Optical monitoring system and method for fermented liquid feed production line

Abstract

The invention discloses an optical monitoring system and method for a fermentation liquid feed production line, belongs to the technical field of fermentation process real-time monitoring and dynamic control, and particularly relates to a fermentation liquid feed production line monitoring system and method based on optical detection and dynamic modeling. The problems that an existing offline chemical analysis method is lagged in detection, tedious in operation and incapable of achieving real-time feedback regulation and control, and an existing online monitoring technology (such as an electrochemical sensor) is prone to being interfered by complex components in fermentation liquor, so that the detection precision is reduced, and production requirements are difficult to meet are solved. The system comprises a data acquisition monitoring unit and a data processing unit. The optical monitoring system and method for the fermented liquid feed production line are suitable for real-time monitoring, regulation and control of the fermented liquid feed production line, and are especially suitable for an application scene in which fermentation liquid data are acquired through near infrared spectrum and turbidity analysis dual channels and production parameters are dynamically optimized by fusing a partial differential equation model.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time monitoring and dynamic control of fermentation processes, and specifically to a fermentation liquid feed production line monitoring system and method based on optical detection and kinetic modeling. The system is particularly suitable for collecting fermentation liquid data through dual channels of near-infrared spectroscopy and turbidity analysis, and integrating partial differential equation models to dynamically optimize production parameters. Background Art

[0002] During the production of fermented liquid feed, it is necessary to monitor in real time: microbial concentration (or bacterial growth concentration, reflecting fermentation activity), nutrient consumption rate (or substrate consumption rate, the nutrient / substrate is such as glucose) and accumulation of metabolites (such as lactic acid, ethanol). These parameters directly affect the nutritional quality of the feed.

[0003] Existing technologies for detecting or monitoring these parameters mainly include: offline chemical analysis methods (such as titration and high-performance liquid chromatography) and online monitoring technologies based on single sensors (such as electrochemical sensors). Existing technologies have the following drawbacks: (1) Offline chemical analysis methods (such as titration and high-performance liquid chromatography) have problems such as detection lag, cumbersome operation, and inability to provide real-time feedback and control.

[0004] (2) Existing online monitoring technologies (such as electrochemical sensors) are easily interfered with by complex components in the fermentation broth, resulting in decreased detection accuracy and difficulty in meeting production needs.

[0005] Therefore, there is an urgent need to develop an efficient, accurate, and real-time optical monitoring system to achieve dynamic monitoring of key fermentation parameters. Summary of the Invention

[0006] The present invention proposes an optical monitoring system and method for a fermented liquid feed production line, which solves the problems of existing offline chemical analysis methods, such as detection lag, cumbersome operation, and inability to provide real-time feedback and regulation. It also solves the problem that existing online monitoring technologies (such as electrochemical sensors) are easily interfered with by complex components in the fermentation liquid, resulting in reduced detection accuracy and difficulty in meeting production needs.

[0007] The optical monitoring system for the fermented liquid feed production line of the present invention comprises the following units: The data acquisition and monitoring unit monitors the state of the fermentation liquid based on the fermentation liquid feed sample data collected at different points, and analyzes the nutrients and metabolites in the fermentation liquid in real time based on the absorption characteristics of the substance to near-infrared light; The data processing unit constructs a dynamic analysis of the fermentation process based on partial differential equations; the dynamic analysis of the fermentation process includes dynamic analysis of bacterial growth, dynamic analysis of substrate consumption, dynamic analysis of boundary conditions and dynamic analysis of product generation.

[0008] Furthermore, a preferred embodiment is provided, wherein the system further comprises a production adjustment unit; Production adjustment unit: According to the data processing results of the data acquisition and monitoring unit and the data processing unit, the production parameters are dynamically adjusted in combination with the composition of the fermentation liquid.

[0009] Furthermore, a preferred embodiment is provided, wherein the dynamic analysis of bacterial growth includes: Assume that the bacterial concentration c(x, t) in the fermentation broth changes with spatial position x and time t; Considering bacterial diffusion and growth metabolic activities, the partial differential equation of bacterial growth is constructed:

[0010] The bacterial growth partial differential equation contains diffusion terms and growth terms; in: D is the bacterial diffusion coefficient, µ(S) is the substrate-dependent specific growth rate, k d is the bacterial death rate constant.

