*4.2. Risk Assessment of Diabetes and its Complications*

A crucial principle in the management of type II diabetes is to retard the progression of the chronic disease as well as to prevent the onset of complications resulting from the condition. Many barriers to the optimal detection of the progression include patients defaulting appointments, non-compliance to medical advice, and financial barriers that include treatment costs and costs of a healthy diet [18]. Therefore, the development of AI in such a setting is beneficial to both doctors and patients as doctors will be able to foretell the course of the disease to individualize and cater to the proper management of the patient based on their risk.

A novel method of achieving the above objective is noted in a paper that studies the application of an artificial neural network model that aims to diagnose type II diabetes mellitus and establishes the relative importance of risk factors. The study was conducted on a cohort of 234 people who were diagnosed with type II diabetes mellitus using glycated hemoglobin levels. A multilayer perceptron artificial neural network that was utilized to highlight the demographic risk factors revealed that risk factors such as age, hypertension, waist circumference, body mass index, sedentary lifestyle, etc. were predictors of type II diabetes. However, the final analysis showed that the most important predictors and risk assessment of diabetes type II were waist circumference, age, BMI, hypertension, stress, smoking, and positive family history of type II diabetes [19].

These risk assessment models developed with the assistance of AI help to improve the detection/screening of diabetes type II by enabling the optimization of health resources for people who are known to be high risk, compared to screening the entire population at large. This not only improves the cost at a population level, but also the landscape of screening for chronic diseases such as diabetes.


*4.6.6 Siamese grain beetle (*Lophocateres pusillus *(Klug, 1832) Coleoptera: Trogositidae)*

*Distribution*: Siamese grain beetle is found in all regions where paddy and different kinds of cereals are cultivated.

*Host range*: The main host is rice and cereals.

*Bionomics:* The Siamese grain beetle is reddish-brown, flattened, elongate, long beetle with a flattened margin of the thorax and wing cover. These are making holes in the rice and hence damage its quality as well as quantity.

*Damage symptoms*: Adults are seen actively walking on and around the food grains.


In terms of the characterization of "failure" and "post-peak" stages in the dynamic failure process, the model results have certain differences from the laboratory test results. The model can be utilized to conduct research on the dynamic damage state of limestone under the action of confining pressure after high-temperature treatment. A laboratory experiment should be carried out with respect to the change features of the "post-peak" stage.

#### **4. Conclusions**

A high-temperature SHPB system was adopted to conduct dynamic compression tests on a limestone specimen under different confining pressures after being heated to different temperatures and naturally cooled to room temperature. Research on the SHPB test and dynamic damage constitutive model of limestone under the action of both confining pressure and temperature was carried out. The major conclusions are as follows:

The uniaxial dynamic stress–strain curve of limestone subjected to high temperatures has an almost identical changing pattern to the one at room temperature. The temperature can significantly impact the dynamic compressive strength of limestone, with a trend of first strengthening and then attenuating as the temperature rises.

After applying the confining pressure, the dynamic stress–strain curve of the limestone showed a consistent tendency with that in the uniaxial state. Through the limitation of lateral deformation, the confining pressure increased the dynamic compressive strength of the limestone. It also intensified the impact of temperature on the dynamic mechanical properties of limestone after high-temperature treatment. With the increase in confining pressure, the dynamic compressive strength also increased, reaching a maximum value at a temperature of 400 ◦C and confining pressure of 2.5 MPa.

A dynamic damage constitutive equation of limestone under the action of confining pressure after high-temperature treatment was established. According to comparisons with the experimental results, the model can reflect the relation among temperature, dynamic strength, strain, confining pressure, and strain rate under the action of confining pressure on the heated limestone.

**Author Contributions:** L.L. and W.S. designed the experiments; R.L. and H.Q. carried out the experiments; L.L. analyzed the experimental results. R.L. analyzed data and developed the analysis tools. W.S. and L.L. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (grant nos. 11862010 and 51964023).

**Data Availability Statement:** The data used to support the findings of this study are included within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Structural Parameter Optimization for Large Spacing Sublevel Caving in Chengchao Iron Mine**

**Yuye Tan 1,2,\*, Mochuan Guo 1,2,\*, Yimin Hao 1,2, Chi Zhang 1,2 and Weidong Song 1,2**

1 School of Civil and Resource Engineering, University of Science and Technology Beijing,

**Abstract:** Non-pillar sublevel caving is beginning to use large structural parameters in China. Appropriate structural parameters can effectively control the loss and dilution of stope and improve ore drawing efficiency. In this study, taking Chengchao Iron Mine as the engineering background, a theoretical calculation, a numerical simulation, and physical similarity experiments were combined to optimize sublevel height, production drift spacing, and drawing space. The optimal structural parameter range, based on the ellipsoid ore drawing theory, was obtained as a theoretical reference for subsequent studies. A "two-step" strategy was used, in which PFC2D software (Itasca Consulting Group, Minneapolis, MN, USA) was used to numerically simulate 20 groups of different sublevel heights and production drift spacing parameters were used to determine the appropriate sublevel height and production drift spacing for the project. Subsequently, the optimization of the ore drawing space was studied using PFC3D (Itasca Consulting Group, Minneapolis, MN, USA) particle unit software, numerical simulation analysis, and similar physical experiments. The results showed that safe and efficient mining can be achieved when the structural parameters of the stope are 17.5 m sublevel height, 20 m production drift spacing, and 6 m drawing space. The findings of this study can further the goal of green and efficient mining, and provide a theoretical reference for the popularization and application of pillarless sublevel caving with large structural parameters at home and abroad. It is an effective measure for the green mining of caving mines.

**Keywords:** sublevel caving; numerical simulation; physical model; structural parameter; green mining

#### **1. Introduction**

Non-pillar sublevel caving offers the advantages of simple operation, high-intensity mining, high mechanization degree, safety and reliability, and relatively low mining costs [1–4]. Therefore, it has been widely used in ore mining at home and abroad [5]. The purpose of mining is to discharge ore under the overburden, leading to a large loss coefficient and dilution ratio [6,7]. Many scholars at home and abroad have conducted in-depth research on reducing loss and dilution. They have successively proposed various ore drawing theories, including ore drawing ellipsoidal and stochastic medium drawing theories [8]. These theories have been widely applied to guide the production of mines in sublevel caving [9].

The three important parameters used in sublevel caving are sublevel height, production drift spacing, and drawing space. Several researchers have studied the adjustment and optimization of mine parameters and mining management to control the recovery effect. Kvapil, R., et al. [10] optimized structural parameters through laboratory experiments on similar materials. Janelle, I. and Kvapil, R. [11] performed an experimental field study on a prototype size based on tracer recovery. David, J., et al. [12] conducted a numerical simulation experiment based on ore drawing theory. These studies focused on the flow principle of ore and rock particles [13]. For the optimization principle of structural parameters, the

**Citation:** Tan, Y.; Guo, M.; Hao, Y.; Zhang, C.; Song, W. Structural Parameter Optimization for Large Spacing Sublevel Caving in Chengchao Iron Mine. *Metals* **2021**, *11*, 1619. https://doi.org/10.3390/ met11101619

Academic Editor: Lijie Guo

Received: 14 September 2021 Accepted: 5 October 2021 Published: 12 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

difference between recovery and dilution rates in the actual production of mines is used as the criterion for determining the optimal structural parameters. In particular, high recovery and low dilution rates indicate large differences and a good recovery effect [14].

