Ruminant rumen methane emission prediction and regulation system based on dynamic Monod model and multi-algorithm collaborative optimization

Abstract

The invention relates to the technical field of ruminant methane emission prediction and regulation and control, in particular to a ruminant rumen methane emission prediction and regulation and control system based on dynamic Monod model and multi-algorithm collaborative optimization. S1, data acquisition and preprocessing; s2, FOM calculation is carried out; s3, the generation amount of H2 and the generation amount of CO2 are calculated; s4, calculating a methanogen kinetic model; s5, multi-algorithm collaborative optimization including simulated annealing and a genetic algorithm; and S6, outputting a result and generating a strategy. According to the method, high-sensitivity parameters can be preferentially regulated and controlled, so that the prediction precision is remarkably improved, the parameter optimization efficiency and the global optimal solution search capability are remarkably enhanced, and the wide applicability and the scene adaptability are better.

Description

Technical Field

[0001] This invention relates to the field of methane emission prediction and regulation technology in ruminants, and in particular to a rumen methane emission prediction and regulation system for ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization. Background Technology

[0002] Current Technological Development: The prediction and regulation of methane emissions from ruminants began in the 1980s, represented by the Molly model (1987) proposed by Baldwin et al. This model, based on rumen fermentation kinetics and microbial metabolism, was the first to systematically integrate the quantitative relationship between substrate degradation, volatile fatty acid (VFA) production, and gas emissions. Subsequent research gradually incorporated theories such as chemometrics (Ellis et al., 2008) and microbiome research (Tapio et al., 2017), forming a prediction system centered on static parameters. Key advancements in current technologies include: 1. Static Model Framework: Methane emissions are estimated by fixing degradation rates (e.g., NDF degradation rate 0.3–0.5) and empirical VFA proportions (e.g., acetic acid 65%), combined with linear equations (IPCC Tier 2 method). 2. Single-Objective Optimization Methods: Parameters are adjusted using gradient descent or genetic algorithms, but these are limited by local optima and convergence speed (Bannink et al., 2010). 3. Simplified environmental parameters: Most models ignore the effects of dynamic fluctuations in rumen pH and temperature on the activity of methanogens (Dijkstra et al., 2018).

[0003] Problems with existing technologies: Despite the achievements of existing technologies, the following core shortcomings still exist:

[0004] 1. Lack of sensitivity guidance: It is impossible to prioritize the control of highly sensitive parameters (such as X_CH4, DMI), resulting in low policy generation efficiency.

[0005] 2. Experience-driven parameter tuning: This method relies on manual adjustment of model parameters to adapt to different feed types, which is highly subjective and requires parameter tuning to improve accuracy.

[0006] Given the aforementioned issues, and driven by global dual-carbon goals and the need for emission reduction in livestock farming, there is an urgent need to develop high-precision and high-efficiency methane prediction and control tools. Based on this, we present a rumen methane emission prediction and control system for ruminants, based on a dynamic Monod model and multi-algorithm collaborative optimization, to address some of the problems existing in current technologies. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a rumen methane emission prediction and regulation system for ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization. It can prioritize the regulation of highly sensitive parameters, thereby significantly improving prediction accuracy, parameter optimization efficiency and global optimal solution search capability, and has better applicability and scenario adaptability.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] A method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization includes the following steps;

[0010] S1, Data Acquisition and Preprocessing;

[0011] S2, FOM calculation;

[0012] Calculation of S3, H2 and CO2 generation;

[0013] S4, Calculation of the kinetic model of methanogens;

[0014] S5, multi-algorithm collaborative optimization: including simulated annealing and genetic algorithm;

[0015] S6, Result Output and Strategy Generation.

[0016] Preferably, the data acquisition and preprocessing operation method in step S1 specifically includes the following steps:

[0017] S11, Static parameter input: DMI and feed composition data are entered through the ranch management system or manually;

[0018] S12, Dynamic parameter acquisition: Fill in degradation rate and VFA ratio by calling literature databases or experimental measurement results;

[0019] S13, Environmental Parameter Synchronization: Real-time reading of rumen pH and temperature sensor data.

