Near Real-Time Interpolative Measurement Algorithm for the Modelling of Environmental Air Quality in Underground Mines
DOI:
https://doi.org/10.17159/Abstract
As real-time air quality monitoring becomes more prevalent in US underground mines, it is important to provide the highest degree of data reliability with the fewest sensors. Real-time sensors remain costly. These costs are not exclusively financial; the time required to install, calibrate and maintain real-time sensors poses a barrier to implementation. Therefore, paradigms for the interpolation of volume constrained data are required. This allows for higher data resolution and reliability while reducing the total number of sensors required to create a well-informed monitoring system. We have developed an algorithm for the deterministic interpolative modeling of real-time sensor data. The Near Real-Time Interpolative Measurement
(NeaRTIMe) Algorithm is a self-optimizing inverse distance weighting method that allows for a higher degree of monitoring across an excavation based on sparse data. The use of self-optimization during interpolation allows for reduced error across a mine. NeaRTIMe is designed for use in mine gases and particulate matter. This algorithm generates and visualizes interpolations across underground mine footprints with minimal delays with the preprocessing of spatial data, with output as two-dimensional or three-dimensional formats. NeaRTIMe is being validated against personal monitoring methods and network sensors to measure the deviation between NeaRTIMe values and measured values.
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Copyright (c) 2024 Kate Willa Brown Requist, Eric Lutz, Moe Momayez
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