Because the structural parameters of the sublevel caving mining method can directly affect the ore recovery ratio and recovery efficiency, and then affect the overall technical and economic indicators and benefits of the mine, many scholars use different methods to optimize the structural parameters of sublevel caving stope [15,16]. To determine the appropriate stope structural parameters for the sublevel caving method, Wu et al. [17] compared and analyzed nine different stope structural parameters by laboratory test and computer numerical simulation. Zhao et al. [18] obtained the optimal structural parameters of an iron ore stope through a single-ore drawing experiment and MATLAB software (MathWorks, Natick, MA, USA) fitting analysis, combined with industrial eccentricity. Using the three ore drawing methods, the no-dilution caving method, the low-dilution caving method, the and the current cutoff caving method under large structural parameters, similar simulation experiments and PFC2D (Particle Flow Code 2D) (Itasca Consulting Group, Minneapolis, MN, USA) are used to simulate ore recovery and rock mixing. The optimal caving step of the three ore drawing methods was obtained by Jin et al. [19]. An improved flow diversion drawing technology can reduce the ore loss rate and dilution ratio [20].

In this study, the exploitation of the Chengchao Iron Mine through sublevel caving without a sill pillar was used as the engineering background. The range of optimal structure parameters was calculated based on ellipsoid drawing theory and the intersection degree of the drawn-out orebody, and a "two-step" strategy was adopted. PFC2D software (Itasca Consulting Group, Minneapolis, MN, USA) simulated the sublevel caving with different sublevel heights and production drift spacing to determine the optimal solution while maintaining the existing drawing space. Next, PFC3D three-dimensional discrete element software (Itasca Consulting Group, Minneapolis, MN, USA) was used to adopt the finite difference method. Combined with the physical ore drawing similar simulation experiment [21,22] and theoretical calculation results with the experimental model ratio of 1:100, and considering the relationship between drawing step and ore loss and dilution, the optimal drawing step was determined by mutual verification from the numerical simulation, physical experiment, and theoretical analysis [23].

#### **2. Theoretical Calculation of Optimal Structural Parameters**

At present, the generally accepted criterion for determining the optimal structural parameters of sublevel caving without a sill pillar is that "the morphology of the caving body should be consistent with that of the drawn-out ore body". Jin et al. [24] modified this criterion, where the optimization of structural parameters refers to optimizing the spatial arrangement of the drawn-out ore body, and the degree of compaction is the best. Theoretically, the drawn-out ore body can be divided into two equivalent and optimal arrangements: high sublevel and large space. The arrangement of large space is advantageous [25] considering mines' current technical equipment conditions in China, as shown in Figure 1.

The relationship between sublevel height and production drift spacing is expressed as:

$$\frac{\mathbf{H}}{\mathbf{B}} = \frac{\sqrt{3}}{6} \frac{\mathbf{a}}{\mathbf{b}} \tag{1}$$

$$\frac{\mathbf{L}}{\mathbf{H}} = \frac{(1 + \sin \theta)(\mathbf{c} + \cos \theta)}{2\mathbf{a}} \tag{2}$$

In this equation, H is the sublevel height, m; B is the production drift spacing, m; a is the long axis of the discharged ellipsoid, m; b is the short axis of the discharged ellipsoid, m; L is the step distance of the ore drawing, m; θ is the included angle (2◦–4◦, θ = 2◦) between the long axis of the quasi-ellipsoid and the end wall; c is the short semiaxis of the quasi-ellipsoid in the direction of vertical access, m.

**Figure 1.** Arrangement of large space of discharged ellipsoid.

In actual ore drawing, the morphology change of the drawn-out ore body is dynamic, and its actual morphology is close to that of Figure 2. Chengchao Iron Mine adopts low dilution ore drawing of sublevel caving without a sill pillar. The optimal structural parameters [26] can be theoretically determined, including the sublevel height, production drift spacing, and the interval of caved ore, based on the geometric relation of the optimal spatial arrangement of the discharged ellipsoid.

**Figure 2.** The ellipsoid arrangement is released in the direction of the vertical access.

According to the drawing theory, the drawn-out ore body is a quasi-ellipsoid, and its size can be determined by the a, b, and c axes of the ellipsoid. The ellipsoidal arrangement in Figure 2 allows as much ore to be recovered as possible and can be expressed in the vertical direction as follows:

$$\mathbf{B} = \mathbf{2c} + \mathbf{p} \tag{3}$$

$$\mathbf{H}\_{\mathbf{f}} = 2\mathbf{H} = 2\mathbf{a} \tag{4}$$

In this equation, Hf is the ellipsoid discharge height, m; p is the access width, m. The caving step L can be determined through geometric analysis, using the following equation:

$$\mathbf{L} = \mathbf{b}\cos\theta + \mathbf{a}\sin\theta \tag{5}$$

The eccentricity of the ellipsoid is expressed as:

$$\mathbf{b} = \mathbf{a}\sqrt{\mathbf{1} - \varepsilon\_{\mathbf{b}}^{2}}\tag{6}$$

$$\mathbf{c} = \mathbf{a}\sqrt{1 - \varepsilon\_{\mathbf{c}}}^2 \tag{7}$$

According to the actual situation of the mine, at the level of −500~−570 m, the sublevel height H is 17.5 m, that is, a = 17.5 m, and the access width p = 3.8 m, through the industrial experiment eccentricity, εb = 0.95, εc = 0.90. These values are substituted in Equations (6) and (7) to yield b = 5.46 m and c = 7.62 m. Next, the production drift spacing B1 is 18.91 m by substituting the values of a and b into Equation (1). Production drift spacing B2 is 19.04 m by substituting the values of b and c into Equation (3). Thus, the theoretical production drift spacing ranges from 18.91 m to 19.04 m. The interval of caved ore L1 is 4.46 m by substituting a, c, and θ into Equation (2), and the interval of caved ore L2 is 6.07 m by substituting a, b, and θ into Equation (5). Therefore, the interval of caved ore ranges is 4.46~6.07 m.

#### **3. PFC Numerical Simulation Study**

#### *3.1. Optimization of Sublevel Height and Production Drift Spacing*

#### 3.1.1. Simulation Scheme

Sublevel caving without a sill pillar was adopted for the mining stage −500~−570 m in Chengchao Iron Mine. The ore body was about 1500 m long and 296◦ in the overall strike; the direction of dip was 214◦, the amount of inclination was 0◦–45◦, and the average thickness was 72.95 m. The ore body mainly comprised marble, with good stability. The mine production scale was 3.4 million t/a, and the ore geological grade was 48%. The sublevel height was 17.5 m, and the production drift spacing was 15 m. Compared with the advanced level at home and abroad, the stope structure parameters can be optimized. Through PFC2D (Itasca Consulting Group, Minneapolis, MN, USA), the numerical simulation was carried out with sublevel heights of 17.5 m, 19 m, 21 m, 23.5 m, and 26 m, and production drift spacing of 15 m, 18 m, 20 m, and 22 m to study the influence of sublevel height and production drift spacing on ore recovery and dilution index.

In this study, the vertical production drift direction profile was selected to establish a calculation model. An ore drawing scheme, with a sublevel height of 21 m and production drift spacing of 20 m, was taken as an example, as shown in Figure 3a. The model's width was 60 m, its height was 126 m, the upper overburden was 42 m, the lower ore body was 84 m (four sublevels), and the edge angle of the middle and deep holes was 60◦. The equivalent radius R of the ore unit in the model was 200 mm, and the equivalent radius R of the overburden unit was 300 mm, using a volume equivalent method. Gravity ore dropping was adopted to make the corresponding collapsed ore move under the action of gravity.