[0020] Preferably, the method for calculating FOM in step S2 specifically includes the following steps:

[0021] S21, Calculate organic matter (OM): OM = 1 - Ash - Moisture; where the default value of Ash is 0.06~0.10 g / g DM, and the default value of Moisture is 0.10~0.15 g / g DM;

[0022] S22, summation of items: FOM = DMI × OM × (C_NDF × d_NDF + C_NFC × d_NFC + C_CP × d_CP).

[0023] Preferably, the specific steps for calculating the H2 and CO2 production amounts in step S3 include:

[0024] S31, Calculate the net H2 production: H2_net=α×FOM×(f_Ac×Y_H2_Ac+f_Pr×Y_H2_Pr+f_Bu×Y_H2_Bu), where Y_H2_Ac=0.0247, Y_H2_Pr=-0.0123, Y_H2_Bu=0.0123mol / g;

[0025] S32, Calculate the total CO2: CO2_total=CO2_fermentation+CO2_respiration-CO2_liquid.

[0026] Preferably, the calculation of the methanogenic bacteria kinetic model in step S4 specifically includes the following steps:

[0027] S41, Monod equation extension: μ_CH4=μ_max×[H2] / (Ks_H2+[H2])×[CO2] / (Ks_CO2+[CO2])×pH_effect×T_effect;

[0028] S42, calculate the amount of methane produced: CH4=Y_CH4×X_CH4×FOM×μ_CH4×24×16.

[0029] Preferably, in step S5, the initial simulated annealing temperature T = 1000, and the constraint condition VFA scaling normalization is: f_Ac + f_Pr + f_Bu = 1.

[0030] Preferably, the result output in step S6 is a visual display; the strategy is to prioritize the adjustment of highly sensitive parameters, where the sensitivity threshold SI>1.0 is a highly sensitive parameter.

[0031] The control system using the above-described method for predicting rumen methane emissions in ruminants based on dynamic Monod model and multi-algorithm collaborative optimization includes an input module, a core computation module, an optimization module, and an output module.

[0032] The input module includes a dry matter intake (DMI) sensor for real-time acquisition of feed intake data of ruminants and a feed composition analyzer for measuring the fiber, carbohydrate, and protein content in the feed.

[0033] The core computing module includes a FOM computing engine and a dynamic Monod model processor for real-time calculation of the specific growth rate of methanogens and the amount of methane produced.

[0034] The optimization module is a simulated annealing-genetic algorithm co-optimizer used for global-local two-stage parameter optimization to generate emission reduction strategies.

[0035] Preferably, the feed composition analyzer includes NDF, NFC, and CP detection.

[0036] Preferably, the output module includes a visualization interface for displaying prediction results, optimization schemes, and sensitivity analysis, specifically charts and reports.

[0037] The beneficial effects of this invention are:

[0038] 1. The prediction method employed in this invention significantly improves prediction accuracy, specifically in the average coefficient of determination (R²) between the model's predicted values ​​and the measured data. 2 The accuracy reaches above 0.75, reducing the error by 15-20% compared to traditional models (such as the Molly model). Dynamically integrating H2 / CO2 concentration, pH, and temperature parameters, it can accurately capture the impact of environmental factors on methane emissions, reducing prediction bias caused by environmental fluctuations. Utilizing an extended dynamic Monod equation, H2 and CO2 concentrations are introduced as dynamic variables into the Monod equation (μ_CH4=μ_max×[H2] / (Ks_H2+[H2])×[CO2] / (Ks_CO2+[CO2])) to quantify the real-time changes in methanogenic bacterial activity. Adding pH and temperature correction factors (Gaussian functions simulating optimal pH and temperature) corrects for the influence of environmental parameters on microbial metabolism.

