An experimental study on the synergic damage of chemical solutions and stress to coal

Changes in the microstructure of coal

Scattering vector-scattering intensity relationship and deviation calibration.

The SAXS system produces two-dimensional scattering images for characterizing the porosity features in coal (Fig. 4). From these images, it can be seen that there are slight differences in the scattering rings of different samples, indicating that there is a disparity in the scattering intensity between different coal samples. With these images, one-dimensional relative scattering intensity data and effective scattering vector q can be further obtained with the Fit2d software²⁴ and S software²². Figure 5 shows the one-dimensional relative scattering intensity of the coal samples. The highest scattering intensity was in the central part of the detector, decreasing as the distance from the center increases. A schematic diagram of a typical SAXS experiment is shown in Fig. 2. The quantitative relationship between the scattering vector q and the scattering angle θ is as follows²⁵:

where q is the scattering vector (nm^-1), λ is the incident wavelength with a value of 1.54 Å, θ is the scattering angle.

Original data of relative scattering intensity of coal samples.

The effective scattering vector q value range was determined to be 0.00805 nm^-1 < q < 1.6452 nm^-1. The quantitative relationship between the scattering vector q value and the corresponding pore size d conforms to the Bragg equation (as shown in Eq. 14)²⁶, calculating the main observed pore size range for this experiment as 4 nm < d < 78 nm.

The fractal geometry principles were used to simplify the complex and disordered scattering pores in coal into spherical structures. The relative scattering intensity I(q) can be calculated using the following integral formula^22,27,28,29. The relative and absolute scattering data of the experimental coal samples are shown in Figs. 5 and 6.

where I(q) is the relative scattering intensity (a.u.), C is the comprehensive constant, r represents the radius of the simplified individual pore (nm), D_V(r) is the distribution function with respect to pore diameter r, \(\varsigma (q,r)\) is the scattering form factor, the calculation method for \(\varsigma (q,r)\) is as follows²⁷:

Relationship curve between scattering vector q and absolute scattering intensity I(q) of coal samples.

There are several different methods to calculate the pore size distribution of coal through Eq. (2). This paper uses the Carlo method³⁰, which does not require an assumption of the type of distribution function.

According to Porod’s Law³¹, for an ideal two-phase system, the two phases have uniform but different electron density distributions with a clear interface. The porous theory mainly describes the relative relationship between scattering intensity and scattering angle. When there is no clear interface between the two phases, the scattering intensity of the test sample will exhibit a positive/negative deviation³². However, coal, as a non-ideal material, has inhomogeneous electron density between its internal matrix and pores. Fluctuations in electron density during the experiment cause additional scattering, leading to a positive slope (porous positive deviation phenomenon) in the porous curve at high q-value angles. Therefore, it is necessary to calibrate for these deviations. The specific scattering intensity deviation correction formula is as follows.

where K is the Porod constant.

According to Fig. 6, which shows the relationship between the effective scattering vector and the absolute scattering intensity of the three coal samples, it can be seen: in the low q-values range, the scattering intensity relationship of the three types of coal samples is I(q)(A) > I(q)(B) > I(q)(C). In the high q-values range, I(q)(C) > I(q)(B) > I(q)(A).

The results indicate that acidic and alkaline solution treatments have a significantly different impact on the scattering intensity of coal. At larger pore scales, the acidic solution results in a greater difference in electron density compared to the other two treatments. However, at smaller pore scales, the alkaline solution leads to a greater difference in electron density. This difference suggests that the porosity, chemical composition, and surface characteristics of coal responded differently to acidic and alkaline solutions, thus leading to differing scattering intensity. However, the specific changes require further analysis. Figure 7 displays the porosity deviations of the three samples and their calibration curves. It can be observed that there are regions of uneven electron density between the pores and solid particles in the coal samples, leading to the coal exhibiting a non-ideal two-phase system of solids and gases.

Porod deviation and calibrated curves.

Changes in pore size distribution

Coal is a complex porous material composed of a matrix and internal pores and fissures. Based on SAXS experiments, the porosity of coal can be calculated using the following equation³³:

where r_e is the Thomson electron radius, approximately equal to 2.8179 × 10^–13 cm, Δp_e is the electron density difference between the solid matrix and the pore of the scattering body (e/ų), p is the porosity of the scattering body (%), \(\left( \partial \Sigma/\partial \Omega\right)\left( q \right)\) is the absolute scattering intensity of the scattering body (cm^-1). The absolute scattering intensity and the relative scattering intensity can be calculated using the following equation³⁴:

where A is the irradiated area of the sample (cm²), ΔΩ is the solid angle corresponding to a single pixel on the detector, η is the efficiency of the detector, T is the transmission rate of the sample, D is the thickness of the scattering body being measured (cm).

The pore size distribution in coal can be calculated using the maximum entropy probability method^1,35. Figure 8 shows the pore size distribution of nanopores in coal after treatment with distilled water, HCl solution, and NaOH solution. It can be seen that the pore size distributions of the B and C samples show a unimodal distribution. The peak of the former is higher than that of the latter, and its post-peak decline is faster. The pore size distribution of B shows a bimodal distribution, with a rapid decline after the second peak. Figure 9 shows the distribution of nanopores in different pore size ranges after treatment with the three solutions, excluding the nanopores distributed in the 1 ~ 10 nm and 80 ~ 100 nm ranges due to their low quantity. It can be observed that nanopores in different size ranges have increased or decreased to varying degrees, indicating that acidic and alkaline solution treatments have affected the internal pore structure of coal.

