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Article

Research on Algae Enhancing Biogenic Methane Production from Coal

by
Liu Zhu
1,2,
Wangjie Diao
1,*,
Yi Liu
1,
Peilin Zhu
1 and
Chenyao Gong
1
1
School of Mines, China University of Mining & Technology, Xuzhou 221116, China
2
Yunlong Lake Laboratory of Deep Underground Science and Engineering, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(17), 7960; https://doi.org/10.3390/su17177960
Submission received: 31 July 2025 / Revised: 21 August 2025 / Accepted: 1 September 2025 / Published: 3 September 2025
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Abstract

The low efficiency of the microbial gasification of coal limits the application of bio-coal bed methane technology. The co-fermentation of coal and biomass provides a new approach for improving the degradation rate of coal. In this study, a co-fermentation system comprising five different coal orders with five microalgae was constructed in the laboratory, and the methanogenic characteristics of coal–algae co-fermentation and its microbiological mechanism were systematically investigated in terms of gas production, soluble organic matter, and microbial community characteristics. The results showed that the combination of lignite and Nannochloropsis exhibited optimal methane production, with a methane yield of 26.43 mL/g coal. Biogenic methane yields for lignite–Porphyra and anthracite–Porphyra were 23.43 mL and 21.28 mL, respectively, demonstrating the potential for algae to enhance gas production even in high-rank coals. pH monitoring revealed that algal species played a critical role in the acidification process. Dunaliella caused a continuous pH decrease, reaching 3.76 by day 30, while Nannochloropsis maintained a neutral pH of 6.95, optimizing the fermentation environment. Significant differences in soluble organic matter were observed between the lignite and anthracite fermentation systems, with lignite systems producing more volatile fatty acids, including acetic and butyric acids. Microbial community analysis revealed that Methanosarcina, an acetic acid-utilizing methanogen, was dominant in lignite and anthracite systems, while Syntrophomonas played a key role in lignite–Nannochloropsis co-fermentation. These findings provide valuable insights into optimizing coal microbial gasification and selecting appropriate algal species to enhance methane production efficiency.

1. Introduction

The direct combustion of coal produces large amounts of toxic gases and particulate matter. Clean utilization of coal is a challenge for scholars [1]. Methane is a cleaner energy source than coal. Laboratory tests have shown that microorganisms can gradually degrade coal into biomethane [2,3,4]. The use of microorganisms to convert coal to biomethane can facilitate the green mining of coal and increase the production of natural gas [5,6,7]. However, coal is a polymer that is difficult for microorganisms to degrade. The current biomethane production rate is much lower than the extraction rate. Biomethane production can be increased through coal pretreatment [8,9,10,11], microbial enhancement [12], nutrient enhancement [12,13,14], and the application of an electric field [15,16].
Coal is a high-carbon organic matter in terms of its elemental composition. Anaerobic fermentation of coal as a single feedstock has several drawbacks, including unstable anaerobic fermentation operations, limited year-round availability of certain feedstocks, and low methane production [17]. Anaerobic co-fermentation overcomes the disadvantages of single-feedstock fermentation by simultaneously digesting two or more feedstocks. Co-fermentation promotes a diverse microbial community in the reaction system, which can enhance system stability, balance the system’s carbon-to-nitrogen ratio, replenish trace elements, and improve the buffering capacity of the fermentation solution. Anaerobic co-digestion of coal with other organic materials is a promising approach for increasing the amount of methane produced by coal producers [18,19,20]. Rice straw, sweet sorghum stover, wheat straw, and corn stover co-fermented with coal produced higher biomethane yields than those from single-feedstock fermentation [21,22]. The cumulative methane production from mixed anaerobic fermentation of kitchen waste oil and lignite can be increased by 377.86% compared to that from anaerobic fermentation with lignite alone [23]. In another study, a moderate amount of coal slurry increased the gene abundance of key enzymes in the metabolic pathways during methane metabolism and accelerated the initiation of fermentation [24]. The above studies demonstrate that co-fermenting coal with other organic materials can increase methane production, providing a new strategy for enhancing methane production efficiency.
Algal biomass has great potential to enhance the production of biomethane from coal [25,26,27]. The mineral composition of algae can fulfill the nutritional requirements of anaerobic microflora. In addition to carbon, nitrogen, and phosphorus, which are the main components of algae, micronutrients such as iron, cobalt, and zinc can stimulate microbial methanogenesis [28]. Algal biomass has a very low carbon-to-nitrogen ratio and can be co-fermented with carbon-rich organic matter to balance the carbon-to-nitrogen ratio in the reaction system [18]. Algae enhance methane production in the same manner as yeast extracts [29,30]. A recent study evaluated both gas production and microbiota in terms of microalgal concentrate (Chlorella sp. strain SLA-04) that enhanced biomethane production from coal with different thermal maturities in the United States [31]. This stimulation approach has expanded the scope of coal microbial gasification technology applications. Although a wide variety of algae exists, it remains unclear which are more effective in enhancing methane production and how algae affect methane production in fermentation systems. China has abundant coal resources [32], and the effectiveness of algae in enhancing methane production across different coal grades still needs to be evaluated. In this study, five mature coal samples and five algal species from China were selected for anaerobic fermentation experiments in the laboratory to investigate algae-enhanced biomethane production from coal with different thermal maturity levels in terms of gas production, liquid-phase products, and microbial communities.

2. Materials and Methods

2.1. Coal and Algae Samples

As shown in Figure 1, coal samples were collected from the major coal-producing provinces in China and transported to the laboratory for backup after on-site sampling. The coal samples were crushed to a particle size of 0–40 mm using a pressure testing machine. The crushed coal samples were frozen with liquid nitrogen to prevent the high-temperature oxidation of each sample and then placed into a motorized crusher to further crush the coal samples into powder. The coal samples were sieved to pass through a 200 mesh screen using a motorized sieving machine [33]. The algae were bought from biotech companies on Alibaba (https://www.1688.com/, accessed on 21 October 2024). Coal and algae samples are crushed to a particle diameter of less than 0.075 mm and dried at 100 °C. The results of the elemental analyses of the coal samples and algae are shown in Table 1. The specular group reflectance results of the coal samples are listed in Table 2. The maturity of the coal samples was classified according to international standards. The coal samples of groups BYH, XZ, MDL, LX, and ZLS were lignite, subbituminous, bituminous A, bituminous B, and anthracite, respectively.

