Research progress on degradative solvent extraction of low-rank coals
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摘要:
低阶煤的热溶萃取提质是利用溶剂在温和条件下对低阶煤进行热萃取，通过对原煤的脱水与多级分离，可得到无水、无灰、高热值与良好热塑性的萃取产物与低水分的萃余煤。同时萃取产物在配煤炼焦、高级燃料及炭材料制备等多个领域均具有实际应用优势，拥有较高的附加值，并且该技术中溶剂具备可循环利用的优势。因此，热萃取提质是实现低阶煤分级分质转化利用的有效途径之一。本综述首先介绍了现有各类低阶煤提质处理方式；然后梳理了热溶萃取提质发展脉络，重点综述了低阶煤热溶萃取提质的各类影响因素、反应机理、现有工艺以及产物的利用途径；最后利用“Web of Science核心合集”作为数据源，使用CiteSpace科学计量软件刻画了煤溶剂萃取的知识图谱，通过对研究主题进行分析，总结研究方向并预测研究热点，为低阶煤热溶萃取提质研究提供一定的参考价值。综合分析表明：新型低成本绿色溶剂的选取、萃取产物结构表征及高值利用等领域具有较高的研究趋势，同时需要对萃取机理及萃取物特性定向调控开展深入研究，进一步推动工艺规模化生产进程。
Abstract:The degradative solvent extraction of low rank coal is the use of solvents to extract low rank coal under mild conditions, through the dehydration and multi-stage separation of the raw coal: the extraction products with no water content, no ash content, high calorific value properties and excellent thermoplastic properties, and low moisture of the extractive residual coal can be obtained. At the same time, the products have practical application advantages in many fields such as coal coking, advanced fuel and carbon material preparation, which have high added value, and the solvent in this technology has the advantage of being recyclable. Therefore, degradative solvent extraction is one of the effective ways to realize the graded fractionated conversion and utilization of low-rank coal. This review firstly introduces the existing low rank coal upgrading methods, and then reviews the development of degradative solvent extraction, focusing on the various influencing factors, reaction mechanisms, existing processes and product utilization of low rank coal upgrading by degradative solvent extraction. Finally, using the "Web of Science core collection" as the data source, the knowledge graph of coal solvent extraction was carved using CiteSpace scientometric software, and the research themes were analyzed to summarize the research directions and predict the research hotspots, providing some reference value for the research of degradative solution extraction of low-rank coal. The comprehensive analysis shows that: the selection of new low-cost green solvents, structural characterization and high-value utilization of extraction products have high research trends, while in-depth research on extraction mechanism and targeted regulation of extractant properties is needed to further promote the process of large-scale production.
