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An optical fiber (OF) sensor with enhanced dynamic range and high sensitivity is realized to detect varying range of relative humidity (RH). For precise and independent estimation/prediction of RH from its nonlinear response, feed forward artificial neural network (ANN) model is developed. Preprocessed stable sensor outputs and corresponding commercial sensor outputs were used to train ANN. Results show that ANN has been effective in successfully predicting RH from the response of OF sensor. Overall, neural network approach showed better performance in comparison to alternative calibration method. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 641–645, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24132

The West Valley melter has been taken out of service. Its design is the direct ancestor of the current melter design for the Hanford Waste Treatment Plant. Over its eight years of service, the West Valley melter has endured many of the same challenges that the Hanford melters will encounter with feeds that are similar to many of the Hanford double shell tank wastes. Thus, inspection of the West Valley melter prior to its disposal could provide valuable — even crucial — information to the designers of the melters to be used at the Hanford Site, particularly if quantitative information can be obtained. The objective of Mississippi State University’s Diagnostic Instrumentation and Analysis Laboratory’s (DIAL) efforts is to develop, fabricate, and deploy inspection tools for the West Valley melter that will (i.) be remotely operable in the West Valley process cell; (ii.) provide quantitative information on melter refractory wear and deposits on the refractory; and (iii.) indicate areas of heterogeneity of deposits, requiring more detailed characterization. A collaborative arrangement has been established with the West Valley Demonstration Project (WVDP) to inspect their melter.