[0039] 2. The parameter optimization efficiency and global optimal solution search capability of the control system of this invention are significantly enhanced, specifically in the following ways: A multi-stage collaborative strategy combining simulated annealing (global search) and genetic algorithm (local optimization) improves parameter optimization efficiency by 60% compared to a single algorithm, avoiding getting trapped in local optima; it supports rapid generation of emission reduction strategies (such as reducing X_CH4 to 1.0%), reducing optimization time to 1 / 3 of traditional trial-and-error methods. Utilizing a multi-algorithm collaborative optimization framework, simulated annealing explores the global parameter space through high-temperature random jumps to determine the initial value range; the genetic algorithm performs selection, crossover, and mutation operations based on the initial values ​​to finely adjust parameter combinations; and constraint embedding forces VFA proportional normalization (f_Ac + f_Pr + f_Bu = 1) to ensure the physical rationality of the solution.

[0040] 3. This invention offers superior applicability and scenario adaptability, covering various feed types such as high-fiber, high-starch, and mixed rations, and is suitable for different ruminants such as dairy cows and beef cattle, with low model generalization error. The parameter range is based on empirically measured values ​​from the literature (e.g., d_NDF = 0.3–0.5, f_Pr = 0.15–0.40), supporting flexible adjustments to adapt to regional farming differences. Utilizing a dynamic parameter coupling mechanism, the input parameters are designed hierarchically (static parameters, such as DMI and feed composition; dynamic parameters, such as VFA ratio and X_CH4), separating fixed and adjustable variables. Sensitivity-driven optimization prioritizes optimizing highly sensitive parameters (such as X_CH4 and DMI), improving convergence efficiency in different scenarios.

[0041] 4. This invention provides practical guidance and visualization support, offering clear decision-making support for aquaculture practitioners and researchers. In practical applications, visualizations such as heat maps (…) Figure 3 This technology can quickly identify high or low methane emission ranges under specific d_NDF and d_NFC combinations, thereby guiding adjustments to feed formulations to achieve emission reduction targets. For example, when d_NDF is 0.35 and d_NFC is 0.88, methane emissions are at a moderate level. Farmers can use this information, combined with factors such as herd productivity and feed costs, to weigh the pros and cons and develop an optimal feed formulation. Attached Figure Description

[0042] Figure 1 This is the output of the prediction results before and after parameter optimization in Embodiment 2 of the present invention.

[0043] Figure 2 This is a sensitivity parameter diagram for the test parameters in Embodiment 2 of the present invention.

[0044] Figure 3 Optimize the prediction of methane emissions driven by highly sensitive parameters (such as d_NDF, d_NFC). Detailed Implementation

[0045] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0046] This invention provides a rumen methane emission prediction and regulation system for ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization. The regulation system specifically includes an input module, a core calculation module, an optimization module, and an output module.

[0047] The input module includes a dry matter intake (DMI) sensor for real-time acquisition of feed intake data of ruminants and a feed composition analyzer for determining the fiber, carbohydrate, and protein content in feed.

[0048] The core computational module includes a FOM computational engine (which generates data according to the formula FOM = DMI × OM × (C_NDF × d_NDF + C_NFC × d_NFC + C_CP × d_CP)) and a dynamic Monod model processor for real-time calculation of the specific growth rate (μ_CH4) of methanogens and the amount of methane produced;

[0049] The optimization module is a simulated annealing-genetic algorithm co-optimizer used for global-local two-stage parameter optimization to generate emission reduction strategies;

[0050] The output module is a visualization interface (charts, reports) used to display prediction results, optimization schemes, and sensitivity analysis.

[0051] The above system is used to build a unique structural feature:

[0052] ① Hierarchical data processing workflow:

[0053] 1) Data input layer: Standardization of sensor and manually entered data.

[0054] 2) Model calculation layer: FOM→H2 / CO2→μ_CH4→CH4 are calculated step by step.