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Keywords:
- low-rank coal /
- degradative solvent extraction /
- extracts application /
- CiteSpace /
- knowledge graph
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0. 引 言
露天煤矿适用于开采范围广、煤层厚度大的煤田。为了提高煤炭资源开发效率，通常把煤田分成若干独立的露天煤矿[1-2]。然而受开采技术、设备规格、产量规模、煤层赋存条件以及经济效益等各种因素影响，且为避免大型露天煤矿整体开采时运输距离大、运费高、工作线长度大、开采成本高、初期投资时间长、投资费用大等缺点，在满足技术经济条件下，对露天煤矿进行合理的采区划分[3-5]。采区过大，则过长的工作线和过大的运距导致露天煤矿开采过程中经济效益降低；而采区过小，频繁的采区接续增大生产管理难度影响生产效率。因此，划分合理的采区关系是提高露天煤矿生产效率和经济效益的基础。刘宪权等[6]基于近水平露天煤矿采区划分提出了坑底纵向搭桥及转向期反向内排开采程序；白润才等[7-8]基于熵值法与Topsis法构建了采区划分方案评价模型；张瑞新[9-10]等优化了露天煤矿开采程序设计相关计算机模型，对哈尔乌素露天煤矿的开采程序提出了多方案动态综合优化方法，并对安太堡和安家岭的开采程序提出了递阶优化方法；叶义成[11]采用双基点法对开采程序进行多目标综合排序，并确定了敏感指标；刘桐等[12]采用Delphi-Topsis法构建了霍林河露天煤矿采区划分方案优选模型。以新疆准东某露天煤矿扩能目标为研究背景，提出多种采区划分方案，结合该露天煤矿具体开采条件确定采区划分方案评价指标，并构建综合评价模型，确定最佳的采区划分方案，以满足扩能至50 Mt/a的生产能力要求。
1. 研究区概况
以新疆准东某露天煤矿为研究对象，矿区南北最大长达40.78 km，东西最大宽达37.69 km，面积1 127.32 km2。矿田主要可采煤层为B5、B3、B2三层煤层，且均为巨厚煤层，可采煤层平均厚度为44.2 m。其中剥离工艺采用单斗−卡车间断工艺；采煤工艺采用单斗−卡车−半移动式破碎站−带式输送机半连续开采工艺[13-15]。该露天煤矿年产原煤量19.85 Mt，为进一步做好露天煤矿生产经营长远规划，该生产能力拟核增至50 Mt/a。
如以当前工作线长度1 500 m继续向前推进，每年的推进度将达到600 m/a左右，超出《煤炭工业露天矿设计规范》规定的小于400 m/a要求。将造成外排运距过大且破碎站的移设频繁。因此，应以适应产能需求的工作线长度对采区进行优化，提高生产效率及经济效益。
2. 工作线长度优化
2.1 技术可行的工作线长度
由年产量、容重、含煤率、采出率、煤厚及推进度，确定技术可行工作线长度，应满足[16]：
$$ {L_{\text{k}}} = \frac{Q}{{{v_{\text{t}}}{h_{\text{m}}}\gamma \eta }} $$ (1) 式中：Q为年产量，t/a；$ L_{{\rm{k}}} $为技术可行原煤工作线长度，m；vt为推进度，m/a；$ h_{\mathrm{m}} $为煤层平均厚度，m；γ为原煤容重，t/m3；η为煤层采出率。
根据该矿产能核增要求，以目前1500 m工作线长度，分别计算露天矿产能20 Mt/a与计划产量增至为50 Mt/a时推进度为
$$ {v_{{\rm{t1}}}}{\text{ = }}\frac{{2 \times {{10}^7}}}{{44.2 \times 1\;500 \times 1.32 \times 0.98 \times 0.966\;6}} = 241\;{{\text{m}} / {\text{a}}} $$ (2) $$ {v_{{\rm{t2}}}}{\text{ = }}\frac{{5 \times {{10}^7}}}{{44.2 \times 1\;500 \times 1.32 \times 0.98 \times 0.966\;6}} = 603\;{{\text{m}} / {\text{a}}} $$ (3) 由此可见，产能核增后，以当前工作线长度生产将导致推进度过大，应合理增大工作线长度以降低高产能条件下的推进度。结合《煤炭工业露天矿设计规范》要求，设计生产能力为50 Mt/a时的推进度宜控制在241～400 m/a，进而确定产能核增后技术可行工作线长度为2261.7～3 753.9 m，如图1所示。
2.2 经济合理的工作线长度
通过该露天煤矿的端帮边坡角、上覆岩层剥离物与煤层厚度、爆破、采装及运输费用、排弃路线系数以及排弃的影响距离，确定经济合理工作线长度[17-18]：
$$ {L_{\text{h}}} = \sqrt {\frac{{(H + {h_{\text{m}}})\cot \;\beta ({c_1} + {c_2}b)}}{{1\;000{c_2}a}}} $$ (4) 式中：Lh为经济合理采煤工作线长度，km；H为剥离层平均厚度，m；β为端帮边坡角，(°)；c1为穿孔爆破、采装、排土费，元/m3；c2为运输费，元/(m3·km)；a为排弃路线系数，双环内排时取0.5，单环内排时取1；b为排弃影响距离，km。
