Pre-treatment of alcohol precipitation wastewater of Chinese patent medicine by anaerobic digestion and microbial community changes during the pre-treatment process Open Access

According to data released by the National Bureau of Statistics of China, more than 4,000 types of proprietary Chinese medicines have been manufactured to date (Huang et al. 2021). In 2021, the production of Chinese patent medicine (CPM) in China exceeded 2.3 million tons. However, the manufacturing process of CPM can result in the discharge of wastewater with elevated levels of chemical oxygen demand (COD), intense coloration, and substantial biological toxicity. If released untreated, this wastewater could lead to significant pollution of natural water bodies (Jin et al. 2021). The wastewater generated during CPM production primarily originates from the cleaning and extraction phases (Feng 2018). Notably, ethanol is predominantly employed as a solvent during the extraction phase, resulting in the generation of alcohol precipitation wastewater. This wastewater is particularly challenging to treat due to its COD concentration, which can reach up to 30–40 g/L, and its potent biological inhibition. Currently, biological treatment remains the most cost-effective and efficient method for treating CPM wastewater. However, alcohol precipitation wastewater notably compromises the operational stability of integrated biological wastewater treatment processes. Therefore, it is imperative to pre-treat high-concentration alcohol precipitation wastewater to enhance the effectiveness of subsequent biochemical treatment (Wang 2021a).

The physicochemical approach stands as the dominant methodology for pre-treating CPM wastewater. This encompasses iron–carbon micro-electrolysis, Fenton oxidation, ozone catalytic oxidation, and wet oxidation. Previous studies have shown that iron–carbon micro-electrolysis offers low operational costs and simple procedures. However, its efficiency is generally modest (Feng et al. 2012). Fenton oxidation excels in chromaticity removal, but it requires multiple pH adjustments, involves cumbersome procedures, and yields substantial amounts of chemical sludge (Chen et al. 2021). Ozone catalytic oxidation can introduce new products through redox reactions, thus increasing wastewater chrominance (He et al. 2019). On the other hand, wet oxidation demands high-temperature and high-pressure conditions (Zhu et al. 2023).

Anaerobic digestion has thus emerged as an ideal technology for managing high-concentration organic wastewater. This strategy not only conserves energy but also enhances production capacity, aligning with global carbon reduction strategies. Due to these advantages, anaerobic digestion has found widespread application across diverse sectors, including high-concentration industrial wastewater, residual sludge from sewage plants, food waste, lignocellulosic biomass residues, and agricultural waste treatment (Li et al. 2015; Azizan et al. 2021; Granzotto et al. 2021; Paritosh et al. 2021; Singh et al. 2022). Su et al. (2015) employed a novel double-circulation anaerobic digestion reactor (DC) to address CPM wastewater, achieving a COD removal efficiency of 67% at a volumetric loading rate (VLR) of 13.0–15.0 kg COD/(m³·d). Chen et al. (2019a, b) utilized a spiral symmetric flow anaerobic bioreactor for CPM wastewater treatment, demonstrating its capacity to withstand higher VLRs compared with an IC reactor under identical conditions. Li et al. (2007) explored the anaerobic–aerobic treatment of pharmaceutical wastewater through a combination of anaerobic cross-baffle reactors, moving bed biofilm reactors, and membrane bioreactors. The anaerobic units achieved a COD removal rate of 55%. Cui et al. (2015) conducted a pilot-scale experiment using UASB-MBR technology for berberine wastewater. Following Fenton pre-treatment, the influent COD concentration ranged from 2,300 to 3,600 mg/L, and the UASB process achieved an approximately 50% COD removal rate for berberine wastewater. Nevertheless, although anaerobic digestion can be employed for the biological treatment of CPM wastewater, its efficiency remains limited. Notably, no previous attempts have been made to apply anaerobic digestion in the treatment of alcohol precipitation wastewater.

In this study, we focused on the alcohol precipitation wastewater produced by a CPM manufacturing company. Our main objective was to utilize the anaerobic digestion process as a means of biochemical pre-treatment for this wastewater. Through an extended debugging period, we identified the key operational parameters that would unlock the maximum potential of anaerobic treatment for alcohol precipitation wastewater. Using spectral detection technology, we analyzed alterations in the major components of the wastewater. Furthermore, the microbiota structure was investigated through 16S rRNA high-throughput sequencing. This dual approach allowed us to delve into the degradation mechanism of pollutants during the anaerobic digestion of alcohol precipitation wastewater. Ultimately, our findings offer valuable theoretical insights for real-world wastewater treatment projects.

