Hydro-physico-chemical Grouping of Cachar Paper Mill Effluents in Assam Using Multivariate Statistical Model

2018 ◽  
pp. 203-213
Author(s):  
Sangeeta Dey ◽  
Manabendra Dutta Choudhury ◽  
Suchismita Das
Keyword(s):  
Statistical Model ◽  
Paper Mill ◽  
Multivariate Statistical ◽  
Paper Mill Effluents ◽  
Physico Chemical ◽  
Multivariate Statistical Model

Multivariate Statistical Model for Predicting Occurrence and Location of Broken Rails

2003 ◽  
Vol 1825 (1) ◽  
pp. 48-55 ◽  
Author(s):  
C. Tyler Dick ◽  
Christopher P. L. Barkan ◽  
Edward R. Chapman ◽  
Mark P. Stehly
Keyword(s):  
Statistical Model ◽  
Service Failure ◽  
Quantitative Prediction ◽  
Strongly Correlated ◽  
Santa Fe ◽  
Multivariate Statistical ◽  
Hazardous Materials Transportation ◽  
Service Failures ◽  
Multivariate Statistical Model ◽  
Improve Service Quality

Broken rails are the leading cause of major accidents on U.S. railroads and frequently cause delays. A multivariate statistical model was developed to improve the prediction of broken-rail incidences (i.e., service failures). Improving the prediction of conditions that cause broken rails can assist railroads in allocating inspection, detection, and preventive resources more efficiently, to enhance safety, reduce the risk of hazardous materials transportation, improve service quality, and maximize rail assets. The service failure prediction model (SFPM) uses a combination of engineering and traffic data commonly recorded by major railroads. A Burlington Northern Santa Fe Railway database was developed in which the locations of approximately 1,800 service failures over 2 years were recorded. The data on each location were supplemented with information on other engineering and traffic volume parameters. A complementary database with the same parameters was developed for a randomly selected set of locations at which service failures had not occurred. The combined databases were analyzed using multivariate statistical methods to identify the variables and their combinations most strongly correlated with service failures. SFPM accuracy in predicting service failures at specific locations exceeded 85%. Although further validation is necessary, SFPM is promising in the quantitative prediction of broken rails, thereby improving a railroad’s ability to manage its assets and risks.

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