**Figure 3.** Schematic of the model: (**a**) schematic of model size, m; (**b**) 2D numerical model diagram.

 The particle unit diagram of the 2D numerical simulation of the ore drawing scheme is shown in Figure 3b.

PFC2D modeling requires debugging micromechanical parameters. The micromechanical parameters assumed by the model are first assigned to conduct numerical tests and

then are matched with the macro test parameters obtained from laboratory tests to enable continuous debugging. These micromechanical parameters can be applied to the numerical calculation when the calculated results are consistent with the laboratory test results [26,27]. The size of the 3D ore drawing numerical model adopted in this study was consistent with the laboratory test, and the contact-stiffness model was adopted for the particle interaction model. The modeling parameters of this model mainly included ore and rock particle radius, normal stiffness, shear stiffness, friction coefficient, density, and color [28,29]. The micromechanical parameters in Table 1 are consistent with the macroscopic mechanical parameters after repeated debugging.

**Table 1.** Micromechanical parameters of the model.

Before ore drawing, the cutoff condition of simulated ore drawing was determined based on the principle of equal ore volume dilution ratio. The specific calculation process of the ore drawing cutoff condition is expressed as follows:

$$\mathbf{C}\_{\mathbf{i}} = \frac{\mathbf{C}\_{\mathbf{k}}}{\mathbf{W}} \times \mathbf{W}\_{\mathbf{k}} + \frac{\mathbf{C}\_{\mathbf{y}}}{\mathbf{W}} \times \mathbf{W}\_{\mathbf{y}} \tag{8}$$

$$\mathbf{W} = \mathbf{W}\_{\mathbf{k}} + \mathbf{W}\_{\mathbf{y}} \tag{9}$$

$$\mathbf{W\_{y}} = \mathbf{1.67W\_{k}} \tag{10}$$

In this equation, according to the actual situation of the mine, Ci is the cutoff ore grade, 18%; Ck is the ore geological grade, 48%; Cy is the surrounding rock grade, 0; Wk is the current ore weight; and Wy is the current rock weight.

The ore is mined by free falling. Equation (10) can be obtained from Equations (8) and (9), when the ratio of the waste rock mass to the ore mass reached 1.67, the ore was stopped. The drawing process with sectional height and route spacing of 19 m × 20 m was selected as an example, as shown in Figure 4.

**Figure 4.** Numerical simulation of ore drawing process: (**a**) subparagraph 1; (**b**) subparagraph 2; (**c**) subparagraph 3; (**d**) subparagraph 4.

#### 3.1.2. Analysis of Simulation Results

The ore drawing statistical results of 20 simulation schemes are shown in Table 2. From the statistical results of ore drawing in each sublevel, only the sublevel height × production drift spacing of 19 m × 20 m are presented as an example because of space limitations, as shown in Table 3.

**Table 2.** Statistical table of ore drawing results of each simulation scheme.

**Table 3.** Statistical data of 19 m × 20 m ore discharge results.

#### (1) Single-factor analysis

According to the numerical simulation drawing results, the single factor analysis of 20 schemes was carried out [30]. The difference between recovery and dilution ratio Y and sublevel height H and production drift spacing B is shown in Figure 5.

By analyzing the changing trend of the curve in Figure 5a, it can be seen that the difference between recovery and dilution ratio at each production drift spacing first increased and then decreased with the increase in sublevel height. The difference between recovery and dilution ratio when the production drift spacing was 20 m constantly remained the largest compared with other production drift spacings with the increase in sublevel height, indicating that the mining effect was optimal when the production drift spacing was 20 m, and the maximum value was 86.54% at the sublevel height of 19 m. The difference between recovery and dilution ratio increased from 85.16% to 86.54%, with a growth ratio of 1.62% and a small change when the production drift spacing was 20 m and the sublevel height increased from 17.5 m to 19 m, indicating that the values ranging from 17.5 m to 19 m were the appropriate sublevel heights.

By analyzing the changing trend of the curve in Figure 5b, it can be observed that the difference between recovery and dilution ratio at each sublevel height first increased and then decreased with the increase in production drift spacing. The difference between recovery and dilution ratio when the sublevel height was 19 m constantly remained the largest compared with other sublevel heights with the increase in production drift spacing, indicating that the stope had the best mining effect when the sublevel height was 19 m.

**Figure 5.** Relationship of the difference between recovery and dilution ratio Y and sublevel height H and production drift spacing B: (**a**) relationship curve of sublevel height H and the difference between recovery and dilution ratio Y; (**b**) relationship curve of production drift spacing B and the difference between recovery and dilution ratio Y.

(2) Comprehensive analysis

Table 2 demonstrates a quadratic polynomial with MATLAB (MathWorks, Natick, MA, USA), and the results are shown in Figure 6. The vertex of the surface in the figure was at a sublevel height of approximately 19 m, and the production drift spacing was approximately 20 m. The quadratic function relation of the difference between recovery and dilution ratio Y concerning sublevel height H and production drift spacing B can be obtained through fitting analysis, which can be expressed as:

$$\text{Y} = -0.13\text{H}^2 - 0.157\text{B}^2 + 5.594\text{H} + 6.517\text{B} - 0.018\text{HB} - 36.172(\text{R}^2 = 0.8756) \tag{11}$$

**Figure 6.** Relation curve of the difference between recovery and dilution ratio Y and sublevel height H and production drift spacing B.

In this fitting function, partial derivatives of H and B were calculated, and the maximum difference between recovery and dilution ratio was 84.15% when H = 20.18 m and B = 19.61 m. The results of theoretical calculation, numerical simulation, and function prediction are shown in Table 4.

**Table 4.** Statistical table of the range of structural parameters obtained by each method.

Combined with the occurrence of ore bodies (Figure 7) ranging from −500 m to −570 m, the changes in size and morphology of the ore body were large with the increase in sublevel height and production drift spacing. The edge of the ore body was difficult to control, resulting in a decrease in the ore recovery ratio and an increase in the dilution ratio. The sublevel height of 17.5 m in the development stage was defined as the mine design and was difficult to adjust. Thus, the sublevel height was kept constant at 17.5 m. As shown in Table 4, the optimal production drift spacing ranged from 18.91 m to 20 m. The crossing space of the −500 m level in the east of Chengchao Iron Mine is 60 m. The crossing space should be an integral multiple of the production drift spacing. The production drift spacing was set to 20 m based on the ellipsoid drawing theory [31] to ensure that the production drift spacing of the upper and lower sublevels formed a diamond layout. In particular, the optimal structure parameters of the stope were 17.5 m of sublevel height and 20 m of production drift spacing.

**Figure 7.** Top view of 3D visual model of No. II, III, and IV ore bodies from −500 m to −570 m.

#### *3.2. Optimization of the Caving Step*

#### 3.2.1. Simulation Scheme

The interval of the caved ore of the stope was 3.0 m, and the caving step was approximately 4.0 m. For the simulation study, nine structural parameters, namely, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, and 7.0 m, were selected to determine the optimal caving step. The drawing model parameters are shown in Table 5, and the micromechanical parameters were the same as in Table 1.

Figure 8 shows the diagram of a single model of 17.5 m × 20 m. The actual drawing height was the sum of the sublevel and ridge heights formed through sublevel blasting. After the calculations, the actual drawing height was set to 33 m.

Figure 9 shows the initial diagram of the single model. The production drift direction is the *x*-axis, the vertical production drift is the *y*-axis, and the direction of the model height is the *z*-axis. The yellow particles are the overburden, the blue particles are the frontal waste rock, and the red particles are the ore. Under the action of the initial gravity field, the ore and rock flow from the lower ore discharge port to form recovery mining and stop when the cutoff grade is reached.

