Analysis of the Organic Chemical Fractions of Three Coal Extracts

Abstract

Coal is an important fossil energy source in the world, which provides important support for the development of industry. However, the chemical composition of coal is complex, and it may cause harm to the human body and environment during the process of mining and utilization, especially some aromatic hydrocarbons in coal that are strongly carcinogenic to human beings; thus, it is necessary to analyze the organic chemical compositions of coal so as to realize the clean and harmless utilization of coal. In this article, three different coal samples were extracted by seven solvent-graded extractions, and then the extracts were tested by gas chromatography–mass spectrometry (GC-MS). According to the results of the GC-MS test, it was found that CS₂ could dissolve a large amount of aromatic hydrocarbons in the coal, n-hexane could dissolve a larger amount of aliphatic hydrocarbons, methanol could dissolve a larger amount of ketones, benzene could extract phenolic compounds in the coal, acetone could dissolve alcoholic compounds, and the mixed solvent methanol/THF could dissolve coal esters. Then, by analyzing these extracts, researchers can clearly understand the microscopic organic components of coal, which have a significant role in the development of the coal chemical industry and ecological environment protection.

1. Introduction

Coal is generally formed by the long-term deposition of plant remains by physicochemical biology and other long-term actions; the composition inside the coal is complex, and most coals have a high content of organic matter [1,2]. Because some of the aromatic hydrocarbons contain a certain degree of toxicity, the human body and the environment suffer greater harm [3,4], which has become a major problem in the clean utilization of coal. China is a large coal-producing country, mainly concentrated in Shaanxi, Shanxi, and other northwestern regions, and coal is an indispensable resource for us [5]. Therefore, the clean and efficient use of coal requires the removal of toxic or hazardous substances from the coal. To achieve this goal, the principle of similar solubility [6,7], with the help of various types of substances in coal in different solvents with different solubilities, will be utilized for the extraction of coal resources for “purification” treatment [8].

Solvent extraction has been the most commonly used method to dissolve organic substances in coal over the years [9,10]. V.K. [11] used NMP solvent to extract Indian coking coal after ultrasonic or microwave pre-treatment and used EDA as a co-solvent. The final extraction results showed that the clean coal yield reached the highest, compared with the extraction effect of the untreated coal, which increased by 4%. Zheng [12] showed that DME had the shortest extraction time, the lowest energy consumption and the highest extraction rate (16.2%) by Soxhlet extraction of direct coal liquefaction residue at room temperature. The extracts obtained by liquefying DME and acetone were characterized by high carbon, low sulfur, low oxygen, and low ash (<0.1%), which finally showed that liquefied DME has the advantages of low energy consumption, short extraction time, high extraction rate, and high extractant properties, and it is a highly efficient and economical solvent for extracting DCLR. Li [13] extracted bituminous coal by using a solvent mixture of CS₂ and acetone, with a final extraction rate of about 12%. The extracted material is mainly alkanes, aromatic hydrocarbons, olefins, and some heteroatomic compounds, of which the aromatic hydrocarbons have the highest content, accounting for 76% of the total extract, and most of them are aromatic hydrocarbons with two and seven rings. When the number of times of extraction is increased, the extracted substances’ molecular weight increases.

Researchers usually use gas chromatography–mass spectrometry (GC-MS) technology to detect the composition of extracts after the extraction of coal. GC is a separation technology that can separate substances according to the differences in their boiling points, but it provides little information about the detection of substances and cannot be used to detect unknown compounds alone. MS, in contrast, happens to be an excellent detector; MS converts substances to charged ions through an ionization source, and MS sends substances to a detector through the ionization source. The detector, which detects and identifies the substances according to the difference in their mass-to-charge ratios, finally obtains the mass spectra of each compound by comparing them with a database and then determines the type and content of the substances according to their mass spectra. Therefore, GC-MS is one of the most commonly used methods for the detection of unknown compounds [14,15,16]. Ju [17] carried out Soxhlet extraction, ultrasound-assisted extraction, and microwave-assisted extraction of Daxing coal with carbon tetrachloride. The extracted fractions were analyzed by gas chromatography–mass spectrometry (GC-MS), and the GC-MS analysis showed the following: (1) The SE method extracted the least amount of substances from the coal; on the other hand, most of the extracted compounds were chlorinated, which can be explained by the free radical mechanism. (2) MAE extracted 75 organic compounds, of which 53 were oxygenated substances. The amount of non-alkanes was low (1.19%), contrary to the other two extraction methods. In addition, a number of biomarker compounds were identified, including hexaoxane, 2-methylcholan-3-ene, 6,9,12-tripropylheptadecane, and 17α-21β-28,30-bisnorbornane

