查看更多>>摘要：Thermal runaway (TR) propagation is a critical challenge in the safety application of lithium-ion batteries (LIBs). In this study, the battery modules with different connection modes are designed to reveal TR propagation mechanisms, and a passive strategy based on thermal insulation is proposed to inhibit TR propagation. The temperature, voltage, heat transfer of battery module, as well as the equivalent flux power during TR propagation are captured and analyzed. The batteries in parallel experience fiercer combustion and propagation in comparison with the batteries without connection, which is because the parallel connection mode intensifies the exothermic reactions inside the battery. Particularly, the energy from the former battery contributes to the dominant heat source for triggering TR of its adjacent battery, accounting for 52 %- 67 %. Compared to the module without connection, the module in parallel releases much higher heat flux to adjacent batteries, leading to shorter TR propagation time and severer TR propagation. Furthermore, the aerogel can completely prevent TR propagations with different connection modes. The average flux power of the former battery to its neighboring battery can be reduced from 400 W to 35 W by inserting aerogel. The results provide new insights into TR propagation mechanism and its prevention, which are beneficial to the safety design of battery modules.
查看更多>>摘要：This paper proposes a physics-based machine learning model to analyze the safety and reliability of solid propellant casting molding processes. The model identifies the relationship between process variables that may lead to failure events and process safety. The fuzzy fault tree analysis (FFTA), as a typical physical model, can provide reasonable physical criteria and reliable a priori knowledge for back propagation neural network (BPNN). All information mapped into BPNN is used to explore the nonlinear relationships of the data and establish dynamic rules. The particle swarm optimization (PSO) algorithm is used to improve the performance of the BPNN model (PSO-BPNN), and a risk prediction model with a maximum error of 0.0006 is obtained. The results show that the proposed model can provide high precision evaluation results. A sensitivity analysis is also performed based on the mean impact value (MIV) algorithm. The importance of curing temperature, casting vacuum, curing time, casting time, and vacuum degree is determined. The above methods help realize dynamic risk analysis of the solid propellants production process and provide timely warning and feasible reference for unsafe processes.
查看更多>>摘要：Chemical clusters are attributed with large inventories of hazardous materials whose release could result in catastrophic events, as observed in the Tianjin Port accident. Such events are typically high-impact low-probability (HILP) accidents since multiple robust safety barriers can significantly ensure the integrity of installations. However, the consequences are extremely serious if the safety barriers broke down due to disaster factors. The current risk assessment methods cannot capture the complex multi-hazard scenarios and the interaction of escalation factors causing domino effects. To overcome such gaps, the present study proposes a quantitative vulnerability assessment method for multi-hazard scenarios triggered by natural events. The vulnerability assessment method considers the exposure of hazards, the sensitivity of causes, and the resilience of asset in modelling the primary event and the possible domino accidents. The proposed method assists in analyzing the risk of domino effects triggered by natural disasters and optimizing the deployment of safety barriers in chemical clusters. The application of the method is demonstrated through a detailed case study.
查看更多>>摘要：SiO/C anode materials for high-energy-density lithium-ion batteries (LIBs) have attracted considerable attention. However, battery capacity degradation and thermal safety problems caused by the large volume variation in the SiO/C anode during the long cycle limit its application. We propose the use of two composite additives to overcome the limitations of the current SiO/C anode materials. The electrochemical performance and thermal stability of the blank electrolyte (BE) and two composite additives were systematically compared using electrochemical, characterisation, and thermokinetic methods. The results revealed the synergistic effect of (2-cya-noethyl) triethoxysilane (TEOSCN) and 4, 5-difluoro-1, 3-dioxolan-2-one (DFEC) improved the cycling and thermal stability of the cells. The use of the additives resulted in the formation of a dense and thin SEI layer with LiF as the main material on the surface of the anode, which significantly improved the cycling stability of Li/ SiO@C batteries. In addition, differential scanning calorimetry (DSC) measurements and thermokinetic analysis indicated that the addition of TEOSCN/DFEC significantly enhanced the thermal stability of the cells, which was mainly manifested as the delay of the exothermic peak and the increase in the activation energy of the mixture of the SiO/C anode and electrolyte. Our results suggested that the multifunctional additive offers a viable approach for developing LiBs with high energy density and excellent safety.
查看更多>>摘要：Recently, industrial sectors that stage occupational and environment safety critical tasks, such as the oil and gas industry, have been interested in monitoring biological parameters to prevent human errors and enhance process safety with emergency preparedness and response. In this context, human reliability plays a fundamental role to avoid possible catastrophic accidents triggered by human factor, for example workers' fatigue. Drowsiness, as a main causes of fatigue, maybe recognized through patterns in electroencephalogram (EEG) signal. In this paper, we propose a drowsiness recognition system that combines information from different EEG signal channels and machine learning in an ensemble methodology, novel for this context. We consider two ensemble approaches: the bagging, using five and three channels, and the voting, using a single channel. To validate the proposed system, DROZY, a real and public database containing drowsiness data, was used in three cases: (1) evaluated in all available subjects;; (2) evaluated in specific subjects with general model; and (3) evaluated for specific subjects and dedicated models. The results show that our proposed system has high accuracy above 90%, in most subjects for Case 3. While for Cases 1 and 2, the ensemble model is equivalent to the best results of the classifiers from the single-channels. Furthermore, collecting many channels of EEG signals is often expensive and cumbersome for humans, and the schemes using many channels of EEG signals do not necessarily lead to better performances.
