Cryogenic Air Supply Feasibility for a Confined Space: Underground Refuge Alternative Case Study

A breathable air source is required for a confined space such as an underground refuge alternative (RA) when it is occupied. To minimize the risk of suffocation, federal regulations require that mechanisms be provided and procedures be included so that, within the refuge alternative, the oxygen concentration is maintained at levels between 18.5% and 23% for 96 h. The regulation also requires that, during use of the RA, the concentration of carbon dioxide should not exceed 1%, and the concentration of carbon monoxide should not exceed 25 ppm. The National Institute for Occupational Safety and Health (NIOSH) evaluated the cryogenic air supply's ability to provide breathable air for a refuge alternative. A propane smoker was used to simulate human breathing by burning propane gas which will consume O2 and generate CO2 and H2O. The rate of propane burned at the smoker was controlled to represent the O2 consumption rate for the breathing of a certain number of people. Two 96-h tests were conducted in a sealed shipping container, which was used as a surrogate for a refuge alternative. While burning propane gas to simulate human oxygen consumption, cryogenic air was provided to the shipping container to determine if the cryogenic air supply would keep the O2 level above 18.5% and CO2 level below 1% inside the shipping container as required by the federal regulations pertaining to refuge alternatives. Both of the 96-h tests simulated the breathing of 21 persons. The first test used the oxygen consumption rate (1.32 cu ft of pure oxygen per hour per person) specified in federal regulations, while the second test used the oxygen consumption rate specified by (Bernard et al. 2018, "Estimation of Metabolic Heat Input for Refuge Alternative Thermal Testing and Simulation," Min. Eng., 70(8), pp. 50-54) (0.67 cu ft of pure oxygen per hour per person). The test data shows that during both 96-h tests, the oxygen level was maintained within a 21-23% range, and the CO2 level was maintained below 1% (0.2-0.45%). The information in this paper could be useful when applying a cryogenic air supply as a breathable air source for an underground refuge alternative or other confined space. [DOI: 10.1115/1.4064062].

Mathematical Modeling for Carbon Dioxide Level Within Confined Spaces

Federal regulations require refuge alternatives (RAs) in underground coal mines to provide a life-sustaining environment for miners trapped underground when escape is impossible. A breathable air supply is among those requirements. For built-in-place (BIP) RAs, a borehole air supply (BAS) is commonly used to supply fresh air from the surface. Federal regulations require that such a BAS must supply fresh air at 12.5 cfm or more per person to maintain the oxygen concentration between 18.5% and 23% and carbon dioxide level below the 1% limit specified. However, it is unclear whether 12.5 cfm is indeed needed to maintain this carbon dioxide level. The minimal fresh air flow (FAF) rate needed to maintain the 1% CO2 level will depend on multiple factors, including the number of people and the volume of the BIP RA. In the past, to predict the interior CO2 concentration in an occupied RA, 96-h tests were performed using a physical human breathing simulator. However, given the infinite possibility of the combinations (number of people, size of the BIP RA), it would be impractical to fully investigate the range of parameters that can affect the CO2 concentration using physical tests. In this paper, researchers at the National Institute for Occupational Safety and Health (NIOSH) developed a model that can predict how the %CO2 in an occupied confined space changes with time given the number of occupants and the FAF rate. The model was then compared to and validated with test data. The benchmarked model can be used to predict the %CO2 for any number of people and FAF rate without conducting a 96-h test. The methodology used in this model can also be used to estimate other gas levels within a confined space.

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic combined prediction method of gas concentration. Spearman's rank correlation coefficient is applied for the dynamic optimization of prediction indicators. The time series and spatial topology features of the optimized indicators are extracted and input into the combined prediction model of gas concentration based on indicators dynamic optimization and Bi-LSTMs (Bi-directional Long Short-term Memory), which can predict the gas concentration for the next 30 min. The results show that the other gas concentration, temperature, and humidity indicators are strongly correlated with the gas concentration to be predicted, and Spearman's rank correlation coefficient is up to 0.92 at most. The average R² of predicted value and real value is 0.965, and the average prediction efficiency R for gas abnormal or normal emission is 79.9%. Compared with the other models, the proposed dynamic optimized indicators combined model is more accurate, and the missing alarm of gas abnormal emission is significantly alleviated, which greatly improves the early alarming accuracy. It can assist the safety monitoring personnel in decision making and has certain significance to improve the safety production efficiency of coal mines.

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.

Thermal Boundary Layer Effects on Line-of-Sight Tunable Diode Laser Absorption Spectroscopy (TDLAS) Gas Concentration Measurements

The effects of thermal boundary layers on tunable diode laser absorption spectroscopy (TDLAS) measurement results must be quantified when using the line-of-sight (LOS) TDLAS under conditions with spatial temperature gradient. In this paper, a new methodology based on spectral simulation is presented quantifying the LOS TDLAS measurement deviation under conditions with thermal boundary layers. The effects of different temperature gradients and thermal boundary layer thickness on spectral collisional widths and gas concentration measurements are quantified. A CO₂ TDLAS spectrometer, which has two gas cells to generate the spatial temperature gradients, was employed to validate the simulation results. The measured deviations and LOS averaged collisional widths are in very good agreement with the simulated results for conditions with different temperature gradients. We demonstrate quantification of thermal boundary layers' thickness with proposed method by exploitation of the LOS averaged the collisional width of the path-integrated spectrum.

Gas Measurement Using Static Fourier Transform Infrared Spectrometers

Online monitoring of gases in industrial processes is an ambitious task due to adverse conditions such as mechanical vibrations and temperature fluctuations. Whereas conventional Fourier transform infrared (FTIR) spectrometers use rather complex optical and mechanical designs to ensure stable operation, static FTIR spectrometers do not require moving parts and thus offer inherent stability at comparatively low costs. Therefore, we present a novel, compact gas measurement system using a static single-mirror Fourier transform spectrometer (sSMFTS). The system works in the mid-infrared range from 650 cm−1 to 1250 cm−1 and can be operated with a customized White cell, yielding optical path lengths of up to 120 cm for highly sensitive quantification of gas concentrations. To validate the system, we measure different concentrations of 1,1,1,2-Tetrafluoroethane (R134a) and perform a PLS regression analysis of the acquired infrared spectra. Thereby, the measured absorption spectra show good agreement with reference data. Since the system additionally permits measurement rates of up to 200 Hz and high signal-to-noise ratios, an application in process analysis appears promising.