海底深部金矿采矿方法优化(1).pdf

Trans. Nonferrous Met. Soc. China 292019 2160−2169 Optimization of mining in subsea deep gold mines Acase study Wei-zhang LIANG1, Guo-yan ZHAO1, Hao WU1, Ying CHEN2 1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China; 2. School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China Received 28 February 2018; accepted 31August 2019 Abstract The mining optimization in subsea deep gold mines was studied. First, an index system for subsea mining selection was established based on technical feasibility, security status, economic benefit, and management complexity. Next, an uation matrix containing crisp numbers and triangular fuzzy numbers TFNs was constructed to describe quantitative and qualitative ination simultaneously. Then, a hybrid model combining fuzzy theory and the Tomada de Deciso Interativa Multicritrio TODIM was proposed. Finally, the feasibility of the proposed approach was validated by an illustrative example of selecting the optimal mining in the Sanshandao Gold Mine China. The robustness of this approach was demonstrated through a sensitivity analysis. The results show that the proposed hybrid TODIM is reliable and stable for choosing the optimal mining in subsea deep gold mines and provides references for mining optimization in other similar undersea mines. Key words subsea deep mining; mining ; fuzzy theory; hybrid TODIM 1 Introduction With the substantial reduction of available land resources, the exploitation of marine resources has measurably increased [1]. Mineral resources in oceans are mainly distributed in seawater, marine mud and bedrock. In particular, plentiful mineral resources are found in the bedrock near coastlines with many countries interested in accessing them [2]. Selecting suitable mining s is one of the most important processes inmininganddirectlyinfluencesthesafetyand efficiency of the work. Problems may arise because of an inappropriate mining choice, such as inefficiency, high production costs, and even water inrush disasters [3]. Mining s must adapt for different mining depth. When mining near the sea floor, due to water inrush hazards, it is essential to retain enough pillars and prevent roof damage [4]. In comparison, it is possible to cancel pillars and change support measures for deep mining because the mining disturbance on the waterproof rock ation is diminished [5]. Moreover, owing to the distinctiveness of subsea mining, the mining technology used in land is not easily utilized directly. Hence, it is significant to research mining optimization in subsea deep mines. On the basis of statistics, more than 100 subsea coal mines exist globally [6]. The history of subsea coal mining is rich, long wall mining and room and pillar mining s are widely employed. In contrast, there are relatively few subsea metal mines. Examples include the Qajasalolcroix Iron Mine in Finland, Levent Tin Mine in England, Dove Tungsten Mine in Australia, and Sanshandao Gold Mine in China [4,6]. As the filling material can restrict the deation of the surrounding rock, several filling s are chief subsea mining s. Additionally, certain s for mining under rivers or reservoirs can be used for reference. However, as conditions between mines vary, mining s cannot be used indiscriminately. Many researchers regard mining selection as a multi-criterion decision making MCDM problem because it is affected by multiple factors [7,8]. In the processofminingselection,twovital components are contained the index system and decision making . It is essential to establish an index Foundation item Project 2018dcyj052 supported by Survey Research Funds of Central South University, China; Project 51774321 supported by the National Natural Science Foundation of China; Project 2018YFC0604606 supported by the National Key Research and Development Program of China Corresponding author Guo-yan ZHAO; Tel 86-13507311842; E-mail gyzhao DOI 10.1016/S1003-63261965122-8 Wei-zhang LIANG, et al/Trans. Nonferrous Met. Soc. China 292019 2160−21692161 systemfirst[9].However,fewresearchershave established an index system for subsea mining selection. LIU et al [10] selected 10 indicators in consideration of the geologic conditions, technology, economy and safety production in a subsea gold deposit. Nevertheless, their index system does not reflect the significant