Quality Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR

Abstract

Wasteless mining has become the mainstream mode of China’s mining industry, which is an environmentally friendly and sustainable mining method. To effectively evaluate the construction of a green mine, the wasteless mining evaluation method was proposed, based on the intuitionistic fuzzy set theory and the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) decision-making method in this study. Firstly, the intuitionistic fuzzy sets for six indicators (such as waste rock utilization, tailing sand utilization and the current mining situation) of the Dongguashan Copper Mine were established, based on the intuitionistic fuzzy set theory and the national standard system. Secondly, referring to the green mine evaluation system, the index weights were calculated with the Analytic Hierarchy Process (AHP) method. Finally, the above intuitionistic fuzzy set matrix was ranked by the VIKOR method. The calculation results show that the group utility value of this mine is 0.1943 ∈ (0,0.2), which is at level I; the individual regret value is 0.0916 ∈ (0.075,0.15), which is at level II; the compromise value is 0.2193 ∈ (0.2,0.4), which is at level II. Through comprehensive assessment, the final evaluation of the wasteless mining in the Dongguashan Copper Mine is Grade II. The Dongguashan Copper Mine is one of the first mining enterprises subject to the transformation into wasteless mining in China. The results of the final evaluation are consistent with the current status of wasteless mining in the Dongguashan Copper Mine.

1. Introduction

Green mine construction is an important part of China’s development strategy. To promote the progress of green mine construction, different industry standards and green mine management measures were issued [1]. The primary objective of green mine construction is to realize green mining, and the core of green mining for mining enterprises is to solve the environmental problems caused by solid wastes caused by mining activities, and to realize the sustainable development of mining enterprises [2,3]. Among them, the green mining mode dominated by wasteless mining has been widely studied in the field.

Wasteless mining aims to improve the resource utilization rate and optimizes the resource allocation on the basis of ensuring both economic and ecological benefits. To promote the construction of green mines and the development of wasteless mining, Hongren Wang et al. [4] conducted a study on the countermeasures of wasteless mining, based on the theory of the circular economy. Shunfa Hu et al. [5] carried out a study on the comprehensive utilization technology of wasteless mining for the medium and low-grade phosphate ore. Ganghe Qi et al. [6] put forward the scientific mining mode of “short-wall filling & long-wall mining” to achieve “mining without coal pillars” and “zero discharge of solid waste”. Based on the concept of “collaborative mining”, Xilong Xue et al. [7] proposed a wasteless collaborative mining model for hard rock uranium ore. Shida Xu et al. [8] carried out the design and application of wasteless mining in the Hongtushan Copper Mine. Dengpan Qiao [9] et al. claimed that wasteless mining is the direction of development for green mining, and discussed the advantages of backfill mining technology. Izabela Kotarska et al. [10] performed a feasibility analysis on mining waste recycling management by using the concept of the circular economy.

At present, some of the mining enterprises in China are trying to transform to wasteless mining production. To standardize the industry standard of wasteless production in mines, the evaluation method of wasteless mining should also be gradually established. In this regard, Peng Huaisheng [11] proposed a comprehensive index evaluation of the wasteless mining and established neural network evaluation methods and judging criteria. Huaichang Zheng [12] conducted a quantitative analysis of wasteless mining in mines. Huang et al. [13,14] carried out a study on wasteless mining evaluation and established a numerical model for the comprehensive evaluation of wasteless mining, based on a fuzzy mathematical method. Through the Phillips environmental sustainability mathematics (PESM) model, Naseem and Sobia et al. [15] investigated the impact of Khewra mine (the second largest salt mine in the world) on the surrounding environment. The above assessment methods have practical value, but also have some limitations. For example, the neural network algorithm is computationally tedious and may ignore the original data, which affects the accuracy of the evaluation results [16]; the fuzzy hierarchical analysis method is subjective in determining the index weights; in the TOPSIS method, the weight of the distance between the evaluation object and the positive and negative ideal solutions in the calculation process is not considered, and the optimal solution obtained may not be the closest to the ideal point [17].

Both the affiliation, non-affiliation and hesitation degrees are considered in intuitionistic fuzzy sets. Compared with fuzzy sets, intuitionistic fuzzy sets can efficiently express information and characterize the uncertainty of evaluators. In the VIKOR algorithm, the situation can be avoided in which the individual negative indicators are ignored and neutralized by other indicators, and the compromise evaluation can be performed according to the order of group utility maximization and individual regret value minimization. As a result, the VIKOR algorithm has a higher accuracy and is beneficial for the improvement of the rationality of evaluation, which can effectively solve the data incommensurability and obtain the optimal compromise solution. Based on this, a mine wasteless mining evaluation model based on intuitionistic fuzzy sets and VIKOR was proposed in this study. A series of intuitionistic fuzzy sets were established, based on mine-related indicators and their classification, and then evaluated by using the VIKOR method. The evaluation results visually reflect the overall level of wasteless mining and the level of individual indicators. This study promotes the construction of a green wasteless mining evaluation system in mines.

2. Theory of Wasteless Mining Methods

2.1. Overview of Wasteless Mining

The wasteless production process is a comprehensive resource utilization approach proposed by the United Nations Economic Commission for Europe in 1984. It is defined as a process technology that minimizes waste, reduces emission, does not discharge or makes comprehensive utilization of it. The connotation of the wasteless production process mainly includes the following aspects [13]: (i) emphasizing the economy and durability of the mining enterprises; (ii) attaching importance to the comprehensive utilization of resources; (iii) minimizing waste output; (iv) promoting the utilization of waste.

