Method and device for monitoring stability of anaerobic digestion system in real time

Abstract

The invention discloses a method and a device for monitoring the stability of an anaerobic digestion system in real time. The method comprises the following steps: 1, carrying out non-targeted metabonomics analysis on the anaerobic digestion mVOCs category by adopting headspace solid-phase microextraction in combination with gas chromatography-high resolution mass spectrometry; 2, screening differential mVOCs markers based on principal component analysis and orthogonal partial least squares discriminant analysis; and 3, constructing a system stability real-time monitoring system by using an electronic nose sensor array, and when the difference mVOCs marker concentration deviates from a threshold value or difference categories are continuously accumulated, triggering early warning and recommending an optimization strategy by the system. The method and the device can monitor the state of the anaerobic reactor in real time, early warn the methane production inhibition risk in advance compared with traditional parameters (pH, volatile fatty acid and the like), improve the biogas yield and reduce the operation and maintenance cost, are suitable for intelligent management of anaerobic digestion scenes of kitchen garbage, municipal sludge, agricultural waste and the like, and can also be expanded to other anaerobic biological fermentation fields.

Description

Technical Field

[0001] The present invention relates to the technical field of anaerobic digestion system stability monitoring, and in particular to a real-time monitoring method and device for anaerobic digestion system stability. Background Art

[0002] Anaerobic digestion is a core technology for the resource utilization of organic waste. It is widely used in the treatment and resource utilization of kitchen waste, agricultural and forestry waste, sewage sludge, and food processing waste. Through microbial co-metabolism, it converts organic matter into biogas and stabilizes residual waste, combining environmental benefits with energy recovery value. However, the microbial communities within anaerobic digestion systems are highly sensitive and susceptible to factors such as temperature fluctuations, disturbances in the organic loading rate, and the accumulation of toxic substances, which can lead to metabolic instability, decreased gas production efficiency, and even system collapse. Therefore, the efficient and stable operation of anaerobic digestion systems depends on real-time monitoring of key parameters.

[0003] However, traditional monitoring indicators (such as pH, volatile fatty acids, gas production, etc.) have a response lag and are unable to reflect the dynamic changes in the metabolic state of microorganisms in a timely manner. In addition, the detection of traditional indicators mostly relies on offline sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also difficult to meet the real-time monitoring needs of large-scale engineering applications. Therefore, it is urgent to develop a new type of early warning monitoring technology with high sensitivity, real-time, and intelligent technology to break through the limitations of traditional monitoring methods and provide reliable guarantees for the stable operation of anaerobic digestion systems.

[0004] Microbial volatile organic compounds (mVOCs) are volatile small molecules released during microbial metabolism, encompassing thousands of substances such as alkanes, alkenes, alcohols, aldehydes, ketones, and aromatic compounds. Studies have shown that the production of mVOCs is directly related to the metabolic activity of microbial communities, interspecies interactions, and responses to environmental stresses. Changes in their composition and concentration can sensitively reflect physiological instability or functional abnormalities in microbial communities. However, the feasibility of using mVOCs as a new early warning monitoring indicator for anaerobic digestion is currently unknown, and there is a lack of systematic research ideas, technical solutions, and supporting equipment. Summary of the Invention

[0005] The purpose of the present invention is to provide a real-time monitoring method and device for the stability of an anaerobic digestion system based on mVOCs, which can achieve early warning and process optimization by dynamically detecting the mVOCs characteristic spectrum and combining it with microbial community data.

[0006] To achieve the above objectives, the present invention provides the following technical solutions:

[0007] A method for real-time monitoring of anaerobic digestion system stability comprises the following steps:

[0008] In the first step, non-targeted metabolomics analysis of mVOCs in the anaerobic reactor was performed using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-high-resolution mass spectrometry (GC-Orbitrap / MS). Target detection categories included alkanes, alkenes, alcohols, and aromatic compounds.

[0009] Step 2: Differential mVOCs markers related to system instability were screened based on principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA);

[0010] Step three: The anaerobic reactor's system stability is monitored using a monitoring device (including an online sampling module, a detection module, a data analysis unit, and an early warning execution module). If the concentration of a differential mVOC marker deviates from the threshold by more than 20%, the device triggers an alarm and recommends an optimization strategy.

[0011] A further improvement of the present invention is that in step 1, the extraction fiber coating of the headspace solid phase microextraction (HS-SPME) is selected from one or more combinations of polydimethylsiloxane (PDMS), polyacrylate (PA) or divinylbenzene-carbon molecular sieve-polydimethylsiloxane (DVB / CAR / PDMS).

[0012] A further improvement of the present invention is that in step 2, during the process of screening and determining differential mVOCs markers by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), a cross-validation method is used to evaluate the stability and predictive ability of the model, and the screened differential mVOCs markers include but are not limited to alkenes, ketones, benzene derivatives, etc.

