Enhanced Operations and Maintenance of Pollution Control Equipment

Abstract

Laws and regulations mandate that Army installations monitor emissions from industrial processes, and maintain their processes within emissions standards. Army installations commonly use pollution control equipment (PCE) to monitor emissions, and to stay within regulatory and legal limits. Alternatives to use of PCE include using advanced technologies to detect problem conditions, collecting data to predict and determine the cause of failures, using dynamic modeling techniques to model the system or components that have a higher than expected frequency of failure, and verifying the efficacy of the model with data collected from test runs. This study summarized recent attempts to model large systems by using a model based on queuing theory. By studying the methods being used now for reliability centered maintenance (RCM) and the military's predilection to run-to-failure maintenance, this research has produced a linear program from the queuing model that can help reduce equipment downtime. These recent methods may allow U.S. Army installations to optimize maintenance policy for minimal ecological impact.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1999
Accession Number
ADA372744

Entities

People

  • Jearldine I. Northrup
  • Joyce C. Baird
  • Mark D. Ginsberg

Organizations

  • Construction Engineering Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Cognition
  • Downtime
  • Electron Tubes
  • Electronic Circuits
  • Engineers
  • Fabrication
  • Failure Mode And Effect Analysis
  • Frequency
  • Linear Programming
  • Maintenance
  • Maintenance Personnel
  • Manufacturing
  • Neural Networks
  • Operations Research
  • Reliability
  • Statistical Analysis
  • Time Intervals

Readers

  • Aerospace Test and Evaluation
  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.