Methods and applications for constructing synthetic ecological networks in deep coal CO2 sequestration zones
By identifying functional targets and optimizing the synthetic ecological network in a simulated real geological environment, the stability problem of microbial systems in deep CO2 sequestration environments was solved, achieving a long-term, synergistic, stable, and efficient biomethane production process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ANHUI UNIV OF SCI & TECH
- Filing Date
- 2026-02-24
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies struggle to construct long-term stable microbial systems in deep CO2 sequestration environments. Existing methods cannot ensure that exogenous microbial communities work synergistically with native communities, and they also suffer from short-lived environmental regulation effects or neglect of ecosystem compatibility.
By constructing a closed-loop dynamic optimization method for native communities, functional targets are identified, synthetic ecological networks are designed, and collaborative verification and iterative optimization are carried out in a simulated real geological environment to ensure system stability.
This study achieved long-term synergistic stability of the microbial system and continuity of the biomethane production process in a deep CO2 sequestration environment, improved methane conversion efficiency and system robustness to environmental fluctuations, and reduced the uncertainty of technology application.
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Figure CN121747726B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of bioengineering and carbon sequestration technology, and in particular to a method for constructing and applying a synthetic ecological network in deep coal CO2 sequestration zones. Background Technology
[0002] Injecting carbon dioxide (CO2) into deep, unminable coal seams for geological sequestration and then converting it into methane through microbial processes is a promising carbon reduction and energy recovery technology.
[0003] However, deep storage environments typically involve multiple extreme conditions such as high pressure, strong acid, and supercritical CO2 fluids, which severely inhibit the survival and metabolism of microorganisms, resulting in inefficient biomethane production processes that are difficult to maintain stably in the long term. This is the core bottleneck restricting the development of this technology.
[0004] Currently, there are two main technical approaches to solving this problem. One is to regulate the environment by injecting nutrient solutions or chemical reagents, but this method is crude and short-lived, failing to fundamentally maintain the homeostasis of the microbial community. The second approach is to directly introduce exogenously domesticated functional microbial communities into the underground environment, but this method often neglects compatibility with the existing complex native microbial ecosystem. The introduced microbial communities may fail to colonize due to competitive exclusion or niche conflicts, and their metabolic activities may even interfere with the functions of the original microbial communities, ultimately leading to a decline or failure of the entire system's gas production function after a period of operation.
[0005] A further limitation is that existing methods for constructing and optimizing microbial communities are mostly open-loop, static designs. These methods typically involve one-time design under standard laboratory conditions, lacking the crucial step of verifying synergistic function and iteratively optimizing systems that simulate extreme real-world geological environments and include native communities. Therefore, existing technologies cannot ensure that the constructed microbial systems can achieve long-term, stable synergistic interaction with native communities in actual underground environments. Summary of the Invention
[0006] The purpose of this invention is to provide a method and application for constructing a synthetic ecological network in deep coal CO2 storage areas. It overcomes the shortcomings of existing open-loop static design by establishing a closed-loop dynamic optimization method oriented towards the native community. Through rational design, experimental verification, and iterative feedback, it constructs functional modules that can coordinate and stabilize with the underground in-situ microbial ecosystem in the long term, thereby ensuring the sustainability and reliability of the biomethane production process in the deep CO2 storage environment.
[0007] To achieve the above objectives, this invention provides a method for constructing a synthetic ecological network in deep coal CO2 storage areas, comprising the following steps:
[0008] Step S1: Based on the in-situ microbial community data of the target coal seam, identify functional targets for introducing exogenous synthetic ecological network integration;
[0009] Step S2: Design a preliminary synthetic ecological network design scheme;
[0010] Step S3: The preliminary synthetic ecological network design scheme and the representative native microbial community of the target coal seam are co-cultured in a system simulating the deep CO2 sequestration environment, and their synergistic operation status is monitored.
[0011] Step S4: Evaluate the synergistic stability of the synthetic ecological network and the native community based on monitoring data;
[0012] Step S5: If the evaluation does not meet the collaborative stability criterion, then based on the collaborative failure analysis, return to adjust the target screening strategy in step S1 and the preliminary synthetic ecological network design scheme in step S2, and re-verify until the final synthetic ecological network design scheme that meets the collaborative stability criterion is obtained.
[0013] Preferably, step S1 specifically includes:
[0014] Step S11: Obtain in-situ microbial samples from the target coal seam, perform high-throughput sequencing, and obtain species composition data;
[0015] Step S12: Based on species composition data, construct a microbial coexistence network using the SparCC algorithm;
[0016] Step S13: Calculate the intra-module connectivity of each node in the microbial coexistence network. Values and inter-module connectivity value;
[0017] Step S14, based on Value and The value identifies the network location of a node that meets one of the following conditions as a functional target: inter-module connectivity. A value less than 0.5 indicates a weak network connection or poor connectivity within a module. Potentially functionally enhanced regions with a value greater than 2.5 and a relative abundance of the mcrA gene in methanogenic archaea within the module of the region of less than 1%.
[0018] Preferably, step S2 specifically includes:
[0019] Step S21: Based on the functional targets identified in step S1, define the functions of the environmental buffer module, substrate processing module, electron transport enhancement module, and methanogenesis core module.