[0055] 3) Optimize the decision-making level: provide parameter adjustment suggestions from the multi-objective optimization algorithm.

[0056] ② Algorithm call structure:

[0057] 1) Simulated annealing: The first stage is a global search to determine the initial value range.

[0058] 2) Genetic Algorithm: Local optimization in the second stage, fine-tuning parameters.

[0059] The physical property parameters are shown in Table 1.

[0060] Table 1 System physical property characteristics table

[0061]

[0062] The process for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization is as follows:

[0063] (1) Data Acquisition and Preprocessing

[0064] ① Step description:

[0065] 1) Static parameter input: DMI and feed composition data are entered through the ranch management system or manually.

[0066] 2) Dynamic parameter acquisition: Call literature databases or experimental measurement results to fill in degradation rate and VFA ratio.

[0067] 3) Environmental parameter synchronization: Real-time reading of rumen pH and temperature sensor data.

[0068] ② Process parameters:

[0069] 1) Data standardization: unify units (e.g., convert NDF percentage to g / g OM).

[0070] 2) Handling missing values: Use the default values ​​from the literature or interpolation to fill in the missing values.

[0071] (2) Calculation of FOM (Fermentable Organic Matter)

[0072] ① Step description:

[0073] 1) Calculate organic matter (OM): OM = 1 - Ash-Moisture.

[0074] 2) Sum of items: FOM = DMI × OM × (C_NDF × d_NDF + C_NFC × d_NFC + C_CP × d_CP).

[0075] ② Process parameters:

[0076] 1) Ash content (Ash) default value: 0.06–0.10 g / g DM.

[0077] 2) Moisture (default value): 0.10–0.15 g / g DM.

[0078] (3) Calculation of H2 and CO2 production

[0079] ① Step description:

[0080] 1) Net H2 generation:

[0081] H2_net=α×FOM×(f_Ac×Y_H2_Ac+f_Pr×Y_H2_Pr+f_Bu×Y_H2_Bu),

[0082] Among them, Y_H2_Ac=0.0247, Y_H2_Pr=-0.0123, and Y_H2_Bu=0.0123mol / g.

[0083] 2) Total CO2:

[0084] CO2_total=CO2_fermentation+CO2_respiration-CO2_liquid,

[0085] in:

[0086] a. CO2 fermentation: produced by substrate fermentation (NDF: 0.06, NFC: 0.12, CP: 0.03 mol / g)

[0087] b. CO2_respiration: Microbial respiration (μ_max × X_microbe × 0.85 × 24)

[0088] c.CO2_liquid: Dissolution equilibrium calculation (29.4×(1 / (1+10^(pH-pKa))×rumen_volume)

[0089] ② Process parameters:

[0090] 1) α = 0.15–0.30 mol / g (default 0.22).

[0091] 2) Rumen volume = 125L (adjustable).

[0092] (4) Calculation of the kinetic model of methanogens

[0093] ① Step description:

[0094] 1) Monod equation extension:

[0095] μ_CH4=μ_max×[H2] / (Ks_H2+[H2])×[CO2] / (Ks_CO2+[CO2])×pH_effect×T_effect,

[0096] in:

[0097] a.pH_effect: Gaussian function correction (pH_opt = 6.8, standard deviation 0.3).

[0098] b. T_effect: Gaussian function correction (T_opt = 39℃, standard deviation 2.0).

[0099] 2) Methane production:

[0100] CH4=Y_CH4×X_CH4×FOM×μ_CH4×24×16.

[0101] ② Process parameters:

[0102] 1)μ_max=0.04h-1, Ks_H2=7.5μM, Ks_CO2=3.0mM.

[0103] 2) Y_CH4 = 0.25–0.35 mol / g.

[0104] (5) Multi-algorithm collaborative optimization

[0105] ① Step description:

[0106] 1) Simulated annealing (global search):

[0107] a. Initial temperature T = 1000, decay rate = 0.95, iterations 500 times.

