Coal dust is a fugitive and combustible pollutant leading to risks of both explosion and air pollution. Dust explosions are one of the most serious potential disasters in coal mining operations. A substantial literature on coal mine safety covers issues related to the origin, distribution, and transport of coal dust. This paper conducted a review of recent theoretical and experimental literature that are related to the current status and progress in coal dust management strategies. Empirical models and simulations have been used to investigate how dust concentration and particle sizes impact dust propagation in areas prone to dust production, such as mechanised mining faces. The key characteristics of coal dust that are vital for their generation, dispersion, and associated risks are summarised. Better understanding of these characteristics can lead to more effective control in coal dust and hence improved health outcomes for workers and reduced risks at an industry-wide level. Some gaps in the research literature were identified, such as those regarding dust-settling and dedusting. Current research efforts are primarily concentrated on systems, such as water injection, spray dust suppression, ventilation, and dust collector. The advantages and limits of different methods for dust management are compared. Biological dedusting, a novel technology that is still in its early stage, was also reviewed. The paper also discussed potential areas of future research that were needed. 

Recent progress and perspectives on coal dust sources, transport, hazards, and controls in underground mines

Innovative and environmentally friendly MICP surface curing: enhancing mechanical and durability properties of concrete

A new method for extracting the Brillouin frequency shift (BFS) from the Brillouin gain spectrum (BGS), the modified backward correlation method (MBWC), is presented. The possibilities of using MBWC, and MBWC in combination with the Lorentzian curve fitting (LCF) based on Levenberg–Marquardt (LM) method, are studied. The effectiveness of the new method, and its combination with LM, has been demonstrated for processing spectra with a low signal-to-noise ratio (SNR). The experiments, which were in good agreement with the performed simulation, showed that at SNR = 0 dB, the combined (MBWC + LM) method provided the BFS extraction error of less than 4 MHz, while the state-of-the-art LM algorithm extracted it with the error greater than 4.5 MHz. The advantage of correlation methods becomes more significant with the decreasing SNR: at SNR = −2 dB, the LM’s error is 14.3 MHz, and that of the combined one is 8.1 MHz.

/pdf/a-refinement-of-backward-correlation-technique-for-precise-hal2mf5s.pdf

A Refinement of Backward Correlation Technique for Precise Brillouin Frequency Shift Extraction

Brillouin optical time-domain reflectometry (BOTDR) is widely used for strain and temperature measurements in various fields. However, the accuracy and reliability of the measurements are often limited by the noise in the sensor signals. Dynamic measurement of BOTDR requires small averaging number and fast measurement, and hence noise reduction is more significant in dynamic measurement. Small gain stimulated Brillouin scattering (SBS) can enhance the Brillouin signal power in BOTDR to realize dynamic measurement, but noise reduction is still important in system. In this work, we investigate the denoising of Brillouin gain spectrum (BGS) images using convolutional neural networks (DnCNN) to improve the accuracy of the small gain SBS STFT-BOTDR measurement of strain vibration. It is shown that the denoising of BGS images along the time axis can result in better detection of the strain vibration compared with denoising of BGS images along the fiber length. The denoising performance was evaluated using frequency uncertainties and R-squared values. The best denoising performance was achieved with a DnCNN network with 8 layers and 200 epochs, leading to a frequency uncertainty of 2.32 MHz and an R-squared value of 0.907. The frequency uncertainty is improved to about 45% of the original value.

Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR

Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.

Fast Feature Extraction Method for Brillouin Scattering Spectrum of OPGW Optical Cable Based on BOTDR

The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs.

/pdf/denoising-of-botdr-dynamic-strain-measurement-using-18gz40fr.pdf

Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks

Aggregate is an indispensable raw material for concrete. Microbial induced carbonate precipitation (MICP) is an environmentally friendly aggregate modification method, which could modify recycled aggregate (RA) to replace natural aggregate for the preparation of concrete in the future. The urea hydrolysis is the key to modified RA by MICP. Biological calcium carbonate is generated during mineralization reaction in modification solution to fill the weak part of aggregate, which will enhance the performance of RA. The bacterial solution and urea solution, as the sources of CO32- in biological CaCO3, jointly affect the modification effect of RA. In this paper, the different ratios of bacterial solution concentration (×108 cells/mL) to urea concentration (mg urea/mL) are used for exploring the improvement effect and the cooperative enhancement mechanism of recycled coarse aggregate (4.75 mm-9.5 mm). The results show that the bacterial mineralization efficiency is most improved when bacterial solution concentration/urea concentration (B/U) is in the optimal interval of 5–6 because 1 mg urea provides a moderate number of bacteria during the modification period. In the interval, the precipitation amount of biological CaCO3 and effective CaCO3 that could modify RA is larger, and the pores of the attached mortar of RA are better filled and blocked. Therefore, the macro performance of RA is greatly improved in the internal. When the B/U is outside the optimal interval, the modification effect is poor because the amount of biological CaCO3 and effective CaCO3 is too less to fill pores or too more to enter pores. 

Evaluation of urea hydrolysis for MICP technique applied in recycled aggregate: Concentration of urea and bacterial spores

Microbially induced calcium carbonate precipitation (MICP) has attracted significant attentions in recent years. In the realm of MICP advancement, bioaugmentation and biostimulation have been adopted. Biostimulation presents a potentially more cost-effective and sustainable avenue in contrast to the exogenous introduction of microbes under certain circumstances. Within the scope of the current investigation, a series of elemental-scale sand column batch tests were conducted under diverse treatment recipes (i.e., concentration of enrichment media and cementation solution), aiming to calibrate the varying apparent ureolysis kinetics parameters (i.e., the half saturation constant Km and the maximum ureolytic activity vmax) during the biostimulated MICP treatment under the combined actions of flushing and encapsulation, and the varying apparent ureolytic kinetics parameters were incorporated into modelling for the first time. The proposed model was then validated by average cementation content of the biostimulated treated sand columns. The simulated results are consistent with the data measured in terms of average cementation content after different treatment cycles, indicating that the proposed model is feasible to simulate the kinetically controlled biostimulated MICP process. Additionally, a comprehensive parametric sensitivity analysis is undertaken to evaluate the influence of some selected key parameters on the average content of cementation and its spatial uniformity. 

Soil improvement using biostimulated MICP: Mechanical and biochemical experiments, reactive transport modelling, and parametric analysis

Recycled use of dredged sludge for river bank slope protection: Role of water adsorbents

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