[0020] Step S22: Screen or design strains that can form metabolic complementarity with the native community and assign them to the corresponding modules;
[0021] Step S23: With the optimization objective of maximizing methane yield and minimizing the accumulation of metabolic intermediates, calculate and determine the inoculation ratio of strains within and between modules;
[0022] Step S24: Output a preliminary synthetic ecological network design scheme that includes specific strain composition, module affiliation, and inoculation ratio.
[0023] Preferably, the environmental buffer module is used to secrete extracellular polymers and regulate the micro-region pH to above 5.0; the substrate processing module is used to degrade coal-derived aromatic compounds into hydrogen, carbon dioxide and acetic acid; the electron transport enhancement module is used to increase electron flux through direct interspecies electron transport or soluble electron shuttles; and the methanogenic core module is used to convert the substrate into methane through hydrogen-trophic and acetic acid-trophic pathways.
[0024] Preferably, step S3 specifically includes:
[0025] Step S31: According to the preliminary synthetic ecological network design scheme output in step S24, co-inoculate with representative native microbial communities and maintain the environmental conditions simulating deep CO2 sequestration.
[0026] Step S32: Continuously monitor the metabolite indicators, environmental indicators, and microbial abundance indicators of the co-culture system to assess the cooperative operation status;
[0027] Among them, the metabolite indicators include methane yield, acetic acid concentration, and dissolved hydrogen concentration;
[0028] Environmental indicators include pH value;
[0029] Microbial abundance indicators include the gene copy number of synthetic network marker strains and native methanogenic archaea as determined by quantitative PCR.
[0030] Preferably, in step S4, the long-term stability criterion for the collaborative function includes:
[0031] Criterion for methane yield fluctuation coefficient: The methane yield fluctuation coefficient from day 15 to day 30 of the monitoring period is less than or equal to 15%;
[0032] Criteria for determining metabolic intermediates: During the monitoring period, the concentration of acetic acid was below 50 mg / L;
[0033] Criterion for microbial community stability: The gene copy number of synthetic network marker strains decreases by no more than one order of magnitude.
[0034] Preferably, in step S5, the feedback optimization rule based on the collaborative failure analysis is specifically as follows:
[0035] If the methane yield fluctuation coefficient criterion is violated, return to step S1 and filter the connectivity between modules. The threshold for the value was increased from 0.5 to 0.7;
[0036] If the metabolic intermediate criterion is violated, return to step S2 to re-screen or validate the strain by increasing the tolerance standard to the key metabolic intermediate, or return to step S23 to increase the ratio of strain cell number in the substrate processing module to the methanogenic core module.
[0037] If the microbial community stability criterion is violated, return to step S2 to increase the survival rate verification of the strain under pH 3.5 conditions, or return to step S23 to increase the inoculation ratio of the environmental buffer module.
[0038] The present invention also provides an application of the synthetic ecological network constructed by the above method in enhancing the long-term synergistic stability of the biomethane conversion process in the CO2 sequestration area of deep coal seams. After obtaining the final synthetic ecological network design scheme, all functional strains in the final synthetic ecological network design scheme are co-cultured and scaled up, and combined with a slow-release carrier composed of calcium alginate and activated carbon to prepare a solid microsphere ecological preparation with a particle size of 2 mm to 5 mm.
[0039] Therefore, the present invention employs the above-mentioned method for constructing and applying a synthetic ecological network in deep coal CO2 sequestration zones, and the beneficial technical effects are as follows:
[0040] (1) This invention analyzes the network topology of the native community and identifies specific functional targets, enabling the subsequent designed synthetic ecological network to precisely supplement the weak links of the original system. The modular design adopted clearly defines the metabolic interaction relationship between each module and the native community, ensuring that the newly introduced functional microbial communities can be integrated into the original ecological niche in a complementary rather than competitive manner, thereby improving the colonization success rate.
[0041] (2) This invention requires the preliminary design of the synthetic network and the in-situ representative microbial community to be co-cultured and validated in a system that highly simulates real geological conditions, and evaluated based on clear long-term synergistic stability criteria. This closed-loop process ensures that any design that does not meet the stability requirements can be identified in the laboratory stage and iteratively improved through feedback optimization, thereby mitigating the risk of system failure and ultimately obtaining a solution with experimentally verified long-term operational reliability.
[0042] (3) In constructing the synthetic network, this invention uses model tools such as metabolic flux balance analysis for rational calculations to determine key parameters with the goal of optimizing the overall system performance. This model-based design method improves the predictability and accuracy of the regulation of complex microbial systems, which not only helps to improve methane conversion efficiency, but also enhances the system's robustness to environmental fluctuations through the synergy of various functional modules.
[0043] (4) This invention covers the entire process from underground ecosystem diagnosis, rational design of functional modules, experimental verification of synergistic stability to preparation of engineered formulations. This provides a standardized and repeatable operating procedure that closely connects laboratory research and development with field applications, significantly reducing the uncertainty and engineering risks of large-scale application of the technology. Attached Figure Description
[0044] Figure 1 This is a flowchart of the method for constructing a synthetic ecological network in a deep coal CO2 sequestration zone according to the present invention;
[0045] Figure 2 A diagram illustrating the mechanism of synergistic integration between synthetic ecological networks and native communities;
[0046] Figure 3 This is a curve comparing the daily methane production in the experimental and control areas. Detailed Implementation
[0047] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.