[0108] b. Objective function: Minimize the MSE between the prediction and the actual result.

[0109] 2) Genetic Algorithm (Local Optimization):

[0110] a. Population size = 50, crossover probability = 0.5, mutation probability = 0.1.

[0111] b. Elite retention rate = 0.01, iteration 300 times.

[0112] ②Constraints:

[0113] 1) VFA scaling normalization: f_Ac + f_Pr + f_Bu = 1.

[0114] 2) Parameter physical range: such as d_NDF∈[0.3,0.5].

[0115] (6) Results Output and Strategy Generation

[0116] ① Step description:

[0117] Visualization: Generate scatter plots, heatmaps, sensitivity ranking charts, etc.

[0118] ② Emission reduction strategy recommendations:

[0119] 1) Prioritize adjusting highly sensitive parameters (e.g., reduce X_CH4 to 1.0%).

[0120] 2) Optimize the diet formulation (e.g., increase the starch content to increase f_Pr).

[0121] 3) Output example:

[0122] a. Forecast Report: CH4 emissions, optimized emission reduction rate, and key parameter adjustments. Strategy List: Specific operational steps (such as additive usage, feed ratio adjustments), input data and parameters (“raw materials”), and the types and sources of raw materials are shown in Table 2.

[0123] Table 2. Types and Sources of Raw Materials

[0124]

[0125]

[0126] The raw materials used (parameter range and typical values) are shown in Table 3.

[0127] Table 3 Raw Material Usage

[0128] parameter Scope / Dosage Typical value DMI 12–25 kg / day 15kg / day NDF proportion 30–60％ 45％ d_NDF 0.3–0.5 0.42 f_Pr 0.15–0.40 0.28 Rumen pH 5.5–7.5 6.8

[0129] The process parameters are summarized in Table 4.

[0130] Table 4 Summary of Process Parameters

[0131] Process Phase Key parameters Value / Range Data preprocessing Ash content 0.06–0.10 g / g DM FOM calculation NFC degradation rate (d_NFC) 0.8–0.95 <![CDATA[H2 / CO2 generation]]> <![CDATA[α (H2 basic production rate)]]> 0.15–0.30 mol / g Methanogenic kinetics Temperature correction standard deviation 2.0℃ Optimization Algorithm Simulated annealing initial temperature T＝1000 Output Results Sensitivity threshold (SI) SI > 1.0 indicates a high sensitivity parameter.

[0132] Example 1

[0133] (1) High-fiber diet scenario

[0134] Input parameters: DMI = 15 kg / day, NDF = 60%, d_NDF = 0.42, f_Pr = 0.28, X_CH4 = 0.015.

[0135] Output results: CH4 = 285g / day (actual value 290g / day, error 1.7%), emission reduction rate after optimization is 12%.

[0136] (2) Parameter sensitivity verification

[0137] X_CH4 decreased from 1.5% to 1.0%: CH4 decreased by 28% (320 → 230g / day)

[0138] 4. Summary of the advantages of the technical solution

[0139] The parameter range is clearly defined: based on literature and algorithm optimization, the feasibility and reliability of the model are ensured.

[0140] Modular structure: The hierarchical design of input-computation-optimization-output facilitates expansion and adaptation to different hardware platforms.

[0141] Standardization of physical property parameters: The dynamic model parameters (μ_max, Ks) and correction factors (pH, T) are all cited from authoritative literature to ensure scientific accuracy.

[0142] Example 2

[0143] The verification test was conducted according to this technical solution, and the test results are as follows.

[0144] Table 5 Verification Test Form

[0145]

[0146]

[0147]

[0148] The above test parameters and sensitivity parameters are as follows Figure 2 As shown;

[0149] The output of the above test results before optimization is as follows: Figure 1 As shown in the left figure, R 2 =0.718;

[0150] The output of the above test results before optimization is as follows: Figure 1 As shown in the right figure, R 2 =0.755.