The pore size distribution of coal after treatment with different solutions.

Statistical graph of the multi-pore size distribution of nanopores in coal after treatment with different solutions.

The pore size distribution of coal sample B (Fig. 8b) begins to increase at 8 nm, starts to rise rapidly at 11 nm, reaches its peak at 31 nm, then decreases swiftly, and drops to its minimum value around 74 nm. This indicates that the nanopores of this sample are mainly distributed between 11 and 74 nm, but there are more nanopores distributed near a pore size of 31 nm. The pore size distribution of coal sample A (Fig. 8c) begins to increase at 5 nm, starts growing at 9 nm, reaches the first peak at 27 nm, then decreases, attains a second peak at 51 nm, followed by a rapid decline, and drops to its minimum value around 84 nm. This indicates that the nanopores of this sample are primarily distributed between 5 and 84 nm, but there are more nanopores distributed near pore sizes of 27 nm and 51 nm. The pore size distribution of coal sample C (Fig. 8d) begins to increase at 3 nm, starts to grow rapidly at 9 nm, reaches its peak at 30 nm, then decreases swiftly, and drops to its minimum value around 77 nm. This indicates that the nanopores of this sample are mainly distributed between 3 and 77 nm, but there are more nanopores distributed near a pore size of 30 nm.

From the Fig. 9, it can be seen that a large number of new nanopores structures appear in coal after treatment with acidic and alkaline solutions, and there are significant changes in the distribution of existing nanopores in the 20 ~ 70 nm pore size range. After soaking in acidic solution, the distribution of nanopores in the coal samples gradually decreases in the 20 ~ 50 nm pore size range, and increases in the 50 ~ 80 nm range. After soaking in alkaline solution, the distribution of nanopores in the coal samples mainly increases in the 10 ~ 30 nm and 50 ~ 80 nm pore size ranges, while decreasing in the 30 ~ 50 nm range. Combining the experimental results from Figs. 8 and 9 indicates that soaking in chemical solutions with different pH values causes varying degrees of change in the nanopore structure in coal, leading to different levels of changes in the internal pore distribution across various pore sizes in the coal.

Changes in the mechanical properties of coal samples

The changes in the microstructure of coal influence its mechanical properties^16,36. In the mechanical experiments, the stress–strain curves of the coal samples under loading and the corresponding AE characteristics were obtained, as shown in Figs. 10 and 11. It is observed that the time required for the compaction phase of coal samples follows the trend C > A > B > Raw coal, with samples soaked in NaOH and HCl solutions exhibiting longer compaction times compared to those soaked in distilled water and the untreated raw coal. The uniaxial compressive strength of the coal samples shows a trend of Raw coal > B > A > C. After soaking in distilled water, NaOH solution, and HCl solution for 7 days, the uniaxial compressive strength of the coal samples decreased by 24.39%, 39.19%, and 47.26%, respectively. This indicates that alkaline solutions have the largest weakening effect on coal, followed by acidic solutions and distilled water. Statistical analysis using one-way ANOVA revealed significant differences between the groups (p < 0.05), confirming that the type of solution has a notable impact on the weakening of coal strength. These differences were further supported by standard deviation and variance analysis, which demonstrated the reliability of the compressive strength reduction trends.

Stress–strain curve of coal samples.

AE response characteristics of coal under uniaxial compression conditions.

From Fig. 11, it can be seen that for the coal samples soaked in distilled water (Fig. 11c), the AE ringing count shows multiple peaks as the stress increases, indicating sudden structural changes or rapid crack propagation in the coal at corresponding stress levels. For the coal samples soaked in HCl solution (Fig. 11b), the AE ringing count reaches a peak in the early stages of the stress–strain curve. This indicates that even under lower stress levels, the acidic solution has already caused changes in the microstructure of the coal, making it more brittle. In the experiment, coal samples soaked in NaOH solution exhibited a lower AE ringing count, which may be due to the formation of numerous defects inside the coal samples during the soaking process. This is consistent with the results of the SAXS experiment mentioned earlier, where these defects are compacted in the initial stage of stress application. However, as the stress increases, these defects begin to connect, leading to a sharp rise in the stress–strain curve at high strain stages (Fig. 11d). This suggests that coal samples soaked in alkaline solution may suddenly become unstable and rapidly fail after reaching a certain level of strain. The slow increase in the cumulative ringing count also reflects the process of gradual accumulation of what leading to sudden release. The AE ringing count data were statistically analyzed, revealing significant differences in AE activity patterns among the groups. Coal samples soaked in alkaline solution exhibited a significantly lower average ringing count during the early stages of loading compared to those soaked in distilled water and acidic solutions, with p-values < 0.05.

In summary, coal samples soaked in distilled water exhibit typical mechanical and AE characteristics. Samples soaked in acidic solution show early structural weakening, while those soaked in alkaline solution, despite a lower frequency of AE activity initially, may ultimately fail suddenly due to the accumulation of internal defects. The differences in compressive strength among the groups are attributed to the distinct chemical interactions between the coal and the solutions. Alkaline solutions promote the propagation and connection of microcracks, resulting in more severe structural damage under stress. Acidic solutions, on the other hand, primarily dissolve mineral components, increasing brittleness at lower stress levels. Distilled water causes less significant changes, maintaining relatively stable mechanical characteristics. The findings highlight the risks associated with coal soaked in alkaline solutions, where the accumulation of internal defects can lead to sudden failure, posing significant challenges in engineering applications. This underscores the need for careful monitoring and preventive measures in coal mining operations involving chemical interactions.