[0011] Further, a preferred embodiment is provided, wherein the dynamic analysis of substrate consumption includes: Assume that the substrate concentration S(x, t) in the fermentation broth varies with spatial position x and time t; Partial differential equation for substrate consumption:

[0012] Where D is the substrate diffusion coefficient, Y S / c is the yield coefficient of substrate to bacteria; The partial differential equation for substrate consumption contains diffusion terms and growth terms.

[0013] Furthermore, a preferred embodiment is provided, wherein the dynamic analysis of the boundary conditions includes: Assume that the diffusion flux at the reactor wall is zero, that is, no material penetrates: ▽c·n=0, ▽S·n=0, where n is the wall normal vector; the substrate concentration at the inlet is customized S in , the bacterial concentration is C in , using continuous fermentation; The partial differential equation for dissolved oxygen transport is:

[0014] Where: k Lα is the volumetric oxygen transfer coefficient, is the dissolved oxygen concentration in equilibrium with the gas phase, rO2 is the oxygen consumption rate of the bacteria; Partial differential equation for temperature distribution:

[0015] Where: α is the thermal diffusivity, Q met is the heat production rate of bacterial metabolism, U is the heat transfer coefficient, A is the heat exchange area, V is the volume of fermentation liquid, ρc p is the specific heat capacity of the fermentation broth, T cool is the cooling medium temperature.

[0016] Further, a preferred embodiment is provided, wherein the dynamic analysis of the product generation is as follows: Assuming that the generation of product P is related to bacterial growth, establish the partial differential equation for product generation:

[0017] Where: α is the yield coefficient of non-growth coupling, β is the yield coefficient of growth coupling, D P is the product diffusion coefficient.

[0018] The present invention also provides an optical monitoring method for a fermented liquid feed production line, the method comprising the following steps: The data collection and monitoring step includes monitoring the fermentation liquid state based on the fermentation liquid feed sample data collected at different points, and analyzing the nutrients and metabolites in the fermentation liquid in real time based on the substance's absorption characteristics of near-infrared light; The data processing step is to construct a dynamic analysis of the fermentation process based on partial differential equations; the dynamic analysis of the fermentation process includes dynamic analysis of bacterial growth, dynamic analysis of substrate consumption, dynamic analysis of boundary conditions and dynamic analysis of product generation.

[0019] The present invention also proposes a computer device, comprising: a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute the above-mentioned optical monitoring method for the fermented liquid feed production line by executing the executable instructions.

[0020] The present invention also provides a computer storage medium, in which a computer program is stored. When the computer program is run, the optical monitoring method for the fermented liquid feed production line described above is executed.

[0021] The present invention also provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned optical monitoring method for a fermented liquid feed production line.

[0022] The present invention has the following beneficial effects: 1. The optical monitoring system for a fermented liquid feed production line described in this invention combines mechanism modeling with data-driven methods to construct a dynamic mathematical description of the fermentation process. This breaks through the limitation of the traditional Lambert-Beer law, which only describes "single-point concentration," and enables the calculation of fermentation parameters (such as spatially distributed bacterial activity and substrate utilization efficiency).

[0023] 2. The optical monitoring system for the fermented liquid feed production line described in the present invention is based on multispectral optical detection technology to monitor biomass, nutrients and metabolite concentrations in real time, achieving precise control and optimization of the fermentation process.

[0024] 3. The optical monitoring system for the fermented liquid feed production line described in the present invention uses near-infrared direct detection of metabolites combined with turbidity to indirectly calculate bacterial concentration, achieving dual-channel collaborative monitoring and improving the detection accuracy of complex components in the fermentation liquid.

[0025] 4. The optical monitoring system of the fermented liquid feed production line described in the present invention predicts metabolic bottlenecks in real time through product generation equations, thereby achieving dynamic optimization of the entire fermentation process.