2.2. Experimental Methods

The fermentation system contained 20 g of solids (10 g each of coal powder and algae powder) and 200 mL of liquid (100 mL each of medium and inoculum), placed in an anaerobic chamber using 500 mL brine flasks. The reactor was sterilized at 120 °C, inoculated with the strain, and sealed in an anaerobic chamber. Each group has three parallel samples. The medium consisted of basal medium, trace metal solution, and vitamins. The medium, inocula, and anaerobic bottles were prepared as previously described [7]. The chemical reagents used in the experiment were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). The purity of the chemical reagents was analytical grade. The co-digestion time for coal and algae is 30 days.
The headspace gas compositions of the brine bottles were examined using a 9790Plus gas chromatograph (Fuli, China). CH4 and CO2 are detected using a flame ionization detector, while CO2 and N2 are detected using a thermal conductivity detector. The carrier gas is argon, with a hydrogen flow rate of 30 mL/min and an air flow rate of 300 mL/min. The injection port temperature is 120 °C, the column oven temperature is 100 °C, the FID detector temperature is 200 °C, and the TCD detector temperature is 130 °C.
The fermentation solution was filtered using a 0.7 μm filter membrane, and the pH of the fermentation solution was tested using a pHS-3E tester (Leici Instruments Co., Ltd., Shanghai, China). The volatile fatty acids in the fermentation solution were tested using a GC-7890 gas chromatograph (Agilent Technologies, Santa Clara, CA, USA). The chromatographic column is a stainless-steel column with a mesh size of 60–80. The carrier gas is nitrogen, with a flow rate of 90 mL/min. The air flow rate is 500 mL/min. The vaporization chamber temperature is 240 °C, and the detection temperature is 210 °C. The temperature is heated from 60 °C to 200 °C at a rate of 4 °C/min.
The ammonia nitrogen content in the solution was determined using nano-reagent spectrophotometry. The soluble organic matter in the fermentation solution was tested using 8890-7010B (Agilent Technologies, Santa Clara, CA, USA). The fermented liquid is filtered through a 0.7 μm filter membrane, extracted with dichloromethane, dried with anhydrous sodium sulfate, and concentrated with nitrogen gas. The data were processed using MassHunter 8.0 software and matched to the NIST 20.0 L database to give the results.

2.3. DNA Sequencing

The bacterial 16S rRNA V3–V4 region was amplified using primer 338F (5′-adaptor + barcode + ACTCCTACGGGGAGGCAGCAG-3′) and 806R (5′-adaptor + barcode + GGACTACHVGGGGTWTCTAAT-3′). The length of the bacterial primers was 468 bp. For the archaeal 16S rRNA V3-V4 region, primer 524F10extF (5′-adaptor + barcode + TGYCAGCCGCCGCGGGTAA-3′) and Arch958RmodR (5′-adaptor + barcode + TGYCAGCCGCCGCGGTAA-3′) were used. The archaeal primer length was 434 bp. Microbial community analysis data are the means of three parallel experimental groups.

3. Results

3.1. Methane Production

As shown in Figure 2a, different coal–algae combination systems presented significantly different methane production performances. Methane production showed apparent substrate specificity. The highest single-group gas production (26.43 mL) was obtained for the combination of Nannochloropsis and lignite, which was significantly higher than that of the other algal combinations of the same coal order. Porphyra exhibited exceptional gas production performance of 21.28 mL in the anthracite system, which was 2.3–7.3 times higher than that of other algae in the same system. The bundle of Aphanizomenon showed an unusually high value (6.53 mL) in combination with bituminous B, with a 10–34-fold increase in its production compared to that in other coal rank systems.
The lignite system had the best overall gas production efficiency (13.18 mL on average), with the combinations with Nannochloropsis and Porphyra contributing the most. The bituminous coal system showed noticeable differences, with 23.49 mL and 9.05 mL for Porphyra and bituminous A and B, respectively. The combination of high-rank anthracite and Porphyra produced 21.28 mL of gas, indicating potential for gas production in high-rank coal as well.
The best gas production efficacy was obtained for lignite, and the gas production characteristics of the combinations of lignite and different algae are shown in Figure 2b. The Modified Gompertz model was used to fit the methane production kinetics of the lignite–microalgae co-fermentation system, and the fitting results are shown in Table 3. The R2 of the model for all combinations was ≥0.985, indicating that the model could effectively characterize the dynamics of the fermentation process. The RMSE values of all combinations were lower than 1.34 mL, with the lignite + Schizochytrium system exhibiting the best fit (RMSE = 0.37 mL, AIC = −12.1). The deviation of model parameter A from the experimentally measured maximum gas production was less than 4%, confirming the reliability of the model prediction. The Nannochloropsis system obtained the maximum potential gas production (A = 26.43 mL) and the highest specific methane production rate (μm = 2.23 mL/d); however, its lag time (λ = 10.8 d) was 107–138% longer than that of the other systems. The Porphyra system showed kinetic equilibrium characteristics with a 51.6% shorter λ (5.23 d) than the Nannochloropsis system and maintained a higher gas production rate (1.65 mL/d). Although the Schizochytrium system had the lowest A value (15.70 mL), it exhibited the fastest metabolic initiation (λ = 4.54 d) and optimal model fitness.

3.2. pH

As shown in Figure 2, the pH changes in different coal orders and algae co-fermentation systems showed combination-specific and time-dependent results. In the lignite group fermentation system, the pH of Dunaliella group decreased continuously from 6.00 (day 1) to 3.76 ± 0.05 (day 30), a decrease of 37.3%, which showed a trend of strong acidification (Figure 3a–f). The pH of Nannochloropsis group rose to 6.95 ± 0.12 at day 30, higher than that of the other lignite combinations.
The pH of the anthracite and Porphyra combination was maintained at 6.38–6.96, with a value of 6.96 ± 0.09 at day 30, making the fermentation system more stable (Figure 3e). The pH of the fermentation using the Aphanizomenon group decreased from 6.69 ± 0.04 (day 6) to 6.22 ± 0.06 (day 30), a decrease of 7.0%. The pH of the bituminous A+Schizochytrium (Figure 3c) combination was stable (6.91–7.19), whereas that of the bituminous B+Schizochytrium (Figure 3d) combination decreased to 6.83 ± 0.11 (day 30). The bituminous B+Dunaliella combination showed a sudden rise in pH (6.00 ± 0.43) on day 6 but decreased to 3.78 ± 0.02 on day 30. The Dunaliella group exhibited a strong acidification effect.
At the end of fermentation, Porphyra maintained the highest pH (6.83–6.96) in all coal systems. Dunaliella resulted in a pH decrease to 3.76–4.15 (30.7–37.3%) across the systems. During algal fermentation alone, all algal species had a lower pH than that of the coal-algal co-fermentation system at day 30, demonstrating that the coal substrate was pH buffering.