**Figure 8.** Single model design.

**Figure 9.** Initial diagram of the single model: (**a**) front view; (**b**) side view.

The cutoff grade of the 3D simulated ore drawing was the same as that of the 2D simulated ore drawing. Considering the model's limitations and shovel simulation, ore drawing in the three-dimensional single simulation experiment should be stopped when the mass ratio of waste rock and ore reaches 1.67 two consecutive times to ensure complete ore discharge.

#### 3.2.2. Analysis of Simulation Results

Figure 10 shows the displacements of nine simulation schemes. The experimental results of each simulation scheme are shown in Table 6.

As shown in Figure 10a, the height of the drawn-out ore body increased with the increase in the caving step. The drawn-out ore body remained constant when the caving step reached 5.0 m, indicating that the height of the drawn-out ore body remained unchanged. The overall shape resembled an ellipsoid when the caving step increased to a certain degree.

**Figure 10.** Schematic of the drawn-out ore body: (**a**) front view contrast of the drawn-out ore body; (**b**) side view contrast of the drawn-out ore body.

**Table 6.** Results of recovery indexes in the optimization simulation of caving step.

As shown in the analysis of Figure 10b, the thickness of the drawn-out ore body increased with the increase in the caving step. The thickness of red ore gradually increased, and the waste rock with the blue face slightly decreased. The body's morphology developed from "short and thick" to "long and thin" with the increase in the ore caving step.

As shown in Figure 10a,b, the morphology of the drawn-out ore body of each scheme resembled an ellipsoid, and the ore drawing process reflected the actual situation, indicating the rationality of the simulation study.

The ore recovery indexes, such as ore caving step, recovery ratio, dilution ratio, and the difference between recovery and dilution ratio, were obtained through sorting and summarizing. The data were fitted on MATLAB software (MathWorks, Natick, MA, USA), and the results are shown in Figures 11–13.

**Figure 11.** Relationship between ore recovery ratio and caving step.

**Figure 12.** Relationship between ore dilution ratio and caving step.

**Figure 13.** Relationship of the difference between recovery and dilution ratio Y and caving step.

#### (1) Recovery ratio

Figure 11 shows that the ore recovery ratio first increased and decreased with the increase in the caving step. The fitting curve results show that their relationship was quadratic, and the determination coefficient reached 0.96418. The recovery ratio was 40–60% when the caving step was 3–4 m, indicating that a large amount of waste rock in the front was mixed into the ore to intercept the normal outflow because of the small caving step, leading to premature dilution. The ore recovery ratio reached approximately 90% and remained unchanged when the ore drawing interval was 5–7 m. The recovery slightly declined when the caving step was 6 m, with an average reduction ratio of 1.55%. From the perspective of the fitting function, the recovery reached its maximum when the ore caving step was 6.3 m. Therefore, the recovery ratio showed that an ore caving step of 6–6.3 m was appropriate.

(2) Dilution ratio

As shown in Figure 12, the dilution ratio gradually decreased with the increase in caving step. The fitting curve results show that their relationship was quadratic, and the determination coefficient reached 0.98221. The dilution ratio was higher than 20% when the caving step was 3–3.5 m, indicating that a large amount of waste rock was mixed at this time, which would lead to an increase in mining production costs. The dilution ratio decreased to less than 10% when the caving step was 6–7 m, and the change was small. The average reduction ratio was 13.09%, indicating that the increase in waste rock mixing amount was less than that of the increase in total ore, leading to the decrease in dilution ratio. From the perspective of the fitting function, the dilution ratio was at its minimum value when the caving step was 15.71 m. Therefore, a caving step of 6–7 m was reasonable based on the dilution ratio.

(3) Difference between recovery and dilution ratio

The difference between recovery and dilution ratio is an important indicator for evaluating the effects of mining methods [32]. The greater the difference between the recovery and the dilution ratio, the better the economic effect. As shown in Figure 13, the difference between recovery and dilution ratio increased and decreased with the increase in the caving step. The fitting curve results show that their relationship was quadratic, and the determination coefficient reached 0.98284. The difference between recovery and dilution ratio gradually increased when the caving step was 3–6 m. The caving step was greater than 6 m, and the difference between recovery and dilution ratio showed a downward trend. From the perspective of fitting function, the difference between recovery and dilution ratio was at its maximum when the caving step was 6.63 m. Therefore, the appropriate range of the caving step was 6–6.63 m.

The appropriate range of the caving step of 6–6.3 m was obtained by considering the relationship between the recovery and the dilution ratio and the difference between the recovery ratio, the dilution ratio, and the caving step.

#### **4. Experimental Study on Physical Ore Drawing**

#### *4.1. Experimental Scheme*

In this experiment, the caving steps of 3.0, 4.0, 5.0, 6.0, and 7.0 m were taken under sublevel height and production drift spacing of 17.5 m × 20 m to conduct a simulation study of porous ore drawing. Appropriate suggestions were provided through thorough analysis and comparison of the experimental results.

The multi-segment three-dimensional ore drawing model was used in the experiment. According to the similarity theory, the stope structure parameters, ore and rock particles, and the total amount of ore and rock at the time of mining were simulated. The model size was length × width × height = 800 mm × 140 mm × 1100 mm. The experimental ore rock was Chengchao iron ore field ore rock. Compared with the field block, the particle size was ground at a 1:100 ratio, as shown in Figure 14a. The cross section of the drawing roadway was 38 mm × 36 mm (equivalent to 3.8 m × 3.6 m on site), and the roadway was arranged in a rhombic staggered arrangement. As shown in Figure 14b, the ore flowed downward by weight.

(**a**) Ore and waste rock particles (**b**) Physical drawing model framework

**Figure 14.** Physical ore drawing model. (**a**) Ore and waste rock particles; (**b**) Physical drawing model framework.

Each group of ore drawing test waste rock and ore was loaded at a 1:3 ratio, and three to four production drift roads were set for each sublevel. The ore drawing process is shown in Figure 15.

#### *4.2. Results Analysis*

As shown in Figure 15, the discharged bodies displayed a quasi-ellipsoid morphology and conformed to the ellipsoid theory, thereby confirming that this experiment was theoretically reasonable and feasible. The simulation experiment results of five ore caving steps of 3.0, 4.0, 5.0, 6.0, and 7.0 m were calculated under a sublevel height of 17.5 m and a production drift spacing of 20 m, as shown in Figure 16.

From Figure 16a,c, it can be seen that the variation trend of the recovery ratio and the difference between the recovery and the dilution ratio of the ore in each sublevel with different structural parameters were similar under the same ore drawing method. The residual bodies and recovery indexes in the discharged bodies gradually stabilized with the ore drawing sublevel. These findings indicate that each ore sublevel can be fully recovered under the existing structural parameters [33]. For the structural parameters of 17.5 m × 20 m × 5 m at sublevel II, the recovery ratio and the difference between recovery and dilution ratio were higher than the other structural parameters.

According to Figure 16b, the rock mixing ratio of each sublevel was significantly affected by the structural parameters under the same ore drawing method. Rock with structural parameters of 17.5 m × 20 m × 3 m had the highest mixing ratio. The actual caving step of the mine was approximately 3.5 m, indicating that the caving step of the stope should be increased at the same rate to optimize the recovery indexes.

**Figure 15.** Drawing process diagram (**a**) before drawing, (**b**) at the initial drawing stage, (**c**) at the middle drawing stage, and (**d**) at the end of ore drawing.