The alcohol precipitation wastewater was collected in the extraction workshop of the CPM manufacturing company (Shandong Province, China), which encompassed the production of CPM products such as Ganmaoling and Banlangen granules. The characteristics of the alcohol precipitation wastewater are listed in Table 1. An incremental concentration approach was used to adjust the proportion of alcohol precipitation wastewater. Inoculation sludge utilized in the anaerobic digestion unit was derived from an anaerobic reactor of a starch factory (Kaifeng, China), with the mixed liquor suspended solids (MLSS) concentration and the ratio of mixed liquor volatile suspended solids (MLVSS) to MLSS was 150,000 g/L and 0.75, respectively. The cultivation and domestication of sludge adopted the asynchronous inoculation method. The anaerobic DC underwent inoculation with granular sludge (sludge amount was 30% of the effective volume of the reactor), followed by alcohol precipitation wastewater, which was diluted to 5,000 mg/L COD addition. The reactor was initiated with a mass load of 1.0 kg COD/(m³·d). It took one period for the complete turnover of wastewater within the reactor, and each loading cycle encompassed two to four influent periods. Upon achieving a stable COD concentration in the effluent and a COD removal rate of at least 75%, loading could be escalated.

All samples were filtered through 0.45 μm membrane filters before chemical analysis. COD, NH₄⁺-N, TN, MLSS, and MLVSS were determined according to standard characterization methods (State Environmental Protection Administration 2002). pH values were monitored using a pH/OPR detector (PHSJ-6L). The testing items are shown in Table 2.

Types of organic pollutants were analyzed by a UV spectrophotometer (TU-1900) of PERSEE in China with scanning wavelength from 200 to 800 nm (Niermeier et al. 2018). Chemical profiling of the wastewater was conducted via Fourier-transform infrared spectroscopy (FT-IR) (Zhou et al. 2021). A Nicolet iS20 spectrometer (Thermo Scientific, USA) was used for FT-IR analysis at wave numbers ranging from 400 to 4,000 cm⁻¹. For the FT-IR analysis, KBr pellets were prepared by blending the wastewater samples with KBr. Furthermore, dissolved organic matter (DOM) was quantitatively assessed using three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy (Zhan 2021).

Throughout the debugging process, the anaerobic DC experienced two instances of acidification triggered by shock loads and fluctuations in water quality. Consequently, the analysis was partitioned into three distinct stages. In Stage I, there was an alternating elevation in the influence quantity and influence concentration. This was performed to assess the reactor's operational efficacy under varying conditions. In Stage II, the influence concentration was maintained while the influence quantity was increased. The objective was to gauge the reactor's resilience toward the wastewater. Finally, in Stage III, the influence quantity was retained while the influence concentration was augmented. This allowed for the determination of the reactor's suitability for appropriate operating loads.

Stage Ⅰ spanned from Days 1 to 41. As depicted in Figure 3(a), the COD removal rate remained consistently above 90% for the initial 6 days due to the dilution effect of fresh water within the reactor. By Day 15, the COD removal rate had stabilized at above 85%, at which point loads began to be increased. On Day 39, the influent COD concentration and volumetric load were increased to 14,350 mg/L and 19.5 kg COD/(m³·d), respectively. However, the COD removal rate abruptly declined to 69.8%, with the effluent COD reaching 4,333 mg/L. This led to reactor acidification, evidenced by an effluent VFA of 20 mmol/L. Therefore, the reasons for the swift surge in influent COD concentration were further inspected. In the treatment of traditional Chinese medicine wastewater using an expanded granular sludge blanket (EGSB) reactor, Li et al. (2014) implemented a two-phase loading rate escalation strategy: Phase I (4–10 kg COD/(m³·d)) involved increasing influent COD concentration under constant hydraulic loading, while Phase II (10–20 kg COD/(m³·d)) elevated hydraulic loading with stabilized COD concentration. Analysis of operational parameters during Days 39–42 revealed that influent COD concentration shock induced reactor acidification, manifested by a 20 mmol/L effluent volatile fatty acid (VFA) concentration and concomitant 18.7% reduction in COD removal efficiency. To mitigate persistent acidification risks, corrective measures including sodium hydroxide supplementation (pH stabilization), dual reduction of hydraulic and organic loading rates, and enhanced effluent recirculation (3:1 reflux ratio) were systematically implemented for process recovery (Ge & Wang 2016).