3.3. Volatile Fatty Acids (VFAs)

3.3.1. Lignite Groups

As shown in Figure 4a–e, the accumulation of fatty acids exhibited differences in the lignite fermentation systems with different algae. Acetic acid, butyric acid, and isovaleric acid yields were much higher in the lignite + Aphanizomenon combination than in the other lignite combinations. Acetic acid increased continuously from day 6 (4.24 ± 1.04 g/L) to day 15 (8.42 ± 1.98 g/L), and then decreased slightly on day 30 (7.79 ± 2.25 g/L), butyric acid reached a peak on day 15 (3.37 ± 0.69 g/L), and isovaleric acid showed a similar trend with a maximum on day 15 (1.41 ± 0.28 g/L). The lignite + Dunaliella combination was the other prominent acid-producing combination, with accumulation of acetic acid (day 15: 2.40 ± 0.05 g/L), butyric acid (day 30: 2.30 ± 0.08 g/L), and isovaleric acid (day 15: 0.35 ± 0.09 g/L). Additionally, it was the only combination in the lignite system that consistently produced valproic acid (day 30: 0.21 ± 0.03 g/L). The other lignite combinations (+Nannochloropsis, +Porphyra, and +Schizochytrium) generally accumulated lower levels of VFAs. Notably, VFAs in the lignite + Porphyra combination decreased significantly on day 15, and no acids were detected by day 30. Hexanoic acid was only detected in trace amounts (0.02 ± 0.01 g/L) in lignite + Porphyra on day 6.

3.3.2. Anthracite Groups

The accumulation pattern of VFAs in the fermentation systems of anthracite and different algae differed from that in the lignite system (Figure 4f–j). The combination of smokeless coal and Aphanizomenon also showed higher VFAs production, with accumulation of acetic acid (day 30: 6.27 ± 0.40 g/L), butyric acid (day 30: 3.18 ± 0.06 g/L), and isovaleric acid (day 30: 1.29 ± 0.05 g/L). However, the overall peak level was lower than that of the brown coal and algae combination. The combination of smokeless coal and Dunaliella was equivalent to the combination of lignite and algae in terms of butyric acid accumulation (day 15: 2.34 ± 0.20 g/L; day 30: 2.18 ± 0.11 g/L), accumulation of hexanoic acid was observed in the brown coal system (day 30: 0.32 ± 0.02 g/L), and the production of valeric acid (day 30: 0.32 ± 0.02 g/L) was also higher than that of the brown coal–algae combination. The combination of smokeless coal and Nannochloropsis showed a unique enhancement effect in the later stage (day 30), with high levels of acetic acid (2.96 ± 0.12 g/L), butyric acid (1.39 ± 0.17 g/L), isovaleric acid (0.52 ± 0.09 g/L), and hexanoic acid (0.16 ± 0.01 g/L). The accumulation of VFAs in the combination of smokeless coal and Schizochytrium was generally low. Similarly to in the brown coal system, the VFAs of the anthracite+Porphyra combination also decreased to extremely low or undetectable levels on day 30. However, trace amounts of valeric acid (0.01 ± 0.00 g/L) were detected on day 6 in this combination, which was not observed in the brown coal and algae combination.

3.3.3. Differences Between Lignite and Anthracite Groups

In the lignite fermentation system, lignite + Aphanizomenon showed the strongest accumulation of acetic, butyric, and isovaleric acids, with higher peaks than those of the homozygous anthracite combination. In the anthracite system, although anthracite+Aphanizomenon was still the main acid-producing combination, its peak value was lower than that of its lignite counterpart, and anthracite+Nannochloropsis showed an enhancement in the accumulation of various VFAs at the later stage (30 days). Valproic acid was consistently produced in both the coal substrate and Dunaliella combinations, but both valproic acid and hexanoic acid production were higher in anthracite+Dunaliella. Accumulation of hexanoic acid was a distinctive feature of the anthracite system, with accumulations observed in both the +Dunaliella and +Nannochloropsis combinations (up to 0.32 ± 0.02 g/L). In contrast, the lignite system produced essentially no hexanoic acid, except for trace amounts detected in +Porphyra on day 6. Both the lignite + Porphyra and anthracite+Porphyra combinations exhibited a tendency for VFAs to decrease rapidly with fermentation time, eventually disappearing. However, trace amounts of valeric acid were detected in the anthracite+Porphyra on day 6 (not detected in the lignite homoalgae combination). The accumulation of VFAs was consistently low in lignite + Nannochloropsis, whereas anthracite+Nannochloropsis showed an increase in the accumulation of acetic, butyric, isovaleric, and hexanoic acids in the later stages of fermentation (30 days).

3.4. Total Ammonia Nitrogen (TAN) and VFAs/TAN Ratio

3.4.1. Lignite Groups

As shown in Figure 5a–e, in the lignite fermentation system, the TAN concentrations and VFAs/TAN ratios of the different algal groups showed different patterns of change. The TAN concentration of the Dunaliella group fluctuated with the fermentation time, and reached the lowest value of (0.14 ± 0.01) g/L on day 15, at which point the VFAs/TAN ratio peaked at 35.38, which was 4.8 times higher than that on day 6. The TAN concentration in the Aphanizomenon group showed a continuous increase from 0.21 g/L to 0.24 g/L, and the VFAs/TAN ratio of this group remained at a high level (33.98–57.09) and reached a maximum value (57.09 ± 31.54) on day 15. In the Porphyra group, the TAN concentration increased from 0.65 g/L to 1.00 g/L, an increase of 53.7%. At the same time, the VFAs/TAN ratio decreased exponentially from 1.64 to the lowest value (0.07 ± 0.11) on day 30, and the TAN concentration in the Schizochytrium group increased after a brief decrease on day 15. The TAN concentration in the Schizochytrium group increased after a brief decline on day 15, and its VFAs/TAN ratio peaked at day 15 (5.01 ± 0.01) and then decreased to (2.58 ± 0.01) on day 30.