As shown in Figure 1, a large quantity of waste (such as waste gas, wastewater and solid waste) can be generated in the traditional mining model, which causes great damage to the ecological environment around the mine. The industrial solid wastes, including waste rock, tailings and red mud, occupy a large number of land resources; at the same time, the safety risks of these solid wastes cannot be ignored, such as debris flow from waste rock sites and the leakage of the tailings pond. The drainage and discharge of mine water cause the waste of water resources and the release and migration of heavy metal elements.

Traditional mining is characterized by high extraction, low utilization and high emissions, which is contrary to the concept of green mining development. Under the sustainable development goals, wasteless mining is characterized by a reduction in emissions, less discharge or no discharge, and has become an inevitable trend in the green mining industrial system [18,19,20]. In the production model of wasteless mining, the mine waste is first treated and then discharged to minimize its negative impact on the ecological environment. Many techniques are used in this process. For instance, the cement-filling technology can be used to reduce the tailing waste piles and avoid surface subsidence and collapse; the industrial treatment of waste gas and wastewater is conducted to achieve resource recycling. Figure 2 shows the production model of wasteless mining.

2.2. Planning Principles for Wasteless Mining

The essence of wasteless mining is to fully recycle the mineral resources. To reduce the adverse environmental impact of mining activities, wasteless mining can be realized by the reuse of the generated waste. To this end, the principles of wasteless mining planning include seven main aspects:

(1)

Waste reduction. Under the premise of safety and economic rationality, the number of mining activities and the output of waste rock should be reduced in the preliminary design of the mine, and the mining parameters should be optimized to reduce the loss depletion rate and stabilize the ore input grade;

(2)

Comprehensive utilization of resources. For poor ore or ore with high mining difficulty, reasonable ore blending is required to improve the utilization rate of mineral resources; the waste rock, tailing sand and wastewater should be comprehensively recovered to realize the waste treatment;

(3)

In situ processing and utilization. The waste rock can be treated in the field to avoid waste transportation, and the transportation distance can be reduced as much as possible;

(4)

Control of secondary pollution. The useful components in the tailings and wastewater treatment can be recovered; the mineral processing and wastewater treatment-related technologies can be further explored to improve the degree of the resourcefulness of waste materials; the pollution generated by the treatment should be reduced to avoid secondary pollution;

(5)

Using advanced equipment and techniques. The advanced mining equipment and techniques should be developed in wasteless mining to achieve the mechanization, automation, and intelligent transformation of mines. Finally, the mining efficiency and mining enterprises’ comprehensive benefits can be greatly improved;

(6)

The unity of economic, environmental and social benefits. Wasteless mining can be used to ensure the environmental and social benefits, as well as the economic benefits of the enterprise;

(7)

Adaptation to local conditions and circumstances. During the wasteless mining, optimal planning of wasteless mining for the mining enterprise can be obtained by analyzing the mining enterprise’s own situation and the current economic market [13].

2.3. Reference Standard for Wasteless Mining Evaluation

Referring to the research on the indicators and classification standards of wasteless mining [21,22,23,24], the evaluation indicators in wasteless mining evaluation include the utilization and treatment level of waste rock, tailings, wastewater and waste gas, the impact of the emissions on the surrounding soil and resource recovery. According to the pollution and utilization degrees of wastewater and soil environment, four levels are defined in the evaluation criteria, including the non-pollution, mild pollution, moderate pollution and serious pollution levels. The evaluation criteria for waste gas are also classified into four levels. According to their waste utilization rate and resource recovery rate, the mining recovery and the utilization of solid waste (such as tailings and waste rock) are quantitatively evaluated. Based on the above, combined with the national evaluation standards related to each indicator [25,26,27,28,29,30,31], the following classification criteria are proposed for the six evaluation indicators of wasteless mining:

(1) Evaluation criteria of waste rock tailings:

Waste rock and tailings are the main solid wastes of the mine, and the utilization rate of the waste is taken as the evaluation index. Considering the actual situation of the mine, the utilization rate of the waste rock and tailings in a mine is calculated, and the evaluation criteria are summarized, as shown in

Table 1. Grading standards for contamination level of waste rock and tailings in the mining area.

Grade	Contamination Level	Utilization Rate of Waste Rock and Tailings
I	Non-pollution	80–100%
II	Mild pollution	70–80%
III	Moderate pollution	60–70%
IV	Severe pollution	0–60%

.

(2) Evaluation criteria for wastewater pollution and soil quality:

The pollution degrees of mine wastewater and soil refer to the wastewater discharge standard and soil environmental quality standard. During the evaluation, the discharge wastewater and the surrounding soil of the mining area are monitored and sampled, and compared with the national standards. According to the relationship between the various pollutants and the standard limits, the exceedance percentage in relation to the national standard is calculated, and then a qualitative comprehensive evaluation is carried out.

Table 2. Grading standards for the contamination level of wastewater and soil quality.

Grade	Contamination Level	Percentage of Exceedance/%
I	Non-pollution	Not exceeded
II	Mild pollution	0–200
III	Moderate pollution	200–300
IV	Severe pollution	More than 300

shows the evaluation criteria for the wastewater and soil quality.

(3) Evaluation criteria for exhaust gas pollution:

With the increase in air pollution, exhaust gas pollution is gradually highlighted. and the impact of mine production on air quality is also included in the quality evaluation of wasteless mining. At present, there is no uniform grading standard for mine air quality. To effectively evaluate the mine air quality, the atmospheric air quality standards are referred to.

Table 3. The influence degree of mine waste gas on air quality.

Grade	Degree of Impact
I	Unaggravated
II	Aggravating Grade 1
III	Aggravating Grade 2
IV	Aggravating Grade 3 and above

and

Table 4. Air quality classification standards (concentration unit: mg/m³).