risk of water inrush with subsea bedrock miningduetothedisturbancetooverlyingrock ations.Thisisanessentialconsiderationfor optimizing subsea mining s. Withregardtothedecisionmaking, BALUSA and SINGAM [7] combined wavelet packet modulationWPMandthepreferenceranking organizationforenrichmentuations PROMETHEE to select an applicable mining for a bauxite mine. LIU et al [10] considered a large amount of uncertain ination and proposed a mining optimization model based on unascertained measurement theory. KARIMNIA and BAGLOO [11] proposed a fuzzy analytic hierarchy process FAHP approach to determine the most appropriate mining in the Qapiliq Salt Mine. KABWE [12] selected theoptimalminingforNchanga’sUpper Orebody using an analytic hierarchy process AHP and Yager’s . Furthermore, SITORUS et al [13] discussed the applications and trends of MCDM for the choice problem in mining and mineral processing. Nevertheless, the assessment values in these s are only expressed by crisp or fuzzy numbers, which cannot indicatequalitativeandquantitativeination simultaneously. Generally, the qualitative inds expressed using the scoring do not adequately reflect fuzzy ination.Inthiscase,fuzzytheorycanbe well-adopted to solve such ambiguous problems. For convenience, the fuzzy ination is often transed into triangular fuzzy numbers TFNs in the decision making process [14]. Thus, many MCDM s have been combined with TFNs to solve fuzzy decision making problems. For instance, DONG et al [15] modified an analytic network process ANP with TFNs to identify the key influencing factors in the power generation market; OCAMPO [16] built a decision model for manufacturing sustainability with an FAHP in a triangular fuzzy environment; ZHAO et al [17] assessed battery energy storage systems based on TFNs, the best−worst , and fuzzy-cumulative prospect theory. In addition to the above s, the Tomada de Deciso Interativa Multicritrio TODIM was presentedbyGOMESandLIMA[18]torank alternatives on the basis of prospect theory [19,20]. In recent years, this has been successfully modified with various fuzzy sets to address realistic issues. For example, JI et al [21] selected personnel by integrating multi-valued neutrosophic numbers with the TODIM ; BISWAS and SARKAR [22] proposed an interval-valued Pythagorean fuzzy TODIM approach to deal with multi-criteria group decision making problems; ZHANGetal[23]uatedwatersecurityby employing TODIM with probabilistic linguistic term sets. Considering the complexity of mining selection and the diversity of indicators, a hybrid TODIM for selecting the optimal mining is presented in this study. The goal of this study is to propose an approach for mining optimization in subsea deep gold mines. First, an uation index system for subsea mining selectionisestablished.Then,ahybrid ology combining fuzzy theory and the TODIM ispresented.Afterwards,theproposed ology is adopted to select the optimal mining in the Sanshandao Gold Mine, China. Finally, the effectivenessandrobustnessoftheapproachis demonstrated. 2 uation index system The index system is established in this section according to the specific characteristics of subsea mining s. It is comprised of four criteria technical feasibility B1, security status B2, economic benefit B3, and management complexity B4. The detailed uation index system for subsea mining selection is shown in Fig. 1. 1 Technical feasibility B1 Due to the complex mining conditions, the selected mining should be feasible in the technical level first [7]. Furthermore, because of variation in orebody morphology, thechosenmustbestrongly adaptable. Thus, the sub-criteria of technical feasibility include the degree of feasibility B11 and degree of adaptability B12. 2 Security status B2 There is a particularly high risk of water inrush in subsea mining. Hence, it is essential that disturbances to overlying rock ations should be minimized when employing any mining [5]. The safety of the working surface also needs to be guaranteed, as it directly affects the operation security [10]. Therefore, the sub-criteria of security status include the degree of safety of the working surface B21, ventilation conditions B22, anddegreeofdisturbancetotheoverlyingrock ation B23. 