Accompanied by the rapid development of science and technology, more and more testing instruments can accurately detect samples and provide researchers with more detailed information about the items, providing more analytical processing methods for understanding the composition of substances in coal. Previously, data processing was cumbersome, and it was difficult to find the target compounds, but now people use computers to assist in the analysis, which greatly improves the efficiency of work. Cluster analysis is also the most commonly used method for the statistical and metrological analysis of chemical data nowadays. The principle of this method is to compare the similarities or identify the differences between the extract samples by mathematical methods according to the characteristics and attributes of the samples themselves, to determine the relational distances between the samples, to categorize the samples according to the differences in the sample relationships, and then to cluster the samples with a large degree of similarity into a class. This process is continued until the similarity is low, in turn, clustering until all the samples are analyzed and processed, and finally, according to the data after processing, the required clustering diagram is drawn [18,19]. In cluster analysis, one generally uses the Euclidean distance to indicate the size of the difference between the samples, so that the data are clustered out based on the smallest differences within the class and the largest differences between the classes, and the clustering effect can be seen. The most important feature of cluster analysis is that there is no need to know the classification structure of the data before the analysis; only a batch of data can be processed, and then the appropriate method can be selected following the steps. The results of the processing directly affect the clustering of the picture [20,21]. Zhang [22] carried out simulated distillation and infrared spectroscopy of heavy coal tar, and the tar fraction content changed significantly after simulated distillation at different temperatures. Among them, the light oil content decreased from 4.3 to 0.1%, and the pitch content increased from 77.6 to 90.6%. Based on the infrared spectra of heavy coal tar, the different fractions were analyzed by principal components. The cumulative value was up to 96.93% when double principal components were used. The results showed that PC1 had strong peak signals near 749 cm⁻¹ and 687 cm⁻¹, and PC2 had strong peak signals near 2356 cm⁻¹ and 1143 cm⁻¹. Yu [23] used cyclohexane, methanol, and acetone to solubilize the coal samples and then used mass spectrometry to analyze the dissolved samples. Then, the dissolved material was detected by a mass spectrometer, and a large number of material mass spectra were obtained. He used hierarchical cluster analysis in R and the expected maximum algorithm of the Gaussian model (EMGM) to cluster the extracts, analyzed the seven heterocyclic compounds among them, and inferred the possible models and structures from the differences between the compounds. This method of data processing is very convenient for the study of the molecular structure of coal.

In this paper, three different coal samples were subjected to seven solvent-graded extractions, and the samples completed at each level of extraction were collected. A total of 21 extract samples were obtained, and these extracts were detected and identified using GC-MS. The compounds contained in the extracts were classified according to the family components. Principal component analysis and cluster analysis, which are commonly used as analytical methods in biology, were applied in this paper, which provide new ideas for the compositional analysis methods of coal. In this paper, the soluble organic components of coal were clearly recognized, which is of great significance to realize the clean and harmless use of coal.

2. Materials and Methods

2.1. Experimental Materials

This experiment utilized three types of coal: Shanxi Pingshuo coal (PS), Shanxi Datong coal (DT), and Shandong Geting coal (GT). The coal samples were prepared by crushing them to a particle size of less than 200 mesh (0.075 mm) using a crusher. Subsequently, the samples were dried in an oven at 30 °C for over 4 h. After drying, they were cooled to room temperature and stored in a desiccator containing chameleon silica gel.