[0026] The optical monitoring system and method for a fermented liquid feed production line of the present invention are suitable for real-time monitoring and regulation of a fermented liquid feed production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a schematic structural diagram of an optical monitoring system for a fermented liquid feed production line in one embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the technical solutions and advantages of the present invention more clearly described, the specific embodiments of the present invention will be further described in detail and completely in conjunction with the accompanying drawings. The various embodiments described below are only part of the preferred embodiments of the present invention, rather than all implementation plans; the various embodiments described below are intended to explain the present invention and cannot be understood as limiting the present invention; the reasonable combination of the technical features defined in the various embodiments of the present invention, as well as all other implementation plans obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work, all fall within the scope of protection of the present invention.

[0030] Embodiment 1: An optical monitoring system for a fermented liquid feed production line, comprising the following units: The data acquisition and monitoring unit monitors the state of the fermentation liquid based on the fermentation liquid feed sample data collected at different points, and analyzes the nutrients and metabolites in the fermentation liquid in real time based on the absorption characteristics of the substance to near-infrared light; The data processing unit constructs a dynamic analysis of the fermentation process based on partial differential equations; the dynamic analysis of the fermentation process includes dynamic analysis of bacterial growth, dynamic analysis of substrate consumption, dynamic analysis of boundary conditions and dynamic analysis of product generation.

[0031] In this embodiment, the fermented liquid feed sample data collected at different points are spatial multi-point data collected in real time based on a sensor network.

[0032] The sensor network includes an OD sensor (to monitor bacterial concentration), a dissolved oxygen electrode, a temperature probe, and a pH electrode.

[0033] In the spatial multi-point data, multi-points refer to discrete (sampling) points in the axial or radial direction of the reactor.

[0034] In this embodiment, the OD sensor is used to monitor the bacterial concentration.

[0035] The Lambert-Beer law describes the relationship between light absorption and the concentration of a substance (such as bacteria):

[0036] Where: A is absorbance (OD value); is the molar absorption coefficient; c is the bacterial concentration; L is the optical path length.

[0037] The absorbance (OD value) is monitored online by the OD sensor, and the bacterial concentration is calculated in real time. , and then obtain the curve c(t) of bacterial concentration changing with time.

[0038] In this embodiment, the system is based on multispectral optical detection technology to monitor biomass, nutrients and metabolite concentrations in real time, thereby achieving precise control and optimization of the fermentation process.

[0039] In this embodiment, the system has the following beneficial effects: Safe and reliable: ensure bacterial concentration and metabolite composition.

[0040] Precise control: Small error in monitoring key ingredients, reducing batch differences.

[0041] Intelligent and convenient: Fermentation parameters are calculated through models.

[0042] Cost-effectiveness: Reduces manual sampling and chemical analysis costs.

[0043] Embodiment 2: The system further includes a production adjustment unit; Production adjustment unit: According to the data processing results of the data acquisition and monitoring unit and the data processing unit, the production parameters are dynamically adjusted in combination with the composition of the fermentation liquid.

[0044] Embodiment 3: The dynamic analysis of bacterial growth includes: Assume that the bacterial concentration c(x, t) in the fermentation broth changes with spatial position x and time t; Considering bacterial diffusion and growth metabolic activities, the partial differential equation of bacterial growth is constructed:

[0045] The bacterial growth partial differential equation contains diffusion terms and growth terms; in: D is the bacterial diffusion coefficient, µ(S) is the substrate-dependent specific growth rate, k d is the bacterial death rate constant.

[0046] In this embodiment, the bacterial cell diffusion is material transfer caused by stirring.

[0047] In this embodiment, µ(S) is the substrate-dependent specific growth rate, as shown by the Michaelis-Menten equation µ=µ max S / Ks+S.

[0048] In this embodiment, the substrate-dependent specific growth rate μ(S) is obtained based on the parameters of the Michaelis-Menten equation.

[0049] The Michaelis-Menten equation describes the relationship between the specific growth rate (μ) of a microorganism and the substrate concentration (S): μ = μ max S / Ks+S; where µ max is the maximum specific growth rate, Ks is the half-saturation constant (when the substrate concentration reaches Ks, the growth rate is μ max half of the total cost).

