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Vertical shaft natural ventilation is a common smoke exhaust method in highway tunnel fires. This study investigated the vertical shaft natural smoke exhaust work in highway tunnel fires with the effect of multiple factors through numerical simulation. Using the analysis of the flow field of smoke in nearby areas of the vertical shaft and the quantitative calculation of the gas exhausted through the vertical shaft, considering the impact of shaft division and board height, an optimal vertical shaft arrangement was proposed, and the working conditions of this arrangement in low-pressure environments were discussed. The results show that dividing a single large vertical shaft into multiple small vertical shafts and appropriately adjusting the board height can reduce the incidence of vertical shaft plug holes and significantly enhance the vertical shaft smoke exhaust performance. Meanwhile, the board-coupled shaft (BCS) has excellent working ability in low-pressure environments, and when pressure drops, smoke exhaust efficiency will improve. This research offers a foundation and reference value for improving the vertical shaft smoke exhaust system in highway tunnels. [ABSTRACT FROM AUTHOR]

Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs. [ABSTRACT FROM AUTHOR]

Effective identification of complex power quality disturbances (PQDs) is the premise and key to improving power quality issues in the current complex power grid environment. However, with the increasing application of solid-state switches, nonlinear devices, and multi-energy system generation, the power grid disturbance signals are distorted and complicated. This increases the difficulty of PQDs identification. To address this issue, this paper presents a novel method for power quality disturbance classification using a convolutional neural network (CNN) and gated recurrent unit (GRU). The CNN consists of convolutional blocks, some of which come with a squeeze-and-excitation block (SE), and is used to extract the short-term features from PQDs, where the convolutional block is used to capture the spatial information from PQDs and the SE is used to enhance the feature extraction capability of the convolutional neural network. The GRU network is designed to capture the long-term feature from PQDs, and an attention mechanism connected to GRU's hidden states at different times is proposed to improve the GRU's feature capture ability in long-term sequences. The CNN and GRU are parallelly arranged to perceive the same PQDs in two different views, and the feature information extracted from them is fused and transmitted to the Softmax activation layer for classification. Based on MATLAB-Simulink, a typical multi-energy-source system is constructed to analyze PQDs, and twelve PQDs are simulated to validate the proposed method. The simulation results show that the proposed method has higher classification accuracy in both single and hybrid disturbances and significant advantages in noise immunity. [ABSTRACT FROM AUTHOR]

Rockburst hazards pose a severe threat to mine safety. To accurately predict the risk level of rockburst, a LightGBM−TCN−RF prediction model is proposed in this paper. The correlation coefficient heat map combined with the LightGBM feature selection algorithm is used to screen the rockburst characteristic variables and establish rockburst predicted characteristic variables. Then, the TCN prediction model with a better prediction performance is selected to predict the rockburst characteristic variables at time t + 1. The RF classification model of rockburst risk level with a better classification effect is used to classify the risk level of rockburst characteristic variables at time t + 1. The comparison experiments show that the rockburst characteristic variables after screening allow a more accurate prediction. The overall RMSE and MAE of the TCN prediction model are 0.124 and 0.079, which are better than those of RNN, LSTM, and GRU by about 0.1–2.5%. The accuracy of the RF classification model for the rockburst risk level is 96.17%, which is about 20% higher than that of KNN and SVM, and the model accuracy is improved by 1.62% after parameter tuning by the PSO algorithm. The experimental results show that the LightGBM−TCN−RF model can better classify and predict rockburst risk levels at future moments, which has a certain reference value for rockburst monitoring and early warning. [ABSTRACT FROM AUTHOR]