperature Index (RTI), Supply Heat Index (SHI), and Rack Cooling Index (RCIHI). The results demonstrate that among individual optimization strategies, rack blanking panels achieved the most significant improvement, reducing SHI by 42.61% while effectively eliminating local hotspots. For combined optimization strategies, the rack blanking panels and fully contained cold aisle containment showed optimal performance, improving cooling utilization efficiency by 88.26%. The optimal retrofit solution for this data center is the rack blanking panels with fully contained cold aisle containment. When considering budget constraints, the secondary option would be rack blanking panels with cold aisle top-only containment. These findings provide practical guidance for energy efficiency improvements in similar data center environments.

Cooling load forecasting is a crucial aspect of optimizing energy efficiency and efficient operation in central air conditioning systems for manufacturing plants. Due to the influence of multiple factors, the cooling load in manufacturing plants exhibits complex characteristics, including multi-peak patterns, periodic fluctuations, and short-term disturbances during meal periods. Existing methods struggle to accurately capture the relationships among variables and temporal dependencies, leading to limited forecasting accuracy. To address these challenges, this paper proposes a hybrid forecasting method based on the iTransformer-BiLSTM. First, the Pearson correlation coefficient is employed to select time-series variables that have a significant impact on cooling load. Then, iTransformer is utilized for feature extraction to capture nonlinear dependencies among multivariate inputs and global temporal patterns. Finally, BiLSTM is applied for temporal modeling, leveraging its bidirectional recurrent structure to capture both forward and backward dependencies in time series, thereby improving forecasting accuracy. Experimental validation on a cooling load dataset from a welding workshop in a manufacturing plant, including ablation studies and comparative analyses with other algorithms, demonstrates that the proposed method achieves superior performance compared to traditional approaches in forecasting accuracy. Meanwhile, by integrating the SHAP sensitivity analysis method, the contributions of input variables to the cooling load prediction results are systematically evaluated, thereby enhancing the interpretability of the model. This research provides a reliable technical foundation for energy-efficient control of central air conditioning systems in manufacturing plants.

This study proposes a method based on image segmentation for accurately identifying liquid aluminum leakage during deep well casting, which is crucial for providing early warnings and preventing potential explosions in aluminum processing. Traditional DeepLabV3+ models in this domain encounter challenges such as prolonged training duration, the requirement for abundant data, and insufficient understanding of the liquid surface characteristics of casting molds. This work presents an enhanced DeepLabV3+ method to address the restrictions and increase the accuracy of calculating liquid surface areas for casting molds. This algorithm substitutes the initial feature extraction network with ResNet-50 and integrates the CBAM attention mechanism and transfer learning techniques. The results of ablation experiments and comparative trials demonstrate that the proposed algorithm can achieve favorable segmentation performance, delivering an MIoU of 91.88%, an MPA of 96.53%, and an inference speed of 55.05 FPS. Furthermore, this study presents a technique utilizing OpenCV to accurately measure variations in the surface areas of casting molds when there are leakages of liquid aluminum. In addition, this work introduces a measurement to quantify these alterations and establish an abnormal threshold by utilizing the Interquartile Range (IQR) method. Empirical tests confirm that the threshold established in this study can accurately detect instances of liquid aluminum leakage.

Accurate prediction of the heat load in district heating systems is challenging due to various influencing factors, substantial transmission lag in the pipe network, frequent fluctuations, and significant peak-to-valley differences. An autoencoder—grey wolf optimization—gated recurrent unit (AE-GWO-GRU)-based heat load prediction method for district heating systems is proposed, employing techniques such as data augmentation, lag feature extraction, and input feature extraction, which contribute to improvements in the model’s prediction accuracy and heat load control stability. By using the AE approach to augment the data, the issue of the training model’s accuracy being compromised due to a shortage of data is effectively resolved. The study discusses the influencing factors and lag time of heat load, applies the partial autocorrelation function (PACF) principle to downsample the sequence, reduces the interference of lag and instantaneous changes, and improves the stationary characteristics of the heat load time series. To increase prediction accuracy, the GWO algorithm is used to tune the parameters of the GRU prediction model. The prediction error, measured by RMSE and MAPE, dropped from 56.69 and 2.45% to 47.90 and 2.17%, respectively, compared to the single GRU prediction approach. The findings demonstrate greater accuracy and stability in heat load prediction, underscoring the practical value of the proposed method.