2.2. Instruments and Reagents

In addition to the commonly used glass apparatuses such as beakers, measuring cylinders, funnels, flasks, Soxhlet extractors, spherical condenser tubes, and other commonly used instruments, the main instrumentation used in this experiment is as follows in

Table 1. Experimental apparatuses and equipment.

Equipment Name	Equipment Model	Address	Manufacturer
CNC Ultrasonic Cleaner	KQ-250DE	Kunshan, China	Kunshan Ultrasound Instrument Co., Ltd.
Digital display constant temperature water bath	HH-2	Changzhou, China	Changzhou Yuexin Instrument Manufacturing Co., Ltd.
rotary evaporator	RE-3000	Shanghai, China	Shanghai Yarong Biochemical Instrument Factory
Circulating water multi-purpose vacuum pump	SHB-ⅢS	Zhengzhou, China	Zhengzhou Changcheng Science and Technology Industry and Trade Co., Ltd.
electronic balance	FA2004B	Shanghai, China	Shanghai Jingke Tianmei Scientific Instrument Co., Ltd.
Electrically heated blast drying oven	101-OAB	Tianjin, China	Tianjin Taist Instrument Co., Ltd.
Elemental Analyser	Elementar Vario ELⅡ	Hebi, China	Hebi Yingtai Electronic and Electrical Appliance Co., Ltd.
sulfur meter	5E-S3200	Yuncheng, China	Nanfeng Chemical Group Co., Ltd.
Gas Chromatography/Mass Spectrometry (GC-MS)	7890A-5975C	Santa Clara, CA, USA	Agilent Corporation of the United States

.

The instruments shown in the table are all freely available in the laboratory or school, and all of them are useful in the course of the experiment.

The reagents and specifications used in the experiment are listed below in

Table 2. Experimental reagents and specifications.

Reagent Name	Standard	Address	Manufacturer
CS2	Analytical purity (AR)	Tianjin, China	Tianjin Kemio Chemical Reagent Co., Ltd.
N-hexane			Tianjin Damao Chemical Reagent Factory
Benzene			Tianjin Fuyu Fine Chemical Co., Ltd.
Methanol			Tianjin Fuyu Fine Chemical Co., Ltd.
Acetone			Tianjin Fuyu Fine Chemical Co., Ltd.
THF			Tianjin Fuyu Fine Chemical Co., Ltd.

.

The reagents shown in the table were purchased through regular channels. These reagents are analytically pure for experimental purposes.

2.3. Graded Extraction of Coal

Weigh 10 g of each pre-prepared coal sample, build the Soxhlet extraction device, place the coal samples in a filter paper tube in the Soxhlet extractor, add the first level of extraction solvent CS₂ 150 mL extraction, an extraction temperature slightly lower than the boiling point of the solvent, an extraction time of 60 h, and every 12 h, replace the fresh solvent to prevent saturation of the solvent extraction. Collect the extracts after each level of extraction is completed, and repeat the extraction of the last level of solvent according to the above steps until all 7 levels of solvent extraction are completed. A total of 21 groups of extracts were obtained from three kinds of coal samples, which were PS-CS₂, PS-HEX, PS-BEN, PS-MET, PS-ACE, PS-THF, PS-THF/MET, DT-CS₂, DT-HEX, DT-BEN, DT-MET, DT-ACE, DT-THF, DT-THF/MET, GT-CS₂, GT-HEX, GT-BEN, GT-MET, GT-ACE, GT-THF, and GT-THF/MET.