**Figure 16.** Curves of the recovery indexes of sublevel ore in each structural parameter scheme: (**a**) recovery ratio curve of sublevel ore; (**b**) rock mixing ratio curve of sublevel ore; (**c**) curve of the difference between recovery and dilution ratio of sublevel ore y.

Figure 17 presents the relationship between recovery indexes and ore interval drawing from an overall perspective without considering sublevels. The analysis of the curve showed that the dilution and ore recovery ratio first decreased and then increased. The change law of the difference between the recovery and the dilution ratio was similar to the recovery ratio when the caving step was 5.0 m. The maximum value of the difference between the recovery and the dilution ratio was obtained. Therefore, the caving step of 5.0 m (loose coefficient of 1.3, equivalent to 3.8 m or so of the interval of caved ore) and the recovery effect were optimal when the sublevel height and production drift spacing were 17.5 m × 20 m, based on the difference between the recovery and the dilution ratio.

**Figure 17.** Relationship between recovery indexes and drawing space from an overall perspective.

#### *4.3. Results Comparison between Numerical Simulation and Physical Experiment*

PFC3D software (Itasca Consulting Group, Minneapolis, MN, USA) was used to conduct the numerical simulation research on nine drawing plans and the fitting analysis of each recovery index and ore drawing interval to determine the optimal caving step. A similar experiment of physical ore drawing in the laboratory was designed and combined with the theoretical calculation range of the caving step. The optimal caving step determined by each method is shown in Table 7.

**Table 7.** Various methods are used to obtain the optimal step range.

Table 7 shows that the optimal caving step ranged from 6 m to 6.07 m through theoretical calculation and numerical simulation. The results only serve as a reference because similar physical experiments in practice inevitably involve human error. The optimal caving step, 6 m, was determined (extrusion blasting, loose coefficient of 1.3, equivalent to approximately 4.5 m of the interval of caved ore) by combining the actual effect of mine blasting.

#### **5. Conclusions**

In this paper, the sublevel caving method was studied through theoretical calculation, numerical simulation, and a laboratory test, and the following conclusions were drawn.

**Author Contributions:** Conceptualization, Y.T. and M.G.; methodology, Y.T. and W.S.; software, M.G. and C.Z.; validation, Y.T., M.G. and Y.H.; formal analysis M.G.; investigation, Y.T.; resources, Y.T., data curation, M.G.; writing—original draft preparation, Y.T.; writing—review and editing, W.S.; visualization, Y.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52004019.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Exclude this statement.

**Acknowledgments:** Financial support from the National Key R&D Program of China 2017YFC0602900 to the first author is gratefully acknowledged. The research is also partially supported by the Fundamental Research Fund for the Central Universities (FRF-TP-19-014A3). The authors would also like to thank Tan Y. Y. and Song W. D. for the discussions of the mining method.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Enhancing Arsenic Solidification/Stabilisation Efficiency of Metallurgical Slag-Based Green Mining Fill and Its Structure Analysis**

**Wei Gao 1,2,3, Zifu Li 1,2,3, Siqi Zhang 2,3,4,\*, Yuying Zhang 2,3,4, Pingfeng Fu 2,3,4, Huifen Yang 2,3,4 and Wen Ni 2,3,4,\***

**Abstract:** To dispose of arsenic-containing tailings with low carbon and high efficiency, sodium sulphate (Na2SO4), sodium hydroxide (NaOH), calcium nitrate Ca(NO3)2 and calcium hydroxide Ca(OH)2 were independently added to metallurgical slag-based binder (MSB) solidification/stabilisation (S/S)-treated tailings (MSTs) to enhance the MST arsenic S/S performance. Results showed that only Ca(OH)2 could increase the unconfined compressive strength of MST from 16.3 to 20.49 MPa and decrease the leachate As concentration from 31 μg/L to below 10 μg/L. Na3AsO4·12H2O and NaAsO2 were used to prepare pure MSB paste for mechanism analysis. The results of microstructure analyses showed the high specific surface area and amorphous properties of calcium–sodium aluminosilicate hydrate facilitated the adsorption or solid-solution formation of As(V) and As(III). As(V) formed an inner-sphere complex in ettringite, whereas As(III) formed an outer-sphere complex, and the relatively larger size and charge of As(V) compared with SO4 2− restrict substitution inside channels without affecting the ettringite structure under high loading of As(V). The added Ca(OH)2 promoted the hydration reaction of MSBs and facilitated the formation of a Ca–As(V) precipitate with low solubility, from Ca4(OH)2(AsO4)2·4H2O (*Ksp* = 10−27.49) to Ca5(AsO4)3(OH) (*Ksp* = 10−40.12). This work is beneficial for the application of cement-free MSB in the S/S process.

**Keywords:** metallurgical slag-based binders; solidification/stabilisation; As(III); As(V); calcium hydroxide

#### **1. Introduction**

Arsenic (As) is a toxic and carcinogenic element, which can cause severe effects on human health, such as skin, lungs, kidney and liver cancers, even at a low concentration [1]. The mining, processing and smelting industry of nonferrous metals is the most widespread source of As emissions in China, accounting for 61.8% [2]. Mining wastewater, smelting slag, dust and tailings are piled up in tailings ponds, which have potential environmental risks and occupy a large percentage of soil [3]. Heavy metal pollution from a large number of tailings poses a major threat to the environment [4,5]. The treatment of heavy metal pollution has received increasing attention since the implementation of the Soil Pollution Prevention and Control Law of the People's Republic of China in 2019 [6]. It is imperative to develop effective and economically viable technologies to reduce pollution from the nonferrous metal industry.

Solidification/stabilisation (S/S) technology has been used to dispose of metal(loid) containing waste by forming a less soluble, mobile or toxic product through adsorption, co-precipitation and physical encapsulation [7]. Ordinary Portland cement (OPC) is a

**Citation:** Gao, W.; Li, Z.; Zhang, S.; Zhang, Y.; Fu, P.; Yang, H.; Ni, W. Enhancing Arsenic Solidification/Stabilisation Efficiency of Metallurgical Slag-Based Green Mining Fill and Its Structure Analysis. *Metals* **2021**, *11*, 1389. https:// doi.org/10.3390/met11091389

Academic Editor: Lijie Guo

Received: 8 August 2021 Accepted: 27 August 2021 Published: 1 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

traditionally used binder in S/S processes, while the high carbon footprint of cement clinker manufacture is of concern [8,9]. To achieve an environment-friendly binder for the S/S of As-containing waste, many studies have been conducted to replace cement in part or whole using materials, such as red mud, blast furnace or metakaolin-assisted cement [1,10], cement-free clay-based binders [11], red mud-enhanced magnesium phosphate cement [12], pine sawdust biochar [13], coal fly ash [14], zero-valent iron or magnetic biochar [15]. However, there have been few reports on the use of cement-free metallurgical slag-based binders (MSB) [16,17], which consist of steel slag powder (SSP), ground-granulated blast furnace slag (GGBFS) and flue gas desulphurisation gypsum (FGDG). Steel slag is a byproduct of a steel-making process, known as overburnt clinker because its mineral phase composition is similar to that of cement clinker [18]. In 2019, 149.45 million tonnes of it were produced in China; however, the usage rate is less than 40% [19]. A large accumulation of steel slag pressurises the environment [20]. GGBFS is a by-product of the steel-making industry and is used as a supplementary cementitious material. In the hydration process of MSB, the relatively high alkalinity of SSP facilitates the breakage of Si–O and Al–O bonds in the GGBFS vitreous structure to form SiO4 4− and AlO4 5− [21,22], which react with dissolved Ca2+ to form calcium silicoaluminate hydrate (C–A–S–H) gels [11,23]. The AlO4 5− dissolved in solution transforms from four coordinated aluminium ions to six coordinated aluminium ions [Al(OH)6] 3− in alkaline environments, which reacted with the Ca2+ and SO4 2− dissolved from gypsum to form ettringite [24].