排弃影响距离可按式计算：
$$ b = \frac{{H + {h_{\text{m}}}}}{{2\;000}}\left( {\cot \;\varphi + \cot \;\theta + \cot \;\beta } \right) + m $$ (5) 式中：θ为内排土场帮坡角，(°)；φ为工作帮坡角，(°)；m为坑底安全距离，km。
结合煤厚与上覆岩层厚度，确定经济合理工作线长度为2 286.3～2 496.5 m。
2.3 最佳工作线长度
确定产能核增50 Mt/a时，技术可行工作线长度为2 261.7～3 753.9 m，经济合理工作线长度为2 286.3～2 496.5 m。两者取交集为2 286.3～2 496.5取整后确定核增后最佳工作线长度在2 300～2 500 m内取值。
3. 采区划分方案及分析评价
3.1 采区划分方案
3.1.1 采区划分原则
采区划分应满足开采工艺系统及生产能力的需要，结合矿田的几何形状、地质条件、开采工艺和采区接续等因素，以最佳工作线长度为采区宽度划分依据，同时要综合考虑采区长度及服务时间、采区过渡方式及影响，各采区剥采比变化关系等。
3.1.2 采区划分方案的提出
1）方案一：先以矿区南边界往北划分为四采区，再对剩余未开采矿区从东往西以最佳工作线长度划分成3个采区，各采区工作线长度皆基本满足合理工作线长度，如图2所示。
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图 2 采区划分方案一及各采区剥采比Figure 2. Mining area division scheme 1 and stripping ratio bar chart of each mining area2）方案二：从开采现状逐渐往西南方向推进，西南方向4 000 m左右为首采区；再由此往东至东部边界为二采区；然后剩余未开采矿区东部的南北方向长度均分划分成上下三采区和五采区，最后西部合理工作线长度按开采顺序划分为四采区，如图3所示。
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图 3 采区划分方案二及各采区剥采比Figure 3. Mining area division scheme 2 and stripping ratio bar chart of each mining area3）方案三：首采区、二采区与方案二首采区、二采区采区划分方案相同；然后剩余未开采矿区划分成3个采区。保证三采区、四采区从东往西划分在最佳的工作线长度范围内，最后剩余未开采部分为五采区，如图4所示。
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图 4 采区划分方案三及各采区剥采比Figure 4. Mining area division scheme 3 and stripping ratio bar chart of each mining area4）方案四：首采区、二采区与方案二首采区、二采区的采区划分方案相同；然后直接在剩余未开采矿区南北方向上划分成三采区及四采区，如图5所示。
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图 5 采区划分方案四及各采区剥采比Figure 5. Mining area division scheme 4 and stripping ratio bar chart of each mining area3.2 采区划分方案对比
3.2.1 采区平均工作线长度及剥采比
通过采区划分及剥采比等值线图确定4种方案中各采区的平均工作线长度和剥采比大小见表1。
表 1 各采区平均工作线长度及剥采比Table 1. Average working face length and stripping ratio of each mining area采区 工作线长度/m 剥采比/（m3·t−1） 方案一 方案二 方案三 方案四 方案一 方案二 方案三 方案四 首采区 2099 2147 2147 2147 2.18 1.85 1.85 1.85 二采区 2223 2335 2335 2335 3.28 3.04 3.04 3.04 三采区 2157 2436 2266 2360 3.54 4.02 4.93 3.68 四采区 2226 2193 2142 2226 5.06 4.20 3.97 5.06 五采区 — 2480 1896 — — 5.17 4.38 — 3.2.2 方案对比
结合该矿端帮边坡角34°、内排土场边坡角23°，考虑各采区平均埋深、相邻采区二次剥离长度，基于全压帮内排方式，分析4种采区划分方案的优缺点。
1）方案一：优点：①工作线长度分布较均匀；②各采区形状较规则，便于生产；③采区数量少、过渡次数较少；④容易实现内排；⑤剥采比变化趋势随着采区发展逐渐增大。缺点：①由剥采比等值线图可知各采区剥采比分布不均匀；②采区间接续均需重新拉沟，工程量大耽误组织生产；③外排运距3.1 km，长度过大，降低外排效率。
2）方案二：优点：①首采区剥采比1.8 m3/t，前期剥采比较小减小前期投资，投产快；②较容易实现内排；③外排运距为2.2 km，相对较小；④剥采比变化趋势随着采区往后逐渐增大，趋势较好。缺点：①各采区工作线长度变化较大；②二次剥离量950.7 Mm3，相对较大；③采区数量相对较多继而接续次数多，影响生产。