[0048] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
[0049] Example 1
[0050] like Figure 1 As shown, the method for constructing a synthetic ecological network in a deep coal CO2 sequestration zone includes the following steps:
[0051] Step S1, Synergistic Target Analysis: Based on the in-situ microbial community data of the target coal seam, a native microbial coexistence network is constructed, its topology is analyzed, and functional targets suitable for the integration of exogenous synthetic ecological networks are identified.
[0052] Native microbial communities are complex ecological networks with various metabolic interactions and competitions. By constructing coexistence networks and analyzing their topology, weak points or metabolic bottlenecks can be identified, representing optimal insertion points for exogenous synthetic networks. The SparCC algorithm effectively eliminates spurious correlations in the compositional data, improving the reliability of network inference. (Module connectivity...) This reflects the importance of a node within its own module, and the connectivity between modules. This reflects the connection strength between nodes and different modules; combining both allows for a systematic evaluation of a node's functional role in the network. Specifically:
[0053] Step S11: Obtain in-situ microbial samples from the target coal seam, perform high-throughput sequencing, and obtain species composition data.
[0054] (1) Sampling strategy.
[0055] Spatial distribution: Sampling points were arranged along the strike and vertical of the coal seam at CO2 injection wells, monitoring wells, and along the inferred dominant fluid migration path. At least three independent samples were collected from each sampling point.
[0056] Sample types: coal seam water (fracture water), coal dust, roof and floor cores, etc. After collection, the samples were immediately placed in sterile, anaerobic preservation tubes, filled with N2 for protection, transported to the laboratory at low temperature (4℃), and stored at -80℃ until analysis.
[0057] If the target area exhibits heterogeneity (such as localized high salinity or high temperature gradients), it is necessary to sample and analyze the data in separate zones.
[0058] (2) DNA extraction and sequencing.
[0059] Use a DNA extraction kit (such as the PowerSoil Pro Kit) optimized for environmental samples (especially those rich in humic acid and coal matrix) and set up a blank control in parallel.
[0060] The 16S rRNA gene of prokaryotes (bacteria and archaea) was amplified using universal primers (primer 27F / 1492R). Targeted amplification of the methanogenic gene mcrA was also possible (primer mcrA-f / mcrA-r).
[0061] The sequencing platform uses Pacbio HiFi third-generation high-throughput sequencing, with a sequencing depth of no less than 100,000 valid sequences per sample.
[0062] Step S12: Based on species composition data, construct a microbial coexistence network using the SparCC algorithm.
[0063] Data preprocessing: QIIME2 (version 2022.11) was used for sequence quality control, denoising, chimera removal, and clustering into amplicon sequence variants (ASVs). Species annotation was performed using the Silva 138 database.
[0064] Network construction: The correlation coefficients between ASVs were calculated using the Python package FastSpar (an efficient implementation of SparCC). The number of iterations was set to 50, and the number of bootstraps to 1000 to evaluate significance. Value). Only retain For edges with an absolute correlation coefficient greater than 0.3, construct an undirected weighted network.
[0065] Network visualization and basic topology analysis: Visualization is performed using Gephi software, and overall network properties are calculated, including average degree, average path length, clustering coefficient, and modularity.
[0066] Step S13: Calculate the intra-module connectivity of each node in the microbial coexistence network. Values and inter-module connectivity value.
[0067] Module partitioning: The network is partitioned into modules (resolution parameter set to 1.0) to obtain multiple functional modules (communities).
[0068] and The calculation formula is as follows:
[0069] ;
[0070] ;
[0071] in, Represents a node With the same module The number of connections to other nodes within the module (i.e., the degree within the module). Representation module The average number of intra-module connections for all nodes in the module. Representation module The standard deviation of the number of intra-module connections for all nodes in the module. Represents a node With modules The number of connections in a node. Represents a node Total number of connections (degrees) This indicates the total number of modules in the network.
[0072] Functional annotation assistance: Functional prediction of ASVs within each module (using PICRUSt2 or FAPROTAX) and association analysis with mcrA gene abundance (determined by qPCR).
[0073] Step S14, based on Value and The value identifies the network location of a node that meets one of the following conditions as a functional target: inter-module connectivity. A value less than 0.5 indicates a weak network connection or poor connectivity within a module. Potentially functionally enhanced regions with a value greater than 2.5 and a relative abundance of the mcrA gene in methanogenic archaea within the module of the region of less than 1%.
[0074] If the network as a whole is highly modular (modularity greater than 0.4), the focus can be on identifying weak connection areas.
[0075] If there are multiple dense modules with low methane abundance in the network, multiple functional enhancement regions can be identified, and multi-target integration strategies can be designed.
[0076] This step systematically identifies functionally weak or potentially functionally enhanced regions within the network, providing precise integration targets for the subsequent modular design of the synthetic network. This avoids niche conflicts that may be caused by blindly introducing exogenous strains and improves the compatibility of the synthetic network with the native community.