In this study, As-containing tailings were chosen as the S/S objects, as other hazardous elements' leaching risk is very low after S/S treatment (Table S1). In order to dispose of As-containing tailings with low carbon and high efficiency, the MSB (60 wt% GGBFS, 30 wt% SSP, and 10 wt% FGDG) was chosen as the binder for the As-containing tailings, based on a previous study [25]. SO4 2− (Na2SO4), OH− (NaOH), Ca2+ (Ca(NO3)2 or Ca2+ and OH− (Ca(OH)2) was added to the MSB-S/S treated tailings (MST) to enhance the MST-As-S/S performance. Arsenic in tailing occurs in various states in the natural environment, and its main forms in regard to leaching and migration characteristics are soluble arsenite As(III) and arsenate As(V) compounds [26]. The As-S/S mechanism of MSB and the mechanism of efficiency improvement are both unclear; therefore, Na3AsO4·12H2O and NaAsO2 were used to study the S/S mechanisms through X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscope-energy dispersive spectrometer (SEM-EDS) analysis. This study will improve the engineering application prospects of MSB and reusability of MST.

#### **2. Materials and Methods**

#### *2.1. As-Containing Tailings and Metallurgical Slag-Based Binders*

As-containing tailings were collected from a closed lead-zinc tailings pond in Hechi Nandan, Guangxi (China). The main components of the tailings were quartz, calcite and fluorite, of which the D50 and D90 particle sizes were 42 and 188 μm, respectively (Figure S1). The total concentration of As in the tailings was 2098 mg/kg, as determined using an Agilent 7500a ICP-MS (Agilent Technologies, Santa Clara, CA, USA) after aqua regia digestion (1/3 concentrated HNO3 and HCl (*v*/*v*)) based on Chinese standard method HJ 803-2016. The leaching concentration of As in the tailings after horizontal oscillationleaching tests was 0.57 mg/L, based on the Chinese standard method HJ 557-2010 (Table 1). The MSB consisted of GGBFS, SSP and FGDG. The chemical components of raw materials are shown in Table 1, and the XRD spectra of the binders are shown in Figure S2. The analytical reagents, Na2SO4, NaOH, Ca(NO3)2 and Ca(OH)2, were used as added additive for the binders, respectively. A polycarboxylic acid water-reducer (WR) was used to achieve an acceptable flowability of mortar during S/S treatment. The analytical reagents, Na3AsO4·12H2O and NaAsO2, were used to prepare the pure MSB paste to study the S/S mechanisms of As(III) and As(V).

**Table 1.** Raw material chemical components, blaine fineness, component concentrations and leaching concentration of As.

#### *2.2. Cement-Free S/S Treatment*

For the preparation of MST mortar samples, the different constituents of binders were mixed with tailings at specific ratios (Table 2). MSB with 60 wt% GGBFS, 30 wt% SSP and 10 wt% FGDG was chosen due to its high compressive strength, according to the findings of previous studies [25]. Four additives (Na2SO4, NaOH, Ca(NO3)2 and Ca(OH)2) were incorporated into the MSB mass individually at 5 wt%, 10 wt% and 15 wt%. The binders-to-tailings (B/T), water-to-solids (W/S) and WR-to-solids (WR/S) mass ratios were maintained at 0.25, 0.2 and 0.008, respectively, in all MST samples. After the tailings and binders were mixed for 1 min using a standard cement mortar mixer, distilled water used to dissolve the WR was added, and the blended mortar was mixed for another 5 min, before being cast into steel moulds (40 mm × 40 mm × 160 mm) to prepare the MST samples. To prepare MSB paste samples, 5 wt% concentrations of the analytical reagent, Na3AsO4·12H2O or NaAsO2, was added (Table 2), which were then mixed with water until the mass ratio of water-to-binders (W/B) reached 0.35. The paste samples were stirred for 5 min and then cast into steel moulds (30 mm × 30 mm × 50 mm). All mortar and paste samples were cured in a moist cabinet at (40 ± 2) ◦C and (90 ± 1)% relative humidity (similar to the underground filling environment in Guangxi [27]) for 3 day, and then the samples were demoulded and placed under the same curing conditions until the appropriate degree of sample ageing was achieved.

#### *2.3. S/S Performance and Spectroscopic/Microscopic Analysis*

The S/S performance was evaluated in terms of the unconfined compressive strength (UCS) and As leachability of the MST samples. The UCS of the samples was assessed on the basis of the Chinese standard GB/T 17671-1999 method of testing cements. The determination of UCS on three replicates at each curing time, and the average value with standard deviations of less than 5% was reported. The leachability of MST was assessed using the Chinese standard method HJ 557-2010, wherein the ratio of leachant (deionised water) volume to solid specimen weight (crushed to size < 3mm) was maintained at 10:1 (L/kg). Leachate samples were oscillated horizontally at 110 ± 10 times/min for 8 h and then left to stand at room temperature for 16 h. The pH values were measured, and the leachate was filtered through a 0.45-μm polypropylene microfiltration membrane. Afterwards, the filtered leachate samples were acidified to below pH 2 using concentrated HNO3 and then stored at 4 ◦C in the dark before As detection using a PerkinElmer Optima 8300 ICP-OES (Perkin Elmer, Cumberland, NJ, USA) or an Agilent 7500a ICP-MS (Agilent Technologies, Santa Clara, CA, USA), with all samples analysed in triplicate to ensure

accuracy. Mean results for triplicate sample analysis (with standard deviations of less than 5%) and error bars, are presented in this study. The As curing rate was evaluated using Equation (1):

$$\text{Curing Rate} = (1 - C\_{\text{S}/\text{S}}/C\_T) \times 100\% \tag{1}$$

where *Cs/s* is the As leaching concentration of MST, and μg/L; *CT* is the As leaching concentration of untreated tailings.

**Table 2.** Mixture formulations of MST and MSB samples (wt%).

1 Binder-to-tailings mass ratio.

The hydration of pulverised and sieved paste samples was terminated using alcohol drenching at the specific testing age, with samples then dried at 50 ◦C under a vacuum for 24 h for further characterisation. Mineralogy was detected using a high-resolution powered Rigaku D/max-RB XRD with CuKα radiation, a voltage of 40 kV, current of 200 mA and 2θ scanning, ranging between 10◦ and 70◦. The morphology and elemental mapping of the samples were analysed using SEM-EDS (JSM-6701F). Structural and chemical bond analyses were performed using GX Perkin-Elmer FTIR (Perkin Elmer, Cumberland, NJ, USA) between wavelengths of 400–4000 cm−1 and at a resolution of 0.125 cm−1.

#### **3. Results**

#### *3.1. The S/S Performance of MST Samples*

The UCS results for MST samples (Figure 1a) demonstrate that MST combined with various additives performed differently. The 3, 7 and 28 day UCS results for T were 11, 13.8 and 16.3 MPa, respectively. An increase in concentrations of Na2SO4, NaOH and Ca(NO3)2 reduced the USC of MST at every curing time, especially for the addition of NaOH, which inhibits hydration reactions and reduced the USC of MST (the lowest 28 day USC (T-H3 3.7 MPa) was only 22.7% of T). The addition of Ca(OH)2 enhanced mechanical properties (the highest 28 d UCS (T-C3 20.49 MPa) was 125.7% of T). The USC of all samples could meet the filling requirements (5 MPa, the black line in Figure 1a), except for T-H3.

