General model and method for predicting growth rate of microorganisms in photobioreactor

Abstract

The invention discloses a universal model and method for predicting the growth rate of microorganisms in a photobioreactor. The prediction model comprises a pretreatment module, a fluid dynamics calculation module, an illumination transmission module and a microorganism growth rate mechanism module, firstly, a physical model of the photobioreactor is obtained; secondly, according to the obtained physical model and the calculation area, grid division is carried out, and a pre-processing module is constructed; then, calculating initial values of growth parameters of microbial particles in the photobioreactor; and finally, calculating and predicting the growth rate of the microorganisms in the photobioreactor by calculating fluid dynamics, a light intensity transmission law and a microorganism growth mechanism coupling model. According to the method, on the basis of physical modeling, computational fluid dynamics, light intensity transmission and a microbial growth mechanism are coupled, and the universality of growth rate prediction is expanded; therefore, the model can more accurately predict the growth rate of microorganisms in any photobioreactor.

Description

Technical Field

[0001] The invention relates to the technical field of microorganism cultivation photobioreactors, and in particular to a universal microorganism growth rate prediction model and method in a photobioreactor. Background Art

[0002] Microalgae are a class of autotrophic microorganisms that release oxygen while absorbing carbon dioxide and can produce high-value products such as biodiesel and pigments. Due to their high metabolic flexibility, adaptability to various culture conditions and extremely fast growth rate, they are suitable for wastewater treatment and bioenergy production, and do not compete with crops for land. Therefore, more and more research is focusing on them as a source of products with high biological value.

[0003] The design scheme and control methods of microalgae photobioreactors need to be flexibly selected according to specific circumstances to achieve optimal growth and product accumulation. However, the reality is that all designs and controls need to be based on the mastery of the laws of the bioreactor and the growth mechanism of microalgae, otherwise it will be a disordered, random and rough process. And for large-scale reactors, flow mixing is an important issue that cannot be ignored, and the related content is rarely mentioned in the influencing factors of traditional microalgae growth mechanism. At the same time, the flow situation is also the main reason why various mechanism models of microalgae cannot be used in reactors of different shapes and sizes. Therefore, constructing a comprehensive model that couples fluid mechanics and other influencing factors has important guiding significance for the design and control of large-scale photobioreactors. There are obvious gaps and blanks in the existing design and manufacturing guidance schemes for photobioreactors, which can basically only be carried out through experience and experiments. In addition, the applicability of existing microbial growth and metabolism mechanism models in large-scale reactors is generally poor, and the guidance on the control of various related parameters in the culture process is also missing. The existing strategies are rough and will generate a lot of energy dissipation. Therefore, how to scientifically guide the expansion design of photobioreactors is a key step in the large-scale utilization of microalgae in the future. In addition, during the operation of the photobioreactor, how to match the material flow (aeration concentration, liquid culture medium circulation) with the energy flow (agitation, aeration, hydraulic retention time, and light) and conduct scientific regulation to achieve the most efficient microalgae growth rate is also a key step for large-scale and low-cost applications.

[0004] The literature [Jin M, Xu Y, Chen J, et al. A comprehensive universal model framework of microalgae growth dynamics for photobioreactor scaling-up design and optimization [J]. Energy Conversion and Management, 2024, 299: 117832.] proposed a model for predicting the growth rate of microorganisms in photobioreactors by coupling computational fluid dynamics, light intensity transmission, and growth mechanism. However, the growth mechanism in this model adopts a steady-state assumption, that is, the growth of microorganisms changes rapidly when exposed to changing light intensity, and there is no relaxation time. Therefore, it is inconsistent with the actual process, and the influence of light history and time on it is not considered. Summary of the invention

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a universal model and method for predicting the growth rate of microorganisms in a photobioreactor, which is based on physical modeling and couples computational fluid dynamics, light intensity transmission, and microbial growth mechanism to expand the versatility of growth rate prediction; by coupling computational fluid dynamics, light intensity transmission and a more complete growth mechanism (transient, considering light history and time), the model can more accurately predict the growth rate of microorganisms in any photobioreactor.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A general prediction model for microbial growth rate in photobioreactors, including:

[0008] Pre-processing module: obtain the physical model of the photobioreactor, fluid calculation area, initial value of phase volume fraction, initial value of microbial concentration in the fluid, light intensity boundary conditions, ventilation / agitation mixing conditions during the operation of the photobioreactor, and grid division conditions, which are used to predict the growth rate of microorganisms in the photobioreactor;

[0009] Fluid dynamics calculation module: used to calculate the gas, liquid and solid three-phase flow distribution in the photobioreactor, so as to obtain the accurate flow distribution of solid-phase microorganisms in the photobioreactor;

[0010] Light transmission module: used to quantitatively calculate the precise distribution of light intensity in the photobioreactor;

[0011] Microbial growth rate mechanism module: accurately calculate the growth rate of microorganisms through the light intensity received by the microorganisms;

[0012] The precise flow distribution of solid-phase microorganisms in the photobioreactor obtained by the fluid dynamics calculation module and the precise distribution of light intensity in the photobioreactor obtained by the light transmission module are used to obtain the light intensity received by each solid-phase microorganism in the photobioreactor, thereby calculating the precise microbial growth rate through the microbial growth rate mechanism module, and taking the average of the growth rates of all solid-phase microbial particles in the photobioreactor to obtain the precise growth rate of all microorganisms in the photobioreactor.