Grade	Contamination Level	Concentrations
		Dust Fall	Floating Dust	SO2	NOX	CO	Total Oxidizer
I	Ideal	≤8	≤0.10	≤0.05	≤0.02	≤2	≤0.5
II	Good	≤12	≤0.15	≤0.15	≤0.05	≤4	≤0.10
III	Safety	≤20	≤0.25	≤0.25	≤0.10	≤6	≤0.20
IV	Pollution	≤40	≤0.50	≤0.50	≤0.20	≤12	≤0.40
V	Heavy pollution	>40	>0.50	>0.50	>0.20	>12	>0.40

show the evaluation criteria and air quality grading of mine exhaust pollution.

(4) Evaluation criteria for mineral resource recovery:

In mining, the extraction of the mineral resources should be maximized to obtain the best economic benefits. In the quality evaluation of wasteless mining, the mining loss rate and mineral processing recovery rate are used as evaluation indicators to calculate the comprehensive resource recovery rate and make a quantitative evaluation of the mine resource recovery. The comprehensive resource recovery rate can be calculated as follows.

$$H=H_X(1-H_s)$$

(1)

where H is the resource recovery rate; H_X is the mineral processing recovery rate; and H_S is the mining loss rate.

(5) Comprehensive evaluation of the quality of wasteless mining:

In the comprehensive evaluation of wasteless mining, six indicators of waste rock, tailings, wastewater, waste gas, soil and resource recovery are taken as the guideline layer; combined with their corresponding weights, the comprehensive evaluation result of the wasteless mining for a mine is finally obtained. Generally speaking, if the grading evaluation of all of the six indicators is at the same level, the final comprehensive evaluation must also be at the same level. However, in the actual mining projects, the evaluation results of these indicators are often not at the same level. In this case, hierarchical analysis, fuzzy comprehensive evaluation and the projection tracing method can be selected according to the actual evaluation methods; then the intuitionistic fuzzy set [17,32] and VIKOR can be combined to conduct a comprehensive evaluation of the wasteless mining and to make the necessary analysis of the results.

3. Intuitionistic Fuzzy Sets and VIKOR Method Theory

3.1. Definition of Intuitionistic Fuzzy Sets

Definition 1.

For a domain X, if there are two mappings on X, that is, m_A: X→[0,1] and n_A: X→[0,1], then let the m_A(x) ∈ [0,1] and n_A(x) ∈ [0,1], where x ∈ X, and 0 ≤ m_A(x) + n_A(x) ≤ 1. It can be seen that m_A and n_A form an intuitionistic fuzzy set A on X as follows:

A = {<x, m_A(x), n_A(x)>|x ∈ X}

(2)

where m_A and n_A are the affiliation and non-affiliation functions of A; m_A(x) and n_A(x) are the affiliation and non-affiliation degrees of elements x belonging to A. Meanwhile, all of the intuitionistic fuzzy sets in the theoretical domain X form a set F(X). The affiliation and non-affiliation degrees of each intuitionistic fuzzy set here are basically independent of each other, but both must satisfy that the sum value is no greater than 1.

The degree of hesitation of each A can be denoted by t_A(x) as follows:

t_A(x) = 1 − m_A(x) − n_A(x)

The t_A(x) can be used to evaluate whether x belongs to the degree of hesitation of A and satisfies x ∈ X with the value 0 ≤ t_A(x) ≤ 1.

The intuitive fuzzy sets of m_A(x) ∈ [0,1], n_A(x) ∈ [0,1] and t_A(x) ∈ [0,1] can denote three levels of evidence for, against and neither for nor against, alleviating the uncertainty in the calculation of fuzzy parameters.

Definition 2.

The Euclidean distance of any two intuitionistic fuzzy numbers α₁ = (mα₁, nα₁) and α₂ = (mα₂, nα₂) is defined as follows:

$$d\left({\alpha }_1,{\alpha }_2\right)=\sqrt{\frac{1}{2}\left(\right(m_{\alpha 1}-m_{\alpha 2}){}^2+(n_{\alpha 1}-n_{\alpha 2}){}^2)}$$

(3)

3.2. Intuitive Fuzzy Set Multi-Attribute Decision Making

For a multi-attribute decision problem, it consists of p scenarios x_i(i = 1,2,…,p) composing the set of scenarios X = {x₁,x₂,…,x_p}. Each scenario can be evaluated by q attributes o_j(j = 1,2,…,q), and the set of attributes is noted as O = {o₁,o₂,…,o_q}. The evaluation value of scheme x_i∈X belonging to o_i∈O is written in the form of the intuitionistic fuzzy set as F_ij = {<(o_j,x_i),m_ij,n_ij>}(i = 1,2,…,p; j = 1,2,…,q), abbreviated as F_ij = <m_ij,n_ij>, where m_ij ∈ [0,1] and n_ij∈ [0,1] denote the affiliation degree of x_i∈X with respect to o_j∈O, satisfying 0 ≤ m_ij + n_ij ≤ 1. The matrix form of the multi-attribute decision in the intuitionistic fuzzy sets can be expressed as follows:

$$F=(<m_{ij},n_{ij}>{)}_{p\times q}=\left[\begin{array}{llll}<m_{11},n_{11}>& <m_{12},n_{12}>& \cdots & <m_{1q},n_{1q}>\\ <m_{21},n_{21}>& <m_{22},n_{22}>& \cdots & <m_{2q},n_{2q}>\\ \hfill ⋮\hfill & \hfill ⋮\hfill & \hfill ⋮\hfill & \hfill ⋮\hfill \\ <m_{p1},n_{p1}>& <m_{p2},n_{p2}>& \cdots & <m_{pq},n_{pq}>\end{array}\right]$$

(4)

Weight is a very important factor in the multi-attribute decision problems, while attribute weight is another fuzzy concept, and different results can be obtained by various weighting methods. As a result, it is difficult to determine the weights accurately in practical decision problems. In this study, the intuitionistic fuzzy set VIKOR is combined with Analytic Hierarchy Process (AHP) to obtain weighted indicators to reflect the importance of the different indicators [16,17,32,33].