**Figure 1.** (**a**) UCS of MST samples at 3, 7 and 28 day; (**b**) As leachability of 28 day MST samples.

Figure 1b shows leachate As concentrations and the pH values of 28 day MST samples after the horizontal oscillation-leaching experiments. The leachate As concentration of T samples was 31 μg/L, which exceeded the prescribed As concentration limit value in the Chinese standard GB 5749-2006 for drinking water quality (10 μg/L, the black line in Figure 1b), indicating a relevant risk of environmental pollution. The addition of Na2SO4, NaOH or Ca(NO3)2 enhanced leachate As concentration, particularly that of T-H3, which reached 530 μg/L. The addition of Ca(OH)2 reduced the leachate As concentration, particularly that of T-C2 and T-C3, which was less than 10 μg/L.

#### *3.2. Spectroscopic/Microscopic Analysis*

#### 3.2.1. XRD Pattern Analysis

The XRD patterns of MSB paste hydrated for 3 and 28 day are presented in Figure 2a,b), respectively. The main mineral phases of the B sample, as shown in Figure 2a, are C-S-H gel, ettringite (Ca6Al2(SO4)3(OH)12·26H2O) and un-hydrated gypsum (CaSO4·2H2O), C2F (2CaO·Fe2O3), RO phase ((MgO)0.239(FeO)0.761), akermanite (Ca2MgSi2O7) and C2S (2CaO·SiO2) (Figure S2). With the addition of As(III) (sample B(III)), the peaks of ettringite decreased. However, when As(V) was added (B(V)), the peaks of ettringite disappeared due to the formation of a new phase, (Ca4Al2SO10·16H2O), and the peaks of Ca–As coprecipitation Ca4(OH)2(AsO4)2·4H2O appeared.

**Figure 2.** The XRD patterns of (**a**) B, B(III) and B(V) paste; (**b**) B-C3, B-C3(III) and B-C3(V) paste.

Compared with Figure 1a, the added Ca(OH)2 characteristic peaks appeared in the XRD patterns of B-C3 and B-C3-(III); however, it did not appear in B-C3(V) (Figure 2b), and the peaks of Ca–As co-precipitation Ca5(AsO4)3OH·xH2O appeared.

#### 3.2.2. FTIR Spectra Analysis

Figure 3a displays the FTIR spectra of the B, B(III) and B(V) samples. The spectra of the six samples are similar, presenting analogous adsorption bands. The pure B paste spectra (Figure 3a 1 and 2 ) exhibit a small band at 3639 cm−1 associated with the O–H stretching vibration; bands at 3424 and 1646 cm−1 are related to the O–H stretching and bending modes of molecular interlayer water, respectively, and bands near 1450 (1478, 1428) cm−1 and at 876 cm−1 are related to the anti-symmetric stretching (v3) and out-of-plane bending (v2) modes of CO3 2− ions, respectively. The bands at 973 cm−1 and near 450 (462, 422) cm−1 are associated with anti-symmetric Si–O(Al) stretching vibrations (v3) and in-plane Si–O bending vibrations (v2) in the SiO4 tetrahedra of the C–(A)–S–H gel. The lack of a sharp band at 973 cm−1 indicates the wide distribution of SiQ*n*(mAl) units. Moreover, the 3 day and 28 day infra-red spectra of B samples display large bands at 1114 cm−1 corresponding to the S–O4 stretching mode (v3), with a shoulder at 537 cm−1, potentially due to out-ofplane Si–O bending vibrations (v4).

**Figure 3.** FTIR spectra of (**a**) - and - B sample, - and - B(III) sample and - and - B(V) sample; (**b**) - and - B-C3 sample, - and - B-C3(III) sample and - and -B-C3(V) sample.

Nevertheless, some differences were observed in the infrared spectra of B(III) and B(V) samples. The weak band at 3639 cm−1 in the B sample (Figure 3a 1 and 2 ) disappeared in the B(V) (Figure 3a 5 and 6 ) samples, indicating that OH− reacts with the added As(V), which is consistent with the result of XRD analysis (Figure 2a). The anti-symmetric Si–O(Al) stretching vibrations (v3) of the C–(A)–S–H gel (973 cm−1 for B) decreased to 968 cm−1 for B(III) and 964 cm−1 for B(V), indicating that the added As(III) and As(V) were combined in the microstructure network and changed the degree of polymerisation of the C–(A)–S–H gel. In addition, the O–H stretching band (3424 cm−1 for B) increased to 3448 cm−1 and became broader for B(V) due to the absence of ettringite, leading to the change of interchannel molecular water, which combined with the result of XRD pattern (Figure 2a). Moreover, some new bands were observed, with the appearance of broadband at 802 and 671 cm−1 for B(III), and at 866 cm−1 for B(V) (Figure 3a 3 – 6 ), which was due to the As–O stretching vibration of the As(III) and As(V) species [28,29].

Figure 3b display the FTIR spectra of the B-C3, B-C3(III) and B-C3(V) samples, respectively. These spectra are all similar to those presented in Figure 3a, except that the spectra of the B-C3 and B-C3(III) samples exhibited sharp adsorption bands at 3642 cm−1 due to the addition of Ca(OH)2. However, the sharp adsorption bands at 3642 cm−1 did not appear in B-C3(V), illustrating that Ca(OH)2 reacted with As(V). Bands around 1110 cm−1, corresponding to the S–O4 stretching mode (v3) in Figure 3b 1 – 6 , were offset at 1114 cm−1 in B-C3 sample, 1111 cm−1 in B-C3(III) and 1117 cm−1 in B-C3(V), indicating that, after As(III) and As(V) were added, the hydration products associated with SO4 2− were affected, as shown by the XRD pattern (Figure 2b).

#### 3.2.3. SEM-EDS Analysis

Figure 4 show SEM-EDS images for B, B(V) and B(III) at hydration ages of 3 and 28 day. Dense structures with amorphous gels and needle-like mineral phases are visible in the cement-free B paste in Figure 4a,b, which are calcium-sodium aluminosilicate hydrate (C–(N)–A–S–H) gels and ettringite, combined with the component analysis results in Table S2 (b-1, b-2 and b-3). After As(V) was added, the needle-like ettringite structure was undetected (Figure 4c,d), and a small amount of As(V) was found in the short columnar crystals of sodium sulphate, combined with the component analysis results (Table S2 (d-1)). Moreover, according to the SEM-EDS mapping images (Figure S3) of the red box in Figure 4d, the distribution of As(V) was almost similar to that of Ca, Si and Al, and the distribution of Na was similar to that of S. Therefore, the majority of the added As(V) entered the C–A–S–H gels through isomorphism substitution or was adsorbed and encapsulated by the gels, whereas the tiny minority of that entered the sodium sulphate to form sodium arsenic alunite. After As(III) was added, combined with the component analysis results (Table S2 (e-1,f-3)), different amounts of As(III) were detected in the C–A–S–H, ettringite, Na2SO4 and Na2Ca(SO4)2 (Figure 4e,f).

Figure 5 show SEM-EDS images of B-C3, B-C3(V) and B-C3(III) at hydration ages of 3 and 28 day. In addition, amorphous C–A–S–H gels interwoven with needle-like ettringite structures were detected in the B-C3 paste. The images of B-C3(V) and B-C3(III) are similar to those for B(V) and B(III), except for the appearance of calcium hydroxide flakes in B-C3(V)-3d (Figure 5c) and calcium monosulfoaluminate hydrate (AFm) flakes in B-C3(III)-3d (Figure 5e), combined with the component analysis results in Table S3, which may be due to their content being lower than the detection limit of the XRD technique; both kinds of flakes disappeared as the hydration reaction proceeded.