3）方案三：优点：①首采区剥采比1.85 m3/t，前期剥采比较小减小前期投资，投产快；②较容易实现内排；③外排运距2.2 km，相对较小。缺点：①二次剥离量为1 166.5 Mm3，重复剥离大；②采区数量多，接续复杂，工程量大；③五采区内工作线长度分布不均匀；④各采区工作线长度变化较大；⑤剥采比变化趋势先增后减，趋势较差。
4）方案四：优点：①各采区工作线长度分别分布较均匀；②首采区剥采比1.85 m3/t，前期剥采比较小减小前期投资，投产快；③剥采比变化趋势随着采区往后逐渐增大，趋势较好；④采区数量相对较少，接续简单；⑤外排运距2.2 km，相对较小。缺点：后期采区内剥采比较大。
4. 采区划分方案优选
4.1 采区划分评价指标
通过分析总结多个周边相似露天煤矿采区划分经验，且进一步结合专家评价分析确定下列9个指标作为采区划分影响指标。
1）平均工作线长度。平均工作线长度应结合扩增为50 Mt/a的生产能力确定，工作线长度过长或过短对采运排主要生产环节均有一定影响。
2）二次剥离量。采区边界造成开采过程中上覆岩石的重复剥离，大幅度增加生产成本。
3）前期外排运距。前期外排运距的大小直接影响排弃费用以及外排效率。外排运距过大，则会增加采排费用，降低排卸效率及经济效益。
4）内排难易程度。内排难易程度严重影响对排卸物进行排卸时的效率及经济效益。内排越难，造成的排卸费用就越大。
5）采区接续次数。在露天煤矿生产过程中，频繁的采区接续会浪费大量生产时间及造成复杂的生产管理，严重影响原煤的生产效率。
6）首采区勘探程度。首采区勘探程度不明，在生产推进的过程中，因煤层厚度的骤变或者部分自燃导致生产能力受严重影响。
7）采区接续难易。在采区的过渡接续中，采区接续越难，则对采、运、排等主要生产环节的影响越大，影响生产效率。
8）首采区剥采比。首采区的剥采比大小严重影响露天煤矿开采的前期经济效益。首采区剥采比越小，前期经济效果越好。
9）各采区剥采比变化趋势。随着开采的工作面向前推进，剥采比也应保证随着采区不断过渡接续，不呈大幅度波动变化。
其中，各指标数据见表2。
表 2 采区划分评价指标数据Table 2. Data of division of mining area index方案 平均工作线
长度/m二次剥离
量/Mm3前期外排
运距/km内排难易
程度采区接续
次数首采区勘
探程度采区接续
难易首采区剥采比/
(m3·t−1)各采区剥采比
变化趋势方案一 2176 865.9 3.1 较易 3 一般 较易 2.18 较好 方案二 2318 950.7 2.2 一般 4 较好 一般 1.85 一般 方案三 2157 1166.5 2.2 一般 4 较好 一般 1.85 较差 方案四 2267 878.3 2.2 一般 3 较好 较易 1.85 很好 4.2 指标权重确定
各评价指标对采区划分的影响程度取决于各指标权重，且权重是方案优选的关键性指标。选取集值迭代法确定指标权重，其原理如下[19-20]。
设$ Y = \left\{ {{y_1}} \right.,{y_2}, \cdots ,\left. {{y_q}} \right\} $为有限论域，$ P = \{ {{p_1}},{p_2}, \cdots , {{p_q}}\} $，选初始值$ k\left( {1 \leqslant k \leqslant q} \right) $,然后$ {p_j}\left( {j = 1,2,3, \cdots ,n} \right) $按下完成计算：
①在Y中选pj优选属于A的R1=k个元素，得Y的子集：
$$ Y_1^{\left( j \right)} = \left\{ {y_{{i_1}}^{\left( j \right)}} \right.,y_{{i_2}}^{\left( j \right)}, \cdots ,\left. {y_{{i_k}}^{\left( j \right)}} \right\} \subseteq Y $$ (6) ②在Y中选pj优选属于A的R2=2k个元素，得Y的子集：
$$ Y_2^{\left( j \right)} = \left\{ {y_{{i_1}}^{\left( j \right)}} \right.,y_{{i_2}}^{\left( j \right)}, \cdots ,\left. {y_{{i_2}_k}^{\left( j \right)}} \right\} \supseteq Y_1^{\left( j \right)} $$ (7) ③在Y中选pj优选属于A的Rt=tk个元素，得Y的子集：
$$ Y_t^{\left( j \right)} = \left\{ {y_{{i_1}}^{\left( j \right)}} \right.,y_{{i_2}}^{\left( j \right)}, \cdots ,\left. {y_{{i_t}_k}^{\left( j \right)}} \right\} \supseteq Y_{t - 1}^{\left( j \right)} $$ (8) 假设自然数t满足$ q = tk + c,1 \leqslant c \leqslant k $,则在第t+1步迭代终止。
yj的覆盖频率：