3.3. VIKOR Method

The multi-criteria compromise solution ranking (VIKOR) method is a multi-attribute decision-making method. It is characterized by the maximization of the group benefits and the minimization of individual regrets of opposing views [33]. The detailed decision-making steps are as follows:

(1) Based on the group decision evaluation information, the positive ideal solution f_j⁺ and the negative ideal solution f_j⁻ are determined:

$$f_j^{+}=[(\underset{i}{\max }{f}_{ij}|j\in B),(\underset{i}{\min }{f}_{ij}|j\in C)$$

(5)

$$f_j^{-}=[(\underset{i}{\min }{f}_{ij}|j\in B),(\underset{i}{\max }{f}_{ij}|j\in C)$$

(6)

where f_ij is the jth attribute value of the ith evaluation object; B is the benefits set; and C is the cost-type set;

(2) The group utility value S_i and individual regret value R_i are calculated for each evaluation object;

$$S_i={\displaystyle \sum }_{j=1}^m{w}_j(f_j^{+}-f_{ij})/(f_j^{+}-f_j^{-})$$

(7)

$$R_i=\underset{j}{\max }[w_j(f_j^{+}-f_{ij})/(f_j^{+}-f_j^{-})]$$

(8)

where w_j is the weight of each evaluation index; S_i and R_i are both cost-based variables, the smaller the value of the variable, the better the benefit of the evaluation object;

(3) The tradeoff value Q_i is calculated, the smaller the Q_i, the better the comprehensive benefit of the evaluation object, and the decision coefficient is ν ∈ [0,1]:

$$S^{+}=\underset{i}{\min }{S}_i,S^{-}=\underset{i}{\max }{S}_i,R^{+}=\underset{i}{\min }{R}_i,R^{-}=\underset{i}{\max }{R}_i$$

$$Q_i=\frac{v{S}_i-S^{+}}{S^{-}-S^{+}}+\frac{\left(1-v\right)R_i-R^{+}}{R^{-}-R^{+}}$$

(9)

where v ∈ [0,1] is the decision mechanism coefficient, and it indicates the preference of the decision maker for group opinion and individual regret values. If v > 0.5, it means that the decision-maker prefers comprehensive overall benefits; if v < 0.5, the decision-maker prefers individual regrets; and if v = 0.5, there is no significant preference for the decision-maker;

(4) On the basis of S_i, R_i and Q_i, the evaluation index level is determined, and then the object to be evaluated is rated.

4. Case Study

4.1. Overview of the Dongguashan Copper Mine

The Dongguashan Copper Mine, subordinated to the Tongling Nonferrous Metals Group, is located in Tongling City, Anhui Province. The Dongguashan Copper Mine is now mined to a depth of more than 1200 m. The waste rock of this mine is used for underground backfilling or resourceful sale, and the tailing sand is partially used for underground backfilling. At present, this mine has the characteristics of both modern deep mining and wasteless mining. It insists on scientific mining methods, actively promotes the advanced technology of wasteless mining and is being gradually transformed into a green and ecological modern mine through the recycling of production waste.

(1) Waste rock utilization and treatment

In the design of the mine, wasteless mining with “no tailing sand in the reservoir and no waste rock discharge from the pit” is taken as a goal. In the actual production process, most of the waste rock generated during the development period was used to backfill the empty area, and about 300,000 tons of waste rock for infrastructure construction were put up for sale at a temporary dump in the western depression of Lion Mountain. During the production period from January 2005 to June 2010, the Dongguashan Copper Mine produced approximately 4,626,900 tons of waste rock, of which 4,326,900 tons were used for underground backfill and external sale, and another 300,000 tons of waste rock stripped at the initial stage of mining was pending sale. From the infrastructure to the present, the utilization rate of waste rock in Dongguashan Copper Mine is 93.52%.

(2) Tailings utilization and treatment

As for the utilization of tailing sand, the mined-out area is backfilled with the continuous high-concentration backfilling technology of fluidized full tailings in the vertical sand bill. From January 2005 to December 2008, the Dongguashan Copper Mine produced a total of 8,054,700 tons of tailing sand, of which 7,365,700 tons were used for underground backfilling and 689,000 tons were sent to the Shuimuchong tailing pond for storage; during this period, the tailing utilization rate of the Dongguashan Copper Mine reached 91.45%. In 2011, the amount of tailing sand generated from the Dongguashan Copper Mine was 2,533,400 tons. About 1,566,700 tons of tailing sand were used for underground backfilling and the remaining 966,700 tons were sent to the Lao Yaling tailing pond for storage. During this period, the tailing utilization rate was 61.84%. On the whole, the utilization rate of tailings in the Dongguashan Copper Mine was 84.36%.

(3) Wastewater utilization and treatment

Dongguashan Copper Mine has implemented a variety of measures to control water pollution. To protect the water resources, wastewater was treated and discharged by the following methods: (1) the comprehensive treatment and recycling of wastewater in the concentrator; (2) dense backwater treatment of the sulfur concentrate; and (3) comprehensive treatment of return water in a concentrate dewatering station. The pollution discharge of Dongguashan Copper Mine observed the secondary standard in the Comprehensive Wastewater Discharge Standard (GB8978-1996) [25], and the comparison between the discharge standards and the actual situation is shown in Figure 3.