**Figure 4.** SEM-EDS result for (**a**) B-3d; (**b**) B-28 d; (**c**) B(V)-3 d; (**d**) B(V)-28 d; (**e**) B(III)-3 d; (**f**) B(III)-28 d.

**Figure 5.** SEM-EDS result for (**a**) B-C3-3 d; (**b**) B-C3-28 d; (**c**) B-C3(V)-3 d; (**d**) B-C3(V)-28 d; (**e**) B-C3(III)-3 d; (**f**) B-C3(III)-28 d.

#### **4. Discussion**

This study demonstrates the different As S/S performance of MST samples after four additives (Na2SO4, NaOH, Ca(NO3)2 and Ca(OH)2) were added, as shown in Table S4. The MSB is selective for additives and only Ca(OH)2 exerted a positive effect on the As S/S performance. The addition of Na2SO4 had little effect on the pH values, UCS and As curing rate, probably because gypsum in the MSB system (10 wt% FGDG in B) was sufficient to provide sulphate for the hydration reaction. The addition of NaOH resulted in a pH value above 13, which inhibited the dissolution of Ca2+ and hindered the hydration reaction, leading to a sharp decrease in UCS [30]. The amphoteric nature of arsenic in an alkaline environment and repulsion by OH− during the adsorption process of the hydration products led to the sharp decrease of the As curing rate [31,32]. Neither NaOH

nor Ca(NO3)2 improved the As S/S performance, confirming that the individual addition of OH− without Ca2+ would not improve As S/S efficiency and had a negative effect [33,34].

In addition, this study demonstrates that both the As(V) and As(III) S/S mechanism of MSB, and the effect of Ca(OH)2 addition on As(V) and As(III) S/S are different. The addition of Ca(OH)2 to MSB raised the pH of the reaction mixture (Figure 1), facilitated GGBFS dissolution and provided a source of Ca2+, thus improving the As(V) and As(III) S/S related to the hydration products. Adsorption or solid solution formation of As(V) and As(III) occurred due to the high specific surface area and amorphous properties of C–A–S–H, based on the shift in anti-symmetric Si–O(Al) stretching vibrations (v3) in the discussion above (Figure 3) and SEM-EDS results (Figures 4 and 5).

Anion substitution in ettringite can occur via reaction with surface sites (ligand exchange), replacing Al/Ca coordinated surface OH− or substituting for sulphate inside channels (isomorphic substitution) [35]. This occurs as the ettringite surfaces are negatively charged under alkaline pH range conditions and due to the column and channel-like structure of ettringite, which is composed of columns of Ca6[Al(OH)6]2·24H2O6+ and channels of [(SO4)3·2H2O] 6− [36]. The extent of channel substitution of ettringite may be inversely proportional to the difference in size and electronegativity of the oxyanion compared with SO4 2− (0.29 Å), resulting in As(V) (0.47 Å) being likelier to be channelsubstituted with sulphur than As(III) (0.69 Å) [37]. Thus, As(V) tended to form an innersphere complex in ettringite, whereas As(III) could form an outer-sphere complex [38], and the relatively larger size and charge of As(V) compared with SO4 2− restricts the substitution inside channels without affecting the ettringite structure under high loading of As(V), which is consistent with the XRD, IR and SEM-EDS analysis results.

Previous studies [39–41] have verified the formation of Ca–As precipitates under alkaline conditions. In the present study, the distinct Ca–As(III) mineral phase characteristic peak was not found in the XRD patterns, maybe because of the content being below the detection limit of XRD analysis or mainly existing as an amorphous or more complex structure. When Ca(OH)2 was added, Ca–As(V) changed from Ca4(OH)2(AsO4)2·4H2O (*Ksp* = 10−27.49) to Ca5(AsO4)3(OH) (*Ksp* = 10−40.12) (Figure 2), which restricted As leaching [42].

Based on the findings of this study, alkaline solid waste with high calcium ion equilibrium concentrations and high alkalinity can be used to replace Ca(OH)2 added to MSB for the S/S of high As-containing solid wastes for low carbon and environmental protection.

#### **5. Conclusions**

In this study, the effects of four additives (Na2SO4, NaOH, Ca(NO3)2 or Ca(OH)2) on the As S/S performance of MST to enhance the As S/S efficiency were investigated. As-containing analytical reagents were used to prepare pure MSB paste to study the S/S mechanisms of As(III) and As(V) and the mechanism of efficiency improvement using Ca(OH)2, based on XRD, IR and SEM-EDS analysis. Based on the results obtained from this study, the following conclusions can be drawn:

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/met11091389/s1, Figure S1: (a) XRD pattern and (b) grain size results of tailings, Figure S2: The XRD pattern of raw materials: (a) ground granulated blast furnace slag (GGBFS), C2S: dicalcium silicate; (b) steel slag powder (SSP), C3A: tricalcium aluminate, RO phase: (MgO)0.239(FeO)0.761, C2S: dicalcium silicate, C2F: dicalcium ferrite; (c) flue gas desulfurisation gypsum (FGDG), Figure S3: The SEM-EDS electron image and mapping images of Figure 4 (d), Table S1: Leached concentration of raw materials and T in Table 2, Table S2: Component analysis result of point in the Figure 4, Table S3: Component analysis result of point in the Figure 5, Table S4: Assessment summary of the S/S performance effects of Na2SO4, NaOH, Ca(NO3)2 and Ca(OH)2 at different doses (wt%).

**Author Contributions:** Formal analysis, W.G.; funding acquisition, P.F. and H.Y.; investigation, W.G. and Y.Z.; methodology, W.G.; project administration, S.Z.; resources, S.Z.; supervision, S.Z.; validation, W.G., Z.L., S.Z. and W.N.; writing—original draft, W.G.; writing—review and editing, Z.L., S.Z. and W.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the National Key Research and Development Program of China, grant number 2020YFC1807804 and the National Key Research and Development Program of China, grant number 2019YFC1803503.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Special thanks are addressed to Xuming Ma and Qihui Yan for their support with the interpretation of XRD results.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**4. Results and Discussion**

The Legislative Framework is integrated in Table 2 indicating each of Ostrom's principles, and corresponding sections of the GWMPA, including requirements for IMPs. The most salient individual sections of the statute are identified for each principle. It should be noted that the last of Ostrom's principles—recognition of local rules and nested enterprisesare not coded because their overall design purposes are integrated through the overall approach requiring state (NeDNR) and local (NRD) coordination and cooperation in IMP operationalization. This cross-jurisdictional approach recognizing both local and state responsibility is clearly reflected in the statute's legislative findings, which identify NRDs as the "preferred regulators" for groundwater (§46-702), and state that the objective is that "(a)ll involved natural resource districts, the department, and surface water project sponsors should cooperate and collaborate on the identification and implementation of management solutions" (§46-703 (6)).

**Table 2.** Sections of the GWMPA reflecting CPR design principles.

1 The year 1997 refers to the signing date of the Cooperative Agreement that created the Platte River Recovery Implementation Program (PRRIP) beginning on 1 January 2007. The PRRIP covers the Basin of the Platte River within Colorado, Wyoming and Nebraska. Each state was responsible for developing a plan to mitigate effects of surface and ground water depletions initiated after 1997.

> Six Ostrom design principles include references and field interviews relevant to sections of the GWMPA. The last two principles, Recognition of Local Rules and Nested Enterprises did not receive any mention from the interviewees.