$$ m\left( {{y_i}} \right) = \frac{1}{{n\left( {t + 1} \right)}}\sum\limits_{s = 1}^{t + 1} {\sum\limits_{j = 1}^n {{\chi ^{y_s^{\left( j \right)}\left( {{y_i}} \right)}}} } \left( {i = 1,2, \cdots ,q} \right) $$ (9) 式中：$ {\chi ^{y_s^{\left( j \right)}}} $是集合$ Y_s^{\left( j \right)} $的特征函数，将$ m\left( {{y_i}} \right) $归一化即可得各指标权重。
由5位专家$ P = \left\{ {{p_1}} \right.,{p_2},{p_3},{p_4},\left. {{p_5}} \right\} $对采区划分各项指标进行优先排序，最终得到权重集：W=(w1,w2,w3,w4,w5,w6,w7,w8,w9)=(0.058,0.164,0.076,0.133,0.08,0.04,0.084,0.187,0.178)。
4.3 TOPSIS评价法
TOPSIS原理是在比选方案中选出最优指标和最劣指标，并分别组成理想方案和最劣方案，然后计算出理想方案和各比选方案贴近度，由此给各比选方案进行排序，选出最佳方案[21-25]。
1）评价指标同趋势化。通常把低优指标转化为高优指标，即$ {X'_{ij}} = {1 \mathord{\left/ {\vphantom {1 {{X_{ij}}}}} \right. } {{X_{ij}}}} $。且所有指标按对采区划分的影响程度按小、一般、中等、强烈、极端分别进行1、3、5、7、9定量分析评价。
2）矩阵归一化。
$$ \left\{ \begin{gathered} {a_{ij}} = {{X_{ij}}} / {\sqrt {\sum\limits_{i = 1}^n {X_{ij}^2} } } \\ {a_{ij}} = {{{X'}_{ij}}} / {\sqrt {{{\sum\limits_{i = 1}^n {\left( {{{X'}_{ij}}} \right)} }^2}} } \\ \end{gathered} \right. $$ (10) 式中：aij为评价i在第j指标值；Xij为第i评价对象在第j指标值；$ X_{3} $为经Xij倒数转化后值。
归一化矩阵：
$$ \boldsymbol{A}=\left[\begin{array}{cccc} a_{11} & a_{12} & \cdots & a_{1 m} \\ a_{21} & a_{22} & \cdots & a_{2 m} \\ \vdots & \vdots & & \vdots \\ a_{n 1} & a_{n 2} & \cdots & a_{m m} \end{array}\right]$$ (11) 3）最优、最劣向量。分别取归一化矩阵每行的最大值、最小值为最优向量A+与最劣向量A−：
$$ \begin{gathered} {{\boldsymbol{A}}^ + } = \left( {a_{i1}^ + ,a_{i2}^ + , \cdots ,a_{im}^ + } \right) \\ {{\boldsymbol{A}}^ - } = \left( {a_{i1}^ - ,a_{i2}^ - , \cdots ,a_{im}^ - } \right) \\ \end{gathered} $$ (12) 4）计算出各评价所有指标与最劣方案及模型理想方案的距离$ D_i^ - $与$ D_i^ + $。
$$ \left\{ \begin{gathered} D_i^ - = \sqrt {\sum\limits_{j = 1}^m {{w_j}{{\left( {a_{ij}^ - - {a_{ij}}} \right)}^2}} } \\ D_i^ + = \sqrt {\sum\limits_{j = 1}^m {{w_j}{{\left( {a_{ij}^ + - {a_{ij}}} \right)}^2}} } \\ \end{gathered} \right. $$ (13) 式中：$ D_i^ - $、$ D_i^ + $为第i个评价对象到最劣方案和模型理想方案的距离；wj为第j指标权重。
5）计算各方案与理想方案的贴近度Ci：
$$ {C_i} = \frac{{D_i^ - }}{{D_i^ - + D_i^ + }} $$ (14) 6）贴近度Ci越大，说明越接近模型理想方案，离最劣方案越远，即所比选最佳方案。
4.4 比选方案贴近度确定
通过上述TOPSIS评价法以及各比选方案评价指标参数，计算确定出各比选方案贴近度见表3。
表 3 比选方案贴近度Table 3. Closeness of the comparison scheme方案 $ D_{i}^{-} $ $ D_{1}^{*} $ $ C_{t} $ 方案一 0.170 0.178 0.489 方案二 0.098 0.161 0.378 方案三 0.062 0.225 0.216 方案四 0.223 0.066 0.772 由表3可知，C4=0.772最大，即方案四最接近理想方案，因此方案四为最佳方案。
5. 结 论
1）依据技术可行与经济合理为原则，分别计算了产能扩增至50 Mt/a时的工作线合理范围，综合确定了工作线长度最佳范围为2300～2500 m。
2）基于确定的最佳工作线长度对采区进行划分，提出4种采区划分方案，计算了个采区平均工作线长度及剥采比，确定4种采区划分方案的二次剥离量、前期外排运距、内排难易程度以及各采区划分方案的优缺点。