3.4.2. Anthracite Groups

As shown in Figure 5f–j, the results of the anthracite fermentation system showed that the TAN concentration in Dunaliella group reached the lowest value (0.16039 ± 0.01972) g/L on day 15, coinciding with the peak VFAs/TAN ratio of 30.7 ± 19.72, which was 107% higher than the initial value, while that of Aphanizomenon group showed a continuous increase from 0.28763 g/L to 0.29845 g/L; the VFAs/TAN ratio increased with time, reaching 38.64 ± 0.01268 on day 30; the VFAs/TAN ratio increased with time. The TAN concentration in the Aphanizomenon group showed a continuous increase from 0.28763 g/L to 0.29845 g/L, and the VFAs/TAN ratio increased over time, reaching 38.64 ± 0.01268 on day 30, which was 94% higher than that on day 6. The Porphyra group showed the most accumulation of TAN, with an increase from 0.55679 g/L to 1.21488 g/L, i.e., 118% higher. The VFAs/TAN ratio peaked at 30.7 ± 19.72 on day 30, which was 107% higher than the initial value. The VFAs/TAN ratio decreased to 0 on day 30, indicating that the relevant substances had been completely consumed. In contrast, the concentration of TAN in the Schizochytrium group increased abruptly to 0.43151 ± 0.05628 g/L on day 30, and the VFAs/TAN ratio peaked (5.82 ± 0.01707) on day 15.

3.4.3. Differences Between Lignite and Anthracite Groups

The co-digestion characteristics differed between the lignite and anthracite groups. In terms of TAN accumulation, anthracite+Porphyra showed the highest TAN accumulation (1.21 g/L on day 30), exceeding that of all the lignite systems. The terminal TAN of the lignite system was generally lower than that of the anthracite system. The lignite system had a higher VFA/TAN ratio, which was higher than that of anthracite (up to 38.64 for equivalent algae). Anthracite+Porphyra showed complete depletion of VFAs on day 30 (VFAs/TAN = 0), while lignite + Porphyra still retained trace amounts (VFAs/TAN = 0.07). In terms of algal-specific effects, Aphanizomenon elevated VFAs/TAN in both groups (lignite: 33.98–57.09; anthracite: 19.88–38.64), whereas Porphyra strongly suppressed the ratio (ratio < 1.64 in both groups). The VFAs/TAN of the Schizosaccharomyces pombe system were moderate (lignite: 2.58–5.01; anthracite: 4.08–5.82), and the TAN was at an intermediate level.

3.5. Dissolved Organic Matter

3.5.1. Lignite + Dunaliella Group

As shown in Table 4, Butanoic acid (C4H8O2) was the predominant component in the fermentation broth of this system. The percentage of butanoic acid reached 93.92% on day 6, accompanied by small amounts of butylated hydroxytoluene (C15H24O, 2.73%) and cis-9-octadecenamide (1.30%). With the extension of fermentation time, the proportion of butyric acid continued to increase on days 15 (99.42%) and 30 (99.43%). In contrast, the C15H24O content decreased to 0.35% and 0.57%, respectively, suggesting that the metabolic process of acidification was highly stable in the system.

3.5.2. Lignite + Nannochloropsis Group

The organic composition of the system showed time dependence. p-Cresol (C7H8O, 86.10%) dominated on day 6, while phosphoric acid derivatives (3.82%), indoles (3.61%), and octadecenamide (6.48%) were detected. By day 15, the percentage of p-cresol had decreased to 63.70%, accompanied by the accumulation of 2-piperidone (18.92%), novel adenosine derivatives (2.57% total), and medium-chain fatty acids (hexanoic acid, 1.22%; octanoic acid, 1.35%). A fundamental shift in the system composition occurred after 30 days of fermentation; bicyclo[3.1.1]heptane-2,3-diol (64.80%) and tetradecadienol acetate (26.39%) were the major components, whereas the percentage of p-cresol reduced drastically to 4.52%.

3.5.3. Differences Between Dunaliella and Nannochloropsis Groups

The lignite–Dunaliella fermentation system was dominated by butyric acid (>93%), which was maintained for an extended period of time. In contrast, the lignite–Nannochloropsis system showed metabolic pathway differences before and after fermentation, with aromatic compounds (p-cresol) dominating in the early stage and shifting to terpenes and esters in the late stage. This suggests differences in the structure of the microbial community between the early and late stages and in the way the substrate is utilized.