As shown in Figure 3, the wastewater monitoring value of the total discharge from the sedimentation pond of Dongguashan Copper Mine plant is much smaller than the national standard value, and the wastewater discharge quality here is identified as excellent.

To effectively evaluate the quality of wasteless mining, the influence of the effluent on the surrounding surface water and groundwater should be examined. The surface water treatment of the Dongguashan Copper Mine refers to the Class IV standard in the Surface Water Environmental Quality Standard (GB3838-88) [26,27], and is calibrated to the Class IV standard in the Surface Water Environmental Quality Standard (GB3838-2002) [28], as shown in

Table 5. Surface water environmental quality IV standard indicators and detection values Unit: mg/L (pH is dimensionless).

Project	IV Standard Value		Monitoring Values
	GB3838-88	GB3838-2002	Measurement Point 1	Measurement Point 2	Measurement Point 3
pH	6.5–8.5	6.0–9.0	8.08	7.71	7.7
CODcr	≤30	≤30	9.7	14.7	23.3
SS	38.5–51.5		51.5	39.2	38.5
Petroleum	≤0.5	≤0.5	1.35	1.63	1.13
Sulfide	≤0.5	≤0.5	0.02	0.02	0.02
Total Copper	≤1.0	≤1.0	0.009	0.014	0.05
Total Lead	≤0.05	≤0.05	0.013	0.013	0.013
Total Zinc	≤2.0	≤2.0	0.007	0.021	0.022
Total Cadmium	≤0.005	≤0.005	0.002	0.002	0.002
Chromium	≤0.05	≤0.05	0.004	0.004	0.004
Total Mercury	≤0.001	≤0.001	0.00004	0.00004	0.00004
Total Arsenic	≤0.1	≤0.1	0.0023	0.0053	0.0033
Dissolved iron	≤0.5	≤0.3	0.429	0.235	0.826

Note: Measurement point 1 in the table is 500 m upstream of the confluence of Hongxing River and Dongguashan Copper Mine’s total sewage ditch; measurement point 2 is 1000 m downstream of the confluence of Hongxing River and Dongguashan Copper Mine’s total sewage ditch; measurement point 3 is 4000 m downstream of the confluence of Hongxing River and Dongguashan Copper Mine’s total sewage ditch.

.

Compared with the Class IV standard of the Surface Water Environmental Quality in Table 5, the water quality of the three measurement points meets the relevant standards, indicating that the production of the Dongguashan Copper Mine does have a negative impact on the surface water. The research and monitoring results of the groundwater situation show that the groundwater environmental situation is also good and meets the provisions of the Class III standard in the Groundwater Quality Standard GB(T14848-2017) [29].

In summary, based on China’s national standards, the wastewater treatment effect of wasteless mining in the Dongguashan Copper Mine is obtained as pollution-free, and the mine production does not pollute the groundwater;

(4) Exhaust gas emissions

The impact of dust on air quality is mainly considered in the air pollution evaluation of Dongguashan Copper Mine. For the odorous and toxic disorganized emission gases from the pharmaceutical preparation room, the test room and the laboratory, the enterprise set up facilities such as axial flow fans to reduce the air pollution from the mine production. To evaluate the impact of the mine on air quality, the dust removal unit of the paste-filling mixing station was monitored; meanwhile, the emission outlet of the de-dusting unit of the paste filling and mixing station in the mine abided by the secondary standard of the Comprehensive Emission Standard for Air Pollutants (GB16297-1996) for new sources [30].

Table 6. Statistical table of outlet monitoring results of SX wet triple-effect dust collector.

	Exhaust Flow		Monitoring Factors	Monitoring Results
	Flow Rate Range	Average Flow Rate	Dust	Concentration Range	Average Concentration	Emission Rate
	(N·m3/h)	(N·m3/h)		/(mg/m3)	/(mg/m3)	/(kg/h)
	1726–2121	1972		47–83	64	0.126
Standard limit values					120	34

shows the monitoring results of the outlet of the SX wet triple-effect dust collector and the corresponding emission standards.

The monitoring results show that the dust emission concentrations and emission rates at the outlet of the de-dusting unit of the plant meet the limits of the corresponding standards;

(5) Soil environment

According to the actual situation of the Dongguashan Copper Mine, the construction of the Dongguashan Copper Mine may have had an impact on the soil environment around the mining area. Therefore, the current situation of the soil quality in the dry land along the upstream and downstream of the Red Star River of the mine was monitored.

Table 7. Soil environmental quality monitoring results (Unit: mg/kg).

Monitoring Sites		pH	Cu	Pb	Zn	Cd	As
1	Surface layer 0–20 cm	8.23	88.5	74.4	117	0.280	4.10
	Middle layer 20–60 cm	8.64	45.8	57.9	69.1	0.196	3.85
	Deep layer 60–100 cm	8.50	50.1	49.9	62.6	0.112	3.39
2	Surface layer 0–20 cm	7.96	92.1	161	203	0.293	12.9
	Middle layer 20–60 cm	7.79	81.5	133	187	0.213	11.4
	Deep layer 60–100 cm	7.68	69.7	120	141	0.158	8.85
IV standard value in GB15618-95		>6.5	≤400	≤500	≤500	≤1.0	≤30

shows the monitoring situation and the limit values of the Soil Environment Quality Standard (GB15618-1995) [31] for the different location samples of two monitoring points.

As shown in

Table 8. Evaluation index results of wasteless mining in Dongguashan Copper Mine.

