A method for modifying the biochemical reaction structure of the ADM1 model using metagenomics

Abstract

This invention belongs to the field of biomass energy utilization technology and relates to a method for modifying the biochemical reaction structure of the ADM1 model using metagenomics. The method includes: (1) conducting small-scale biochemical reaction experiments on anaerobic digestion of organic solid waste, and analyzing the relative abundance of dominant microbial communities and key enzyme-encoding genes based on the gene information revealed by metagenomics; (2) precisely correcting the metabolic structure of the biochemical reaction in the ADM1 model; (3) periodically performing routine quantitative analysis on key organic matter components in the system; (4) improving the Pearson matrix of the ADM1 model based on the metabolic structure, inhibition function, kinetic equation, and biochemical rate coefficient, constructing a corrected anaerobic digestion Pearson matrix, and solving it; (5) optimizing the ADM1 model through sensitivity analysis; and (6) conducting accuracy analysis and carrying out continuous experiments on anaerobic digestion of organic solid waste. The method described in this invention can predict and provide early warning of actual operating effects, maintaining high efficiency and stability.

Description

Technical Field

[0001] This invention belongs to the field of biomass energy utilization technology, and relates to solid waste treatment, particularly to a method for modifying the biochemical reaction structure of the ADM1 model using metagenomics. Background Technology

[0002] Anaerobic Digestion Model No. 1 (ADM1) is a structural model based on differential-algebraic equations to describe biochemical and physicochemical processes. It can be used to simulate biochemical reactions in the anaerobic digestion of wastewater. This model calculates the biochemical reaction rates at different stages of the anaerobic digestion system based on input parameters and optimizes operating parameters by simulating and predicting the operational characteristics of anaerobic digestion. However, the metabolic pathways of organic matter in high-solids anaerobic digestion of organic solid waste differ significantly from those in anaerobic digestion of wastewater. Therefore, the mathematical model of biochemical reactions in the wastewater ADM1 model is not applicable to the anaerobic digestion of organic solid waste.

[0003] Existing research methods for modifying the biochemical reactions of the ADM1 model typically involve adjustments based on substrate characteristics or microbial colony structure, or modifications to the model structure incorporating data from actual engineering experience. However, the modified biochemical reaction structure differs significantly from the actual operation of anaerobic digestion of organic solid waste. Summary of the Invention

[0004] To overcome the drawback of significant discrepancies between the biochemical metabolic structure of the modified ADM1 model and actual conditions, this invention discloses a method for modifying the biochemical reaction structure of the ADM1 model using metagenomics.

[0005] This invention analyzes the relative abundance of enzyme-encoding genes for key pathways in organic matter metabolism based on metagenomic information from small-scale anaerobic digestion biochemical reaction experiments. Based on this, it extracts key metabolic pathways from the anaerobic digestion system of organic solid waste and precisely modifies the metabolic structure of the ADM1 model, particularly the hydrolysis, acidification, hydrogen and acetic acid production, and methanogenesis stages, thereby effectively predicting the operational status of anaerobic digestion of organic solid waste.

[0006] The present invention achieves the above objectives through the following technical solutions.

[0007] A method for modifying the biochemical response structure of the ADM1 model using metagenomics includes the following steps:

[0008] (1) Conduct small-scale biochemical reaction experiments of anaerobic digestion of organic solid waste, and analyze the relative abundance of dominant microbial communities and key enzyme-encoding genes in the system based on the gene information revealed by metagenomics.

[0009] (2) Based on the metagenome information analysis of the unique metabolic pathway of anaerobic digestion of organic solid waste, the biochemical reaction metabolic structure in the ADM1 model was precisely modified.

[0010] (3) Regularly perform routine quantitative analysis on key organic components in the system, and compare and optimize the inhibition function, kinetic equation and biochemical rate coefficient in the ADM1 model based on the results of small-scale biochemical experiments;

[0011] (4) Based on the optimized biochemical reaction metabolic structure, inhibition function, kinetic equation and biochemical rate coefficient, the Pearson matrix of the ADM1 model is improved to construct the modified anaerobic digestion Pearson matrix; and the modified ADM1 model is solved.