3.6. Characterization of Microbial Communities in Lignite Group

3.6.1. Venn Diagram Analysis

As shown in Figure 6a, the five-assembly intersection of Aph & Dun & Nan & Sch & Spi contained 65 operational taxonomic units (OTUs) (38.69% relative abundance) centered on the thick-walled bacterial phylum, which mainly included 21 genera such as Bacillus, Clostridium, and Blautia from the Clostridia phylum, and multiple unclassified groups from the Desulfotomaculia phylum. Dunaliella had the highest number of unique OTUs (21, 12.50%), including Lactobacillus and Sphingomonas of Saccharopolyspora and thick-walled bacteria, and five unique OTUs (2.98%) for Sch coal, including Leuconostoc and Sphingomonas (2.98%), including Levilactobacillus and unclassified Clostridia, and seven (4.17%) OTUs unique to Aph algae, dominated by Hydrogenophaga and unclassified Bacillariophyta. Notable diatom combinations included Aph & Dun (two OTUs, 1.19%), Dun & Sch (six OTUs, 3.57%), and Dun & Nan (eight OTUs, 4.76%), whereas Aph & Dun and Sch & Spi (four OTUs, 2.38%) and Dun & Sch & Spi (four OTUs, 2.38%) also detected specific flora were also detected. The remaining combinations (e.g., Nan and Spi only) had OTU counts ≤ 1. The Venn diagram for bacteria reveals significant differences in the bacterial communities across the various coal–algae combinations. The lignite–algae systems showed a higher diversity of bacterial taxa, with multiple unique operational taxonomic units (OTUs) identified, including Clostridium (94.16% relative abundance in Dunaliella). In contrast, the anthracite systems displayed fewer unique bacterial OTUs, particularly in combinations like anthracite+Schizochytrium, where the microbial community was dominated by more ammonia-tolerant genera like Methanosarcina.
The distribution of shared and exclusive archaeal taxa in the fermentation systems of different coal–algae combinations is shown in Figure 6b. Two exclusive OTUs (Methanocella and Methanothrix) with a relative abundance of 16.67% were detected only in the Sch coal samples. One shared OTU was present in the combination of Aph and Dun (unclassified Nitrososphaeraceae), with an abundance of 8.33%, and the Dun coal samples contained one unique OTU (Candidatus Nitrosocosmicus), with an abundance of 8.33%. The Dun & Sch combination contained one shared OTU (Methanobrevibacter) with an abundance of 8.33%; the Dun & Sch & Spi combination contained one shared OTU (Candidatus Nitrosotalea) with an abundance of 8.33%; and the five-portfolio intersection of Aph, Dun, Nan, Sch, and Spi contained six core OTUs (Methanoculleus, Methanosarcina, Methanobacterium, Methanomassiliicoccus, unclassified Bathyarchaeia, and unclassified Archaea) with the highest abundance (50.00%). None of the remaining coal–algal assemblages contained any detectable OTUs. The Venn diagram for archaea indicated differences in the methanogenic communities between the lignite and anthracite systems. The Methanoculleus genus was dominant in the lignite + Nannochloropsis group, which aligns with the observed high methane production rates in this combination. On the other hand, the anthracite system exhibited a higher abundance of Methanosarcina (34.0%). The presence of unique OTUs in the anthracite groups, such as Methanocella and Methanothrix, further underscores the different ecological pressures in the two coal systems, as well as the adaptation of methanogens to coal rank and nitrogen availability.

3.6.2. Species Composition

In the Dun group (Figure 7a), Clostridium (94.16% relative abundance) was the most abundant bacterium, and Haloimpatiens (61.62%) and Enterocloster (18.21%) were more abundant than in the other groups, while the Sch group was characterized by Pseudomonas (52.56%) and Agathobaculum (20.47%), and Faecalicatena (7.49%) was specifically enriched in the Sch_3 samples. The Sch group was characterized by Pseudomonas (52.56%), Agathobaculum (20.47%), and Faecalicatena (7.49%), which were specifically enriched in Sch_3 samples, whereas the Nan group was dominated by Syntrophomonas (16.54%), Enterobacteriaceae unclassified (16.19%), and Caldicoprobacter (9.42%). The Aph group was dominated by Sporanaerobacteria (59%) and Enterocloster (18.21%). Sporanaerobacter (59.48%) and Terrisporobacter (19.69%) were more abundant in the Aph group, while Enterocloster (10.87%) and Haloimpatiens (61.62%) were the dominant taxa in the Spi group. Bacillus was prominent in Dun_3 samples (7.27%), whereas Sedimentibacter was highly distributed in the Spi (7.30%) and Sch (1.51%) groups. The abundance of genera common to all algal groups, such as Clostridium, was generally less than 15.67% outside of the Dun group.
In the lignite co-fermentation system with different algae, the archaeal community composition showed algal group-dependent differences (Figure 7b). The Spi group was dominated by Methanoculleus (mean relative abundance 84.8%, 77.3–88.8%), whereas the abundance of Methanobacterium was lower (10.6%, 7.5–15.7%). The Aph group was dominated by Methanobacterium (63.7%, 38.2–92.8%), followed by Methanoculleus (27.3%, 6.5–59.6%) The Nan group was dominated by Methanosarcina (34.0%, 19.8–55.8%) and Methanomassiliicoccus (29.8%, 0.1–52.5%), while Methanoculleus was the least abundant (12.0%. 1.4–27.5%). The Sch group showed coexistence of multiple genera: Methanoculleus (33.8%, 12.5–51.7%), Methanobacterium (20.7%, 8.0–29.3%), Methanosarcina (24.3%, 12.1–32.8%), and Candidatus Nitrosotalea (13.8%, 7.1–18.5%), the latter being endemic to the group and having the highest abundance. The Dun group was characterized by Methanoculleus (45.9%, 34.1–55.4%) and Methanobacterium (28.4%, 19.0–43.0%), with Candidatus Nitrosotalea in moderate abundance (4.8%, 2.9–7.1%) and as the most abundant species (7.1%), and was the only group in which trace amounts of Candidatus Nitrosocosmicus were detected. In addition, Methanobrevibacter (≤0.14%), Methanothrix (≤0.06%), and Methanocella (≤0.01%) were uniquely found in the Sch group, while rare taxa such as Bathyarchaeia were <0.7% in all groups.

3.6.3. Differences Between Bacterial Groups

The results of linear discriminant analysis (LDA) of bacteria are shown in Figure 8. The bacterial communities of the co-fermentation system of lignite and different algae were marked by intergroup differences in marker species. The Sch group was marked by a core of Ascomycetes (Pseudomonadota, LDA = 5.20) with enrichment of Desulfovibrionaceae (LDA = 3.95), Desulfotomaculia (LDA = 3.63), and Desulfitobacteriia (LDA = 3.63), and Desulfitobacteriia (LDA = 3.41) were enriched. The characteristic taxa of the Nan group were unclassified_p__Bacillota (LDA = 3.41), Caldicoprobacter (LDA = 4.46), and Syntrophomonas (LDA = 4.70). The differential signatures of the Aph group were centered on Peptostreptococcales-Tissierellales (LDA = 5.54). In particular, families XI (LDA = 5.46), Terrisporobacter (LDA = 4.86), and Sporanaerobacter (LDA = 5.42). The dun group was dominated by Clostridiales (LDA = 5.48) and Clostridiaceae (LDA = 5.46), whereas Halobacteroidaceae (LDA = 3.21) was enriched. The Spi group was characterized by Peptococcaceae (LDA = 3.33) and Anaerolineales (LDA = 3.54).

3.6.4. Metabolic Pathway Prediction

Kyoto Encyclopedia of Genes and Genomes Pathway level 3 abundance analysis of the bacterial community in the co-fermentation system of lignite and different algae is shown in Figure 9. Differences in metabolic functions were detected among the different algal groups. Overall, the Spi group showed the highest abundance in most of the core metabolic pathways, including carbohydrate metabolism, pentose phosphate pathway, amino acid metabolism, aminoacyl–tRNA biosynthesis, energy metabolism, and membrane translocation. In contrast, the Sch group was prominent in certain of the environmental stress-responsive pathways, such as the bacterial secretion system and multiple xenobiotic degradation pathways, and the Aph group showed high abundance of certain degradation pathways. The overall metabolic abundances of the Nan and Dun groups were mostly intermediate. Additionally, group sensing and flagellar assembly were enhanced in the Spi group, indicating strong microbial interactions and motility.