[0077] Step S2, as follows Figure 2 As shown, the preliminary design of the modular network is as follows: a preliminary synthetic ecological network design scheme is designed, which includes four functional modules: environmental buffer, substrate processing, electron transfer enhancement and methanogenic core. The preliminary synthetic ecological network design scheme clarifies the metabolic interaction relationship between each module and the in-situ community.
[0078] Modular design breaks down complex functions into independent yet collaborative sub-units, facilitating functional optimization and system control. For example... Figure 2 As shown, the synthetic ecological network is integrated with the native community in units of functional modules. Figure 2 The labels A, B, C, D, and E are merely exemplary strains representing the corresponding functions in each module, and do not refer to any specific strain. Specifically, the environmental buffer module (such as...) Figure 2 A) In this context, the inhibition of the bacterial community by extreme acidity is mitigated by secreting extracellular polymeric substances (EPS) and regulating local pH; substrate processing modules (such as...) Figure 2 In the first part, B) is responsible for converting recalcitrant coal-derived aromatic compounds into methane precursors; the electron transfer enhancement module (such as...) Figure 2 C) By forming or strengthening "nanowires" (a general term in this field for conductive structures involved in direct interspecies electron transfer in microorganisms, such as conductive pili, electron shuttles, etc.) to improve interspecies electron transfer efficiency and avoid electron accumulation inhibiting metabolism; methanogenic core modules (such as...) Figure 2 The D / E (digestive and oxidative processes) in the metabolic pathway are directly responsible for methane synthesis. The modules complement each other, forming a complete metabolic chain from environmental adaptation to the synthesis of the final product. Specifically:
[0079] Step S21: Based on the functional targets identified in step S1, define the specific functions of the four functional modules.
[0080] The environmental buffer module is used to secrete extracellular polymers and regulate the pH of micro-regions to above 5.0; the substrate processing module is used to degrade coal-derived aromatic compounds into hydrogen, carbon dioxide, and acetic acid; the electron transport enhancement module is used to increase electron flux through direct interspecies electron transport or soluble electron shuttles; and the methanogenic core module is used to convert substrates into methane through hydrogen-trophic and acetic acid-trophic pathways.
[0081] Step S22: Screen or design strains that can form metabolic complementarity with the native community and assign them to the corresponding modules.
[0082] Strains were obtained from public culture collections (e.g., *Methanothermobacter thermoautotrophicus*, DSM 1053). Indigenous functional strains were isolated and screened from similar extreme environments (acidic mines, high-temperature oil reservoirs). Existing strains were modified using synthetic biology techniques (e.g., enhancing acid-resistant genes, introducing exogenous genes), but must comply with biosafety regulations.
[0083] Environmental tolerance: Growth curves (OD) were measured under simulated formation conditions (temperature 50℃, pressure 10 MPa, CO2 partial pressure 5 MPa, pH 4.0). 600 ) and survival rate (CFU count).
[0084] Substrate utilization profile: Test the degradation ability of representative aromatic compounds (such as sodium benzoate and phenol) from coal and the product profile (GC-MS analysis).
[0085] Electron transport capacity: The extracellular electron transport rate of the strain was measured by an electrochemical workstation; or its ability to promote the reduction of electron acceptors (such as Fe(III)) was observed by co-culture experiments.
[0086] Metabolic complementarity: Candidate strains are co-cultured in pairs or in multiples with key members of the native community (such as dominant methanogens) enriched from the target coal seam. Changes in methane yield and accumulation of intermediate products are detected to assess synergistic or competitive relationships.
[0087] Step S23: Using a flux balance analysis model based on a genome-scale metabolic network, with the optimization objective of maximizing methane yield and minimizing the accumulation of metabolic intermediates, the inoculation ratio of strains within and between modules is calculated and determined. The model constrains the specific growth rate and specific substrate consumption rate of each strain under simulated stress conditions.
[0088] The objective function is as follows:
[0089] ;
[0090] in, Indicates system performance metrics; Indicates time methane yield (mmol·L) -1 ·h -1 ); Indicates metabolic intermediates (such as acetic acid, propionic acid, etc.) at time The concentration (mmol / L); This represents the penalty coefficient for the accumulation of intermediate products (set to 0.1).
[0091] The constraints are as follows:
[0092] Metabolic flux balance constraints (for each strain) ):
[0093] ;
[0094] in, Indicates strain stoichiometric matrix, This represents its flux vector.
[0095] Strain growth constraints:
[0096] ;
[0097] in, This represents the actual specific growth rate. This represents the maximum specific growth rate under optimal conditions. , , These represent the influencing factors of pH, pressure, and CO2 partial pressure (values range from 0 to 1).
[0098] Substrate consumption constraint:
[0099] ;
[0100] in, Indicates strain The rate of consumption of the substrate Indicates the maximum specific consumption rate. Indicates strain Cell concentration.
[0101] Vaccination ratio constraints:
[0102] ;
[0103] in, Representation module The collection of strains in Representation module The vaccination rate This indicates the total number of cells inoculated.
[0104] Inter-module metabolite transfer constraints (e.g., hydrogen, acetic acid, etc.):
[0105] ;
[0106] in, Representation module The Middle Hydrogen metabolic flux of the strain Representation module The Middle Hydrogen metabolic flux of the strain This indicates the net accumulation rate of hydrogen in the system.