PS-CS₂: Pingshuo Coal CS₂ Extract; PS-HEX Pingshuo Coal Hexane Extract; PS-BEN: Pingshuo Coal Benzene Extract; PS-MET: Pingshuo Coal Methanol Extract; PS-ACE: Pingshuo Coal Acetone Extract; PS-THF: Pingshuo Coal THF Extract; PS-THF/MET: Pingshuo Coal THF mixed with Methanol Extract. DT-CS₂: Datong CS₂ Extract; DT-HEX: Datong Hexane Extract; DT-BEN: Datong Benzene Extract; DT-MET: Datong Methanol Extract; DT-ACE: Datong Acetone Extract; DT-THF: Datong THF Extract; DT-THF/MET: Datong THF and Methanol Mixed Extract. GT-CS₂: Geting CS₂ Extract; GT- HEX: Geting Hexane Extract; GT-BEN: Geting Benzene Extract; GT-MET: Geting Methanol Extract; GT-ACE: Geting Acetone Extract; GT-THF: Geting THF Extract; GT-THF/MET: Geting THF/Methanol mixed Extract.

The extraction rate for each solvent was calculated according to the following equation:

$$\mathsf{\eta }={\displaystyle \frac{100\times W_1}{(100-A_{ad}-M_{ad})\ast W/100}}$$

(1)

where the η is the extraction rate of solvent, %; W₁ is the mass of extract, g; W is the mass of coal sample to be extracted, g; A_ad is the ash of coal, %; M_ad is the moisture content of coal, %.

The process flow of Soxhlet-graded extraction is shown in Figure 1 below.

EXT₁: CS₂ extract, RAF₁: CS₂ extract; EXT₂: n-hexane extract; RAF₂: n-hexane extract; EXT₃: benzene extract; RAF₃: benzene extract; EXT₄: methanol extract; RAF₄: methanol extract; EXT₅: acetone extract; RAF₅: acetone extract; EXT₆: THF extract; RAF₆: THF extract; EXT₇: THF methanol mixed extract; RAF₇: THF methanol mixed extract.

2.4. Test Analysis

Industrial analysis: Adopt the “Industrial Analysis Method of Coal” GBT212-2022 [24] to test.

GC-MS test: An Agilent 7890A-5975C gas chromatography/mass spectrometry (GC-MS) instrument was used; the capillary column was 30.0 m × 250 μm × 0.25 μm HP-5MS; the flow rate of the carrier gas was 1.0 mL/min; helium was used for the carrier gas; and the shunt ratio was 100:1. The ion source was EI, the ion source temperature was 230 °C, the ionization voltage was 70 eV, the mass scanning range was 30–500 amu, the scanning mode was Scan, the injection volume was 1 μL, the temperature of the injection port was 300 °C, and the temperature increase program was as follows: hold at 60 °C for 1 min, then increase to 300 °C at the rate of 10 °C/min, and hold for 10 min.

Compound identification: Based on the results of the GC-MS test, the compounds were searched in the NIST spectral library, and the names and species of the compounds were determined according to the degree of confidence and similarity for qualitative analysis. Then, the compounds present were quantitatively analyzed by the method of external standard, etc., and the species and contents of various compounds were finally determined.

Principal Component Analysis: Using Origin software 2024b to perform principal component analysis on the extracts, the data were downgraded by orthogonal transformation to determine the main components in the extracts.

Cluster analysis: The extract was analyzed by cluster analysis using Origin software, and the close relationship between the components of each family could be clearly seen through cluster analysis.

3. Results and Discussion

3.1. Basic Properties of Coal Samples

Table 3. Industrial and elemental analyses of three coal samples.

Coal Sample	Industrial Analysis/W%				Elemental Analysis/W%, daf
	Mad	Aad	Vdaf	FCad	C	H	N	S	O*
PS	2.97	18.53	29.50	49	79.77	5.60	1.41	0.63	12.59
DT	2.88	16.14	23.96	57.02	82.38	4.35	0.88	2.44	9.95
GT	0.29	10.33	31.14	58.24	81.99	5.46	1.62	0.42	10.51

M_ad: moisture content in coal; A_ad: ash content in coal; V_daf: volatile matter content; FC_ad: fixed carbon content in coal. O* stands for O. The elemental analysis results were obtained by the difference subtraction method.

presents the results of the industrial and elemental analyses of these three coal samples.

From the industrial analysis and elemental analysis test results, it can be seen that all three coal samples contain a large amount of carbon, indicating that all three coal samples have a high calorific value. Geting coal contains a very small amount of water, and Datong coal contains more sulfur compared to the other two coals.