[0013] The present invention also provides a real-time monitoring device for the stability of an anaerobic digestion system, comprising:

[0014] (a) Online sampling module: including an automatic sampling system to achieve the enrichment of headspace volatiles;

[0015] (b) Detection module: including an electronic nose sensor array for qualitative and quantitative analysis of mVOCs;

[0016] (c) Data analysis unit: configured to use machine learning algorithms (PCA, OPLS-DA, ANN) to generate stability reports in real time;

[0017] (d) Early warning execution module: configured to automatically execute an alarm instruction when it is detected that the concentration of the differential mVOCs marker deviates from the threshold by more than 20%.

[0018] The present invention provides a method and device for real-time monitoring of anaerobic digestion system stability based on microbial volatile organic compounds (mVOCs), which has the following benefits:

[0019] 1. By real-time monitoring of the dynamic changes in the mVOCs characteristic spectrum, it is possible to capture system instability signals several days before the system becomes unstable, providing operators with sufficient response time, effectively avoiding a significant drop in gas production efficiency or system crash, and significantly reducing operational risks and economic losses.

[0020] 2. As a direct product of microbial metabolism, changes in the composition and concentration of mVOCs can accurately reflect the metabolic state and functional activity of the microbial community. By analyzing the characteristic spectrum of mVOCs, changes in the activity of key functional microorganisms (such as methanogens) can be identified, providing a scientific basis for system regulation, optimizing operating parameters (such as organic load and hydraulic retention time), and improving overall system efficiency.

[0021] 3. Integrate online sampling, detection and data analysis functions to achieve continuous collection and real-time analysis of mVOCs, breaking through the timeliness bottleneck of traditional offline detection, significantly reducing manual intervention and laboratory analysis costs, and improving monitoring efficiency and management level.

[0022] 4. This method and device can be applied to anaerobic digestion systems of different scales and types (such as kitchen waste, sewage sludge, agricultural and forestry waste treatment) and similar anaerobic biological fermentation systems. It has strong versatility and scalability, and provides technical support for the promotion and application of anaerobic resource utilization technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for real-time monitoring of stability of an anaerobic digestion system based on microbial volatile organic compounds according to an embodiment of the present application is shown;

[0024] Figure 2 The figure shows a schematic structural diagram of a real-time monitoring device for the stability of an anaerobic digestion system based on microbial volatile organic compounds according to an embodiment of the present application;

[0025] Figure 3 The working process of the real-time monitoring device for the stability of an anaerobic digestion system based on microbial volatile organic compounds described in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] See the instructions attached Figure 1 The present application provides a flow chart of a method for real-time monitoring of the stability of an anaerobic digestion system based on microbial volatile organic compounds, comprising the following steps:

[0028] S101: Non-targeted metabolomics analysis of mVOCs in anaerobic reactors using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-high-resolution mass spectrometry (GC-Orbitrap / MS). Target detection categories include alkanes, alkenes, alcohols, and aromatic compounds.

[0029] S102: Screening of differential mVOCs markers associated with system instability based on principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA);

[0030] S103: The system stability of the anaerobic reactor is monitored by a monitoring device (including an online sampling module, a detection module, a data analysis unit, and an early warning execution module). When the concentration of the differential mVOCs is detected to deviate from the threshold by more than 20%, the device triggers an alarm.

[0031] The following examples describe in detail the specific operating steps from the detection and analysis method to the implementation of the online monitoring device, providing technical support for the implementation of the above monitoring method.

[0032] In the specific implementation process, the mVOCs detection and analysis methods include:

[0033] 1. Sample Collection and Pretreatment

[0034] A headspace solid-phase microextraction (HS-SPME) device was used, and a 100 μm PDMS composite-coated extraction fiber was selected. One mL of the mixed solution from the anaerobic digestion reactor was mixed with 0.1 mL of formic acid (3%, v / v) and transferred to a 20 mL headspace injection vial. Sample processing was automated using a TriPlus RSH autosampler. The injection vial was placed in an incubator set to 55°C and equilibrated for 30 minutes, while the extraction fiber was exposed to the headspace to adsorb volatile substances. After adsorption was completed, the fiber was automatically inserted into the gas chromatography inlet (250°C) for thermal desorption, and the desorption time was 3 minutes.

[0035] 2. GC-Orbitrap / MS Analysis

[0036] Chromatographic conditions: TG-WAX capillary column (30 m × 0.25 mm × 0.25 μm), helium carrier gas (flow rate 0.2 mL / min), inlet temperature 250°C, split ratio 10:1. Temperature program: initial 60°C hold for 2 minutes, then increase at 10°C / min to 220°C hold for 3 minutes, then increase at 15°C / min to 325°C hold for 5 minutes. Mass spectrometry conditions: EI ionization mode (70 eV), resolution 60,000, ion source temperature 280°C, transfer line temperature 250°C, scan range m / z 35-500.

