查看更多>>摘要：Magnesium alloy waste dust particles will be generated during alloy grinding, which undergoes a hydrogen production reaction in wet dust removal systems and introduces the risk of hydrogen explosion. To inhibit the generation of hydrogen, environmental friendly sodium alginate (SA) and sodium phosphate (SP) are combined to inhibit the hydrogen evolution reaction of waste magnesium alloy dust and water. The results of the hydrogen evolution experiment and chemical kinetics show that the combined action of 3 g/L sodium alginate and 2 g/L sodium phosphate can completely inhibit the hydrogen evolution reaction of magnesium alloy waste dust. The SEM, EDS, FTIR spectroscopy, XRD and adsorption theory results show that a compact and smooth protective film with sodium alginate as the main body and sodium phosphate as the supplement is formed on the surface of the ZK61 magnesium alloy, blocking the reaction pathway between the external water molecules and Mg2+. This research fills the gap of hydrogen evolution inhibitors for wet dust collectors used in magnesium alloy grinding enterprises and provides a novel, safe and effective method for clean and sustainable production of dust removal system.
查看更多>>摘要：Equipment degradation can lead to reduced mechanical integrity and a lower level of process safety. It is desired to improve system safety and increase productivity through decision planning. However due to system complexity and failure non-linearity, quantifying how different process decisions and equipment conditions affect system safety and performance is challenging which renders production and maintenance planning highly non-trivial. This research provides a Safety-Aware Sustainable Maintenance and Process Optimization (SASUMAPRO) paradigm to help improve system performance. It demonstrates the incorporation of an artificial neural network failure prediction model into a mixed-integer nonlinear programming production and maintenance planning model. It simultaneously maximizes the expected productivity of the plans and minimizes their power consumption while satisfying safety constraints. The methodology is illustrated with a biodiesel production process and results show that the neural network was able to predict equipment mechanical failure with an accuracy of 82.7%. Furthermore, it was seen that the planning model that incorporated the equipment failure model was able to recommend a sequence of decisions that increased expected overall productivity by 31.7% relative to a commonly adopted industrial approach. SASUMAPRO is process-agnostic and can be used to obtain multiple solutions to help improve system performance.
查看更多>>摘要：The frequency and severity of industrial accidents (both process and occupational) are dependent on the effectiveness of risk control systems (RCSs). Despite its importance, the research in this domain is scarce. In this study, a data-driven decision support methodology to evaluate the performance of the RCSs, based on penalty and reward policy, is proposed. The performance influencing factors (PIFs) are identified from incident records and prioritized based on static (expert opinion) and dynamic (data-driven) weights. The static weight is computed using fuzzy best-worst method. The penalty and reward criteria are designed and they correspond to weakness (WI) and effectiveness (EI) index of RCSs, respectively. The values of WI and EI provide information about the performance of RCSs in three levels. The first level provides the overall performance of the RCSs. The second level reports the performance of the individual RCS. The third level provides information about the factors influencing the performance of the RCS. The proposed methodology is applied to process safety incidents reported in a steel manufacturing plant. It is found that the RCS “inspection and maintenance” has poorest performance. The proposed methodology can help industries to objectively assess and monitor the performance of the RCSs, identify the significant factors affecting the performance, and enable management to make informed decisions to improve the safety and safety management system.
查看更多>>摘要：Because of the wide range of applications of lithium ion batteries, the combustible and explosive carbon material dust of a negative electrode material requires additional attention. Few explosion test data of carbon material dust are available. In this paper, the thermodynamic and explosion parameters of allotropes of carbon dust and the same type of carbon material dust with different particle size distributions were determined. The results showed that the explosion pressure of micron/nano carbon material dust ranged from 0.4 to 0.7 MPa; the maximum deflagration index Kst ranged from 4 to 10 MPa · m/s; and the minimum ignition temperature was approximately 600 °C. The proportion of the small dust particles was an important factor that caused a change in the initial oxidation weight loss temperature and apparent activation energy of the same type of carbon dust. The variation trend among the initial oxidation weight loss temperature, apparent activation energy and particle size distribution of different carbon dust materials was not obvious, and mainly depended on the difference in molecular structures. Different molecular structures resulted in different apparent activation energies of the carbon allotropes. The apparent activation energy played a leading role in the change in the dust deflagration index. The smaller the apparent activation energy was, the smaller the energy required for carbon-carbon chemical bond breaking. The faster the carbon-carbon bond-breaking rate was, the larger the deflagration index. The dust particle size distribution, specific surface area and other characteristics had little influence on the deflagration index. Furthermore, the apparent activation energy had a greater impact on the deflagration index than the explosion pressure. The dust explosion pressure was mainly limited by oxygen in the test equipment, and its maximum value remained relatively constant. The minimum ignition temperature of dust clouds decreased with decreasing activation energy and increasing pre-exponential factor. The lower the initial oxidation weight loss temperature was, the lower the measured dust cloud minimum ignition temperature.