[0107] The inter-module transfer constraints for other key metabolic intermediates such as acetic acid are similar.
[0108] Step S3, Cooperative Stability Verification: The preliminary synthetic ecological network design scheme and the representative native microbial community of the target coal seam are co-cultured in a system simulating a deep CO2 sequestration environment, and their cooperative operation status is monitored.
[0109] Real geological environments exhibit extreme conditions such as high pressure, high CO2 partial pressure, and low pH, which significantly affect the metabolic activity and interspecies interactions of bacterial communities. Co-culturing in a simulated environment can expose potential adaptive problems, competitive relationships, or functional disorders that may arise in practical applications of the synthetic network, providing experimental evidence for system optimization. Specifically:
[0110] Step S31: Using a high-pressure bioreactor, inoculate with a representative native microbial community according to the preliminary synthetic ecological network design scheme output in step S24, and maintain environmental conditions simulating deep CO2 sequestration.
[0111] The environmental conditions include: temperature 50±5°C, pressure 10±2MPa, CO2 partial pressure in the system not less than 5MPa, and initial pH value of 4.0±0.5.
[0112] Step S32: Continuously monitor the co-culture system for 30 days. Monitoring parameters include: methane yield, pH value, dissolved hydrogen concentration, acetic acid concentration, and the gene copy number of the synthetic network marker strain and the original methanogenic archaea as determined by quantitative PCR.
[0113] Step S4, Cooperative Stability Assessment: Based on the monitoring data from Step S3, assess whether the combined system of synthetic ecological network and native community meets the preset long-term stability criteria for cooperative function.
[0114] The long-term stability criteria for collaborative functions include:
[0115] Criterion for methane yield fluctuation coefficient: The methane yield fluctuation coefficient from day 15 to day 30 of the monitoring period is less than or equal to 15%;
[0116] Criterion for determining metabolic intermediates: Acetic acid concentration consistently below 50 mg / L;
[0117] Criterion for microbial community stability: The gene copy number of synthetic network marker strains decreases by no more than one order of magnitude.
[0118] Step S5, Dynamic Feedback and Iterative Optimization: If the evaluation does not meet the collaborative stability criterion, then based on the collaborative failure analysis, return to adjust the target screening strategy in step S1 and the preliminary design scheme in step S2, and re-verify until the final synthetic ecological network design scheme that meets the collaborative stability criterion is obtained.
[0119] The feedback optimization rules based on collaborative failure analysis are as follows:
[0120] If the methane yield fluctuation coefficient criterion is violated, return to step S1 and filter the connectivity between modules. The threshold for the value was increased from 0.5 to 0.7;
[0121] If the metabolic intermediate criterion is violated, return to step S2 and add an acetic acid tolerance test at a concentration of 100 mg / L in the strain function verification, or return to step S23 and adjust the ratio of strain cell number in the substrate processing module to the methanogenic core module from 10:1 to 5:1.
[0122] If the microbial community stability criterion is violated, return to step S2 to add verification of the survival rate of the strain under pH 3.5 conditions, or return to step S23 to increase the inoculation ratio of the environmental buffer module by 20%.
[0123] Example 2
[0124] This embodiment aims to illustrate how, when the formation water salinity in the target coal seam CO2 sequestration area is high, the universal construction method of this invention can be applied to construct a synthetic ecological network that can operate stably under high salinity stress for a long time by integrating salt-tolerant functional strains. High salinity (usually characterized by high concentrations of NaCl) is one of the common stress factors in deep geological environments. When coexisting with conditions such as high pressure, high CO2 partial pressure, and low temperature, it severely inhibits microbial activity and is a significant factor leading to instability in the biomethane production process. Therefore, solving the problem of synergistic stability under high salinity is one of the key scenarios for applying the method of this invention in complex actual sequestration areas.
[0125] Formation water salinity analysis of a target coal seam showed a NaCl concentration as high as 8% (w / v), and the high osmotic pressure environment severely inhibited the activity of conventional methanogenic microorganisms. This embodiment improves the long-term synergistic stability and methane conversion efficiency of the system under high salt stress by constructing a salt-tolerant synthetic ecological network.
[0126] Step S1: Synergistic target analysis (for high salt stress).
[0127] First, water and coal samples were obtained from the target coal seam. Ion chromatography analysis confirmed that the main salt was NaCl, with a concentration of 8.2%. Third-generation high-throughput sequencing was then performed on the samples to obtain species composition data.
[0128] A native microbial coexistence network was constructed using the SparCC algorithm. The intra-module connectivity of each node in the network was calculated. Values and inter-module connectivity Values. In-depth analysis revealed a node with a coexisting network with known halophilic bacteria (Halanaerobium genus) and whose functional annotation indicated a connection to acetic acid metabolism. This node exhibited high intra-module connectivity (3.1), but its module had a relatively low abundance of methanogenic genes (mcrA) (0.8%), and the node itself had only 0.3 inter-module connectivity. This location was identified as a functional enhancement target for metabolic pathway disruption caused by salinity stress.
[0129] Step S2: Preliminary design of modular network (integration of salt-tolerant strains).
[0130] Based on the above targets, four functional modules were designed to screen or confirm strains with high salt tolerance in each module.