[0012] (5) Perform sensitivity analysis on the ADM1 model, conduct small-scale biochemical reaction test of anaerobic digestion of organic solid waste, repeat steps (3)-(5), and continue to optimize the relevant parameters with higher sensitivity;

[0013] (6) Analyze the accuracy of the modified ADM1 model and conduct continuous anaerobic digestion experiments on organic solid waste; compare the measured results with the predicted results of the modified ADM1 model to verify the accuracy of the modified ADM1 model. If the accuracy is not good, repeat steps (1)-(5).

[0014] The organic matter metabolic pathways in anaerobic digestion systems are intricate. Metagenomic information can accurately determine the enzyme-encoded abundance of key organic matter metabolic pathways, thereby extracting the organic matter metabolic pathways of the system. Therefore, steps (1)-(2) can accurately improve the biochemical reaction metabolic structure in the organic matter ADM1 model.

[0015] In a preferred embodiment of the present invention, in step (1), the organic solid waste includes kitchen waste, pig manure, straw, and sludge.

[0016] In a preferred embodiment of the present invention, step (1) includes both mesophilic anaerobic digestion (30–40°C, preferably 35°C) and thermophilic anaerobic digestion (48–60°C, preferably 55°C). The temperature selection is based on numerous experiments, which show that 35°C for mesophilic digestion and 55°C for thermophilic digestion are the most effective.

[0017] In a preferred embodiment of the present invention, step (1) includes anaerobic digestion, co-digestion, anaerobic digestion enhanced by additives, anaerobic digestion enhanced by heat treatment, or anaerobic digestion enhanced by alkali treatment.

[0018] In a preferred embodiment of the present invention, in step (2), the biochemical reaction metabolic structure includes a hydrolysis stage, an acid production stage, a hydrogen production and acetic acid production stage, and a methanogenesis stage.

[0019] In a preferred embodiment of the present invention, in step (3), biogas production is recorded daily, and samples are taken every three days to test pH, VFAs, VS and ammonia nitrogen in order to compare and optimize the model parameters.

[0020] In a preferred embodiment of the present invention, in step (5), the relevant parameters with high sensitivity include, but are not limited to, Y_h2, the maximum specific uptake rate of Monod Ks_h2, and the half-saturation value km_h2, which are related to the microbial metabolism of the hydrogen-nutritive methanogenic process.

[0021] Beneficial effects

[0022] The method disclosed in this invention can optimize the existing organic solid waste ADM1 model, improve the accuracy of the modified ADM1 model in predicting the parameters of the anaerobic digestion process of organic solid waste, predict the operation effect of anaerobic digestion of organic solid waste in actual engineering, and provide early warning of potential acidification and ammonia nitrogen inhibition risks in the future. It can also assist operators in regulating the anaerobic digestion system and guide the high-solids anaerobic digestion system towards high efficiency and stability. Attached Figure Description

[0023] Figure 1 Technical roadmap of the present invention;

[0024] Figure 2 Example 1 illustrates the organic matter metabolic pathways for high-solids anaerobic digestion of food waste based on metagenomics.

[0025] Figure 3 The biochemical metabolic structure of the modified ADM1 model in Example 1;

[0026] Figure 4 The accuracy verification of the improved model in Example 1 is shown in Figure a, where a is a comparison between the model calculation results and experimental measurement results of the daily gas production of the reactor, and b is a root mean square error plot of the normalized gas production data. Detailed Implementation

[0027] The present invention will be described in detail below with reference to embodiments, so that those skilled in the art can better understand the present invention, but the present invention is not limited to the following embodiments.