3.2. Extraction Rate of Three Kinds of Coal Samples

Solubility parameters are used to measure the ability of an organic solvent to dissolve a solute, but they are not absolutely proportional, and when the optimal solubility parameter is exceeded the dissolution effect may be reduced.

Table 4. Extraction rate and extractant composition of Pingshuo coal.

Solvent	Extraction Rate/%	Aliphatic Hydrocarbon Content/10−4 g	Aromatic Hydrocarbon Content/10−4 g	Content of Other Compounds/10−4 g	Solubility Parameters/(cal·cm−3)1/2
CS2	2.3159	36.33	223.28	17.56	10
n-hexane	0.3517	55.381	0	1.87	7.3
benzene	0.5333	18.922	0	1.053	9.1
methanol	0.9764	0	0.2145	10.4058	14.6
acetone	2.8124	0.36	4.524	27.224	9.9
THF	8.1693	0	1.38	2.769	9.2
methanol/THF	0.2120	0	0	9.718	11.9

lists the extraction effect of seven solvents on Pingshuo coal. The total extraction rate of seven solvents on Pingshuo coal is 15.371%, and the extraction rate of each solvent on coal is different. The same solvent dissolves different compounds in the coal with different efficiencies, probably due to the molecular forces between them not being the same. For the Pingshuo coal, the dissolution of CS₂, hexane, and acetone is better. CS₂ extracted more than 90% of the aromatic hydrocarbons in the coal; n-hexane and CS₂ extracted more than 80% of the aliphatic hydrocarbons in the coal; and acetone extracted about 50% of the other compounds in the coal, while the other solvents were generally effective in extracting various compounds.

It can be seen from Table 4 above and the following Figure 2 that when the solubility parameter of the solvent is about 9.2, the extraction effect of the solvent on Pingshuo coal is the best, which reaches about 8%; when the solubility parameter is 11.9, the extraction effect of the solvent on Pingshuo coal is the worst, at only 0.2%, which shows that it is not that the higher the solubility parameter is, the better the extraction effect of the solvent on coal. When it is greater than the optimum solubility parameter, the extraction rate will be lower, and it can be seen from the table that for the extraction of Pingshuo coal, between 9 and 10 is better. This shows that the higher the solubility parameter, the better the extraction effect of the solvent on coal. When the optimal solubility parameter is exceeded, the extraction rate will become lower. As can be seen from the table, for the extraction of Pingshuo coal, a solubility parameter between 9 and 10 has better extraction effects.

Table 5. Datong coal extraction rate and composition of extractants.

Solvent	Extraction Rate/%	Aliphatic Hydrocarbon Content/10−4 g	Aromatic Hydrocarbon Content/10−4 g	Content of Other Compounds/10−4 g	Solubility Parameters/(cal·cm−3)1/2
CS2	2.1562	60.78	76.26	7.64	10
n-hexane	0.4722	31.103	0	11.168	7.3
benzene	0.9616	0.648	5.302	3.029	9.1
methanol	0.2195	0.032	0.068	3.005	14.6
acetone	1.1492	1.769	1.712	23.173	9.9
THF	4.3271	0	0.846	10.836	9.2
methanol/THF	0.1570	0	0	8.559	11.9

lists the extraction effects of seven solvents on Datong coal. The total extraction rate of the seven solvents from Datong coal is 9.4428%. For Datong coal, the extraction effects of CS₂, n-hexane, and acetone are also better for the coal samples. CS₂ and n-hexane extracted more than 90% of the aliphatic hydrocarbons; CS₂ extracted more than 90% of the aromatic hydrocarbons in Datong coal; and CS₂, n-hexane, and acetone together extracted about 70% of the other compounds in Datong coal. The extraction of aliphatic hydrocarbons was completed after the first five solvent extractions, and aromatic hydrocarbons were also completed after the first six solvent extractions. When the extraction effects of aromatic hydrocarbons and aliphatic hydrocarbons decreased, the extraction effects of other compounds became higher.