[0013] A general method for predicting the growth rate of microorganisms in a photobioreactor, the specific steps are as follows:

[0014] Step 1: Based on the appearance and operation parameter information of any photobioreactor, a physical model of the photobioreactor, a fluid calculation region, initial values ​​of velocity, pressure, and phase volume fraction in the fluid calculation region, boundary values ​​of velocity, pressure, and phase volume fraction at the boundary of the fluid calculation region, initial values ​​of microbial concentration in the fluid, light intensity boundary conditions, and ventilation / agitation mixing conditions during the operation of the photobioreactor are obtained;

[0015] Step 2: Grid division is performed according to the physical model and fluid calculation area obtained in step 1, and a pre-processing module for predicting the growth rate of microorganisms in the photobioreactor is constructed based on the divided grids, the mixing conditions of aeration / agitation during the operation of the photobioreactor obtained in step 1, the velocity, pressure, and initial values ​​of phase volume fraction in the fluid calculation area, the velocity, pressure, and phase volume fraction boundary values ​​at the boundary of the fluid calculation area, the initial value of the concentration of microorganisms in the fluid, and the light intensity boundary conditions;

[0016] Step 3, based on the pre-processing module constructed in step 2, calculating the initial values ​​of the growth parameters of the microbial particles in the photobioreactor;

[0017] Step 4: Based on the pre-processing module constructed in step 2 and the initial values ​​of the microbial particle growth parameters calculated in step 3, the microbial growth rate in the photobioreactor is calculated and predicted by computational fluid dynamics, light intensity transmission law and microbial growth mechanism coupling model.

[0018] The specific method of step 2 is:

[0019] Step 2.1 Basic mesh construction

[0020] Based on the optimal principle of meshing, mesh the physical model obtained in step 1:

[0021] The physical model dimensions of the photobioreactor are calibrated according to the mesh size, mesh skewness, mesh aspect ratio, mesh dynamic and static partitions, boundary areas and internal areas; the physical model of the photobioreactor is meshed based on the optimal meshing principle; meshes with high mesh skewness and aspect ratio, i.e., poor quality, and mesh flow boundary areas are meshed and re-segmented to improve the meshing quality, and then the nodes, boundaries, sizes, volumes, and topological relationship information of the mesh are extracted to obtain the physical mesh information of the photobioreactor;

[0022] Step 2.2 Multiphase, stirring grid supplementation

[0023] On the basis of the grid divided in step 2.1, based on the ventilation / stirring operation parameters of the photobioreactor obtained in step 1, the air inlet and outlet boundaries and / or stirring areas of the photobioreactor are divided to obtain multiphase and / or dynamic grid partition information; when there is a stirring operation, the grid needs to be divided into dynamic and static areas, wherein the dynamic area includes the stirring components of the photobioreactor, and the static area is other areas, and the dynamic and static areas are coupled at the contact interface. In the subsequent calculation process, the static area grid always remains stationary, and the dynamic area grid actively rotates with the stirring components; when there is a ventilation condition, at the boundary air inlet and outlet, the corresponding area is divided into gas inlet and outlet area boundaries according to the physical model obtained in step 1;

[0024] Step 2.3 Calculation of initial conditions of light intensity field

[0025] According to the initial values ​​of velocity, pressure, phase volume fraction in the fluid calculation area obtained in step 1 and the boundary values ​​of velocity, pressure, phase volume fraction at the boundary of the fluid calculation area, the parameters of the boundary area and the internal area of ​​the grid are directly set; according to the initial concentration conditions and light intensity boundary conditions obtained in step 1, the initial light intensity field conditions in the photobioreactor are calculated as follows:

[0026]

[0027] Wherein, A0 is absorbance, I0 is incident light intensity, I is local light intensity, K is a constant coefficient related to light wavelength, b is the distance from the incident point to the local point, also known as light path, and c is the concentration of the solution where the light is incident. Formula (1) is the description equation of the Bill-Lambert law, which describes the relationship between the incident light intensity and the outgoing light intensity under different concentrations and light paths. When the incident light is not superimposed on each other, that is, the incident light direction is parallel or the solution concentration is too high for the light to penetrate, then formula (1) is directly used to calculate the light intensity field distribution. When the incident light is superimposed, the basic Bill-Lambert law is improved, and the light intensity field in the photobioreactor is calculated by the following discrete summation method:

[0028]

[0029] Among them, I j represents the local light intensity at the jth grid, n represents a total of n incident light points, I i represents the incident light intensity at the i-th incident point, A0 is the absorbance, and l is the distance from the i-th incident point to the j-th grid, i.e., the light path;

[0030] At this point, the pre-processing module is constructed, which includes the grid information of the photobioreactor; the initial values ​​of velocity, pressure, and phase volume fraction in the fluid calculation area; and the boundary values ​​of velocity, pressure, and phase volume fraction and light intensity boundary conditions at the boundary of the fluid calculation area.