Stage Ⅱ encompassed Days 42–139. During the first phase (Days 42–52) of recovering from acidification, the influent COD was maintained at 9,000 mg/L, leading to a COD removal rate of 85%. In the subsequent period (Days 52–90), the influent COD ranged from 9,000 to 11,000 mg/L, coupled with an increase in influent flow from 5 to 7 L/d. However, on Days 64 and 98, the COD removal rate decreased to 50.9 and 48.3%, respectively. Wang et al. (2007) utilized an anaerobic reactor for the treatment of beverage production wastewater demonstrated that fluctuations in wastewater quality during the manufacturing process could lead to abnormal operational performance of the reactor. This was attributed to water quality fluctuations resulting from adjustments in the production process, which hindered the performance of anaerobic sludge microorganisms. Day 98 saw a 75% COD removal rate after 20 days, which was achieved by modifying the water and reducing influent COD concentration. This underscores the importance of ensuring wastewater stability for high-concentration, hard-to-degrade wastewater when initiating anaerobic digestion. Additionally, rapid increases in water volume should be avoided.

Stage III extended beyond Day 140. As depicted in Figure 3(b), reactor debugging was initiated with an influent COD concentration of 11,000 mg/L and a mass loading of 5 kg COD/(m³·d). Gradual increases in influent COD concentration were implemented. After 80 days of loading, the influent COD reached 21,000 mg/L, yielding an effluent COD of 3,200 mg/L and a COD removal rate of 85%. Stabilization occurred at a volumetric loading of approximately 10 kg COD/(m³·d), under which the system operated steadily. Overall, the pre-treatment of alcohol precipitation wastewater through anaerobic digestion was deemed feasible.

According to Figure 4(a), the influent pH value fluctuated between 6.0 and 8.2 during the load increase phase in metabolism, while the effluent pH value ranged from 7.15 to 8.42. A minor decrease in effluent pH occurred during each load increase. This phenomenon was attributed to the heightened production of metabolic acids by acidifying bacteria due to increased loading. Methane-producing bacteria could not promptly utilize these acids, resulting in a temporary decline in pH. However, the pH typically rebounded within two to three influent periods. On Day 37, significant acidification was observed, leading to a substantial decrease in pH. To counter persistent acidification, NaOH was introduced into both the influent and the system on Days 38–40, thus effectively restoring influent and effluent pH values. As illustrated in Figure 4(b), during the transition from a loading of 6.0 to 10.0 kg COD/(m³·d), the effluent VFA concentration remained elevated (4.5–14 mmol/L). However, the COD removal rate consistently remained above 85%. However, further escalating the loading from 10.0 to 11.0 kg COD/(m³·d) resulted in a sharp increase in effluent VFA concentration, coupled with a decline in COD removal rate. This shift was attributed to the cumulative buildup of VFA and the influent COD concentration increase, both impacting the reactor's microbial population and inducing acidification. Figure 4(b) also demonstrates elevated effluent ALK levels. This is due to the conversion of organic nitrogen in the effluent to NH₄⁺-N through anaerobic microbial metabolism. Consequently, the effluent's pH value increased, coinciding with an increase in ALK.

During Stage II, the reactor frequently experienced high and fluctuating effluent VFA concentrations, primarily due to reactor acidification. This resulted in a COD removal rate of only 60–70%, despite a low volumetric loading. Moreover, when VFA/ALK > 0.8, the reactor's buffering capacity weakened (Rao et al. 2014). On Days 64 and 98, as illustrated in Figure 4(c), the effluent exhibited a VFA/ALK ratio of 1.5, underscoring the substantial impact of these two acidification events. Therefore, the load was decreased during Stage III to restore the reactor's stability. This load reduction in Stage III led to a significant decrease in effluent VFA concentration compared with Stage II. The levels were maintained between 5 and 10 mmol/L, with ALK stably remaining within a 10–20 mmol/L range. The VFA/ALK ratio ranged from 0.3 to 0.6, experiencing only minor fluctuations at the onset of each load increase. This indicated that increasing the load by maintaining influent flow and elevating influent concentration yielded favorable outcomes.

