ABSTRACT
Early detection of significant abnormal changes is highly desirable for oil refinery processes, which consist of sophisticated unit operations handling hazardous and flammable inventories and operating at high temperature and pressure. Close monitoring and anomaly detection are vital for avoiding major accidents and losses and enable intervention before failure occurrence. A new big data analytics tool called Plant Health Index (PHI) is proposed in this work. PHI is a statistical anomaly detection software that trains its model using online normal plant operation, then uses statistical analytics to detect anomalies. For detecting the anomalies, a combined method of multivariate analysis of residuals and nonparametric models of the process is employed. The methodology provides a structured representation of the plant variables to ease the detection of problems along with the detection of operation changes of the system. The PHI system has been tested on a hydrotreating units in a refinery, which consists of catalytic reactors and separators. The current implementation tagged 170 process variables and proved effective in capturing the normal operational conditions of the plant. When placed online, PHI was able of detecting anomalies that are difficult to detect using the control system and before being detected by the alarm system.
Subject(s)
Oil and Gas Industry , SoftwareABSTRACT
Mesoporous silica (MSPN12) was prepared by nonionic surfactant micelle-templated gelation of sodium silicate (Na2SiO3) and fluorosilicic acid (H2SiF6) in aqueous solution, characterized by a range of instrumental techniques, and tested as a support for Ni and Rh catalysts in the partial oxidation of methane (POM). Calcined and sintered MSPN12 exhibited well-defined d00l-spacings (3.5-4.39 nm), narrow pore distributions (2.4-3.1 nm), and large specific surface areas (552-1,246 m2 g-1), and was found to be highly thermally stable. Microscopic imaging revealed that MSPN12 comprised spherical particles with a uniform diameter of ~0.7 µm, with each particle featuring firm and regular honeycomb-type pores. MSPN12-loaded Ni and Rh maintained stable POM activity at 700 °C during almost 100 h on stream, which were comparable to those for the commercial Rh(5)/Al2O3 catalyst in terms of methane conversion and H2 formation selectivity. Thus, the combination of structural stability and favorable physicochemical properties resulted in good POM performance.
ABSTRACT
AIMS: The Short Post-traumatic Stress Disorder (PTSD) Rating Interview (SPRINT) is a validated, eight-item, brief global assessment scale for PTSD. This report investigated the psychometric properties of the Korean version of the SPRINT (K-SPRINT). METHODS: Eighty-seven PTSD patients, 47 other psychiatric patients, and 63 healthy control subjects were enrolled in the study. All subjects completed a psychometric assessment package that included the K-SPRINT and the Korean versions of the Clinician-Administered PTSD Scale (CAPS), the Beck Depression Inventory (BDI), and the State Trait Anxiety Inventory (STAI). RESULTS: The K-SPRINT showed good internal consistency (Cronbach's alpha = 0.86) and test-retest reliability (r = 0.82). K-SPRINT showed moderatecorrelations with CAPS (r = 0.71). An exploratory factor analysis produced one K-SPRINT factor. The optimal diagnostic efficiency (91.9%) of the K-SPRINT was found at a total score of 15, at which point the sensitivity and specificity were 90.8% and 92.7%, respectively. CONCLUSIONS: The present findings demonstrate that the K-SPRINT had good psychometric properties and can be used as a reliable and valid instrument for the assessment of PTSD.