查看更多>>摘要：The main objective of this paper is to develop a real-time risk assessment model for havzmat release accidents involving a tank truck. The proposed model can make full use of multiple real-time data and static data for the purpose of quantifying the risk of hazmat release faced by the tank truck at each moment in the driving process. In order to achieve the above function, we improve the risk quantification method in ARAMIS (Accidental Risk Assessment Methodology for Industries System) for real-time risk assessment. For quantifying the possibility, a fuzzy comprehensive evaluation method based on fuzzy rules is used to synthesize multiple evaluation indicators; For quantifying the intensity and vulnerability, a “potential impact zone” model based on the Gaussian plume model is proposed to describe the influence range of toxic gas leak, and point of interest (POI) data is used to identify critical targets. Through the analysis of the road test results, the effectiveness of the proposed model is verified. The significance of this study is to provide a real-time risk quantification tool for tank trucks, which can be programmed and embedded into various driving auxiliary or safety management systems in the future.
查看更多>>摘要：Chemical industrial park (CIP) is a safety-critical system composed of various hazardous materials related units. Once a chemical accident occurs, evacuation is a typical strategy employed to mitigate the casualties. Currently, the emergency evacuation plans in CIPs are lack of quantitative basis risk. Meanwhile, most related studies ignored the temporal-spatial evolution of chemical accidents. The present work raised a multi-source and multi-sink evacuation model (MMEM), which is driven by the dynamic risk assessment. The human injury model and the evacuation behavior model are adopted to quantify the dynamic evacuation risk. Moreover, the spatial-temporal intersection is considered to avoid the path conflicts caused by multiple evacuation crowds, An efficient prior knowledge-based Dijkstra algorithm (PKDA) is proposed for model solving, and the proposed method is demonstrated by a case study. Simulation results show that PKDA is superior to the traditional Dijkstra algorithm (DA) and the Ant Colony algorithm (ACO), which is competitive in both effectiveness and efficiency. The proposed MMEM model is beneficial to the evacuees select a reasonable evacuation path according to the dynamic evolution characteristics of chemical accidents, which effectively improves the efficiency and safety of emergency evacuation.
查看更多>>摘要：Abused and defective Li-ion cells can cause a catastrophic failure of a Li-ion battery (LIB), leading to severe fires and explosions. In recent years, several numerical and experimental studies have been conducted on the explosion hazard related to the vented combustible gases from failed Li-ion cells. Experimentally quantifying fundamental properties for failing LIBs is essential for understanding safety issues; however, it can be costly, time-consuming, and can be partly incomplete. Computational fluid dynamic (CFD) simulations have been an essential tool for studying the risk and consequences in the process industry. In this study, the prediction accuracy of the open-source CFD combustion model/solver XiFoam was evaluated by comparing numerical simulations and experiments of premixed gas explosions in a 1-m explosion channel partly filled with 18650 cell-like cylinders. The prediction accuracy was determined by calculating the mean geometric bias and variance for the temporal pressure evolution, maximum pressure peak, positive impulse, spatial flame front velocity for two different channel geometries, in addition to two gas compositions at several fuel-air equivalence ratios. From this method, the XiFoam model/solver gave an overall acceptable model performance for both geometries and gas composition.
查看更多>>摘要：This research proposes a multi-objective mathematical model for Self-Adaptive Risk-Based Inspection Planning (SARBIP). Risk management is essential to organizations to ensure that a proper maintenance strategy is in place. An inspection plan is required to improve the overall safety and achieve optimal configurations for the investment, repair, and maintenance, in order to minimize the total expenses. To this end, a SARBIP model is proposed to obtain optimal solutions. The proposed model serves a dual purpose by reducing both the expenses and risk level. The proposed algorithm, which is used as the model's solution, is validated by being applied to a real case study in the Iranian petrochemical industry. The model is executed through two algorithms, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Particle Swarm Optimization (MOPSO). The outcomes indicate that MOPSO outperforms other algorithms in large-scale problems.
查看更多>>摘要：Fmoc groups are base labile functional groups attached to amino acids to reduce undesirable reactions in peptide synthesis. As with ordinary amino acids, Fmoc-protected amino acid dusts are combustible, and exhibit the potential for a dust explosion. Dust explosions are a continuing challenge in the process industries and has been the subject of much research. Industrial dusts are usually evaluated for explosion hazard probability on the basis of their minimum ignition energy, the minimum energy an ignition source must supply in order to ignite a dust cloud. Such data is often used in risk assessments and to compare combustible dusts to each other, but there is a lack of data in the literature for minimum ignition energies for both ordinary amino acid dust and Fmoc-protected amino acid dusts. This study experimentally determined minimum ignition energies for the following amino acids and their corresponding Fmoc-protected versions: L-serine, L-proline, glycine, L-glutamic acid, and L-alanine. By comparing the Fmoc-protected amino acid dusts to their ordinary amino acid counterpart, it becomes apparent that the protected variants are much more combustible than the parent molecules. From a perspective of loss prevention, this publication attempts to bring immediate awareness and takes the first step in filling a gap in the published information addressing this topic and contextualize these findings as they pertain to process safety.
NSTL
Elsevier