[0131] Environmental buffer module: Its function is to secrete compatible solutes (such as glycine betaine) to counteract osmotic pressure and regulate micro-zone pH. Selected strains: Bacillus halodurans (Alkali and salt resistant) Halomonas elongata (Obediophilic bacteria).
[0132] Substrate processing module: Its function is to degrade coal-derived compounds into methane precursors under high salinity conditions. Selected strains: Thermosyntropha tengcongensis (Heat-loving, with some salt tolerance) Pelotomaculum thermopropionicum (After adaptive evolution, it can grow in 5% NaCl).
[0133] Electron transport enhancement module: Functions to maintain effective interspecies electron transport in high ionic strength environments. Selected strains: Geopsychrobacter electrodiphilus (Derived from high-salt sediments, it has electrochemical activity).
[0134] Methanogenesis core module: Its function is to produce methane using precursors in a high-salt environment. Selected strains: Methanohalophilus mahii (Halophilic methanogenic archaea) Methanosarcina thermophila (After domestication, it can tolerate 3% NaCl).
[0135] Flux balance analysis (FBA) was performed using a genome-scale metabolic network model, with the objective function of maximizing methane yield and minimizing intermediate product accumulation. A salinity inhibitor (based on measured growth rate data of each strain at 8% NaCl) was incorporated into the model constraints. The optimal cell ratio for each module was calculated as follows: environmental buffer module: substrate processing module: electron transport enhancement module: methanogenic core module = 1.5: 2.0: 0.5: 1.0.
[0136] Step S3: Cooperative stability verification (high-salt simulation environment).
[0137] In a high-pressure bioreactor, the pre-synthesized salt-tolerant network and a representative native community enriched from the target coal seam (containing approximately 15% salt-tolerant / halophilic bacteria) were inoculated according to the above proportions. Simulated environmental conditions were set as follows: temperature 55℃, total pressure 10 MPa, CO2 partial pressure 5 MPa, initial pH 4.2, and NaCl was added to maintain a constant system concentration of 8%. Continuous co-cultivation and monitoring were performed for 30 days.
[0138] Step S4: Evaluation of Cooperative Stability Criteria.
[0139] During the monitoring period, samples were taken and analyzed every 3 days. The stability criteria used for the assessment included:
[0140] Methane yield stability criterion: The methane yield fluctuation coefficient on days 15-30 is ≤15%.
[0141] Metabolic balance criteria: Acetic acid concentration <50 mg / L throughout, glycine betaine (compatible solute) concentration maintained at 1-5 mM.
[0142] Microbial colonization criteria: exogenous marker strains in the synthetic network Methanohalophilus mahii The gene copy number decreases by no more than one order of magnitude within 30 days.
[0143] Step S5: Dynamic feedback and iterative optimization.
[0144] The first round of validation results showed that after an initial increase, the methane yield began to decline and fluctuate significantly on day 18 (fluctuation coefficient reached 28%), and the acetic acid concentration rose to 65 mg / L on day 21, but the bacterial colonization was good. This result violated both criteria 1 and 2.
[0145] Failure analysis: It is speculated that the high-salt environment may have exacerbated the metabolic imbalance between the substrate processing module and the methanogenic core module, resulting in poor consumption of the intermediate product acetic acid.
[0146] Optimization and adjustments:
[0147] Returning to the strain function verification step in S2, a growth test was added to the candidate strains of the substrate processing module under dual stress of 8% NaCl and 100 mg / L acetic acid to screen for more stress-resistant strains.
[0148] Returning to the model calculation stage in S2, the ratio of strain cell number in the substrate processing module to the methanogenic core module is increased from 2.0:1.0 to 2.5:1.0 to enhance precursor supply and promote acetic acid consumption.
[0149] In the next round of validation simulation environment, trace amounts of nickel and cobalt (0.1 mg / L each) were added to enhance the activity of key enzymes in methanogenic archaea.
[0150] After two rounds of iterative optimization, the final synthetic network design scheme obtained runs stably in the same simulated high-salt environment, and all indicators meet the long-term stability criteria.
[0151] Implementation results.
[0152] After co-culturing and scaling up the functional strains of the final synthetic network design, they were combined with calcium alginate-activated carbon carrier to prepare solid microspheres (particle size 3±0.5 mm), which were then applied to a columnar experimental setup simulating high-salt coal seam conditions. The system performance comparisons with the control group without the synthetic network are shown in Table 1.
[0153] Table 1 Comparison Results
[0154]
[0155] The above experiments demonstrate that the synthetic ecological network construction method can effectively solve the problem of inhibiting biomethane production in high-salinity coal seam CO2 sequestration environments by targeted target identification, modular design integrating salt-tolerant functional strains, and closed-loop iterative optimization, thereby improving the long-term synergistic stability and conversion efficiency of the system.
[0156] Example 3
[0157] This embodiment provides a specific scheme for engineering the final synthetic ecological network obtained by constructing and optimizing the method in Example 1, and applying it to enhance the long-term synergistic stability of the biomethane conversion process in deep coal seam CO2 storage areas.
[0158] 1. Preparation of engineered formulations.