[0050] (1) Experimental design Batch culture experiments: Inoculate microorganisms into a constant volume of culture medium and take samples regularly to measure the following indicators: Substrate concentration (S): Determined by spectrophotometry.

[0051] Bacterial cell concentration (X): measured by turbidity method (OD value).

[0052] At least three parallel experiments with different initial substrate concentrations should be set up, covering low, medium, and high concentration ranges to ensure that the data can reflect the saturation characteristics of the Michaelis-Menten equation.

[0053] (2) Parameter fitting method Step 1: Calculate the specific growth rate μ. During the logarithmic growth phase of batch culture, the bacterial concentration increases exponentially with time: X(t) = X0 e µt µ=lnX(t2)-lnX(t1) / t2-t1 Take the bacterial concentration at multiple time points in the logarithmic phase and use linear regression to fit the slope of lnX to time t to obtain µ at the corresponding substrate concentration. max and Ks.

[0054] Step 2: Fit the Michaelis-Menten equation: Substitute the µ data corresponding to different substrate concentrations S into the Michaelis-Menten equation and fit the parameter µ by nonlinear regression (such as least squares method). max and Ks.

[0055] Linearized approximation (simplified method): Transform the Michaelis equation into a double reciprocal form (Lineweaver-Burk equation): 1 / µ = Ks / µ max S+ 1 / µ max , plot 1 / µ against 1 / S, and obtain the intercept 1 / µ through linear regression max and slope Ks / µ max , and then calculate Ks.

[0056] Note: Linearization methods may introduce data bias, and nonlinear regression is more accurate.

[0057] In this embodiment, the bacterial diffusion coefficient D is obtained based on the theoretical model: The bacterial diffusion coefficient describes the random movement ability of bacteria in the medium, and the method of obtaining it depends on the research scale.

[0058] Theoretical estimate (suitable for approximate analysis): For spherical bacteria, the diffusion coefficient can be estimated using the Stokes-Einstein equation: D = k B T / 6πηr; where k B is the Boltzmann constant, T is the absolute temperature, η is the viscosity of the medium, and r is the radius of the bacterial cell. Note: This is only applicable to low Reynolds number (laminar flow) conditions and does not take into account active bacterial movement (such as flagellar swimming).

[0059] In this embodiment, the bacterial death rate constant k d , obtained based on death dynamics experiments: Bacteria death usually conforms to the first-order kinetic model: dX / dt = -k d X; where k d is the death rate constant (unit: h -1 or min -1 .

[0060] (1) Experimental design Batch culture decay phase experiment: Under conditions of nutrient depletion or the presence of inhibitors (e.g., high product concentrations, extreme pH / temperature), culture the bacteria until they die and measure the cell concentration X regularly.

[0061] To eliminate growth interference, high-temperature inactivation or the addition of antibiotics can be used to ensure that the bacteria only die and do not grow.

[0062] (2) Parameter fitting method Take the natural logarithm of the decay period data: ln X(t) = ln X0- k d t, and the slope of ln X(t) versus time t is fitted by linear regression to obtain k d (The slope is negative, k d Take the absolute value).

[0063] Embodiment 4: The dynamic analysis of substrate consumption includes: Assume that the substrate concentration S(x, t) in the fermentation broth varies with spatial position x and time t; Partial differential equation for substrate consumption:

[0064] Where D is the substrate diffusion coefficient, Y S / c is the yield coefficient of substrate to bacteria; The partial differential equation for substrate consumption contains diffusion terms and growth terms.

[0065] In this embodiment, the substrate diffusion coefficient D is: For small molecule substrates (such as glucose, amino acids), the D value of similar systems can be referred to (for example, about 10 in aqueous solution). - 9 m 2 / s level).

[0066] Estimation using molecular dynamics simulation or empirical formulas (such as the Wilk-Zhang equation) requires known parameters such as substrate molecular weight and medium viscosity.

[0067] In this embodiment, the yield coefficient Y of the substrate to the bacteria S / c : Y of common substrates (such as glucose, glycerol) in typical microorganisms (such as Escherichia coli, yeast) S / c There have been a lot of reports (such as the effect of glucose on the Y S / c About 0.4-0.5 g of bacteria / g of glucose) can be obtained through biotechnology manuals or research papers.