From Table 5 above and Figure 3 below, it can be seen that when the solubility parameter is in the range of 9.2 and 10, the solvent has the best extraction effect on Datong coal, and the extraction rates are all over 1%. When the solubility parameter is in the range of 11.9 to 14.6, the solvent has the worst extraction effect on Datong coal, which is only about 0.2%, and it can be seen that for the extraction of Datong coal, the extraction effect of the solubility parameter is between 9 and 10, which is consistent with the conclusion drawn for Pingshuo coal.

Table 6. Extraction rate and composition of extracts of Geting coal.

Solvent	Extraction Rate/%	Aliphatic Hydrocarbon Content/10−4 g	Aromatic Hydrocarbon Content/10−4 g	Content of Other Compounds/10−4 g	Solubility Parameters/(cal·cm−3)1/2
CS2	5.4687	36.507	280.986	27.751	10
n-hexane	0.2170	0.151	0.351	0.232	7.3
benzene	1.5358	0	2.048	85.298	9.1
methanol	0.4819	0	0	4.9075	14.6
acetone	3.1584	0	0.873	268.309	9.9
THF	3.3687	0	95.688	0	9.2
methanol/THF	1.2564	0	5.345	6.763	11.9

lists the extraction effects of seven solvents on Geting coal. The total extraction rate of the seven solvents from Geting coal is 15.4869%. For Geting coal, CS₂, benzene, acetone, and THF have a better extraction effect. CS₂ extracted more than 95% of the aliphatic hydrocarbons in Geting coal, CS₂ and THF extracted more than 90% of the aromatic hydrocarbons in Geting coal, and benzene and acetone extracted more than 90% of the other compounds, such as fatty hydrocarbons, which were extracted after the first two solvent extractions. When the extraction of fatty substances is completed, the extraction of the other compounds quickly increases.

As can be seen from Table 6 above and the following Figure 4, when the solubility parameter is in the range of 9.2 to 10, the solvent has the best extraction effect on Geting coal, and the extraction rate is basically over 3%. When the solubility parameter is between 7.3 and 14.6, the solvent has the worst extraction effect on Geting coal, which is less than 1%, and the aliphatic hydrocarbons are no longer contained in the Geting coal after the previous two solvent extractions.

From the extraction effect table of the above three kinds of coal samples, it can be seen that CS₂ as the first-level solvent extracted a large amount of soluble organic matter in the coal and that the extraction rate of THF was significantly higher than that of the other solvents because of its higher polarity. The solvents extracted coal better when the solubility parameter was between 9 and 10, and a solubility parameter that was too high or low was not conducive to the extraction of coal. The seven solvents had comparable extraction effects on Pingshuo coal and Geting coal; both of them were about 15%, and the extraction effect on Datong coal was only 9%.

3.3. Analysis of the Chemical Composition of Extracts from Three Coal Samples

From the previous analysis, we can only see the content of the extracts after categorizing them into large classes of substances, and some substances in the whole extract are not easy to analyze because of their small content; therefore, the following detailed classification of the data after the GC-MS test was carried out for the family components, and the compounds in the extract were divided into aromatic hydrocarbons (mostly 3–4 rings), aliphatic hydrocarbons (mostly straight chain alkanes or single branched alkanes), oxygen-containing compounds (alcohols, carboxylic acids, esters, ketones, phenols, ethers, furans), nitrogen-containing compounds (amides), sulfur-containing compounds (thiophenes), and other compounds.

Figure 5 below shows the proportion of each component in each level of extracted material after the extraction of Pingshuo coal.

It can be seen from the Figure 5 above that among the seven-stage extracts of Pingshuo coal, the main components in the extract of CS₂ are aliphatic hydrocarbons and aromatic hydrocarbons; the main components in the extracts of hexane and benzene are aliphatic hydrocarbons and acids; the main components in the extract of methanol are phenolic substances and nitrogen-containing compounds; the main components in the extract of acetone are aromatic hydrocarbons, alcohols, ketones, and nitrogenous compounds; and the main components in the extract of THF are aromatic hydrocarbons, ketones, phenolics, and nitrogen-containing compounds.