4. Discussion

4.1. Coal–Algae Synergistic Effect Causing Differences in Methane Yield

The results indicated that the compatibility between the chemical structure of coal and the trophic composition of algae was a key factor in determining methane yield. The highest methane yield (26.43 mL) was obtained from the combination of lignite (low-rank coal) and Nannochloropsis, probably because the richness of lignite in degradable oxygen-containing functional groups and the high lipid content of Nannochloropsis (Table 1) promoted efficient hydrolytic acidification [34]. In the lignite group, the accumulation of short-chain fatty acids, particularly butyric acid (99.43% of the soluble organic matter at day 30 in the Dunaliella group), suggests a highly active acidogenic microbial community that is involved in the hydrolytic breakdown of coal and algae-derived compounds. This accumulation of VFAs promotes an environment favorable to methane production by aceticlastic methanogens such as Methanosarcina, which use acetate as the main substrate for methane generation. However, the continued acidification caused by Dunaliella’s metabolic activity can inhibit methanogenic archaea by lowering the pH, potentially reducing overall methane production rates.
In contrast, anthracite’s higher rank, low oxygen content, and dense aromatic structure (Table 2) make it more resistant to microbial degradation [35]. As a result, the fermentation system with anthracite exhibited slower VFA accumulation and different nitrogen dynamics. For instance, the Porphyra–anthracite combination led to a higher accumulation of TAN (1.21 g/L), which could be due to the slower microbial breakdown of the coal’s organic components. The lower oxygen functional group content of anthracite limits microbial access, requiring more specialized microbial consortia to degrade the coal, including methanogens that are more tolerant to high TAN levels. However, the gas production of the anthracite–Porphyra combination was as high as 21.28 mL, which exceeded that of some low-rank coal systems. Other studies have also shown that previously reported biogenic methane production from higher-order coal may have been underestimated [31]. This finding suggests that higher-rank coals, despite their recalcitrance, might still hold significant methane potential under suitable microbial conditions. The 2.6-fold difference in gas production between the same Porphyra assemblage of bituminous A and B (23.49 mL vs. 9.05 mL), combined with the specular group reflectivity data (Table 2), suggests that coal rock microfractions may inhibit microbial attachment or methanogenic bacterial activity [36].
This comparison between lignite and anthracite emphasizes the influence of coal rank on microbial metabolic pathways. The more reactive lignite supports faster fermentation and higher VFA production, while the more inert anthracite requires specialized microbial communities that can handle the increased nitrogen levels and less accessible organic material. Further validation using XPS and FTIR analyses of the coal surface properties is required in the future. It is recommended that coal samples be pre-oxidized to increase the homogeneity of oxygen-containing functional groups [37,38,39] or that surfactants should be added to enhance the adsorption of extracellular enzymes on hydrophobic coal surfaces.

4.2. Algae Affect the Microenvironment and Microbial Communities in Fermentation Systems

The pH change in the fermentation system was dominated by algal metabolism and regulated by the buffering capacity of the coal stage. The accumulation of VFAs (peak butyric acid 2.30 g/L) and the continuous decrease in pH in the Dunaliella group severely inhibited the activity of the methanogenic archaea [40,41], which caused the fermentation system to rapidly reach the bottleneck of the methanogen production. The Nannochloropsis is a high-nitrogen organic matter, and the pH of the fermentation system in the Nannochloropsis group was close to neutral. The final pH of the Nannochloropsis combination in anthracite was 6.29. The accumulation patterns of VFAs further verified this conclusion. The accumulation of short-chain fatty acids (peak acetic acid 8.42 g/L) in the lignite-Aphanizomenon combination matched with the high expression of phosphotransacetylase gene in Sporanaerobacter (59.48%) and butyric acid kinase activity in Terrisporobacter (19.69%) in the bacterial community. The preference of the anthracite system for long-chain fatty acids (valeric and caproic acids), and, in particular, the caproic acid yield (0.32 g/L) of anthracite+Dunaliella, resulted from activation of the carbon chain-extension pathway. This involved the condensation of acetic acid with ethanol to C4-C6 acids by reverse β-oxidation in the archaeal community of Methanoculleus (45.9%). Additionally, the high triglyceride content of Dunaliella (>30%) provided sufficient ethanol precursors. The rapid VFA depletion in the Porphyra group (not detected on day 30) was directly correlated with its high TAN accumulation (1.21 g/L): at TAN > 0.8 g/L, VFAs/TAN < 1.0 strongly enriched ammonia-resistant methanogenic bacteria, Methanosarcina, which achieves efficient methanogenesis in low VFA environments through a high-affinity acetic acid transporter protein [42]. To prevent deep acidification of the fermentation system, it is recommended that the coal–algae ratio be adjusted to >2:1 to take advantage of the ash buffer in the coal, or that alkaline minerals, such as FeCO3, be dosed to both neutralize acidity and provide Fe2+ to promote electron transfer [43,44].
Algae influence the structure of methanogenic bacterial communities by releasing substrates. In the high-lipid Dunaliella group, the hydrolytic bacterium Clostridium (94.16%) was enriched, leading to the accumulation of short-chain fatty acids and acidification of the fermentation solution. The low C/N ratio triggered TAN accumulation, inhibiting methanogenic bacterial activity (λ = 10.8 days); In the medium-lipid Nannochloropsis group, slow-release substrates support the mutualistic bacterial community of Syntrophomonas (16.54%) and Methanoculleus (45.9%), achieving the highest biogenic methane production rate; The high-protein algae Porphyra group degrades to produce high TAN, enriching the ammonia-tolerant Methanosarcina, ensuring efficient gas production (21.28 mL) in the high-aromaticity anthracite group. Future research should elucidate the specific mechanisms by which algal polysaccharide structures influence the enrichment of Methanosarcina (34.0%).