Figure 5 shows the trends of TN and NH₄⁺-N concentrations for both the influent and effluent during the debugging phase. As illustrated in Figure 5(a), acidification inhibited and inactivated the microorganisms on Day 40, and the release of nitrogen from the autolysis of the bacterium led to a significant increase in the TN concentration of the effluent (Wang et al. 2008), whereas the TN content of the effluent decreased throughout the rest of the day. Following the debugging process, the TN removal rate reached 50%. Wei (2019) also demonstrated that a TN removal efficiency of 50% was achieved due to microbial growth and metabolism requiring nitrogen in the reactor, coupled with nitrogen removal through methanogenesis–denitrification–anaerobic ammonia oxidation. Similarly, Wang et al. (2014) observed NH₄⁺-N removal during UASB treatment of dyeing wastewater, with further analysis of microbial community structure revealing that anaerobic ammonia-oxidizing bacteria (AnAOB) accounted for 1.15% of the total population. Additionally, Yu & Shi (2017) reported that under mesophilic conditions (31–35 °C) and pH 7.8–8.3, after 90–250 days of acclimation using UASB inoculated with anaerobic granular sludge, the synergistic effects of methanogenesis, denitrification, and anaerobic ammonia oxidation were achieved. This resulted in COD, TN, and NH₄⁺-N removal efficiencies of 90, 60, and 24%, respectively. This analysis focused on the nitrogen requirements for microbial growth and metabolism within the reactor, as well as the nitrogen removal mechanisms involving methane-coupled denitrification and anaerobic ammonium oxidation. As shown in Figure 5(b), the NH₄⁺-N concentration in the effluent typically surpassed that of the influent. This divergence can be attributed to the presence of organic nitrogen compounds such as proteins and alkaloids in alcohol precipitation wastewater. During anaerobic digestion, biodegradable organic nitrogen undergoes microbial degradation, producing peptides and other smaller molecules. These are subsequently converted to NH₄⁺-N through ammonification (Hendriksen & Ahring 1996). In Figure 5(c), this process contributes to the decline in TN levels and escalating NH₄⁺-N concentrations observed in our experiments. During Stage II, even as the COD removal rate decreased, the NH₄⁺-N concentration in the effluent continued to rise. This outcome stemmed from the fact that the reactor's resilience against environmental pressures was lower for methane-producing bacteria compared with the removal rates of approximately 30%. Considering the reactor's operational efficiency, our findings suggested the occurrence of methane-coupled denitrification and anaerobic ammonium oxidation within the reactor after an extended operational period.

To explore the degradation of pollutants within alcohol precipitation wastewater through anaerobic digestion, comprehensive analyses were conducted on stabilized influent and effluent samples using 3D fluorescence, FT-IR, and UV–Vis techniques.

The 3D fluorescence spectra of the influent and effluent, diluted 20 times, are depicted in Figure 6. Notably, the 3D fluorescence peaks, designated as Peak A and Peak B, were evident in both the influent and effluent. These peaks were located at λEx/λEm = 440–460 nm/500–540 nm, within region V of the spectral map, which encompasses 3D fluorescence peaks attributed to aromatic substances boasting substantial molecular weights and inherent stability. This observation strongly indicates that the DOM within alcohol precipitation wastewater predominantly consisted of humic acids. Many of these substances contain conjugated double bonds and aromatic ring structures, which possess microorganism-inhibiting characteristics (Chen et al. 2019a, b). Furthermore, these findings suggest that even after anaerobic digestion, the remaining DOM in the wastewater was primarily composed of humic acids. Quantitative characterization of the wastewater's DOM can be achieved based on the standard volume of the fluorescence region integration method (Zhang 2021). The anaerobic treatment resulted in an 82.72% reduction in the fluorescence intensity of the wastewater, thus demonstrating the effective removal of humic acids within the anaerobic digestion unit. Moreover, the integrated standard volume of the fluorescence area experienced a 72.5% decline following treatment. This change, coupled with the phenomenon of blue shift (fluorescence peaks shifting toward shorter wavelengths), indicates the degradation of recalcitrant substances.