[0031] The specific steps of step 3 are as follows:

[0032] Step 3.1 Based on the pre-processing module constructed in step 2, calculate the growth rate of microbial particles in the photobioreactor under the steady-state assumption: that is, couple computational fluid dynamics, light intensity transmission law and steady-state microbial growth mechanism as a coupling model, and evolve the grid according to the rotating grid method in the dynamic change of the grid: after each time step, the dynamic area grid will rotate according to the predetermined angle, that is, the grid is iterated at each time step iteration, the dynamic area grid is changed, and the static area grid is fixed, and the connection surface between the dynamic area grid and the static area grid is coupled by arbitrary grid interface (AMI) surface to avoid grid deformation; then , the flow conditions of culture medium (liquid phase), gas (gas phase) and microorganism (solid phase) in the photobioreactor are calculated by the gas-liquid-solid three-phase model in computational fluid dynamics, where the gas phase and liquid phase are continuous phases and the solid phase is a discrete phase; the light intensity at the local grid where each microbial particle is located is obtained according to the position of each microbial particle in the photobioreactor calculated by fluid dynamics, and the growth rate of each microbial particle is calculated according to the steady-state growth rate calculation formula; the average growth rate of the microbial particles in the reactor under the steady-state assumption is obtained by averaging the growth rates of all microbial particles in the reactor;

[0033] Step 3.2 solves the average growth parameter as the initial value of the transient solution.

[0034] The specific process of step 3.1 is as follows:

[0035] Step 3.1.1 Solve the gas and liquid continuous phase flow conditions according to the NS equation to obtain the gas and liquid continuous phase velocity and pressure flow information:

[0036]

[0037] ρ=αρ1+(1-α)ρ2 (5)

[0038] μ m =αμ1+(1-α)μ2 (6)

[0039]

[0040] 0<α<1 (8)

[0041] in, is the velocity of the fluid; ρ is the density of the fluid; ρ1, ρ2 are the densities of each phase; p is the pressure; τ is the shear stress; g is the gravity; f σ is the surface tension; s is the source term; μ m is the viscosity of the fluid; μ1, μ2 are the viscosities of each phase; α is the volume fraction of one of the phases;

[0042] Step 3.1.2 After obtaining the velocity and pressure flow information of the gas and liquid continuous phases, the velocity of the discrete phase microbial particles is solved using Newton's second law:

[0043]

[0044] in, is the particle velocity, m is the particle mass; is the force exerted on the particles by the fluid; is the drag force; It is gravity; is the pressure gradient; is the virtual mass force; It is other forces;

[0045] Step 3.1.3 Obtain the light intensity field distribution calculated based on the light intensity transmission law

[0046] After solving the velocity of the discrete phase microbial particles in step 3.1.2, the position of the discrete phase microbial particles is known. According to formula (2), the local light intensity basis of each discrete phase microbial particle is obtained to calculate the growth rate of each discrete phase microbial particle in the next step.

[0047] Step 3.1.4 describes the steady-state microbial growth mechanism through the photosynthetic factory (PSF) to calculate the local growth rate of each discrete phase microbial particle:

[0048] The photosynthetic factory (PSF) is the sum of the light-harvesting system, reaction center and related devices, which produces corresponding photosynthetic products under the action of given light energy; PSF has three states: resting state, activated state and inhibited state; the probability of PSF being in resting state, activated state and inhibited state is represented by growth mechanism parameters A, B and C respectively;

[0049] The steady-state microbial growth mechanism is derived by adopting the steady-state assumption for the transient microbial growth mechanism, where the transient microbial growth mechanism equation is described as follows:

[0050]

[0051] A+B+C=1 (12)

[0052] μ=kγB-M (13)

[0053] Under the steady-state assumption, the microbial growth mechanism also satisfies the following assumptions:

[0054]

[0055] Substituting equation (14) into equations (10)-(13) can obtain the following steady-state microbial growth mechanism control equation, and the growth rate of the particle at this time is calculated according to the following equation:

[0056]

[0057] Among them, μ is the growth rate, α, β, δ, γ, k, and M are all model parameters, I represents the light intensity received by the microorganism, and the average growth rate of the photobioreactor is obtained by averaging all the solid particles representing the microorganisms.

[0058] The specific process of step 3.2 is as follows:

[0059] According to the average growth rate of all microorganisms in the photobioreactor Solving for the average growth parameter

[0060]

[0061] Then, according to the average growth parameters Solving for the average light intensity in the reactor

[0062]

[0063] Among them, formula (17) is about the independent variable The quadratic equation has two solutions. The average light intensity value to be solved is constrained according to the incident light boundary conditions; then another average growth parameter of the microorganism is calculated

[0064]

[0065] The average value of the growth parameter obtained by solving the problem is used as the initial value of the transient growth parameter, and thus the initial values ​​of the growth mechanism parameters A and B of the microorganisms in the photobioreactor are obtained.

[0066] The specific steps of step 4 are as follows:

[0067] According to the coupled model of coupled computational fluid dynamics, light intensity transmission law and steady-state microbial growth mechanism described in step 3.1, the growth parameters of each microbial particle are updated based on the historical growth characteristics of each microbial particle, i.e., the growth mechanism parameters A, B, C at the past moment and the light intensity value obtained in step 3.1.3, as well as the discrete time step set by the coupled model calculation:

[0068]

[0069] A+B+C=1 (21)

[0070] μ=kγB-M (22)

[0071] Discretize it explicitly with respect to time:

[0072] A t+1 -A t =Δt(-αI t A t +γB t +δC t ) (twenty three)

[0073] B t+1 -B t =Δt(αI t A t -γB t -βI t B t ) (twenty four)

[0074] C t+1 =1-A t -B t (25)

[0075] Among them, A, B, and C are stored separately with each microbial particle, and the superscript t+1 and t are the values ​​of the current time step and the previous time step respectively, and Δt is the time step length; after updating the growth mechanism parameters A, B, and C at the current moment, the growth rate of the microbial particle can be expressed by the following formula:

[0076] μ t+1 =kγB t+1 -M (26)

[0077] After traversing all the discrete phase microbial particles in the photobioreactor, the growth rates of all the microbial particles are averaged to obtain the overall growth rate of the microbial particles in the photobioreactor.