Figure 6 below shows the proportion of each component in the various levels of extracts of Datong coal after extraction.

It can be seen from Figure 6 above that among the seven-stage extracts of Datong coal, the main components in the extract of CS₂ are aliphatic hydrocarbons and aromatic hydrocarbons; the main components in the extract of n-hexane are aliphatic hydrocarbons, esters, and acids; the main components in the extract of benzene are aliphatic hydrocarbons, aromatic hydrocarbons, phenolic substances, and other compounds; the main components in the extract of methanol are esters, ketones, and acids; the main components in the extract of acetone are aliphatic hydrocarbons, aromatic hydrocarbons, alcohols, and ketones; the main components in the extract of THF are aromatic hydrocarbons, esters, and ketones; and the main components in the mixed solvent extract of THF and methanol are esters and ketones.

Figure 7 below shows the proportion of each component of each level of extract after the extraction of Geting coal.

From Figure 7 above, it can be seen that in the seven-stage extract of Geting coal, the main components in the extract of CS₂ are aliphatic hydrocarbons, aromatic hydrocarbons, and ketones; the main components in the extract of n-hexane are aliphatic hydrocarbons, aromatic hydrocarbons, and esters; the main components in the extract of benzene are phenolics; the main components in the extract of methanol are esters, and nitrogen-containing compounds; the main components in the extract of THF are sulfur-containing compounds and aromatic hydrocarbons; and the main components in the solvent-mixed extract of THF and methanol are aromatic hydrocarbons, ketones, and other compounds.

By classifying the three kinds of substances with detailed family components and drawing the proportion of each component, the proportion of components contained in the extracts at all levels can be seen from the figure, which provides convenience for the subsequent cluster analysis.

3.4. Principal Component Analysis of 21 Extracts

Principal component analysis is a method of mathematical transformation that converts a given set of correlated variables into a set of uncorrelated variables by linear transformation; these new variables are called principal components. The total variance of the variables remains the same, but the first principal component has the largest variance, the second principal component has the next largest variance and is uncorrelated with the first principal component, and so on for the rest.

Figure 8 below shows the principal component analysis performed on the 21 extracts.

After principal component analysis, it can be determined that the first principal component is aromatic hydrocarbons (dominated by Pingshuo coal and Geting coal) with a variance of 27.2%. The second principal component is alcohols (dominated by Datong coal and Geting coal) with a variance of 17.7%. The third principal component is aliphatic hydrocarbons (dominated by Pingshuo coal and Datong coal) with a variance of 12.0%. After the principal component analysis, the main components in the extracts can be easily determined, but the similarity between the extracts could not be seen, so the extracts were then analyzed by clustering.

3.5. Cluster Analysis of the Chemical Composition of Three Kinds of Coal Sample Extracts

The previous analysis reveals the chemical composition of the extracts at all levels, but it is not clear at which level of extraction a certain substance is completely extracted, and it is not easy to see the similarity of the relationship between the extracts. Thus, clustering analysis is the next level of categorization of the extracts, and by plotting the cluster analysis heat map, the features that are close based on the clustering are converted into a class. The heat map is one of the most commonly used methods for data visualization. However, because of the large differences in the content of each component, the extracts were first globally standardized [25] to reduce the differences between the components, and the standardized data were greatly improved compared to the previous one, so that the clustering of the components could be seen more clearly

Figure 9 below shows the clustering heat map for the extracts of Pingshuo coal.

From the above clustering diagram of Pingshuo coal, it can be seen that the main components in the extract are aromatic and aliphatic hydrocarbons. Additionally, from the thermogram results, it can be seen that the extraction efficiency of each solvent varies a lot, and the extraction effect of CS₂ is much higher compared with other solvents, which may be related to the fact that CS₂ acts as the first-stage solvent. After extraction by CS₂, the extraction efficiency of the other solvents was poor. In the clustering results, aromatic hydrocarbons were clustered into a separate class. Aliphatic hydrocarbons were also clustered into a separate class. Alcohols, ketones, and nitrogen-containing compounds with high similarity were clustered into one group. Ester substances, other compounds, and acids were clustered together due to their close similarity, and furans and phenols were clustered separately.