Figure 7 illustrates the FT-IR spectra of the influent and effluent after achieving stability. In Figure 7(a), as observed in the functional group region, 1,413.81 and 1,602.87 cm⁻¹ represent the protein peptide bond stretching vibration peaks and the aromatic compound stretching vibration peaks, 2,929.27 cm⁻¹ is the C–H stretching vibration peak, and 3,420.22 cm⁻¹ is the N–H, C–H stretching vibration peak. In the fingerprint area, 546.57 and 622.59 cm⁻¹ correspond to the stretching vibrations of C–Br or C–Cl bonds, indicating the presence of halogenated hydrocarbons. The peaks at 774.32 and 860.64 cm⁻¹ correspond to the O–H out-of-plane bending vibrations and benzene ring substitution stretching vibrations, respectively, suggesting the presence of phenolic compounds. The peak at 1,040.84 cm⁻¹ is the C–O–C in-plane bending vibration peak of sugar, particularly xylan (Geng et al. 2003). Moreover, the peak at 1,119.58 cm⁻¹ is the C–O–C in-plane bending vibration peak of fat, which is the characteristic peak of lignin. Additionally, alcohol precipitation wastewater contains an array of components such as saturated and unsaturated carbon bonds, benzene rings, hydroxyl groups, and ether bonds. These include alkanes, alkenes, halogenated hydrocarbons, phenols, alcohols, amines, and aromatic compounds. As shown in Figure 7(b), the peak cleavage of the effluent occurs near 1,602.87 cm⁻¹, the peaks of carboxyl functional group stretching vibration appear near 1,924.85 and 2,554.49 cm⁻¹, and the peak of stretching vibration at 3,420 cm⁻¹ decreases. These shifts indicate structural changes in aromatic compounds within the wastewater following treatment. Furthermore, our findings suggested the conversion of macromolecular organics into smaller molecules through biological metabolism, corroborating the effective removal of organic matter and nitrogenous substances through treatment. In conclusion, our findings demonstrated that anaerobic digestion can degrade pollutants present in alcohol precipitation wastewater.

Figure 8 illustrates the UV–Vis spectra of both the influent and effluent (following a 10-fold dilution) upon achieving stability. As shown in Figure 8(a), the influence absorbance is almost negligible within the visible region, whereas strong absorbance was evident in the UV region. This observation underscores the prevalence of organic matter in alcohol precipitation wastewater, primarily consisting of aromatic compounds and unsaturated organics. The UV–Vis spectrum of the influent displays absorption peaks associated with various components. For instance, absorption peaks related to aromatic rings were located at 206, 214, and 221 nm, whereas peaks corresponding to phenolic hydroxyl and chromophores were located at 210 and 217 nm, respectively. Additionally, absorption peaks attributed to conjugated polyenes and –C = C–C = O– bonds were observed at 223 and 227 nm (Wang 2021a, b), respectively. The UV region (200–400 nm) of each segment of the effluent spectrum indicated that the substances were greatly degraded. Within UV–Vis spectra, the molecular weight parameter ‘M’ is the ratio of absorbance at 250–365 nm. This ratio is inversely proportional to molecular weight, with higher M values indicating lower proportions of macromolecular organics (Yu et al. 2016). As shown in Figure 8(b), the influent and effluent exhibited M values of 3.22 and 5.28, respectively. This disparity highlights the efficacy of anaerobic digestion in degrading macromolecular organic components within alcohol precipitation wastewater.

High-throughput sequencing of the 16S rRNA was executed on the anaerobic reactor-inoculated sludge (referred to as Day 1, F-1), sludge from Stage I (Day 40, F-2), Stage II (Day 138, F-3), and Stage III (Day 219, F-4). The objective was to gain insights into microbial diversity and similarity, discern microbial species at both the phylum and genus levels within the anaerobic DC, and scrutinize the variations in microbiota structure during the debugging process.

The alpha diversity of microbial communities is conventionally assessed based on species abundance and evenness. This evaluation encompasses metrics such as the Chao1 index, Dominance, Equitability, Richness index, Simpson index, and Shannon index. The outcomes of these metrics are summarized in Table 3.

As outlined in Table 3, the Chao1 index and Richness index of F-2 were observed to be lower than those of F-1. This reduction was attributed to the inactivation of microorganisms and a decrease in species count triggered by the anaerobic DC's acidification on Day 39. The remaining three samples exhibited the following ascending order in terms of Chao1 and Richness indices: F-1 < F-3 < F-4. This constituted an 8.10% increase in microorganism count during the anaerobic DC's debugging process. The trend of the Shannon index exhibited the following order: F-1 < F-2 < F-3 < F-4. These findings underscored the increased microbial diversity within the anaerobic DC. This elevation indicated the emergence of functional microorganisms targeting the degradation of substances within the wastewater over the course of the study.