[0028] Example 1

[0029] A method for modifying the biochemical response structure of the ADM1 model using metagenomics includes the following steps:

[0030] (1) A completely mixed anaerobic digestion reaction was carried out in a 7L anaerobic digester at a reaction temperature of 35±2℃. The daily gas production volume was measured using a collection device. Metagenomics technology was used to reveal the system's microbial information. Metagenomic information can be found in [link to metagenomic data]. Figure 2 ;

[0031] (2) Based on the information revealed by metagenomics, the hydrolysis, acid production, hydrogen and acetic acid production, and methanogenesis stages in the ADM1 model were precisely modified to include biochemical reaction processes unique to the high-solids anaerobic digestion system of food waste, as revealed by metagenomics. The modified biochemical reaction structure of the ADM1 model is as follows: Figure 3 ;

[0032] (3) Based on the results of the small-scale test, the inhibition function, kinetic equation and biochemical rate coefficient in the ADM1 model were compared and optimized. The variables Sla, Sme, Xc_SS, Xc_FW, Xpro2, Xac1, Xme and Xla were added to the matrix. Sla represents the concentration of soluble lactate in the system, Sme represents the concentration of methyl compounds in the system, Xc_SS represents the concentration of complex organic particles in the inoculum in the system, Xc_FW represents the concentration of complex organic particles in the kitchen waste in the system, Xpro2 represents the concentration of microorganisms in the newly added propionic acid metabolic degradation pathway in the system, Xac1 represents the concentration of microorganisms in the newly added acetic acid metabolic degradation pathway in the system, Xme represents the concentration of microorganisms related to the metabolism of methyl compounds in the system and Xla represents the concentration of microorganisms related to the metabolism of lactic acid in the system. The relevant parameters are shown in Table 1.

[0033] (4) Based on the optimized biochemical reaction metabolic structure, inhibition function, kinetic equation and biochemical rate coefficient, the Pearson matrix of the ADM1 model was improved to construct the improved anaerobic digestion Pearson matrix, as shown in Table 2.

[0034] (5) Implement model calculations and solve the modified ADM1 model. First, the initial value is determined by experimentally based on its COD equivalent concentration. The model is extended based on the biochemical rate and kinetic rate equations of the standard ADM1 model.

[0035] The modified ADM1 model disclosed in this invention can monitor the methane production process of food waste based on the measured concentration of the input. The relative error between the theoretical and actual methane production values ​​of food waste analyzed by the ADM1 model corrected for methane production is less than 20%, although RMSE analysis shows... Figure 4 The model calculations showed some discrepancy with the initial experimental results. The RMSE reached its maximum on day 12, gradually decreasing from day 24, and stabilizing below 0.15 after 40 days. However, when the organic load increased to 2.85–3.71 gVS / (L·d), the model calculations gradually matched the experimental results, with an RMSE of approximately 0.10. This indicates that the modified ADM1 model has better predictive performance under high organic load conditions.

[0036] Table 1. Overview of relevant parameters for the improved model

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[0047] Example 2

[0048] A method for modifying the biochemical response structure of the ADM1 model using metagenomics includes the following steps:

[0049] (1) After pretreating the kitchen waste with 5% red mud, an anaerobic digestion test was carried out in a 500mL reactor, and the reaction temperature was maintained at 35±2℃.

[0050] (2) Metagenomic analysis was conducted on the system, and the ADM1 model was modified in a targeted manner based on the relative abundance of its metabolic pathways. The hydrolysis, acid production, hydrogen production, acetic acid production, and methanogenesis stages in the ADM1 model were modified respectively. The ADM1 model was modified by adding conductive materials to enhance the direct interspecies electron transfer methanogenesis pathway of anaerobic digestion of kitchen waste through metagenomic technology, and corresponding microbial metabolic processes and death processes were added, including the syntrophic acetic acid oxidation process, the propionic acid DIET process, the butyric acid DIET process, the microbial death process involved in the syntrophic acetic acid oxidation process, the microbial death process involved in the propionic acid DIET process, and the microbial death process involved in the butyric acid DIET process.