[0037] Data acquisition was performed using Xcalibur software, with retention indices calibrated using n-alkane standards (C7-C30). Peak alignment and deconvolution were performed using TraceFinder 5.0 software. Compound identification was performed using the NIST MS library and the ThermoFisher Orbitrap HRAM library. Mass spectrometric peak intensities were sum-normalized, and samples with relative standard deviations (RSDs) greater than 30% were excluded.

[0038] In some embodiments, the process of screening for differential mVOCs markers includes:

[0039] The mVOCs data after total peak area normalization were first evaluated for overall data distribution by principal component analysis (PCA, SIMCA software), and principal components (PC1-PC3) with cumulative contribution rates >85% were extracted. Orthogonal partial least squares discriminant analysis (OPLS-DA) was then used to screen for differential mVOCs markers, with the criteria being: VIP > 1, Student's T test p < 0.05, and |log2FC| > log2(1.5). The model stability (Q 2 Finally, the dimensionality reduction effect was verified by non-metric multidimensional scaling (NMDS, Bray-Curtis distance), and the structure was considered credible when the stress value was <0.2.

[0040] In some embodiments, the implementation of the online monitoring device based on differential mVOCs includes:

[0041] 1. Hardware Integration

[0042] As the instruction manual Figure 2 As shown, the anaerobic digestion system stability monitoring device provided in the embodiment of the present application includes:

[0043] Online sampling module 201: automatic sample injector (flow rate 0.5-2 mL / min) connected to PTFE corrosion-resistant sampling tube, equipped with 0.22 μm particulate filter;

[0044] Detection module 202: The electronic nose array contains 12 metal oxide sensors (e.g., TGS2602 for volatile organic compounds, TGS823 for hydrogen sulfide, etc.), with a single sensor response time of <30 seconds;

[0045] Data Analysis Unit 203: Embedded industrial computer (Intel i5, 8GB RAM, Ubuntu 20.04) pre-installed with OPLS-DA machine learning model;

[0046] Early warning execution module 204: RS485 interface is connected to the PLC controller.

[0047] 2. Real-time monitoring process

[0048] As the instruction manual Figure 3 As shown, the workflow of the anaerobic digestion system stability monitoring device based on microbial volatile organic compounds provided in the embodiment of the present application includes the following steps:

[0049] S301: The electronic nose sensor array automatically collects differential mVOCs marker signals regularly and generates characteristic response spectra through ADC conversion;

[0050] S302: Calling the OPLS-DA model to calculate the Mahalanobis distance between the current sample and the historical normal data set (dynamic baseline);

[0051] S303: When the concentration of any differential mVOCs marker deviates from the baseline value by ≥20% for 3 consecutive cycles, the system triggers an early warning.

Claims

1. A real-time monitoring method for the stability of an anaerobic digestion system, characterized in that The following steps are involved: In the first step, non-targeted metabolomics analysis of mVOCs in the anaerobic reactor was performed using headspace solid-phase microextraction combined with gas chromatography-high-resolution mass spectrometry. The target detection categories included alkanes, alkenes, alcohols, and aromatic compounds. Step 2: Screen differential mVOCs markers related to system instability based on principal component analysis and orthogonal partial least squares discriminant analysis; Step 3: The anaerobic reactor system stability is monitored using a monitoring device. When the concentration of the differential mVOCs marker is detected to deviate from the threshold by more than 20%, the device triggers an alarm and recommends an optimization strategy.

2. The method for real-time monitoring of anaerobic digestion system stability according to claim 1, characterized in that: In the step 1, the extraction fiber coating of the headspace solid phase microextraction is selected from one or more combinations of polydimethylsiloxane, polyacrylate or divinylbenzene-carbon molecular sieve-polydimethylsiloxane.

3. The method for real-time monitoring of anaerobic digestion system stability according to claim 1, characterized in that: In the step 2, in the process of screening and determining the differential mVOCs markers by principal component analysis and orthogonal partial least squares discriminant analysis, the stability and predictive ability of the model are evaluated by cross-validation method, and the screened differential mVOCs markers include one or more combinations of olefins, ketones, and benzene derivatives.

4. A real-time monitoring device for the stability of an anaerobic digestion system, used to implement the method according to any one of claims 1 to 4, characterized in that include: (a) Online sampling module: including an automatic sampling system to achieve the enrichment of headspace volatiles; (b) Detection module: including an electronic nose sensor array for qualitative and quantitative analysis of mVOCs; (c) a data analysis unit configured to process the analysis results of the detection module through a machine learning algorithm to generate a stability report in real time; (d) Early warning execution module: configured to automatically execute an alarm instruction when it is detected that the concentration of the differential mVOCs marker deviates from the threshold by more than 20%.

5. The real-time monitoring device for anaerobic digestion system stability according to claim 4, characterized in that: The electronic nose sensor array supports modular replacement to be suitable for different anaerobic digestion scenarios.

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