In terms of species uniformity, the Simpson index of the samples exhibited the following order: F-1 > F-2 > F-3 > F-4, displaying a decrease from 0.034 to 0.010. This trend suggests a tendency toward more uniform species abundance distribution. The significant reduction in the Simpson index of F-2 implies that alcohol precipitation wastewater possesses a potent ability to select and eliminate microbial species throughout the anaerobic digestion process. This effect became particularly pronounced after 40 days. This phenomenon can be attributed to the heightened toxicity of alcohol precipitation wastewater. In terms of dominance and equitability, the samples exhibited the following order: F-1 < F-2 < F-3 < F-4. Larger values in both metrics indicate a lower species abundance distribution. This pattern is closely related to the elimination of microorganisms ill-suited for alcohol precipitation wastewater, allowing adapted species to persist and thrive.

Figure 9 shows the distribution of microbiota OTU in different samples. It is evident that the microbial sequences were organized into clusters, yielding a total of 11,594 OTUs. Within these OTUs, the specific counts of OTUs in F-1, F-2, F-3, and F-4 were 3,239, 2,944, 3,400, and 3,482, respectively. Notably, the total number of OTUs across the four samples amounted to 2. These analyses were conducted because F-2 is a microbial sample captured during the acidification phase of the anaerobic DC. Acidification tends to deactivate microorganisms, thus generating substantial dissimilarity between the microbial compositions during this phase and those in other periods. Consequently, the total number of OTUs was notably lower. Meanwhile, there were 281 shared OTUs between F-1 and F-4, representing 4.18% of the combined OTUs for both samples. This finding indicated that there is a certain degree of similarity between the microbial populations in the anaerobic DC post-stabilization and the initial microbial populations. Additionally, the OTUs for the four samples were 2,519, 2,320, 2,653, and 2,755, respectively. This variation indicates a considerable shift in microbial species during the debugging process. The higher OTU counts suggest that the anaerobic DC hosts a diverse array of microbial species. Furthermore, the unique OTUs within these samples accounted for 21.73, 20.01, 22.89, and 23.76% of the total OTUs, respectively. Among these, F-4 accounted for the highest percentage, suggesting that F-4 has the most unique microbial species (Xu et al. 2016). This phenomenon can be attributed to the intricate composition of alcohol precipitation wastewater. Over prolonged periods of domestication and screening within the anaerobic DC, microorganisms have evolved into a broader range of species, resulting in heightened species diversity. This rich diversity is advantageous for ensuring the stable operation of the anaerobic DC.

Figure 10 shows the relative abundance of microbiota at the phylum level in the samples. Our results revealed that the 10 dominant phyla in the samples (in descending order according to their relative abundances) were Firmicutes, Proteobacteria, Bacteroidetes, Chloroflexi, Euryarchaeota, Actinobacteria, Synergistetes, Planctomycetes, Spirochaetes, and Cloacimonetes. Among them, Firmicutes, a vital bacterial phylum in the acidification phase of anaerobic hydrolysis due to its potential to enhance methane yield and COD removal rate (He 2020), exhibited an increase in relative abundance from 13.94 to 34.55%. Similarly, Proteobacteria, which primarily participate in consuming organic acids such as acetic acid, propionic acid, and butyric acid during anaerobic digestion, experienced an increase from 12.71 to 19.31%. Conversely, if the relative abundance decreased, so did the amount of organic acid consumed (Tang et al. 2019). For instance, a decline in Proteobacteria to 11.08% was evident in F-2, which was consistent with the sample's acidification phase. Bacteroidetes, which play a key role in the hydrolysis and acidification steps that break down macromolecular organics into smaller molecules, saw their relative abundance rise from 10.28 to 19.84% (Luo et al. 2019). These shifts in relative abundances, particularly for Firmicutes, Proteobacteria, and Bacteroidetes, demonstrate the stabilized operation of the anaerobic DC.

Verrucomicrobia, known for their involvement in lignin degradation (Wang 2017), exhibited a relative abundance of 0.29%, indicating effective lignin degradation within the anaerobic DC. Certain genera within Synergistetes can decompose organic acids into acetic acid (Sousa et al. 2007), whereas Euryarchaeota play a key role in methane-producing. The relative abundance of these two phyla increased to 7.21 and 12.42%, respectively, demonstrating that the proliferation of these microorganisms is crucial for anaerobic digestion within the reactor. Notably, some genera of Planctomycetes facilitate anaerobic ammonium oxidation (Yu & Shi 2017), as evidenced by their relative abundance of 1.67%. This can explain the observed anaerobic ammonia oxidation and removal of ammonia nitrogen during reactor operation.