[0051] (3) The state variables Xc_fw, Xc_ss, X_ac1, X_ac2, X_pro_ac, X_bu_ac, and Xc_fw in the model represent the concentration of complex organic particles in the kitchen waste in the system, Xc_ss represents the concentration of complex organic particles in the inoculum in the system, X_ac1 represents the concentration of acetic acid-producing methanogens in the system, X_ac2 represents the concentration of microorganisms involved in symbiotic acetic acid oxidation in the system, X_pro_ac represents the concentration of microorganisms involved in the generation of methane from propionic acid via the DIET pathway in the system, and X_bu_ac represents the concentration of microorganisms involved in the generation of methane from butyric acid via the DIET pathway in the system.

[0052] (4) Based on the optimized biochemical reaction metabolic structure, inhibition function, kinetic equation and biochemical rate coefficient, the Pearson matrix of the ADM1 model is improved to construct the modified anaerobic digestion Pearson matrix.

[0053] (5) Perform model calculations and solve the modified ADM1 model. First, the initial value was determined experimentally based on the COD equivalent concentration. Then, the model was extended based on the biochemical rate and kinetic rate equations of the standard ADM1 model.

[0054] (6) The ADM1 model based on metagenomics can predict the methane production of the input food waste based on its concentration. The RMSE of the modified ADM1 model is less than 0.1 during the anaerobic digestion operation phase, and the simulation results are accurate.

[0055] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made using the present invention specification, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for modifying the biochemical reaction structure of the ADM1 model using metagenomics, characterized in that, Includes the following steps: (1) Conduct small-scale biochemical reaction experiments of anaerobic digestion of organic solid waste, and analyze the relative abundance of dominant microbial communities and key enzyme-encoding genes in the system based on the gene information revealed by metagenomics. (2) Based on the metagenome information analysis of the unique metabolic pathway of anaerobic digestion of organic solid waste, the biochemical reaction metabolic structure in the ADM1 model was precisely modified. (3) Regularly perform routine quantitative analysis on key organic components in the system, and compare and optimize the inhibition function, kinetic equation and biochemical rate coefficient in the ADM1 model based on the results of small-scale biochemical experiments; (4) Based on the optimized biochemical reaction metabolic structure, inhibition function, kinetic equation and biochemical rate coefficient, the Pearson matrix of the ADM1 model is improved to construct the modified anaerobic digestion Pearson matrix; and the modified ADM1 model is solved. (5) Perform sensitivity analysis on the ADM1 model, conduct small-scale biochemical reaction test of anaerobic digestion of organic solid waste, repeat steps (3)-(5), and continue to optimize the relevant parameters with higher sensitivity; (6) Analyze the accuracy of the modified ADM1 model and conduct continuous anaerobic digestion experiments on organic solid waste; compare the measured results with the predicted results of the modified ADM1 model to verify the accuracy of the modified ADM1 model. If the accuracy is not good, repeat steps (1)-(5).

2. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 1, characterized in that: In step (1), the organic solid waste includes kitchen waste, pig manure, straw, and sludge.

3. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 1, characterized in that: In step (1), the anaerobic digestion includes both mesophilic anaerobic digestion and thermophilic anaerobic digestion.

4. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 3, characterized in that: In step (1), the temperature of the mesophilic anaerobic digestion is 30-40°C, and the temperature of the hyperthermic anaerobic digestion is 48-60°C.

5. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 4, characterized in that: In step (1), the temperature of the mesophilic anaerobic digestion is 35°C, and the temperature of the hyperthermic anaerobic digestion is 55°C.

6. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 1, characterized in that: In step (2), the biochemical reaction metabolic structure includes a hydrolysis stage, an acid production stage, a hydrogen production and acetic acid production stage, and a methanogenesis stage.

7. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 1, characterized in that: In step (3), biogas production is recorded daily, and pH, VFAs, VS and ammonia nitrogen are sampled and tested every three days to compare and optimize the model parameters.

8. The method for modifying the biochemical reaction structure of the ADM1 model using metagenomics according to claim 1, characterized in that: In step (5), the highly sensitive relevant parameters include, but are not limited to, Y_h2, the maximum specific uptake rate of Monod Ks_h2, and the half-saturation value km_h2, which are related to the microbial metabolism of the hydrogen-nutritive methanogenic process.

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