[0159] The final synthetic ecological network design scheme, verified through synergistic stability in Example 1, includes all functional strains—that is, strains containing environmental buffer modules. Bacillus haynesii and Sporosarcina pasteurii strain and substrate processing module Pelotomaculum thermopropionicum and Syntrophothermus lipocalidusStrains, electron transport enhancement modules Thermincola ferriacetica and Thermincola potens Strains, methanogenic core module Methanothermobacter thermautotrophicu s and Methanothermobacter wolfeii Strains were co-cultured for scale-up. In a 100L fermenter, using an optimized composite culture medium, the strains were cultured to the plateau phase under simulated formation stress conditions (pH 4.5, total pressure 10MPa) to obtain a highly active mixed bacterial solution.
[0160] Microsphere preparation:
[0161] The bacterial culture obtained from the co-culture was centrifuged and concentrated 10 times, and then mixed evenly with an equal volume of 3% (w / v) sodium alginate solution to form a bacterial-sodium alginate suspension.
[0162] When the suspension is dripped into a 0.1M calcium chloride solution through a vibrating nozzle, sodium alginate undergoes instantaneous ionic cross-linking to form gel microspheres.
[0163] During the microsphere curing process, 1% (w / v) of powdered activated carbon is added to the calcium chloride solution to embed the activated carbon particles into the interior and surface of the microspheres.
[0164] After solidification for 30 minutes, the microspheres were collected, washed with sterile physiological saline, and lightly dried at 4°C for 12 hours to obtain a solid microsphere ecological preparation with a water content of approximately 60% and a particle size of 3.0 ± 0.5 mm. The total viable count in this preparation was greater than 10. 9 CFU / g, activated carbon serves as an endogenous adsorption matrix, which can buffer toxins and adsorb metabolites.
[0165] This embodiment describes a specific method for applying the constructed and engineered synthetic ecological network (i.e., solid microsphere ecological preparation) to enhance the long-term synergistic stability of the biomethane conversion process in a deep coal seam CO2 sequestration area.
[0166] Application process:
[0167] Site preparation and injection: Select a production / monitoring well near the CO2 injection well in the target coal seam. First, inject a certain amount of nutrient buffer solution (mainly containing phosphate, nitrogen source and trace metals) into the target coal seam through this well to provide basic nutrition for microbial activity.
[0168] Ecological agent injection: The prepared solid microsphere ecological agent was mixed with sterile carrier water to form a 5% (w / v) suspension. Using a downhole booster pump, the suspension was continuously injected into the target coal seam through a monitoring well at a flow rate lower than the formation fracture pressure. The total dry weight of the injected ecological agent was approximately 500 kg, which is expected to form multiple bio-enhancing sites in the coal seam fractures and pores.
[0169] CO2 sequestration and bioactivation: Subsequently, CO2 injection is restored and enhanced. The displacement effect of supercritical CO2 helps to further carry the ecological preparation microspheres and nutrient solution into the deeper coal seam. The sequestered CO2 serves as both a carbon source and the carbonic acid formed from its dissolution is gradually neutralized by the environmental buffer module.
[0170] Long-term monitoring and effectiveness evaluation:
[0171] Monitoring indicators: Continuously monitor the gas production components (CH4, CO2 ratio) and gas production of the production well, and regularly take water samples to analyze pH value, acetic acid concentration and microbial community structure (third-generation high-throughput sequencing).
[0172] Comparison setting: This test area (injected with ecological agents) is compared with another control test area in the same coal seam that has not been injected with ecological agents.
[0173] Long-term performance comparison Figure 3 As shown, compared with the untreated control area, the experimental area injected with the synthetic ecological network formulation exhibited a significant and stable yield increase: the daily methane production in the experimental area showed a significant inflection point in the third month (approximately 90 days) after CO2 injection and continued to rise, entering a stable plateau period from the sixth month onwards, maintaining at 1.5 ± 0.05 Nm³ for an extended period. 3 The high level of methane production (1.48 Nm³ / d) resulted in a smooth curve with minimal fluctuations; while the control area's production peaked in the third month and then entered an irreversible decline, essentially ceasing production by the twelfth month. Quantitative analysis showed that the average daily methane production in the test area during the entire monitoring period (months 3-12) was... 3 / d) is the control area (0.24Nm 3 The gas production efficiency was 6.2 times that of the previous generation (d), and the fluctuation coefficient of the gas production curve was reduced by more than 60%, demonstrating the dual advantages of this invention in improving gas production efficiency and long-term stability. Furthermore, chemical analysis of the produced water showed that the pH in the experimental area remained stable at 5.2-5.8, and the acetic acid concentration was consistently below 40 mg / L. High-throughput sequencing further confirmed the continued presence of signals from the introduced synthetic network functional strains, and the abundance of in-situ methanogenic archaea increased by approximately 2 times, indicating a close and mutually beneficial synergistic metabolic relationship between the exogenous network and the native community.
[0174] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.
[0175] Therefore, this invention adopts the above-mentioned synthetic ecological network construction method and application in deep coal CO2 storage areas. Through closed-loop dynamic optimization design, a functional module that can coordinate and stabilize with the underground native microbial community in the long term is constructed. This provides an innovative, reliable, and systematic solution for solving the instability of biomethane production processes under deep CO2 storage environment, and promotes the synergistic effect of carbon storage and clean energy production.