4.3. Microbial Interaction Networks Determine Metabolic Efficacy

Bacterial and archaeal communities are correlated with gas production performance through mutualistic relationships. High biogenic methane production (A = 26.43 mL) in the lignite–Nannochloropsis system was significantly and positively correlated with the mutualistic bacteria Syntrophomonas (LDA = 4.70) and Tissierella, which synergistically degrade long-chain fatty acids. The mutualistic bacterium Syntrophomonas β-oxidized long-chain acids to H2/acetic acid, while the hydrogenotrophic archaeon Methanoculleus (45.9%) maintained a low hydrogen partial pressure through interspecies hydrogen transfer [45]. The Dunaliella system was dominated by the acid-producing bacterium Clostridium (94.16%), which led to overaccumulation of butyric acid (>93%) and was negatively correlated with biogenic methane production.
The structure of the methanogenic bacterial community was correlated with the algal species. In this study, the Porphyra fermentation system with the acetate-nutrient Methanosarcina (24.3%) had a higher abundance of methanogens, and this fermentation system consumed VFAs (down to 0 on day 30) faster and produced large amounts of methane. The Aphanizomenon system accumulated high concentrations of VFAs (peak acetic acid 8.42 g/L) but was inefficient in acetic acid conversion because the archaea were dominated by the hydrogenotrophic Methanobacterium (63.7%). Degradation products of high-protein algae (Porphyra, >40% protein) were subjected to the Stickland Reaction, which produced high TAN (>1.21 g/L), inhibited the VFAs/TAN ratio (<0.07), and promoted the growth of the ammonia-resistant Methanosarcina. Low-nitrogen algae (Aphanizomenon, C/N > 15) maintained a high VFA/TAN (>33.98), supporting the dominance of gas production by acidifying flora (e.g., Clostridium) via the acetoclastic pathway. Other studies have also found that the algal type affects the methanogenic pathway and microbial community structure, with lake-dominated algae tending to produce more methane than river-dominated species [46].

5. Conclusions

The following conclusions were drawn by systematically investigating the biogenic methane production characteristics of the co-fermentation system of different coal orders and five microalgae, combined with an analysis of the microbial community structure and metabolic function.
(1)
The coal–algae synergistic effect affected the biogenic methane production efficiency. The combination of lignite and Nannochloropsis showed optimal biogenic methane production performance (26.43 mL). This was attributed to the synergistic effect of the lignite-rich degradable organic matter and the high lipid content of Nannochloropsis. The combination of anthracite and Porphyra also exhibited excellent gas production performance (21.28 mL), and specific algae could enhance the stabilizing structure of the microorganisms for the effective degradation of high-order coal.
(2)
The pH dynamics of the fermentation system show algal species specificity. The Dunaliella group resulted in continuous acidification of the fermentation system (final pH ≤ 4.15), while Nannochloropsis could maintain a neutral environment (pH 6.95–6.99) in the fermentation system. Lower-order coals (lignite and subbituminous) showed a pH buffering capacity.
(3)
Microbial community analyses revealed the succession patterns of key functional groups. The Porphyra system was enriched in acetic acid-trophic Methanosarcina (24.3%), which was consistent with its efficient conversion capacity for VFAs, whereas the mutualistic bacteria showed a positive correlation with biogenic methane production (r > 0.7).
(4)
The study of the biogenic methane production mechanism of the coal–algae co-fermentation system provides theoretical support for the efficient bioconversion of coal resources, and the research results are of great scientific significance for promoting the clean utilization of coal. Future studies can be combined with multi-omics analysis to further analyze the molecular mechanisms of the coal degradation process.