3 Economic benefit B3 The perance of a mining should be reflected in terms of economic benefit. That is to say, high efficiency and low cost should be achieved with the Wei-zhang LIANG, et al/Trans. Nonferrous Met. Soc. China 292019 2160−21692162 Fig. 1 uation index system for subsea mining selection optimalmining[10].Consequently,the sub-criteria of economic benefit contain the mining efficiency B31, mining cost B32, ore loss rate B33, and ore dilution rate B34. 4 Management complexity B4 Asminingisacomplngineeringsystem, excellentmanageabilityisrequired.Moreeasily managed systems generally are correlated to smoother operations. Mining s with less complexity in processmanagementareweightedhigher[8]. Accordingly,thesub-criterionofmanagement complexity is the process complexity B41. 3 Hybrid TODIM A hybrid model combining fuzzy theory and the TODIM is presented. The framework of this hybrid TODIM is illustrated in Fig. 2. The specific steps of the hybrid TODIM model for ranking mining s are described as follows 1 Step 1 Establish initial uation matrix Forthecomprehensiveassessmentofmining s, several sub-criteria, e.g., B31, B32, B33and B34, can be denoted by quantitative values. Nevertheless, sub-criteria with uncertain ination including B11, B12, B21, B22, B23and B41are more suitably denoted by qualitative values. However, most decision makers are accustomed to using linguistic phrases, such as “very good”, “good”, “poor”, and so on [24]. In this study, the linguistic terms were converted into TFNs according to the transation rule shown in Table 1 [25]. The triangular fuzzy number TFN s  can be denoted as a triplet s  [sO, sP, sT], and the membership function s xis represented as follows [26] O O OP PO T PT TP T 0, , , 0, s xs xs sxs ss x sx sxs ss xs                   1 where sOand sTare the lower and upper bounds of the available area, respectively, and sO η5η4η3, the optimal mining was A1. Consequently, the room-pillar alternation upward level cut and fill stopping was selected as the optimal mining in the Xinli district, which is detailed in Fig. 5. The practice demonstrates that the selected mining is effective and capable of greater economic benefit. Table 2 Sub-criteria of five mining s Mining Sub-criteria B11B12B21B22B23B31/tshift−1B32/YUANt−1B33/B34/B41 A1GSGVGGF42.1260.246.06.0SP A2GGGGSP38.2759.238.27.0SG A3FPSGGVG42.7052.9719.08.5F A4SGGGFG20.2764.735.07.0G A5GGGSGP17.2053.4017.26.0F Table 3 Linguistic ratings of all sub-criteria Professional Sub-criteria B11B12B21B22B23B31B32B33B34B41 D1SHMHSHVHHHHSHM D2HHHHVHHVHHMSH D3SHSHHHVHHVHHHSH D4SHSHHSHVHHHVHSHH D5HSHHSHVHVHHHSHM Table 4 Dominance of each alternative over other alternatives ParameterA1A2A3A4A5 A10−1.1129−0.0526−0.7421−1.7409 A2−3.32730−1.0990−0.6499−2.2954 A3−6.2567−5.03120−4.0848−4.8307 A4−5.8371−4.2695−3.36260−4.3485 A5−4.1836−2.4209−1.4192−1.44960 Wei-zhang LIANG, et al/Trans. Nonferrous Met. Soc. China 292019 2160−21692167 Fig. 5 Room-pillar alternation upward level cut and fill stopping 5 Discussion Tapping the resources of subsea bedrock deposits is becoming increasingly vital and widespread. To account for the distinctiveness of subsea bedrock deposits, a hybrid TODIM model was proposed to select the optimal mining . Considering the influence of parameter θ in Eq. 13, a sensitivity analysis is provided to demonstrate the robustness of the proposed . Here, the value of θ is assumed to be θ1. However, other values of θ have been adopted in the literatures [29,30]. Therefore, to verify the stability of the results, other θ values were also chosen for comparison. In general, if θ1, the influence of loss is weakened; if θA5A4A3A1A3 0.4A1A2A5A4A3A1A3 0.6A1A2A5A4A3A1A3 0.8A1A2A5A4A3A1A3 1.0A1A2A5A4A3A1A3 2.0A1A2A5A4A3A1A3 4.0A1A2A5A4A3A1A3 6.0A1A2A5A4A3A1A3 8.0A1A2A5A4A3A1A3 Wei-zhang LIANG, et al/Trans. Nonferrous Met. Soc. China 292019 2160−21692168 The hybrid TODIM was employed to select the optimal mining for the Xinli district of the Sanshandao Gold Mine. Alternative A1was chosen as the best mining . Currently, the mine is producing ore, both safely and efficiently. The selected filling s are being used, whereas certain deations may still occur, especially in steep and thick orebody. MA et al [31] monitored the surface settlement in the Jinchuan Nickel Mine using global positioning system GPS monitoring system.Theirresultsshowedthatthemaximum settlement reached 2403 mm, despite the use of the back-fillingmet