Detection of incipient self-ignition process in solid fuels through gas emissions methodology

The aim of this study is to propose an experimental methodology to detect incipient self-ignition processes in solid fuels. This methodology is based on the gases emissions of different solid fuels, varying the degree of compaction and the grain size of the materials. To achieve this goal, a procedure for the collection and analysis of the gases emitted by samples of various fuels has been developed, analysing the temperatures at which these emissions begin. The results obtained for different materials show that it is possible to detect incipient spontaneous combustion processes using measurements of CO and CO₂ emissions during heating process, and then to set alarm thresholds based on the concentrations of these gases. Those results have been compared with results from conventional thermogravimetry and differential scanning calorimetry tests and it is shown that the proposed methodology detect the self-ignition process start point in advance.     

Lower explosion limit of hybrid mixtures of burnable gas and dust

Hybrid mixtures – mixtures of burnable dusts and burnable gases – pose special problems to industries, as their combined Lower Explosion Limit (LEL) can lie below the LEL of the single substances. Different mathematical relations have been proposed by various authors in literature to predict the Lower Explosion Limit of hybrid mixtures (LEL_hybrid). The aim of this work is to prove the validity or limitations of these formulas for various combinations of dusts and gases. The experiments were executed in a standard 20 L vessel apparatus used for dust explosion testing. Permanent spark with an ignition energy of 10 J was used as ignition source. The results obtained so far show that, there are some combinations of dust and gas where the proposed mathematical formulas to predict the lower explosible limits of hybrid mixtures are not safe enough.     

Analysis of dust distribution in silo during axial filling using computational fluid dynamics: Assessment on dust explosion likelihood

In this study, the dust distribution in a silo during axial filling was modelled using a commercial computational fluid dynamics (CFD) code. The work focused on the dust concentration distribution in the silo, for evaluating the likelihood of a dust explosion in the silo. The simulation was conducted using a combination of renormalized (RNG) k-epsilon and discrete phase models, with standard pressure interpolation and a second order upwind scheme. The predicted dust concentration distribution showed a good agreement with experimental data adopted from the literature. It was found that the dust concentration distribution was influenced by mean velocity and turbulence flow. The simulation results suggest that the cornstarch concentration inside the silo was always above the lower explosion limit (LEL), hence requiring a mitigating action or a control system to reduce the explosion risk.     

拟除虫菊酯气相色谱手性拆分方法及其影响因素分析

拟除虫菊酯具有较强的手性特征,包含多种异构体,不同异构体具有不同的生物活性,因此,其异构体的手性拆分具有重要的意义。气相色谱分析法因其分辨率高,峰容量大,灵敏度高、分析时间短,选择性强等特点,已被广泛应用于拟除虫菊酯的手性拆分中。文章通过阐述基于氢键作用、配位作用、包结络合作用的3种手性固定相的拆分机制,综述了气相色谱用于拟除虫菊酯手性分离的研究状况。此外,文章还分析了有机溶剂、水、温度等对拟除虫菊酯在拆分过程中差向异构现象的影响。     

Dynamic response characteristics and structural optimization of isolation seal under the impact of gas explosion

Either in the chemical process plants or in the underground infrastructures, the isolation seal is regularly used to separate the working sections and inactive sections, or to isolate the possible explosion sites in order to avoid any domino effects. Due to differences in accumulation space or ignition point locations, pressure on the seal can vary when an explosion occurs. Thus, the safety and reliability of seals are crucial to maintaining safety in process industry. This paper focuses on seals constructed with concrete and loess materials, and examines the dynamic response characteristics of the gas explosion wave on the seal through sample experiments and numerical simulation metods. The study proposes an optimized design for the explosion-proof structure of the wedge-shaped and spherical seal, which can provide a technical basis for the explosion-proof and anti-explosion measures of various sealed walls. These research findings can also serve as a basis for improving the construction quality of seals.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

原油储罐火灾事故环境风险评价分析

通过运用ADMS模型,对2万m3原油储罐发生火灾爆炸事故时有毒有害气体及燃烧次生污染物的环境影响进行预测。结果表明,其产生的废气污染物对环境影响较大;在火灾持续6h的情况下,产生的主要废气污染物在距离事故中心120m范围内的人员将有半数死亡;距120～240m范围内造成组织缺氧;距240～4725m人员不会有生命危险;距离4725m范围内污染物的浓度超出车间最高允许浓度要求。同时提出了相应的环境风险管理措施,为今后类似事故环境影响评价作借鉴,为环保部门环境管理提供参考。     

HPLC和GC-MS法测定环境空气中醛酮类化合物比对分析

对2,4-二硝基苯肼吸附衍生-高效液相色谱（DNPH-HPLC）法和罐采样/气相色谱-质谱（GC-MS）法的测定条件进行优化,将两种方法测定环境空气中13种醛酮类化合物（OVOCs）的结果做比对分析。结果表明,HPLC法和GC-MS法均可实现一次进样13种OVOCs全分析,方法检出限分别为0.26μg/m3～1.39μg/m3和0.49μg/m3～1.10μg/m3,加标回收率分别为95.9%～111%和52.0%～138%。两种方法测定实际样品,检出组分的测定结果无显著性差异;HPLC法适合测定环境空气中低碳类（C1～C3）低浓度OVOCs,GC-MS法适合测定多碳类（C4～C8）低浓度OVOCs。     

2种环境空气中甲烷自动监测方法的比对分析

光腔衰荡光谱法(CRDS)和气相色谱法（GC）均被广泛应用于环境空气中甲烷（CH₄）的测定。采用CRDS和GC这2种自动监测方法对CH₄标准气体和环境空气样品进行分析比对。结果表明,通过使用统一的标准气体和校准方法,2种方法测定CH₄标准气体的不确定度均＜0.5%,CRDS法的不确定度更低;2种方法测定CH₄环境空气样品结果的平均相对误差为0.28%,Z检验法显示,2种方法没有显著性差异,并具有很高的相关性和一致性。提出,对于测量精度和稳定性更高的大气CH₄监测领域,建议优先选用CRDS法或经过比对达到同等性能的方法;而对于测量精度和稳定性要求稍低的CH₄排放源及周边等监测领域,可以采用GC法。     

哈尔滨市管道煤气事故分析及其预防

对我市管道煤气事故的原因进行了分析,提出了相应的预防措施。