Beijing Institute of Petrochemical Technology

About: Beijing Institute of Petrochemical Technology is a education organization based out in Beijing, China. It is known for research contribution in the topics: Catalysis & Corrosion. The organization has 2468 authors who have published 1937 publications receiving 19270 citations.

Observation of the Decay X(3872)→π^{0}χ_{c1}(1P).

Abstract: Using a total of 9.0 fb(-1) of e(+)e(-) collision data with center-of-mass energies between 4.15 and 4.30 GeV collected by the BESIII detector, we search for the processes e(+)e(-) gamma X(3872) with X(3872) -> pi(0)chi(cJ) for J = 0, 1, 2. We report the first observation of X(3872) -> pi(0)chi(c1), a new decay mode of the X(3872), with a statistical significance of more than 5 sigma for all systematic fit variations. Normalizing to the previously established process e(+)e(-) -> gamma X(3872) with X(3872) -> pi(+) pi(-) J/psi, we find B(X(3872) -> pi(0)chi(c1))/B(X(3872) -> pi+ pi(-) J/psi) = 0.88(-0.27)(+0.33) +/- 0.10, where the first error is statistical and the second is systematic. We set 90% confidence level upper limits on the corresponding ratios for the decays to pi(0)chi(c0) and pi(0)chi(c2) of 19 and 1.1, respectively.

Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning

Abstract: Fault feature can be extracted by traditional manifold learning algorithms, which construct neighborhood graphs by Euclidean distance (ED). It is difficult to get an excellent dimensionality reduction result when processed data has strong correlations. In order to improve the effect of dimensionality reduction and increase accuracy of bearing fault diagnosis in mechanical systems, an improved manifold learning method based on Mahalanobis distance (MD) is proposed. In this paper, we use time-domain analysis and frequency-domain analysis to construct high-dimensional feature vectors in the first step. Then, MD is used to replace ED in neighborhood construction of manifold learning. After using the improved manifold learning method, low-dimensional feature vectors can be extracted. Finally, fault diagnosis of rolling element bearing can be made by applying the K-nearest neighbor classifier. In part of experiment, to verify the efficiency of the improved manifold learning methods, artificial data sets and rolling element bearing fault data are adopted. The experimental comparison results of the improved manifold learning algorithm and the traditional algorithm prove that the proposed method is more effective in rolling element bearing fault diagnosis.

Research progress on multi-overpressure peak structures of vented gas explosions in confined spaces

Abstract: Explosion venting of flammable gases within confined spaces, which may be an explosion hazard or a hazard mitigation measure, has gained widespread attention from researchers in various fields. A substantial number of scientific studies have found that complex initial and boundary conditions lead to complex and diverse peak structures in vented gas explosion pressure curves. This poses major challenges for the accurate prediction and scientific research on vented gas explosion overpressures as well as incident prevention and control. By reviewing the existing literature, we analysed the distribution characteristics of multi-overpressure peak structures and the formation mechanisms of typical peak structures in vented gas explosion pressure curves. On this basis, the influence of different factors on various peak structures are described in detail, and a quantitative method for evaluating the correlation between influencing factors and peak structures is proposed. Our analysis revealed that peaks Pb, Pext, Pmfa, and Pac had the highest occurrence frequencies in the vented explosion pressure curves and were the dominant peaks during a vented gas explosion process. Influencing factors with the highest degrees of correlation with Pb, Pext, Pmfa and Pac include vent opening pressure, ignition location, obstacles, and gas concentration. In particular, gas concentration was identified as a key condition that influenced all typical pressure peak structures. Our conclusions may serve as a reliable theoretical basis for future research on safety relief theories and prevention of gas explosion incidents.

Analysis of the safety barrier function: Accidents caused by the failure of safety barriers and quantitative evaluation of their performance

Abstract: An evaluation of the safety barrier system currently in place in the modern workplace is required to prevent major accidents and present new recommendations regarding safety levels. Safety barriers were classified and their components were described to evaluate their performance. We established a new evaluating method that included three indicators, namely the degree of confidence, the effectiveness and the economic impact. A calculation method is developed to assess each indicator using fuzzy mathematic theory. We described the progression of an accident considering the failure of safety barriers and used the observations to devise proper barriers to stop the propagation of unexpected events. The proposed method is applied to simulate a catastrophe involving the explosion of an oil storage facility which constitutes our case study. The obtained results are practical and applicable and show a high degree of quality and flexibility.