Evaluation Indicators	Waste Rock Utilization Rate	Tailings Utilization Rate	Wastewater Pollution	Exhaust Emissions	Soil Environment	Resources Recovery Rate
Mine situation	93.52%	84.36%	No wastewater	No exhaust gas	Great quality	73.10%

, the soil environment around the Dongguashan Copper Mine is in line with the standard limits; and the mining of the mine has not caused any negative impact on the soil environment;

(6) Resource recovery efficiency

In terms of resource recovery, the Dongguashan Copper Mine mainly adopted the backfilling method to mine the deposits, so that the resources can be recovered to the maximum extent. At the same time, the visualization simulation of the large-scale mining process of the deep shafts was realized, and the simulation software was applied to the design of the deep shaft retrieval to optimize the retrieval sequence for the safe and efficient mining of the deep shafts under high-stress conditions; besides, the mine has also carried out a strict and reasonable mining plan to extract the mineral resources, so that the deposit resources can be fully recovered. To quantify the calculation of resource recovery efficiency, the mining loss rates and the beneficiation technical indexes of Dongguashan Copper Mine are obtained, as shown in Figure 4.

(7) Evaluation of indicator results

Based on the above, the utilization rate of the mine waste rock is 93.52%, and the utilization rate of the tailing sand is 84.36%. The mining loss rate of copper is 15%, and the recovery rate of beneficiation is 86%. By combining the two indicators, the resource recovery rate is calculated as 73.1% by Equation (1). The status of mine wastewater pollution, exhaust gas emission, and soil environment all meet the national standards, and the three levels of wasteless mining are characterized as the greatest level. Table 8 shows the final evaluation results of wasteless mining in Dongguashan Copper Mine.

4.2. Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR

The proposed evaluation method of the wasteless mining in this study can be conducted as follows. Firstly, the national standard system and the current mining situation of the Dongguashan Copper Mine were combined and the intuitive fuzzy sets were established. Referring to the green mine evaluation system, the index weights were calculated by the AHP method. Secondly, the above intuitionistic fuzzy set matrix was ranked by the VIKOR method to determine the level of wasteless mining in the mine. Finally, a sensitivity analysis was performed to verify the stability of the VIKOR method in the application. Figure 5 shows the process of the waste-free mining evaluation.

(1) Building the intuitionistic fuzzy sets

To represent each evaluation index level with intuitionistic fuzzy sets, the affiliation degree is assigned according to the classification of each index in Section 2.3. Based on the existing calculation method of the affiliation degree and the actual situation, the six evaluation indicators are used as a domain to assign the affiliation degree to the evaluation criteria of wasteless mining. For the upper limit of level I, the affiliation degree is assigned to 1; for the lower limit of level V, the affiliation degree is assigned to 0. The affiliation degree of the wasteless mining quality of the other levels is between 0 and 1, and its value is assigned according to the detailed level.

Table 9. Affiliation assignment for indicator classification.

Grade	Waste Rock and Tailing Sand Utilization/%	Wastewater Exceeds the Standard/%	Exhaust and Soil Quality	Resource Recovery/%	Affiliation	Non-Affiliation
I*	100	No wastewater	No exhaust gas	100	1	0
I	80	0	Unaggravated	80	0.8	0.2
II	70	200	Aggravated Grade 1	70	0.6	0.4
III	60	300	Aggravated Grade 2	60	0.4	0.6
IV	0	>300	Aggravated Grade 3 and above	0	0	1

shows the values of the six evaluation indexes.

In Table 9, the quality of the wasteless mining is divided into four levels. The level of I* is the ideal state of wasteless mining, followed by I–IV. As shown in Table 9, waste rock, tailing sand and resource recovery can be well classified by the numbers, while the wastewater, waste gas and soil quality are graded in the qualitative description.

According to the actual mining of the mine, the affiliation and non-affiliation degrees of the waste rock, tailing sand, wastewater and waste gas of Dongguashan Copper Mine are calculated with reference to the affiliation and non-affiliation degrees of the evaluation criteria. They are calculated as follows:

$$x_{ij}^{*}=y_l^{*}+\frac{x_{ij}-y_l}{y_k-y_l}(y_k^{*}-y_l^{*})$$

(10)

where $x_{ij}^{*}$ is the affiliation degree of wasteless mining quality indicators; x_ij is the actual affiliation degree of wasteless mining quality indicators; y_l, $y_l^{*}$ are lower limit values of x_ij in the interval of evaluation criteria and their affiliation degree; y_k, $y_k^{*}$ are the upper limits of x_ij in the interval of evaluation criteria and their affiliation degree.

The wastewater, waste gas and soil quality can be qualitatively obtained from the actual index data of the mine to get the corresponding grading, their affiliation and non-affiliation degrees.

Combining the actual production of Dongguashan in Section 4.1 with its evaluation index levels in Table 9, it can be concluded that: the quantitative indicators, such as waste rock utilization, tailing sand utilization resource recovery and other indicators can be directly transformed into the affiliation degree after standard normalization. For the resource recovery rate, the mining loss rate and the beneficiation recovery rate are only considered, therefore, a hesitation degree of 0.05 is used. The qualitative indicators, such as wastewater, waste gas and soil pollution are classified as Class I, according to the actual monitoring results of the mine, with affiliation degrees between 0.8 and 1.0. Considering that the mine production still causes certain impacts on the water bodies, air and soil, these indicators are calculated with the lowest affiliation degree of their level, and a hesitation degree of 0.05 is also used.

Table 10. Affiliation and non-affiliation degrees of six indicators.

	Waste Rock Utilization	Tailings Utilization	Wastewater Exceeds the Standard	Exhaust Gas	Quality of Soil	Recovery of Resource
Affiliation	0.9352	0.8436	0.8000	0.8000	0.8000	0.7310
Non-affiliation	0.0648	0.1564	0.1500	0.1500	0.1500	0.2190

shows the affiliation and non-affiliation degrees of the six evaluation indicators of Dongguashan Copper Mine after the calculation.