[0068] In this embodiment, µ(S): the culture conditions (temperature, pH, dissolved oxygen, etc.) are kept constant, and the bacterial cell concentration C(t) is monitored in real time by turbidity measurement.

[0069] The specific growth rate can be calculated by taking the derivative of C(t) and obtaining dC / dt, which is combined with µ = dC / dt / C.

[0070] Change the initial substrate concentration S0 and repeat the experiment to obtain the µ values ​​corresponding to different S.

[0071] Implementation 5: The dynamic analysis of the boundary conditions includes: Assume that the diffusion flux at the reactor wall is zero, that is, no material penetrates: ▽c·n=0, ▽S·n=0, where n is the wall normal vector; the substrate concentration at the inlet is customized S in , the bacterial concentration is C in , using continuous fermentation; The partial differential equation for dissolved oxygen transport is:

[0072] Where: k L α is the volumetric oxygen transfer coefficient, is the dissolved oxygen concentration in equilibrium with the gas phase, rO2 is the oxygen consumption rate of the bacteria; Partial differential equation for temperature distribution:

[0073] Where: α is the thermal diffusivity, Q met is the heat production rate of bacterial metabolism, U is the heat transfer coefficient, A is the heat exchange area, V is the volume of fermentation liquid, ρc p is the specific heat capacity of the fermentation broth, T cool is the cooling medium temperature.

[0074] In this embodiment, during the fermentation process, the spatial distribution of dissolved oxygen (DO) and temperature affects bacterial metabolism and needs to be described by partial differential equations.

[0075] In this embodiment, for the partial differential equation of dissolved oxygen transfer, if the spatial distribution is considered, a diffusion term D needs to be added. DO ▽ 2 C D O.

[0076] In this embodiment, for the partial differential equation of temperature distribution, α is the thermal diffusion coefficient: α = k / ρ c p ; Where: k is the thermal conductivity of the fermentation liquid, ρ is the density, c p is the specific heat capacity.

[0077] For water-based fermentation broth, α = 10-7 m 2 / s (25℃), it needs to be appropriately lowered when the bacterial concentration is high.

[0078] In this embodiment, the dynamic analysis of the boundary conditions further includes: dynamic analysis of metabolic heat production rate; Metabolic heat production rate: estimated by the standard enthalpy change of substrate oxidation (e.g., the oxidation enthalpy of glucose is approximately -2816 kJ / mol) combined with the substrate consumption rate: Q met = Y S / C· Substrate oxidation enthalpy·µc, which needs to take into account the differences in enthalpy changes in metabolic pathways (e.g., aerobic respiration, fermentation).

[0079] Embodiment 6: The dynamic analysis of the product generation is as follows: Assuming that the generation of product P is related to bacterial growth, establish the partial differential equation for product generation:

[0080] Where: α is the yield coefficient of non-growth coupling, β is the yield coefficient of growth coupling, D P is the product diffusion coefficient.

[0081] In this embodiment, the generation of product P is related to bacterial growth, that is, it is growth-coupled.

[0082] It should be noted that the data processing unit primarily outputs results from the fermentation process, which uses the Lambert-Beer law to monitor bacterial cell concentration. Partial differential equations (PDEs) are incorporated into the data processing unit to calculate fermentation parameters, combining fermentation kinetics models, mass and heat transfer characteristics, and bacterial growth patterns. Essentially, this approach combines mechanistic modeling with data-driven methods to construct a dynamic mathematical description of the fermentation process.

[0083] This method of the data processing unit breaks through the limitation of the traditional Lambert-Beer law that only describes "single-point concentration" and realizes the calculation of fermentation parameters (such as spatially distributed bacterial activity and substrate utilization efficiency).

[0084] It should be noted that during the fermentation process, bacterial growth, substrate consumption, product production and environmental parameters (such as temperature, pH, dissolved oxygen) all change dynamically, and partial differential equations need to be introduced to characterize the multivariable coupling relationship.