查看更多>>摘要：Investigating suitable waste management processes is essential nowadays. Anaerobic digestion and pyrolysis are among waste treatment processes that have demonstrated promising potentials. The objective of this study is to evaluate the integration of pyrolysis and anaerobic digestion comprehensively in terms of energy/exergy analysis and comparing the integrated energy system with bare systems. To that end, novel pyrolysis and anaerobic digestion plants are designed and proposed. MATLAB was used for developing a code that simulated the plants and meanwhile, Aspen Plus provided thermodynamic properties. Results showed that the exergy efficiency of the integrated plant is 45.71%, while this parameter is 27.60% and 88.71% for the simple pyrolysis and anaerobic digestion plants, respectively. Furthermore, to make pyrolysis plant energy-independent and maximize bio-oil production, the optimum chemical composition of biomass feedstock is obtained. Seven samples were scrutinized, of which the sample with 46.00 wt% cellulose, 29.33 wt% hemicellulose, and 24.67 wt% lignin showed the optimal conditions. This composition could raise the exergy efficiency of the pyrolysis plant to 40.03%, while more interestingly exergy efficiency of the integrated system would reach 51.15%. Taken together, the findings suggested that the integration of pyrolysis and anaerobic digestion improves both exergy efficiency and methane production.
查看更多>>摘要：Membrane distillation (MD) is a promising technology for seawater desalination, integrating the advantages of both membrane segregation and thermal distillation. High energy consumption is one of the key barriers to the evolution of MD. The concern of utilizing solar thermal heating techniques for feed water heating in MD systems is increasing worldwide for sustainable freshwater production and lowering of energy consumption. In this review, the recent advances and latest developments in solar-powered MD technology have been highlighted. A special focus has been considered for hybridization configurations, energy performance evaluation, and economic analyses of solar MD systems. The combination of different solar thermal units with MD systems;; including, solar flat plate collectors, evacuated tube collectors, hybrid photovoltaic/thermal collectors, high concentrating solar collectors, salt-gradient solar ponds, solar distillers, ...etc., has been examined. Accordingly, reviewed results and related comparisons for the different solar feed heating techniques are critically discussed and panoramically tabulated. Then the bottlenecks of the system's performance and the literature gap in previous studies are also discussed. Overall, this survey sums up the status of solar-based MD research looking at the perspective strategies to realize the next generation of solar MD systems that can address the future demands of MD and achieve a highly more cost-efficient desalination process.
查看更多>>摘要：Offshore wind farm (OWF) site selection is critical to the successful development of offshore wind energy and manifests as a complex multi-criteria decision-making (MCDM) process. To promote the further healthy development of offshore wind energy, this study developed a new integrated MCDM framework to better evaluate and rank OWF sites. The main contributions are as follows. First, the interval 2-tuple linguistic (I2TL) provides a simple, interpretable, and precise approach to linguistic information handling and effectively prevents information missing and distortion. Second, different opinions are aggregated using a modified similarity aggregation method (SAM), reducing the errors due to neglecting the effect of individual differences on consistency. Third, a new integrated weighting method combining the simplified best-worst method (SBWM) and method based on the removal effects of criteria (MEREC) is adopted to acquire evaluation criteria weights, which comprehensively reveals the relative importance of criteria. Fourth, the proximity indexed value (PIV) method is expanded with I2TL for alternative ranking to minimize the rank reversal problem. Finally, the applicability and robustness of the proposed framework are validated through a case study in China as well as sensitivity and comparative analyses. The proposed MCDM framework can provide beneficial support for managers to analyze and select the optimal OWF site.
查看更多>>摘要：Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire detection and forecasting approach through cameras is discussed with extracting and predicting fire development characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to characterize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network (RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accuracies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error (MRE) of extraction by RCNN for the three parameters are around 4-13%, 6-20% and 11-37%, respectively. Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4-13%, 11-33% and 12-48%, respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire development and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics.
查看更多>>摘要：The use of expansive pipeline networks guarantees domestic and industrial users for accessing a continuous flow of valuable liquids and gases. These pipeline systems were considered the most economical and safest pipeline of transport for oil and gas and are of great strategic importance. The risks during operating conditions need to be controlled to handle the pipeline safely and smoothly in the complex globalized economy. Accordingly, life prediction is an essential issue in the pipeline network of the maintenance systems. Recently proposed data-driven and statistical approaches for pipeline's life prediction continue to rely on previous information to create health indicators and define thresholds, which is inefficient in the significant data era. Thus, a recurrent neural network (RNN)-based method for predicting the life of equipment with corrosion dimension classes exposed to critical condition monitoring is proposed in this research. The RNN model uses the multiple condition monitoring observations at the current and previous inspection points as its inputs and the pipe life condition and corrosion type as its outputs. Further, to improve the predictability of the proposed model, a validation mechanism is introduced during the training process. The proposed RNN method is validated using the data from oil and gas fields that have been collected in real-time. A critical sensitivity analysis with missing parameters is performed to evaluate the model's effectiveness in forecasting the life situation when inputs are absent. A comparative study is also carried out between the proposed RNN method and an adapted version of the reported method. The results show the advantage of the proposed method in achieving an actual life expectancy which can reduce the annual maintenance costs and help take necessary actions for better protection and safety of a pipeline.
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