• Automatic detection of sensor calibration errors in mining industry

      Pothina, Rambabu; Ganguli, Rajive; Ghosh, Tathagata; Lawlor, Orion; Barry, Ronald (2017-12)
      Sensor errors cost the mining industry millions of dollars in losses each year. Unlike gross errors, "calibration errors" are subtle, develop over time, and are difficult to identify. Economic losses start accumulating even when errors are small. Therefore, the aim of this research was to develop methods to identify calibration errors well before they become obvious. The goal in this research was to detect errors at a bias as low as 2% in magnitude. The innovative strategy developed relied on relationships between a variety of sensors to detect when a given sensor started to stray. Sensors in a carbon stripping circuit at a gold processing facility (Pogo Mine) in Alaska were chosen for the study. The results from the initial application of classical statistical methods like correlation, aggregation and principal component analysis (PCA), and the signal processing methods (FFT), to find bias (±10%) in "feed" sensor data from a semi-autogenous (SAG) grinding mill operation (Fort Knox mine, Alaska) were not promising due to the non-linear and non-stationary nature of the process characteristics. Therefore, those techniques were replaced with some innovative data mining techniques when the focus shifted to Pogo Mine, where the task was to detect calibration errors in strip vessel temperature sensors in the carbon stripping circuit. The new techniques used data from two strip vessel temperature sensors (S1 and S2), four heat exchanger related temperature sensors (H1 through H4), barren flow sensor (BARNFL) and a glycol flow sensor (GLYFL). These eight sensors were deemed to be part of the same process. To detect when the calibration of one of the strip vessel temperature sensors, S1, started to stray, tests were designed to detect changes in relationship between the eight temperature sensors. Data was filtered ("threshold") based on process characteristics prior to being used in tests. The tests combined basic concepts such as moving windows of time, ratios (ratio of one sensor data to data from a set of sensors), tracking of maximum values, etc. Error was triggered when certain rules were violated. A 2% error was randomly introduced into one of the two strip vessel temperature data streams to simulate calibration errors. Some tests were less effective than others at detecting the simulated errors. The tests that used GLYFL and BARNFL were not very effective. On the other hand, the tests that used total "Heat" of all the heat exchanger sensors were very effective. When the tests were administered together ("Combined test"), they have a high success rate (95%) in terms of True alarms, i.e., tests detecting bias after it is introduced. In those True alarms, for 75% of the cases, the introduction of the error was detected within 39.5 days. A -2% random error was detected with a similar success rate.
    • A farmers guide to evaluate soil health using physical, chemical, and biological indicators on an agricultural field in Alaska

      Cole, Cory J.; Zhang, Mingchu; Matney, Casey; Karlsson, Meriam (2018-12)
      Farmers across Alaska face many challenges. These challenges include climate extremes, wind and water erosion, weed pressure, crop pests, and nutrient-poor soils. Cover crops, crop rotation, crop residue, and tillage management are common conservation practices used to address soil related resource concerns. Research in the continental United States has shown that these soil conservation practices improve soil health. Resource managers are trying to determine the usefulness of soil health indicators to assess conservation practices in Alaska. The objective of this project was to provide Alaskan farmers, conservation planners, and land managers with a background on soil health, soil health indicators, soil health assessments, and the use of conservation practices to improve soil health. Establishing linkages between soil conservation practices and soil health indicators will allow individuals to focus conservation efforts on improving soil conditions, evaluate soil management practices and techniques over time to determine trends, make qualitative comparisons of soil health among management systems, and provide tested measures of soil health (indicators) that will allow farmers and land managers to make more informed resource decisions. Numerous studies were conducted across Alaska to gauge the success of cover cropping, crop rotation, and reduced tillage (no-till). Improvements in physical, chemical, and biological indicators were documented. After one year of study, most cover crops resulted in lower bulk density at the soil surface compared to conventional tillage. Among the cover crop treatments, the perennial forage grass Timothy (Phleum pratense var. Engmo) ranked highest in soil organic matter, soil water content, and improvement to the soil structure. Preliminary data from this project has been gathered to develop an Alaska specific Soil Health Assessment Card and supplementary User Guide.