[0085] It should be noted that the fermentation process involves spatial distribution (such as concentration and temperature differences in different areas of the reactor) and temporal changes (such as bacterial growth rate and substrate consumption rate), and partial differential equations are used to describe the dual variable dynamics of space and time.

[0086] It should be noted that due to the complexity of the fermentation system, partial differential equations usually need to be solved by numerical methods. The overall steps are summarized as follows: Parameter estimation: Optimize the unknown parameters (such as D, k) in the equation by using historical data or online measurements through least squares method, maximum likelihood estimation or machine learning algorithms (such as neural networks). L α, µ max ).

[0087] Real-time inversion: Based on the currently measured OD value and other parameters, the state variables in the equation (such as substrate concentration and metabolic heat rate) are inferred, enabling online calculation of fermentation parameters (such as bacterial growth, substrate consumption, boundary conditions, and product formation).

[0088] During real-time inversion: First, the partial differential equation model is initialized: the geometric parameters of the fermentation tank, the initial substrate concentration S0, the temperature setting value, etc. are input.

[0089] Next, numerical solutions were performed: partial differential equations were used to simulate the spatial distribution of bacterial growth and substrate consumption (assuming uniform mixing within the tank, this can be simplified to ordinary differential equations (ODEs)). The specific growth rate µ = 1 / c · dc / dt was calculated to determine the growth phase (lag phase, logarithmic phase, or stationary phase). The oxygen consumption rate rO2 was inferred from the dissolved oxygen data to assess bacterial metabolic activity.

[0090] Finally, the results are output: real-time display of parameters such as bacterial concentration, substrate remaining amount, metabolic heat rate, etc., providing a basis for feeding strategy or temperature control.

[0091] In this embodiment, the production monitoring of the fermented liquid feed comprehensively applies physical and mathematical theories to achieve accurate monitoring and control of the key parameter of biomass.

[0092] In this embodiment, the monitoring system not only supports the stable operation of the production line, but also optimizes the fermentation process and improves the feed quality (such as nutrient conversion rate and probiotic activity).

[0093] Embodiment 7: An optical monitoring method for a fermented liquid feed production line, comprising the following steps: The data collection and monitoring step includes monitoring the fermentation liquid state based on the fermentation liquid feed sample data collected at different points, and analyzing the nutrients and metabolites in the fermentation liquid in real time based on the substance's absorption characteristics of near-infrared light; The data processing step is to construct a dynamic analysis of the fermentation process based on partial differential equations; the dynamic analysis of the fermentation process includes dynamic analysis of bacterial growth, dynamic analysis of substrate consumption, dynamic analysis of boundary conditions and dynamic analysis of product generation.

[0094] Embodiment 8: A computer device comprises: a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to perform the above-mentioned optical monitoring method for a fermented liquid feed production line by executing the executable instructions.

[0095] Embodiment 9: A computer storage medium, wherein a computer program is stored in the storage medium. When the computer program is run, the optical monitoring method for the fermented liquid feed production line described above is executed.

[0096] Embodiment 10: A computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for optical monitoring of a fermented liquid feed production line.

[0097] This embodiment provides a computer device or system, the hardware device of this part is a general model and is not shown in the form of a diagram. The system includes a processor and a memory, wherein the processor and the memory can be connected through a bus or other means. The memory is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, and corresponding program instructions / modules. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, so as to realize the data space entity resolution data quality enhancement method in the above method embodiment.

[0098] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, an intranet, a mobile communication network, and combinations thereof.

[0099] One or more modules are stored in the memory. When the processor executes, the method steps in the embodiment are executed. In this way, the purpose of the invention can be achieved through the method, device and process of the present invention. The specific details of the above-mentioned computer equipment can be understood by referring to the corresponding descriptions and effects in the embodiment, and will not be repeated here.

[0100] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The storage medium can also include a combination of the above-mentioned types of memory.