The affiliation and non-affiliation degrees of the parameters are expressed in the form of an intuitionistic fuzzy set decision matrix:

F = (<0.9352,0.0648><0.8436,0.1564><0.8,0.2><0.8,0.2><0.8,0.2><0.731,0.269>)

A set of intuitive fuzzy number matrices for the evaluation of Dongguashan Copper Mine can be obtained according to the affiliation degree assignment of the index classification and the affiliation and non-affiliation degrees of the six evaluation indexes of Dongguashan Copper Mine, as follows:

$$F*=\left[\begin{array}{l}<1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00>\\ <0.80,0.20><0.80,0.20><0.80,0.20><0.80,0.20><0.80,0.20><0.80,0.20>\\ <0.60,0.40><0.60,0.40><0.60,0.40><0.60,0.40><0.60,0.40><0.60,0.40>\\ <0.40,0.60><0.40,0.60><0.40,0.60><0.40,0.60><0.40,0.60><0.40,0.60>\\ <0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00>\\ <0.94,0.06><0.84,0.16><0.80,0.15><0.80,0.15><0.80,0.15><0.73,0.22>\end{array}\right]$$

(2) Determination of weighting factors

In the evaluation of the green mine, the AHP, binomial coefficient method, principal component analysis method, deviation and mean square deviation method are used to determine the weight of the evaluation index. Referring to the research results of the index weight in the literature on wasteless mining and the green mine evaluation system, the intuitionistic fuzzy set VIKOR method is used to calculate the index weight in this study [34,35]. The AHP method is now used to calculate the weight values of each index as shown in

Table 11. AHP method to calculate index weights.

O j	O 1	O 2	O 3	O 4	O 5	O 6	wj
	Waste Rock Utilization	Tailings Utilization	Wastewater Exceeds the Standard	Exhaust Gas	Quality of Soil	Recovery of Resource
O 1	1	1	1/3	1/3	1/2	1/6	0.062
O 2	1	1	1/3	1/3	1/2	1/6	0.062
O 3	3	3	1	1	2	1/2	0.195
O 4	3	3	1	1	2	1/2	0.195
O 5	2	2	1/2	1/2	1	1/3	0.114
O 6	6	6	2	2	3	1	0.372
λmax = 6.013; CI = 0.0026; CR = 0.00206 < 0.01; consistency check passed

.

Based on the above study, the index weights of the waste rock utilization, tailing sand utilization, wastewater exceedance, waste gas, soil quality and resource recovery in the intuitive fuzzy set are obtained as follows:

W = (0.062, 0.0.062, 0.195, 0.195, 0.114, 0.372)^T

(3) VIKOR method sorting

The first five rows of the weighted intuitionistic fuzzy matrix are the weighted intuitionistic fuzzy sets of the evaluation criteria, and the sixth row is the weighted intuitionistic fuzzy set of the Dongguashan Copper Mine. After that, the VIKOR method is used to rank the effectiveness of the wasteless mining, the ideal effectiveness and the worst effectiveness of the comprehensive evaluation matrix are as follows:

A⁺ = (<1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00>)

A⁻ = (<0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00>)

To balance the importance of the group utility value and the individual regret value in the quality evaluation of wasteless mining, Equation (3) and Equations (7)–(9) are combined, and ν = 0.5 is used to calculate the values of S, R and Q. The calculation results are shown in

Table 12. Evaluation results of wasteless mining quality.

	S	R	Q
I*	0	0	0
I	0.2	0.0744	0.1992
II	0.4	0.1488	0.3984
III	0.6	0.2232	0.5976
IV	1	0.372	0.996
Dongguashan Copper Mine	0.194348715	0.091613261	0.219325372
Affiliation Level	I	II	II

.

The above results show that the relative proximity intervals of the wasteless mining evaluation criteria are as follows:

Class I wasteless mining:	0.0 ≤ Si ≤ 0.2, 0.000 ≤ Ri ≤ 0.074 and 0.000 ≤ Qi ≤ 0.199
Class II wasteless mining:	0.2 ≤ Si ≤ 0.4, 0.074 ≤ Ri ≤ 0.149 and 0.199 ≤ Qi ≤ 0.398
Class III wasteless mining:	0.4 ≤ Si ≤ 0.6, 0.149 ≤ Ri ≤ 0.223 and 0.398 ≤ Qi ≤ 0.598
Class IV wasteless mining:	0.6 ≤ Si ≤ 1.0, 0.223 ≤ Ri ≤ 0.372 and 0.598 ≤ Qi ≤ 0.996

Through the calculation, the group utility value is 0.1943 ∈ (0,0.2), belonging to level I; the individual regret value is 0.0916 ∈ (0.074, 0.149), belonging to level II; the compromise value is 0.2193 ∈ (0.199, 0.398), belonging to level II. The S, R and Q indicators of Dongguashan are ranked according to the above numerical data, and the level of wasteless mining at Dongguashan Copper Mine is evaluated finally as Grade II on a comprehensive basis.

(4) Sensitivity Analysis

The value of the decision mechanism coefficient ν has a great influence on the decision outcome. In this study, ν = 0.5 is set to combine the maximum grouping benefits and the minimum individual regret. To verify the stability of the decision results, a value at every 0.1 in ν ∈ [0,1] is taken to conduct a sensitivity analysis. The variation of Q with the decision mechanism coefficient ν is shown in Figure 6 and

Table 13. The variation of the value of Q with the change of v.