[0078] Compared with the prior art, the present invention has the following beneficial effects:

[0079] The present invention combines computational fluid dynamics, light intensity transmission, and microbial growth mechanism, and the three work together to enhance the versatility of the model. The workflow of the coupled model of computational fluid dynamics, light intensity transmission law, and microbial growth mechanism can be summarized as follows:

[0080] In the fluid dynamics calculation module, the flow of culture medium (liquid phase), gas (gas phase) and microorganisms (solid phase) in the photobioreactor is calculated through the gas-liquid-solid three-phase model, and stirring is achieved through dynamic changes in the grid.

[0081] The light intensity transmission law is used to calculate the light intensity distribution in the photobioreactor, and an accurate solution for the high gradient distribution of light intensity in the photobioreactor can be obtained.

[0082] As the time step progresses, the transient growth mechanism parameters carried by each microbial particle are updated, and the growth rate is calculated based on the growth mechanism parameters. Since the particles store the growth parameters of the previous time step, this method specifically considers the effects of light history and light time on growth.

[0083] The key technical point of this method is the modeling based on the laws of physics, the coupling of computational fluid dynamics, light intensity transmission, and microbial growth mechanism, which has strong versatility and expands the versatility of growth rate prediction, so that the model can more accurately predict the growth rate of microorganisms in any photobioreactor. Specifically, the simulation method based on computational fluid dynamics, light intensity transmission, and microbial growth mechanism has important advantages and beneficial effects in the following aspects:

[0084] 1. High-precision and high-generality prediction capability: By adopting a model method based on computational fluid dynamics, light intensity transmission, and microbial growth mechanism, the model as a whole is described by a physical model, which can more accurately and universally predict the growth rate of microorganisms in the photobioreactor. This is of great significance for the scale-up design of the photobioreactor and the dynamic regulation of operating parameters during operation.

[0085] 2. Explainability and understandability: Compared with empirical coupling models and machine learning methods, this method based on the description of physical laws can provide more interpretable results. It can reveal the relationship between the growth rate of microorganisms in photobioreactors and factors such as shape, size, flow boundary conditions, and light intensity boundary conditions, providing a deeper understanding of the scale-up design of photobioreactors and the dynamic regulation of operating parameters.

[0086] 3. Low cost: Compared with the traditional empirical coupling model, the model parameters need to be experimentally measured for different reactors, and the experiment needs to be carried out under reactors of the same size and shape. This method only needs to obtain the microbial growth mechanism parameters to predict the growth rate of photobioreactors of any size, any lighting conditions, and any flow conditions. This greatly reduces the experimental cost.

[0087] 4. Flexibility and scalability: The present invention is composed of three independent modules coupled with each other, so replacing a single module or adding a new module will not affect the feasibility of the model as a whole, and it is very simple and convenient. For example, the traditional Beer-Lambert law can be improved to consider a more advanced and comprehensive light intensity transmission model for the light intensity transmission model, or the growth mechanism can be further improved from the light intensity single factor full transient mechanism to a dual factor model, or modified to a single factor model affected by other factors as needed.

[0088] In summary, the simulation method based on computational fluid dynamics, light intensity transfer, and microbial growth mechanism can effectively provide important data support, theoretical guidance, and decision-making basis for the scale-up design of photobioreactors, the prediction of operating parameter regulation, and the prediction of growth rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is the overall flow chart of the algorithm of the present invention.

[0090] Figure 2 It is the core flow chart of the algorithm of the present invention.

[0091] Figure 3 This is an example of a selected reactor diagram of the present invention.

[0092] Figure 4 It is the physical model corresponding to the physical diagram of the present invention.

[0093] Figure 5 It is the grid corresponding to the physical model of the present invention. DETAILED DESCRIPTION

[0094] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] A general prediction model for microbial growth rate in photobioreactors, including:

[0096] Pre-processing module: obtain the physical model of the photobioreactor, fluid calculation area, initial value of phase volume fraction, initial value of microbial concentration in the fluid, light intensity boundary conditions, ventilation / agitation mixing conditions during the operation of the photobioreactor, and grid division conditions, which are used to predict the growth rate of microorganisms in the photobioreactor;

[0097] Fluid dynamics calculation module: used to calculate the gas, liquid and solid three-phase flow distribution in the photobioreactor, so as to obtain the accurate flow distribution of solid-phase microorganisms in the photobioreactor;

[0098] Light transmission module: used to quantitatively calculate the precise distribution of light intensity in the photobioreactor;

[0099] Microbial growth rate mechanism module: accurately calculate the growth rate of microorganisms through the light intensity received by the microorganisms;

[0100] The precise flow distribution of solid-phase microorganisms in the photobioreactor obtained by the fluid dynamics calculation module and the precise distribution of light intensity in the photobioreactor obtained by the light transmission module are used to obtain the light intensity received by each solid-phase microorganism in the photobioreactor, thereby calculating the precise microbial growth rate through the microbial growth rate mechanism module, and taking the average of the growth rates of all solid-phase microbial particles in the photobioreactor to obtain the precise growth rate of all microorganisms in the photobioreactor.