[0151] and

[0152] This shows that the prediction effect is better after parameter optimization.

[0153] All technical features in this embodiment can be modified in appearance according to actual needs.

[0154] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

Claims

1. A method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization, characterized by: Includes the following steps; S1, Data Acquisition and Preprocessing; S2, FOM calculation; Calculation of S3, H2 and CO2 generation; S4, Calculation of the kinetic model of methanogens; S5, multi-algorithm collaborative optimization: including simulated annealing and genetic algorithm; S6, Result Output and Strategy Generation.

2. The method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 1, characterized in that: The data acquisition and preprocessing operation method in step S1 specifically includes the following steps: S11, Static parameter input: DMI and feed composition data are entered through the ranch management system or manually; S12, Dynamic parameter acquisition: Fill in degradation rate and VFA ratio by calling literature databases or experimental measurement results; S13, Environmental Parameter Synchronization: Real-time reading of rumen pH and temperature sensor data.

3. The method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 1, characterized in that: The method for calculating FOM in step S2 specifically includes the following steps: S21, Calculate organic matter (OM): OM = 1 - Ash - Moisture; where the default value of Ash is 0.06~0.10 g / g DM, and the default value of Moisture is 0.10~0.15 g / g DM; S22, summation of items: FOM = DMI × OM × (C_NDF × d_NDF + C_NFC × d_NFC + C_CP × d_CP).

4. The method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 1, characterized in that: The specific steps for calculating the H2 and CO2 production in step S3 include: S31, Calculate the net H2 production: H2_net=α×FOM×(f_Ac×Y_H2_Ac+f_Pr×Y_H2_Pr+f_Bu×Y_H2_Bu), where Y_H2_Ac=0.0247, Y_H2_Pr=-0.0123, Y_H2_Bu=0.0123mol / g; S32, Calculate the total CO2: CO2_total=CO2_fermentation+CO2_respiration-CO2_liquid.

5. The method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 1, characterized in that: The calculation of the methanogenic bacteria kinetic model in step S4 specifically includes the following steps: S41, Monod equation extension: μ_CH4=μ_max×[H2] / (Ks_H2+[H2])×[CO2] / (Ks_CO2+[CO2])×pH_effect×T_effect; S42, calculate the amount of methane produced: CH4=Y_CH4×X_CH4×FOM×μ_CH4×24×16.

6. The method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 1, characterized in that: In step S5, the initial simulated annealing temperature is T = 1000, and the constraint condition VFA is proportionally normalized: f_Ac + f_Pr + f_Bu = 1.

7. The method for predicting rumen methane emissions in ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 1, characterized in that: In step S6, the result output is a visual display; the strategy is to prioritize the adjustment of highly sensitive parameters, where the sensitivity threshold SI>1.0 is a highly sensitive parameter.

8. A control system using the rumen methane emission prediction method for ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in any one of claims 1-7, characterized in that: It includes an input module, a core computing module, an optimization module, and an output module; The input module includes a dry matter intake (DMI) sensor for real-time acquisition of feed intake data of ruminants and a feed composition analyzer for measuring the fiber, carbohydrate, and protein content in the feed. The core computing module includes a FOM computing engine and a dynamic Monod model processor for real-time calculation of the specific growth rate of methanogens and the amount of methane produced. The optimization module is a simulated annealing-genetic algorithm co-optimizer used for global-local two-stage parameter optimization to generate emission reduction strategies.

9. The control system for the rumen methane emission prediction method for ruminants based on dynamic Monod model and multi-algorithm collaborative optimization as described in claim 8, characterized in that: The feed composition analyzer includes NDF, NFC, and CP detection.

10. The control system for the rumen methane emission prediction method for ruminants based on a dynamic Monod model and multi-algorithm collaborative optimization as described in claim 8, characterized in that: The output module includes a visualization interface for displaying prediction results, optimization schemes, and sensitivity analysis, specifically charts and reports.

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