Figure 10 below shows the heat map of the clustering of Datong coal extracts.

From the above Datong coal cluster diagram, it can be seen that the main components in the extract were aliphatic hydrocarbons, aromatic hydrocarbons, and alcohols. Additionally, from the thermogram results, it can be seen that the extraction effect of CS₂ was the best, and CS₂ and n-hexane extracted nearly all the aliphatic hydrocarbons and aromatic hydrocarbons in the coal samples. CS₂ extracted all the furans, nitrogenous compounds, and sulfurous compounds, and the other solvents extracted a small amount of organic compounds in the coal. The other solvents extracted a small amount of organic compounds from the coal. From the clustering results, the aliphatic hydrocarbons and aromatic hydrocarbons in the extracts had a high degree of similarity, so they were clustered into one group. Alcohols, esters, and ketones were clustered separately because of the large similarity gap; acids, phenols, and esters were clustered. Finally, sulfur-containing compounds, other substances, furans, and nitrogen-containing compounds were clustered into one group due to their close similarity.

Figure 11 below shows the heat map of the clustering performed for the extract of Geting coal.

From the above clustering diagram of Geting coal, it can be seen that the main components in the extracts are aliphatic hydrocarbons, aromatic hydrocarbons, and sulfur-containing compounds. The results of the thermograms show that CS₂ had the best extraction, accounting for about 40% of the total extract, and that CS₂ extracted all the furans in the coal and a large amount of ketone compounds and aliphatic hydrocarbons. CS₂ extracted close to all the aromatic hydrocarbons in the coal with THF. Hexane, benzene, and acetone each extracted a large amount of phenolic compounds and sulfur-containing compounds from the coal. From the clustering results, aliphatic hydrocarbons and ketones with high similarity were clustered into one group. Alcohols, esters, other compounds, acids, furans, and nitrogen compounds were clustered into one group. Phenols, sulfur-containing compounds, and aromatic hydrocarbons were clustered separately because of the large difference in similarity.

4. Conclusions

In this paper, three kinds of coal samples from Pingshuo, Datong, and Geting were subjected to Soxhlet hierarchical extraction, and GC-MS tests were performed on the extracts to detect the composition of organic components in the extracts, which were then classified into aromatic hydrocarbons (mostly 3–4 rings), aliphatic hydrocarbons (mostly straight-chained alkanes or single-branched-chained alkanes), oxygen-containing compounds (alcohols, carboxylic acids, esters, ketones, phenols, ethers, and furans), nitrogen-containing compounds (amides), sulfur-containing compounds (thiophenes), and other compounds. Finally, the extracts were systematically analyzed by principal component analysis and cluster analysis. From the results of principal component analysis, the main components were aromatic hydrocarbons, alcohols, and aliphatic hydrocarbons. From the results of cluster analysis, it could be seen that the aliphatic hydrocarbons and aromatic hydrocarbons in Pingshuo coal have a high degree of similarity and were clustered into one category; the aliphatic hydrocarbons, aromatic hydrocarbons, and alcohols in Datong coal have a high degree of similarity; and the sulfur-containing compounds, aliphatic hydrocarbons, and aromatic hydrocarbons in Geting coal have a high degree of similarity. Through the research of this paper, people can clearly recognize the soluble organic components in coal and understand the similarity between the components, which provides a basis for researchers to recognize the microscopic composition of coal, provides a reference for people to extract the organic compounds in coal and carry out the separation of family components, and enriches the theoretical knowledge for the coal mining and utilization industry, which can realize the clean and pollution-free utilization of coal. In this thesis, only three kinds of coal samples were evaluated, and there is no sufficient condition to prove the universality for all coal samples, which will be widely studied in subsequent research work.