[0101] A general method for predicting the growth rate of microorganisms in a photobioreactor, the specific steps are as follows:

[0102] Step 1, based on the shape and operation parameter information of any photobioreactor, a physical model of the photobioreactor is obtained; a fluid calculation region; initial values ​​of velocity, pressure, and phase volume fraction in the fluid calculation region; boundary values ​​of velocity, pressure, and phase volume fraction at the boundary of the fluid calculation region; initial values ​​of microbial concentration in the fluid; light intensity boundary conditions and ventilation / agitation mixing conditions during the operation of the photobioreactor;

[0103] Step 2: Grid division is performed according to the physical model and calculation area obtained in step 1, and a pre-processing module for predicting the growth rate of microorganisms in the photobioreactor is constructed based on the divided grids and the mixing conditions and speed, pressure, initial value and boundary value of the phase volume fraction, initial value conditions of the microorganism concentration in the fluid, and light intensity boundary conditions of the aeration / agitation of the photobioreactor obtained in step 1;

[0104] Step 3, based on the pre-processing module constructed in step 2, calculating the initial values ​​of the growth parameters of the microbial particles in the photobioreactor;

[0105] Step 4: Based on the pre-processing module constructed in step 2 and the initial values ​​of the microbial particle growth parameters calculated in step 3, the microbial growth rate in the photobioreactor is calculated and predicted by computational fluid dynamics, light intensity transmission law and microbial growth mechanism coupling model.

[0106] Furthermore, the specific method of step 2 is:

[0107] 2.1 Basic grid construction

[0108] Based on the optimal principle of meshing, mesh the physical model obtained in step 1:

[0109] According to the mesh size, mesh skewness, mesh aspect ratio, mesh dynamic and static partitions, boundary areas and internal areas; the physical model size and shape information of the photobioreactor is analyzed and calibrated through a meshing platform or meshing software, and the physical model of the photobioreactor is meshed based on the optimal meshing principle; mesh skewness, high aspect ratio, i.e., poor quality meshes and mesh flow boundary layer areas are meshed and re-segmented to improve the meshing quality; when the quality of automatic meshing cannot be improved by the software, manual meshing is performed, and then mesh nodes, boundaries, sizes, volumes, and topological relationship information are extracted to obtain the physical mesh information of the photobioreactor;

[0110] 2.2 Multiphase and stirring grid supplementation

[0111] On the basis of the grid divided in step 2.1, based on the ventilation / stirring operation parameters of the photobioreactor obtained, the air inlet and outlet boundaries or stirring areas of the photobioreactor are divided to obtain multiphase or dynamic grid partition information; when there is a stirring operation, the grid needs to be divided into dynamic and static areas, where the dynamic area needs to include the reactor components that actively cause stirring behavior, and the static area is other areas, and the dynamic and static areas are coupled at the contact interface. In the subsequent calculation steps 3 and 4, the static area grid always remains stationary, and the dynamic area grid actively rotates with the stirring device; when there is a ventilation condition, at the boundary air inlet and outlet, the corresponding area is divided into gas inlet and outlet area boundaries according to the physical model obtained in step 1;

[0112] 2.3 Calculation of initial conditions of light intensity field

[0113] According to the initial values ​​of velocity, pressure, phase volume fraction in the fluid calculation area obtained in step 1 and the boundary values ​​of velocity, pressure, phase volume fraction at the boundary of the fluid calculation area, the parameters of the boundary area and the internal area of ​​the grid are directly set; according to the initial concentration conditions and light intensity boundary conditions obtained in step 1, the initial light intensity field conditions in the photobioreactor are calculated as follows:

[0114]

[0115] Wherein, A0 is absorbance, I0 is incident light intensity, I is local light intensity, K is a constant coefficient related to light wavelength, b is the distance from the incident point to the local point, also known as light path, and c is the concentration of the solution where the light is incident. Formula (1) is the description equation of the Bill-Lambert law, which describes the relationship between the incident light intensity and the outgoing light intensity under different concentrations and light paths. When the incident light is not superimposed on each other, that is, the incident light direction is parallel or the solution concentration is too high for the light to penetrate, then formula (1) is directly used to calculate the light intensity field distribution. When the incident light is superimposed, the basic Bill-Lambert law is improved, and the light intensity field in the photobioreactor is calculated by the following discrete summation method:

[0116]

[0117] Among them, I j represents the local light intensity at the jth grid, n represents a total of n incident light points, I i represents the incident light intensity at the i-th incident point, A0 is the absorbance, and l is the distance from the i-th incident point to the j-th grid, i.e., the light path;

[0118] At this point, the pre-processing module is constructed, and the pre-processing module includes the grid information of the photobioreactor, the initial value and boundary conditions of the model (including velocity, pressure, concentration, and light intensity).