Among the dominant genera, there were noteworthy shifts in relative abundances. Specifically, the relative abundances of Bacteroides and Sphaerochaeta increased significantly, from 0.49 to 11.44% and from 0.17 to 2.70%, respectively. These two genera play a crucial role in degrading complex macromolecular organics, such as cellulose (Su et al. 2020). Their constant enrichment thus confirms their active contribution to the anaerobic digestion process. Similarly, the relative abundance of Aminivibrio initially increased and then decreased to 1.08%. Honda et al. (2013) indicated that in anaerobic environments, Aminivibrio can metabolically convert organic nitrogen or amino acids into NH₄⁺-N. This phenomenon aligns with the observation that the NH₄⁺-N concentration in the effluent of the anaerobic DC was higher than that in the influent of the anaerobic DC at Stages Ⅰ and Ⅱ, but lower than that in the influent of the anaerobic DC at Stage III. This variation can be attributed to the shifting relative abundance of Aminivibrio, which affects the conversion of organic nitrogen compounds during the anaerobic digestion process.

Desulfovibrio can oxidize acetic acid to CO₂ and reduce sulfate to sulfide using nitrate and nitrite as electron acceptors and acetic acid and ethanol as electron donors during anaerobic digestion, and its relative abundance increased from 0.24 to 1.92%, which is attributed to two main reasons: (1) the sulfate content of the alcohol precipitation wastewater and (2) the generation of nitrite by Nitrosomonas, which provides electron acceptors for Desulfovibrio. The relative abundance of Longilinea increased to 0.81% due to the high content of aromatic compounds in the CPM alcohol precipitation wastewater and the removal of aromatic compounds by Longilinea (Cao & Hao 2012). Methanobacterium, Methanothrix, and Methanomethylovorans, all belonging to Euryarchaeota, are sensitive to environmental changes. They constitute the primary functional groups responsible for methane production in the anaerobic digestion process. The total relative abundance of this group has risen from 0.51 to 4.02%, indicating the establishment of a stable internal environment and operational efficiency within the anaerobic DC. The total relative abundance of Pseudomonas, Nocardia, Sphingobium, and Rhodococcus, which are involved in lignin degradation (Liu 2014), increased to 0.24%, and the relative abundance of Candidatus_Cloacamonas, Syntrophobacter, Prevotella, and Geobacter, which have the functions of degradation of aromatics, metabolism of cellulose, and oxidation of methane (Qiu et al. 2004), also reached 1–2%, suggesting that bacteria in the anaerobic DC had diversified genera and functions. After the debugging process, the relative abundances of Nitrosomonas, Nitrospira, Paracoccus, Hyphomicrobium, and Candidatus_Kuenenia were 2.47, 0.25, 0.46, 0.48, and 0.06%, respectively. Notably, these bacteria play crucial roles in nitrosation, nitrification, denitrification, and anaerobic ammonium oxidation (Yu & Shi 2017). Additionally, the increased TN content and removal rate of NH₄⁺-N in the anaerobic DC were closely related to the increase in their relative abundances.

In this study, the anaerobic digestion process was employed as pre-treatment of alcohol precipitation wastewater. After 220 days of debugging, the reactor facilitated remarkable COD (>85%) removal with the influent COD concentration of 21,000 mg/L and a volumetric loading of 10 kg COD/(m³•d). Results of 3D fluorescence, FT-IR, and UV–Vis analyses demonstrated that humic acid in alcohol precipitation wastewater was degraded, aromaticity was reduced, and macromolecular organics in wastewater were significantly removed. The analysis of microbial communities through high-throughput sequencing revealed that the microbial community increased diversity and homogeneity, with dominant microorganisms, including Methanothrix, Methanobacterium, Bacteroides, Sphaerochaeta, and Aminivibrio, collectively accounting for a relative abundance of 21.88%. The study's findings have implications for using the anaerobic digestion process to treat CPM wastewater as pre-treatment.

X.J.: Writing – original draft; data curation. L.B.: making charts. Y.S.: writing – review and editing. Y.T.: investigation; carrying out experiments; data curation. J.G.: project administration; resources. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Science and Technology Project of Henan province (222102320152, 24102320120).

This work was supported by the Science and Technology Project of Henan province (222102320152). The authors would like to thank all the reviewers who participated in the review.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.