[0101] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable changes and improvements to the present invention, reasonable combinations of implementation methods and equivalent replacements based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Optical monitoring system for fermented liquid feed production line, characterized by: The system includes the following units: The data acquisition and monitoring unit monitors the state of the fermentation liquid based on the fermentation liquid feed sample data collected at different points, and analyzes the nutrients and metabolites in the fermentation liquid in real time based on the absorption characteristics of the substance to near-infrared light; The data processing unit constructs a dynamic analysis of the fermentation process based on partial differential equations; the dynamic analysis of the fermentation process includes dynamic analysis of bacterial growth, dynamic analysis of substrate consumption, dynamic analysis of boundary conditions and dynamic analysis of product generation.

2. The optical monitoring system for a fermented liquid feed production line according to claim 1, characterized in that: The system also includes a production regulation unit; Production adjustment unit: According to the data processing results of the data acquisition and monitoring unit and the data processing unit, the production parameters are dynamically adjusted in combination with the composition of the fermentation liquid.

3. The optical monitoring system for a fermented liquid feed production line according to claim 1, characterized in that: The dynamic analysis of bacterial growth includes: Assume that the bacterial concentration c(x, t) in the fermentation broth changes with spatial position x and time t; Considering bacterial diffusion and growth metabolic activities, the partial differential equation of bacterial growth is constructed: The bacterial growth partial differential equation contains diffusion terms and growth terms; in: D is the bacterial diffusion coefficient, µ(S) is the substrate-dependent specific growth rate, k d is the bacterial death rate constant.

4. The optical monitoring system for a fermented liquid feed production line according to claim 1, characterized in that: The dynamic analysis of substrate consumption includes: Assume that the substrate concentration S(x, t) in the fermentation broth varies with spatial position x and time t; Partial differential equation for substrate consumption: Where D is the substrate diffusion coefficient, Y S / c is the yield coefficient of substrate to bacteria; The partial differential equation for substrate consumption contains diffusion terms and growth terms.

5. The optical monitoring system for a fermented liquid feed production line according to claim 1, characterized in that: The dynamic analysis of the boundary conditions includes: Assume that the diffusion flux at the reactor wall is zero, that is, no material penetrates: ▽c·n=0, ▽S·n=0, where n is the wall normal vector; the substrate concentration at the inlet is customized S in , the bacterial concentration is C in , using continuous fermentation; The partial differential equation for dissolved oxygen transport is: Where: k L α is the volumetric oxygen transfer coefficient, is the dissolved oxygen concentration in equilibrium with the gas phase, rO2 is the oxygen consumption rate of the bacteria; Partial differential equation for temperature distribution: Where: α is the thermal diffusivity, Q met is the heat production rate of bacterial metabolism, U is the heat transfer coefficient, A is the heat exchange area, V is the volume of fermentation liquid, ρc p is the specific heat capacity of the fermentation broth, T cool is the cooling medium temperature.

6. The optical monitoring system for a fermented liquid feed production line according to claim 1, characterized in that: The dynamic analysis of the product generation is as follows: Assuming that the generation of product P is related to bacterial growth, the partial differential equation for product generation is established: Where: α is the yield coefficient of non-growth coupling, β is the yield coefficient of growth coupling, D P is the product diffusion coefficient.

7. An optical monitoring method for a fermented liquid feed production line, characterized in that: The method comprises the following steps: The data collection and monitoring step includes monitoring the fermentation liquid state based on the fermentation liquid feed sample data collected at different points, and analyzing the nutrients and metabolites in the fermentation liquid in real time based on the substance's absorption characteristics of near-infrared light; The data processing step is to construct a dynamic analysis of the fermentation process based on partial differential equations; the dynamic analysis of the fermentation process includes dynamic analysis of bacterial growth, dynamic analysis of substrate consumption, dynamic analysis of boundary conditions and dynamic analysis of product generation.

8. A computer device comprising: A processor and a memory, characterized in that the memory is used to store executable instructions of the processor, and the processor is configured to perform the optical monitoring method for the fermented liquid feed production line according to claim 7 by executing the executable instructions.

9. A computer storage medium, characterized in that The storage medium stores a computer program, and when the computer program is run, the optical monitoring method for the fermented liquid feed production line according to claim 7 is executed.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the optical monitoring method for a fermented liquid feed production line according to claim 7 are implemented.

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