[0119] Furthermore, the specific steps of step 3 are as follows:

[0120] 3.1 Based on the pre-processing module constructed in step 2, the growth rate of microbial particles in the photobioreactor under the steady-state assumption is calculated; that is, the computational fluid dynamics, the law of light intensity transmission, and the steady-state microbial growth mechanism are coupled as a coupling model, and the grid is evolved according to the rotating grid method in the dynamic change of the grid: after each time step, the dynamic area grid will rotate according to a predetermined angle, that is, the grid is iterated at each time step iteration, and the dynamic area grid is changed to avoid grid deformation; then, the flow of culture medium (liquid phase), gas (gas phase), and microorganism (solid phase) in the photobioreactor is calculated by the gas-liquid-solid three-phase model in computational fluid dynamics, where the gas phase and liquid phase are continuous phases and the solid phase is a discrete phase; according to the calculated position of each microorganism in the photobioreactor, the light intensity at the local grid where it is located is obtained, and the growth rate of each microorganism is calculated according to the steady-state growth rate calculation formula; by averaging the growth rates of all microorganisms in the reactor, the average growth rate of microorganisms in the reactor under the steady-state assumption can be obtained;

[0121] Since the mixing tank belongs to the rotating type, the rotating mesh method is used. The mesh will be iterated at each time step iteration, and the mesh of the active stirring blade area will be changed, so as to accurately solve the transient state for each time step. The sliding mesh model is theoretically the most accurate method to simulate the rotating flow and can correctly describe the entire transient startup process. Two separate mesh regions will be generated (the first is the cylindrical mesh containing the rotating parts (inner zone), and the second is the other mesh (outer zone). Each mesh region is bounded by an interface, which connects the relative mesh regions. The two mesh regions will slide relative to each other along the mesh interface in discrete steps. The arbitrary mesh interface (AMI) works by projecting the geometry of one of the interfaces onto the other interface. The two mesh regions are geometrically separated but numerically connected by the AMI, ensuring that the values ​​of the general field are the same on both sides of the interface. The outer mesh of the outer zone does not move during the calculation, and the inner zone rotates during the simulation. After each time step, the inner mesh is rotated by a predetermined angle, so the movement of the gear surface is also correctly reproduced in this way. The advantage of the rotating mesh method is that it ensures the best accuracy while avoiding mesh deformation.

[0122] The specific steps are as follows:

[0123] 3.1.1 Solve the gas and liquid continuous phase flow conditions according to the NS equations. Take the gas and liquid two-phase model described by the VOF method as an example to obtain the gas and liquid continuous phase velocity and pressure flow information:

[0124]

[0125] ρ=αρ1+(1-α)ρ2#(5)

[0126] μ m =αμ1+(1-α)μ2#(6)

[0127]

[0128] 0<α<1#(8)

[0129] in, is the velocity of the fluid; ρ is the density of the fluid; ρ1, ρ2 are the densities of each phase; p is the pressure; τ is the shear stress; g is the gravity; f σ is the surface tension; s is the source term; μ m is the viscosity of the fluid; μ1, μ2 are the viscosities of each phase; α is the volume fraction of one of the phases;

[0130] 3.1.2 After obtaining the velocity and pressure flow information of the gas and liquid continuous phases, the velocity of the discrete phase microorganisms (solid phase) is solved by Newton's second law:

[0131]

[0132] in, is the particle velocity, m is the particle mass; is the force exerted on the particles by the fluid; is the drag force; It is gravity; is the pressure gradient; is the virtual mass force; It is other forces;

[0133] 3.1.3 Obtaining the light intensity field distribution calculated based on the light intensity transmission law

[0134] After solving the velocity of the discrete phase microbial particles in step 3.1.2, the position of the discrete phase microbial particles is known. According to formula (2), the local light intensity basis of each discrete phase microbial particle is obtained to calculate the growth rate of each discrete phase microbial particle in the next step.

[0135] 3.1.4 Describing the steady-state microbial growth mechanism through the photosynthetic factory (PSF), the local growth rate of each discrete phase microbial particle is calculated:

[0136] The photosynthetic factory (PSF) is the sum of the light-harvesting system, reaction center and related devices, which produces corresponding photosynthetic products under the action of given light energy; PSF has three states: resting state, activated state and inhibited state; the probability of PSF being in resting state, activated state and inhibited state is represented by growth mechanism parameters A, B and C respectively;

[0137] The steady-state microbial growth mechanism is derived by adopting the steady-state assumption for the transient microbial growth mechanism, where the transient microbial growth mechanism equation is described as follows:

[0138]

[0139] A+B+C=1 (12)

[0140] μ=kγB-M (13)

[0141] PSF A PSF in the resting state can be stimulated and transferred to the activated state by capturing a photon. The activated PSF has two possible paths: either receive another photon to become inhibited, or transfer the acquired energy to the receptor to start photosynthesis and then return to the resting state. Meanwhile, the inhibited PSF can return to the resting state. Since nutrient saturation is already guaranteed, light is the only variable. The reaction rate of each transition involving photons (i.e., A→B and B→C) is assumed to be a first-order function of the photon flux density. The other two processes are assumed to be zero-order.