[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for constructing a synthetic ecological network in deep coal seam CO2 sequestration zones, characterized in that, Includes the following steps: Step S1: Based on the in-situ microbial community data of the target coal seam, identify functional targets for introducing exogenous synthetic ecological network integration; Step S2: Design a preliminary synthetic ecological network design scheme; Step S3: The preliminary synthetic ecological network design scheme and the representative native microbial community of the target coal seam are co-cultured in a system simulating the deep CO2 sequestration environment, and their synergistic operation status is monitored. Step S4: Evaluate the synergistic stability of the synthetic ecological network and the native community based on monitoring data; Step S5: If the evaluation does not meet the synergistic stability criterion, then based on the synergistic failure analysis, return to adjust the target screening strategy in step S1 and the preliminary synthetic ecological network design scheme in step S2, and re-verify until the final synthetic ecological network design scheme that meets the synergistic stability criterion is obtained. Step S1 specifically includes: Step S11: Obtain in-situ microbial samples from the target coal seam, perform high-throughput sequencing, and obtain species composition data; Step S12: Based on species composition data, construct a microbial coexistence network using the SparCC algorithm; Step S13: Calculate the intra-module connectivity of each node in the microbial coexistence network. Values and inter-module connectivity value; Step S14, based on Value and The value identifies the network location of a node that meets one of the following conditions as a functional target: inter-module connectivity. A value less than 0.5 indicates a weak network connection or poor connectivity within a module. Potentially functionally enhanced regions with a value greater than 2.5 and a relative abundance of the mcrA gene in methanogenic archaea within the module of the region of less than 1%; Step S2 specifically includes: Step S21: Based on the functional targets identified in step S1, define the functions of the environmental buffer module, substrate processing module, electron transport enhancement module, and methanogenesis core module. Step S22: Screen or design strains that can form metabolic complementarity with the native community and assign them to the corresponding modules; Step S23: With the optimization objective of maximizing methane yield and minimizing the accumulation of metabolic intermediates, calculate and determine the inoculation ratio of strains within and between modules; Step S24: Output a preliminary synthetic ecological network design scheme containing specific strain composition, module affiliation, and inoculation ratio; In step S5, the feedback optimization rules based on the collaborative failure analysis are as follows: If the methane yield fluctuation coefficient criterion is violated, return to step S1 and filter the connectivity between modules. The threshold for the value was increased from 0.5 to 0.7; If the metabolic intermediate criterion is violated, return to step S2 to re-screen or validate the strain by increasing the tolerance standard to the key metabolic intermediate, or return to step S23 to increase the ratio of strain cell number in the substrate processing module to the methanogenic core module. If the microbial community stability criterion is violated, return to step S2 to increase the survival rate verification of the strain under pH 3.5 conditions, or return to step S23 to increase the inoculation ratio of the environmental buffer module.
2. The method for constructing a synthetic ecological network in a deep coal CO2 sequestration zone according to claim 1, characterized in that, The environmental buffer module is used to secrete extracellular polymers and regulate the pH of micro-regions to above 5.0; the substrate processing module is used to degrade coal-derived aromatic compounds into hydrogen, carbon dioxide, and acetic acid; the electron transport enhancement module is used to increase electron flux through direct interspecies electron transport or soluble electron shuttles; and the methanogenic core module is used to convert substrates into methane through hydrogen-trophic and acetic acid-trophic pathways.
3. The method for constructing a synthetic ecological network in a deep coal CO2 sequestration zone according to claim 1, characterized in that, Step S3 specifically includes: Step S31: According to the preliminary synthetic ecological network design scheme output in step S24, co-inoculate with representative native microbial communities and maintain the environmental conditions simulating deep CO2 sequestration. Step S32: Continuously monitor the metabolite indicators, environmental indicators, and microbial abundance indicators of the co-culture system to assess the cooperative operation status; Among them, the metabolite indicators include methane yield, acetic acid concentration, and dissolved hydrogen concentration; Environmental indicators include pH value; Microbial abundance indicators include the gene copy number of synthetic network marker strains and native methanogenic archaea as determined by quantitative PCR.
4. The method for constructing a synthetic ecological network in a deep coal CO2 sequestration zone according to claim 3, characterized in that, In step S4, the long-term stability criteria for the collaborative function include: Criterion for methane yield fluctuation coefficient: The methane yield fluctuation coefficient from day 15 to day 30 of the monitoring period is less than or equal to 15%; Criteria for determining metabolic intermediates: During the monitoring period, the concentration of acetic acid was below 50 mg / L; Criterion for microbial community stability: The gene copy number of synthetic network marker strains decreases by no more than one order of magnitude.
5. The application of a synthetic ecological network constructed according to any one of claims 1 to 4 in enhancing the long-term synergistic stability of biomethane conversion processes in deep coal seam CO2 sequestration areas, characterized in that, After obtaining the final synthetic ecological network design scheme, all functional strains in the final synthetic ecological network design scheme were co-cultured and scaled up, and combined with a slow-release carrier composed of calcium alginate and activated carbon to prepare solid microsphere ecological preparations with a particle size of 2 mm to 5 mm.