ν	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
I	0.1986	0.1987	0.1989	0.1990	0.1992	0.1994	0.1995	0.1997	0.1998
II	0.3971	0.3974	0.3978	0.3981	0.3984	0.3987	0.3990	0.3994	0.3997
Q	0.2393	0.2343	0.2293	0.2243	0.2193	0.2143	0.2093	0.2043	0.1993

[16].

As shown in Figure 6 and the results of the sensitivity analysis in Table 13, the maximum grouping benefit is stable between class I and class II; when ν = 0.9, the ranking order is I; when ν is not 0.9, the ranking order is II. In summary, it can be seen that the VIKOR ranking method is relatively insensitive to the decision coefficient ν, and the decision results are stable.

(5) Evaluation results of TOPSIS algorithm

The VIKOR algorithm uses group utility values, individual regret values and trade-off values to rank the overall evaluation of the projects, while the TOPSIS algorithm ranks each project by the distance of positive and negative ideal solutions. Based on the overview of the Dongguashan Copper Mine in Section 4.1 and the analysis of evaluation criteria in Section 2.3, the final TOPSIS algorithm ranking results are obtained, as shown in

Table 14. Evaluation results of wasteless mining quality based on TOPSIS algorithm.

	Positive Distance	Negative Distance	Relative Proximity	Affiliation Level
I*	0.0000	0.5244	1.0000	I
I	0.1049	0.4195	0.8000	I
II	0.2098	0.3146	0.6000	II
III	0.3146	0.2098	0.4000	III
IV	0.5244	0.0000	0.0000	IV
Dongguashan Copper Mine	0.1286	0.4109	0.7616	II

.

When the TOPSIS algorithm is used to evaluate the current status of wasteless mining at Dongguashan Copper Mine, its wasteless mining grade is finally rated as Grade II, which is consistent with the VIKOR algorithm.

Although the evaluation results of the two evaluation methods are the same, the evaluation process is slightly different. In the TOPSIS algorithm, the overall evaluation is focused, and the project indicators are eventually ranked based on the Euclidean distance between the overall project indicators and the optimal ideal solution, but the worst indicators of the project are not reflected. In the VIKOR algorithm, the group utility value S is initially evaluated as grade I; due to the low resource recovery index of the Dongguashan Copper Mine, a low individual regret value R is eventually obtained, and the final compromise value Q is evaluated as grade II. In the wasteless evaluation, multiple aspects need to be considered, and the evaluation of the comprehensive mining level also needs to focus on the worst indicators. When the TOPSIS algorithm is used for evaluation, if the other indicators are excellent, and a few of them are poor, there will be excellent indicators that blur the worst indicators, thus affecting the overall evaluation results. Therefore, when considering the overall level and worst index of wasteless mining, the combination of the intuitionistic fuzzy set and the VIKOR evaluation algorithm, which integrates the SRQ three indicators, is more advantageous.

(5) Comprehensive quality evaluation of wasteless mining of Dongguashan Copper Mine

The above evaluation results show that there are inconsistencies in the evaluation grades of the three indicators of the SQR of the VIKOR algorithm. This is because the resource recovery rate of the Dongguashan Copper Mine is inferior, which determines the final individual regret value and affects the compromise value, Q. Since the resource recovery rate still cannot reach the optimal standard, the quality of the wasteless mining quality in Dongguashan Copper Mine is finally evaluated as grade II.

The existing evaluation results have shown that the wasteless mining quality of the Dongguashan Copper Mine has reached the best level. However, the evaluation result of this study is level II, rather than the level I. The reasons for this difference mainly include:

(i) When the mine was designed, the generated waste rock and tailings could be used as filling materials for backfilling or selling in the extraction area. However, the actual situation is that the waste rock and tailings are not entirely used for underground backfilling, there is still a part of ore piled on the surface and a large amount of the tailings goes into the tailings pond;

(ii) For the pollution-free wastewater, waste gas and soil quality, the evaluation result of this study is at the dividing point between Class I and Class II, because of the environmental capacity factor, but the pre-evaluation is directly evaluated as the best class;

(iii) In terms of resource recovery, the actual mining recovery rate of the mine is 85%, while in the original pre-evaluation of the Dongguashan Copper Mine, the mining recovery rate was 90%, which affects the wasteless mining level of the mine.

Therefore, the beneficiation technology and process still need to be improved in the Dongguashan Copper Mine to recover mineral resources; through pillar mining and rational planning, the residual ore resources should be recovered to improve the mining recovery rate; the tailings should be fully used to fill the underground goaf. In this way, the quality of the wasteless mining in the mine can be further improved.

5. Conclusions and Prospects

(1) In this study, six indicators of waste rock, tailings, wastewater, waste gas, soil and resource recovery are used; based on the current situation of wasteless mining in Dongguashan Copper Mine, a set of intuitively fuzzy set evaluation matrices of wasteless mining level in Dongguashan Copper mine are established, and the VIKOR method is used to rank and grade the above matrices. This study provides a reference for the quality evaluation of the wasteless mining of green mines, which is conducive to improving the assessment mechanism of green mines [1];

(2) The limitations of this study are: (i) The selected indicators are not comprehensive. In addition to the above-mentioned indicators, wasteless mining also involves economic development, advanced technology, management and operation. Therefore, more indicators should be evaluated for the comprehensive evaluation; (ii) It is difficult to establish uniform standards. Due to regional differences, policy differences and wasteless mining standards, it is difficult to unify the standards of wasteless mining, and it is still urgent to realize the unification and fairness of the evaluation system;

(3) To promote wasteless mining production, various influencing factors should be considered and a quality evaluation system for wasteless mining should be established. In future research on wasteless mining evaluation, more effective multi-attribute decision-making methods should also be introduced.