[0142] Under the steady-state assumption, the microbial growth mechanism also satisfies the following assumptions:

[0143]

[0144] Substituting equation (14) into equation 10-13, the following steady-state microbial growth mechanism control equation can be obtained. The growth rate of the particle at this time is calculated according to the following equation:

[0145]

[0146] Among them, μ is the growth rate, α, β, δ, γ, k, and M are all model parameters, which are related to the type of microorganism and all external environments except light intensity in which it grows. I represents the light intensity received by the microorganism. The average growth rate of the photobioreactor is obtained by averaging all the solid particles representing the microorganisms.

[0147] 3.2 Solve the average growth parameter as the initial value of the transient solution:

[0148] According to the average growth rate of all microorganisms in the photobioreactor Solving for the average growth parameter

[0149]

[0150] Then, according to the average growth parameters Solving for the average light intensity in the reactor

[0151]

[0152] Among them, formula (17) is about the independent variable The quadratic equation has two solutions. The average light intensity value to be solved is constrained according to the incident light boundary conditions; then another average growth parameter of the microorganism is calculated

[0153]

[0154] The average value of the growth parameter obtained by solving the problem is used as the initial value of the transient growth parameter, and the values ​​of the initial growth parameters A and B of the microorganisms in the reactor are obtained.

[0155] Furthermore, the specific steps of step 4 are as follows:

[0156] According to the coupled model of coupled computational fluid dynamics, light intensity transmission law and steady-state microbial growth mechanism described in step 3.1, the growth parameters of each microbial particle are updated based on the historical growth characteristics of each microbial particle, i.e., the growth mechanism parameters A, B, C at the past moment and the light intensity value obtained in step 3.1.3, as well as the discrete time step set by the coupled model calculation:

[0157]

[0158] A+B+C=1 (21)

[0159] μ=kγB-M (22)

[0160] By explicitly discretizing it with respect to time, we get the following equation:

[0161] A t+1 -A t =Δt(-αI t A t +γB t +δC t ) (twenty three)

[0162] B t+1 -B t =Δt(αI t A t -γB t -βI t B t ) (twenty four)

[0163] C t+1 =1-A t -B t (25)

[0164] A, B, and C are the probabilities of being in each of the three stages described for the growth mechanism of a single microorganism, and are stored separately with each particle. t+1 and t are the values ​​of the current time step and the previous time step respectively, and Δt is the time step length; after updating the growth parameters A, B, and C at the current moment, the growth rate of the particle can be expressed by the following formula:

[0165] μ t+1 =kγB t+1 -M (26)

[0166] After traversing all discrete phase particles in the photobioreactor, the growth rates of all particles are averaged to obtain the overall growth rate of the photobioreactor.

[0167] The overall process framework of the present invention is as follows Figure 1As shown in the figure, the core algorithm flow is as follows Figure 2 shown.

[0168] First, for any photobioreactor, this example uses Figure 3 The physical model of the photobioreactor shown is extracted as follows Figure 4 The mesh division results are as follows: Figure 5 As can be seen from the figure, we select the gas-liquid fluid area inside the reactor as the calculation area, and the physical size and shape and other information are obtained through measurement. At the same time, there is a stirring paddle device in the center of the reactor, and there is also a gas-liquid interface. First, we mesh the entire fluid area, and after obtaining a good quality mesh, we supplement the multiphase inlet and outlet areas and the stirring mesh. The top of the calculation area, that is, the top of the reactor, is set as the gas outlet area. Since ventilation is not used in this example, no air intake setting is performed. Regarding the supplement of the stirring mesh, first we divide a cylindrical area on the outside of the stirring paddle to ensure that the area can completely cover the stirring device. This area is set as the mesh dynamic area, and the remaining area is set as the mesh static area. And the connecting surface between the two partitions is coupled by AMI surface.

[0169] Secondly, the actual initial conditions and boundary conditions are obtained according to the physical conditions and operating conditions. Since the reactor has no inlet setting, the inlet including the wall is set to a no-slip boundary condition, the outlet is set to a pressure outlet boundary condition, the dynamic and static grid coupling boundary is set to a sliding grid velocity boundary condition, the stirring paddle is set to a moving wall velocity boundary condition, and the value is the same as the dynamic grid rotation speed. The stirring shaft is set to a rotating wall velocity boundary condition, and the angular velocity is the same as the dynamic grid rotation speed. At the same time, the phase volume fraction is set according to the inoculated liquid level. The phase below the liquid level is set as the liquid phase, and the phase above the liquid level is set as the gas phase.

[0170] The initial concentration value is the concentration value after inoculation on the zeroth day of the experiment. The light intensity boundary condition adopts the value measured under the experimental light intensity condition. Then the light intensity distribution in the photobioreactor is calculated according to the multi-light source method:

[0171]

[0172] Since this case uses a circular lighting arrangement, the above formula can be simplified to:

[0173]

[0174] in, I0 is the incident light intensity, I(r) is the local light intensity at a distance r from the center of the cylinder, A0 is the absorbance, l(r,θ) is the light path from any incident point to a distance r from the center of the cylinder, and θ is the angle between the line where the light path is located and the diameter of the local point. Based on this, the light intensity field distribution in the reactor can be solved. So far, the pre-processing module of the model has been obtained.