Author Contributions

Conceptualisation, writing the original draft, funding acquisition, L.Z.; conceptualisation, W.D.; investigation, Y.L.; methodology, P.Z.; data curation, C.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Yunlong Lake Laboratory of Deep Underground Science and Engineering Project, grant number 104024003; the National Natural Science Foundation of China, grant number 52404149; the Natural Science Foundation of Jiangsu Provincial Basic Research Program, grant number BK20220024; the Jiangsu Funding Program for Excellent Postdoctoral Talent, grant number 2024ZB177 and the Postdoctoral Fellowship Program of CPSF, grant number GZC20241927.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Sampling location and coal sample crushing process.
Figure 1. Sampling location and coal sample crushing process.
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Figure 2. Methane production: (a) average cumulative methane production of all experimental groups; (b) methane production curve of lignite and algae co-fermentation. Error bars represent one standard deviation of measurements from triplicate microcosms.
Figure 2. Methane production: (a) average cumulative methane production of all experimental groups; (b) methane production curve of lignite and algae co-fermentation. Error bars represent one standard deviation of measurements from triplicate microcosms.
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Figure 3. pH changes in fermentation solutions: (a) lignite with algae; (b) subbituminous with algae; (c) bituminous A with algae; (d) bituminous B with algae; (e) anthracite with algae; (f) only algae. Error bars represent one standard deviation of measurements from triplicate microcosms.
Figure 3. pH changes in fermentation solutions: (a) lignite with algae; (b) subbituminous with algae; (c) bituminous A with algae; (d) bituminous B with algae; (e) anthracite with algae; (f) only algae. Error bars represent one standard deviation of measurements from triplicate microcosms.
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Figure 4. Volatile fatty acids in the fermentation solutions: (a) Lignite and Dun; (b) Lignite and Nan; (c) Lignite and Aph; (d) Lignite and Por; (e) Lignite and Sch; (f) Anthracite and Dun; (g) Anthracite and Nan; (h) Anthracite and Aph; (i) Anthracite and Por; (j) Anthracite and Sch. Error bars represent one standard deviation of measurements from triplicate microcosms.
Figure 4. Volatile fatty acids in the fermentation solutions: (a) Lignite and Dun; (b) Lignite and Nan; (c) Lignite and Aph; (d) Lignite and Por; (e) Lignite and Sch; (f) Anthracite and Dun; (g) Anthracite and Nan; (h) Anthracite and Aph; (i) Anthracite and Por; (j) Anthracite and Sch. Error bars represent one standard deviation of measurements from triplicate microcosms.
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Figure 5. Total nitrogen in fermentation solutions and volatile fatty acids to total nitrogen ratio. The ratio is calculated based on the average values of three parallel experimental groups. Error bars represent one standard deviation of measurements from triplicate microcosms. (a) TAN of Dun group; (b) TAN of Nan group; (c) TAN of Aph group; (d) TAN of TAN of Aph group; (e) TAN of Sch group; (f) VFAs/TAN ratio of Dun group; (g) VFAs/TAN ratio of Nan group; (h) VFAs/TAN ratio of Aph group; (i) VFAs/TAN ratio of Por group; (j) VFAs/TAN ratio of Sch group. Error bars represent one standard deviation of measurements from triplicate microcosms.
Figure 5. Total nitrogen in fermentation solutions and volatile fatty acids to total nitrogen ratio. The ratio is calculated based on the average values of three parallel experimental groups. Error bars represent one standard deviation of measurements from triplicate microcosms. (a) TAN of Dun group; (b) TAN of Nan group; (c) TAN of Aph group; (d) TAN of TAN of Aph group; (e) TAN of Sch group; (f) VFAs/TAN ratio of Dun group; (g) VFAs/TAN ratio of Nan group; (h) VFAs/TAN ratio of Aph group; (i) VFAs/TAN ratio of Por group; (j) VFAs/TAN ratio of Sch group. Error bars represent one standard deviation of measurements from triplicate microcosms.
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Figure 6. Venn diagram and bar graph species at the genus level. The overlapping areas in the Venn diagram represent the number and percentage of shared species, while the vertical axis of the bar chart represents the number of species: (a) bacteria; (b) archaea. The data in the figure are the means of three parallel experimental groups.
Figure 6. Venn diagram and bar graph species at the genus level. The overlapping areas in the Venn diagram represent the number and percentage of shared species, while the vertical axis of the bar chart represents the number of species: (a) bacteria; (b) archaea. The data in the figure are the means of three parallel experimental groups.
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Figure 7. Species composition of (a) archaea and (b) bacteria at the genus level.
Figure 7. Species composition of (a) archaea and (b) bacteria at the genus level.
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Figure 8. Linear discriminant analysis effect size of bacteria.
Figure 8. Linear discriminant analysis effect size of bacteria.
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Figure 9. Metabolic pathway prediction heatmap.
Figure 9. Metabolic pathway prediction heatmap.
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Table 1. Ultimate analysis of coal and algae samples.
Table 1. Ultimate analysis of coal and algae samples.
SampleC, %N, %H, %S, %
BYH46.741.093.930.99
XZ62.590.844.110.67
MDL60.420.934.440.29
LX65.160.673.670.21
ZLS73.811.262.880.61
Dunaliella (Dun)45.431.006.790.16
Nannochloropsis (Nan)42.846.366.250.89
Aphanizomenon (Aph)54.4912.847.211.04
Porphyra (Por)31.383.064.755.59
Schizochytrium (Sch)52.952.626.972.89
Table 2. Vitrinite reflectance test of coal samples.
Table 2. Vitrinite reflectance test of coal samples.
SampleAverage Reflectivity, %Reflectivity, %Coal Rank
BYH0.2500.25–0.30Lignite
XZ0.4590.45–0.50Subbituminous
MDL0.5040.50–0.55Bituminous A
LX0.7620.75–0.80Bituminous B
ZLS2.6712.65–2.70Anthracite
Table 3. Model and parameters for fitting the methane production in the lignite group.
Table 3. Model and parameters for fitting the methane production in the lignite group.
GroupModelModel ParametersEvaluation
AμmλR2RMSEAIC
Lignite +
Nannochloropsis		Modified Gompertz26.432.2310.80.9851.3413.11
Lignite + PorphyraModified Gompertz23.431.655.230.9910.844.951
Lignite + SchizochytriumModified Gompertz15.701.034.540.9960.37−12.1
Table 4. Soluble organic matter and average percentage in the fermentation liquid.
Table 4. Soluble organic matter and average percentage in the fermentation liquid.
GroupTimeCompoundMolecular FormulaPercent, %
Lignite + Dunaliella6 daysButanoic acidC4H8O293.92
Butylated HydroxytolueneC15H24O2.73
9-Octadecenamide, (Z)-C18H35NO1.3
15 daysButanoic acidC4H8O299.42
Butylated HydroxytolueneC18H35NO0.35
30 daysButanoic acidC4H8O299.43
Butylated HydroxytolueneC15H240.57
Lignite + Nannochloropsis6 daysPhosphonic acid, (p-hydroxyphenyl)-C6H7O4P3.82
p-CresolC7H8O86.1
Indole, 3-methyl-C9H9N3.61
9-Octadecenamide, (Z)-C18H35NO6.48
15 daysAdenosine, 4′-de(hydroxymethyl)-4′-[Nethylaminoformyl]-C20H22N6O61.12
Adenosine, 4′-de(hydroxymethyl)-4′-[Nethylaminoformyl]-C20H22N6O61.45
Ethyl isoallocholateC26H44O51.29
Hexanoic acidC6H12O21.22
Phenyl-.beta.-D-glucosideC12H16O62.48
p-CresolC7H8O63.7
Octanoic acidC8H16O21.35
2-PiperidinoneC5H9NO18.92
Indole, 3-methyl-C9H9N6.39
30 daysp-CresolC7H8O4.52
Bicyclo(3.1.1)heptane-2,3-diol, 2,6,6- trimethyl-C10H18O264.8
7-Methyl-Z-tetradecen-1-ol acetateC17H32O226.39
9-Octadecenamide, (Z)-C18H35NO1.67
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Zhu, L.; Diao, W.; Liu, Y.; Zhu, P.; Gong, C. Research on Algae Enhancing Biogenic Methane Production from Coal. Sustainability 2025, 17, 7960. https://doi.org/10.3390/su17177960

AMA Style

Zhu L, Diao W, Liu Y, Zhu P, Gong C. Research on Algae Enhancing Biogenic Methane Production from Coal. Sustainability. 2025; 17(17):7960. https://doi.org/10.3390/su17177960

Chicago/Turabian Style

Zhu, Liu, Wangjie Diao, Yi Liu, Peilin Zhu, and Chenyao Gong. 2025. "Research on Algae Enhancing Biogenic Methane Production from Coal" Sustainability 17, no. 17: 7960. https://doi.org/10.3390/su17177960

APA Style

Zhu, L., Diao, W., Liu, Y., Zhu, P., & Gong, C. (2025). Research on Algae Enhancing Biogenic Methane Production from Coal. Sustainability, 17(17), 7960. https://doi.org/10.3390/su17177960

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