3）构建了基于集值迭代法−TOPSIS法的采区划分方案评价模型，计算了各种方案的贴近度，并确定了从开采现状逐渐往西南方向推进4000 m左右为首采区，再由此往东至东部边界为二采区，然后剩余未开采矿区在南北方向长度均分划分成上下三采区和四采区的最佳采区划分方案。
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图 7 非极性溶剂对低阶煤热溶萃取提质过程
Figure 7. Schematic of degradative solvent extraction of low rank coal with non-polar solvent
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图 9 煤的热溶萃取提质-多级分离工艺示意
Figure 9. Schematic of degradative solvent extraction and multistage separation process of coal
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图 11 煤与生物质共热溶萃取提质工艺流程
Figure 11. Process flow of coal and biomass co-hot-melting extraction process
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煤样 萃取收率（daf）/% 烟煤 Oakycreek 66.7 Gregory 60.8 Illinois No.6 62.4 Upper Freeport 72.5 Enshu 69.2 Guregory 68.8 低阶煤 Warkworth 31.5 Pasir 37.8 Mullia 35.8 Nantun 39.2 Longkou 38.0 Yakut 9 30.2 
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表 2 酸预处理对热溶萃取率及萃取物灰分的影响[19]
Table 2 The effect of acid pretreatment on degradative solvent extraction yield and the ash content of the extract[19]
溶剂 煤种 萃取收率/% 产物灰含量/% 原煤 酸处理煤 提高量 原煤 酸处理煤 LCO WY 33.5 43.4 9.9 <0.1 <0.1 PA 42.0 43.2 1.2 <0.1 <0.1 AD 37.3 41.5 4.2 <0.1 <0.1 CMNO WY 41.3 60.5 19.2 <0.1 <0.1 PA 60.3 55.1 0.8 0.21 <0.1 AD 47.1 54.0 6.9 0.13 <0.1 注：LCO为轻循环油；CMNO为粗甲基萘油；WY为美国阿尔贡优质煤；PA为巴西煤；AD为印度尼西亚阿达罗煤。
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煤样 萃取率/% 1-MN DMN LCO CMNO UF 63.6 74.0 44.0 80.7 EN 57.4 62.3 55.8 78.9 IL 53.7 69.3 55.8 74.6
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供电子官能团（代表物质） 受电子官能团（代表物质） 富电性π轨道（吡咯、噻吩环） 缺电性π轨道（芳香环、吡啶环） 含氧官能团（酚、醚、羧基） 含质子官能团（酚—OH、硫醇） 含氮官能团（胺、吡啶环） 含硫官能团（噻吩、硫醇）
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表 5 煤的溶剂萃取关键词突现
Table 5 Keyword emergence diagram of solvent extraction of coal
关键词 年份 强度 开始时间 结束时间 CS2-N-methyl-2-pyrrolidinone mixed solvent 1999 10.74 1999 2004 Coal liquefaction 1999 5.65 1999 2008 Supercritical fluid extraction 1999 5.55 1999 2006 Solubility 1999 7.66 2000 2002 Rank 1999 7.01 2000 2006 Room temperature 1999 6.02 2000 2004 Sample 1999 5.5 2000 2002 Conversion 1999 4.92 2005 2007 Hypercoal 1999 5.11 2006 2017 Solvent 1999 5.59 2007 2014 Trace element 1999 5.03 2008 2015 350 ℃ 1999 4.63 2008 2014 Lignite 1999 4.84 2015 2021 Stone coal 1999 7.97 2017 2021 Rare earth element 1999 4.58 2019 2021
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