Uranium


Robot designed for faster, safer pipe cleanup at U.S. Cold War-era uranium plant

The Japan Times

COLUMBUS, OHIO – Ohio crews cleaning up a massive former Cold War-era uranium enrichment plant in Ohio plan this summer to deploy a high-tech helper: an autonomous, radiation-measuring robot that will roll through kilometers of large overhead pipes to spot potentially hazardous residual uranium. Officials say it's safer, more accurate and tremendously faster than having workers take external measurements to identify which pipes need to be removed and decontaminated at the Portsmouth Gaseous Diffusion Plant in Piketon. They say it could save taxpayers tens of millions of dollars on cleanups of that site and one near Paducah, Kentucky, which for decades enriched uranium for nuclear reactors and weapons. The RadPiper robot was developed at Carnegie Mellon University in Pittsburgh for the U.S. Department of Energy, which envisions using similar technology at other nuclear complexes such as the Savannah River Site in Aiken, South Carolina, and the Hanford Site in Richland, Washington. Roboticist William "Red" Whittaker, who began his career developing robots to help clean up the Three Mile Island nuclear power accident and now directs Carnegie Mellon's Field Robotics Center, said technology like RadPiper could transform key tasks in cleaning up the country's nuclear legacy.


Robot Designed for Faster, Safer Uranium Plant Pipe Cleanup

U.S. News

Officials say using the RadPiper robot is safer, tremendously faster and more accurate than the current method of workers taking external measurements. They also say it could save tens of millions of public dollars on cleanups of that site and one near Paducah, Kentucky.


Application of the PROSPECTOR system to geological exploration problernst

Classics (Collection 2)

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. Here we describe several applications of the PROSPECTOR consultation system to mineral exploration tasks. One was a pilot study conducted for the National Uranium Resource Estimate program of the U.S. Department of Energy. This application estimated the favourability of several test regions for occurrence of sandstone uranium deposits. For credibility, the study was preceded by a performance evaluation of the relevant portion of PROSPECTOR's knowledge base, which showed that PROSPECTOR's conclusions agreed very closely with those of the model designer over a broad range of conditions and levels of detail.


Using Existing HEI Techniques to Predict Pilot Error: A Comparison of SHERPA, HAZOP and HEIST

AAAI Conferences

At the moment, there appears to be no human error identification (HEI) techniques developed specifically for use in aviation. Similarly, there appears to be very little research concerning the prediction of pilot error in the cockpit. This paper investigates the potential use of existing HEI methods for predicting pilot error and describes a comparative study of three existing HEI techniques, SHERPA, HAZOP and HEIST when used to predict potential pilot error on an aviation landing task using the'autoland' system. The study aims to demonstrate that existing HEI methods developed for use in highly complex systems, such as nuclear power plants and chemical processing plants, can be used effectively in an aviation context.


Application of the PROSPECTOR system to geological exploration problems

Classics

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.