[0175] After obtaining the model pre-processing module, it is necessary to solve the initial value conditions of the microbial particle growth parameters. Here, it is necessary to solve the steady-state growth rate of the model first. First, the velocity distribution of the gas-liquid continuous phase fluid is solved by the following set of equations:

[0176]

[0177] ρ=αρ 1 +(1-α)ρ2#

[0178] μ m =αμ1+(1-α)μ2#

[0179]

[0180] 0<α<1#

[0181] After obtaining the velocity distribution of the continuous phase fluid, the velocity and position distribution of the discrete phase particles are calculated by the following equations:

[0182]

[0183] The local light intensity of each discrete phase microorganism particle is obtained by calculating the velocity position and other flow characteristics of the discrete phase microorganism (solid phase) in computational fluid dynamics, and the growth rate of the particle is solved according to the local light intensity of the grid where all particles are located at different times:

[0184]

[0185] And average all growth rates, select the value of the stable stage as the steady-state growth rate of the reactor. Then solve the initial value of the growth parameter B by the following formula:

[0186]

[0187] After obtaining the initial value of B, the characteristic light intensity is solved by the following formula:

[0188]

[0189] The two solved light intensity values ​​are constrained according to the surface light intensity, and the characteristic light intensity value that conforms to the reality is selected. Then the growth characteristic parameter A is solved by the selected characteristic light intensity:

[0190]

[0191] At this point, the initial values ​​of all growth characteristic parameters are obtained, and the growth characteristic parameters of all discrete phase particles representing microorganisms in the reactor are assigned this value, and then the transient growth rate prediction calculation is started. The calculation process is as follows Figure 2 As shown, the PIMPLE velocity-pressure loop iteration algorithm is first used to solve the NS equations. The inner loop includes dynamic feature evolution of the grid, changing the grid topology and shape, and then calculating the volume fraction of the gas-liquid phase. Then, the velocity field and pressure field of the continuous phase are iteratively solved, and the inner loop ends. Then, the force on each particle is calculated according to the grid where each particle is located. After obtaining the force, the particle acceleration is calculated, and then the velocity and coordinates are updated. Combined with the time each particle stays under the light intensity where it is located, the growth parameters of the particle are updated by the following formula:

[0192] A t+1 -A t =Δt(-αI t A t +γB t +δC t )

[0193] B t+1 -B t =Δt(αI t A t -γB t -βI t B t )

[0194] C t+1 =1-A t -B t

[0195] The growth rate is calculated for the updated parameters using the following formula:

[0196] μ t+1 =kγB t+1 -M

[0197] The dynamically stable value was selected as the growth rate value under each condition.

[0198] The comparison between the predicted values ​​of this model and the experimental values ​​and the aforementioned steady-state values ​​is shown in the following table:

[0199] Growth rate OD=0.1 OD=0.5 OD=1.0 OD=1.5 This model 1.95e-5 7.0e-6 2.0e-6 -5.0e-7 Steady-state model 1.82e-5 3e-6 -0.5e-6 -1.7e-6 Experimental value 1.99e-5 6.85e-6 2.10e-6 <0

[0200] By comparison, it can be seen that the predicted growth rates of the transient model at the four characteristic concentrations selected during the entire growth stage of the experiment are close to the growth rates measured by the experimental values, especially for medium and high concentrations (OD = 0.5, 1.0), which greatly reduces the model prediction error compared to the steady-state model. This also verifies the limitations of the aforementioned steady-state assumption, and that when microorganisms are exposed to changing light intensities, they can instantly transform into steady-state growth characteristics under the light intensity. This is only applicable to low concentrations with relatively uniform light intensity distribution. For medium and high concentrations, the accuracy of model prediction will be greatly reduced due to the strong non-uniformity of light intensity distribution. This model greatly improves the accuracy of model prediction by eliminating the steady-state assumption and performing calculations based on the full transient growth mechanism.

Claims

1. A general prediction model for microbial growth rate in a photobioreactor, characterized in that: include: Pre-processing module: obtain the physical model of the photobioreactor, fluid calculation area, initial value of phase volume fraction, initial value of microbial concentration in the fluid, light intensity boundary conditions, ventilation / agitation mixing conditions during the operation of the photobioreactor, and grid division conditions, which are used to predict the growth rate of microorganisms in the photobioreactor; Fluid dynamics calculation module: used to calculate the flow distribution of gas, liquid continuous phase and solid discrete phase in the photobioreactor, so as to obtain the accurate flow distribution of solid phase microorganisms in the photobioreactor; Light transmission module: used to quantitatively calculate the precise distribution of light intensity in the photobioreactor; Microbial growth rate mechanism module: accurately calculate the growth rate of microorganisms through the light intensity received by the microorganisms; The precise flow distribution of solid-phase microorganisms in the photobioreactor obtained by the fluid dynamics calculation module and the precise distribution of light intensity in the photobioreactor obtained by the light transmission module are used to obtain the light intensity received by each solid-phase microorganism in the photobioreactor, thereby calculating the precise microbial growth rate through the microbial growth rate mechanism module, and taking the average of the growth rates of all solid-phase microbial particles in the photobioreactor to obtain the precise growth rate of all microorganisms in the photobioreactor.

2. A general method for predicting the growth rate of microorganisms in a photobioreactor, characterized in that: The specific steps are as follows: Step 1, based on the shape and operation parameter information of any photobioreactor, a physical model of the photobioreactor is obtained; a fluid calculation region; initial values ​​of velocity, pressure, and phase volume fraction in the fluid calculation region; boundary values ​​of velocity, pressure, and phase volume fraction at the boundary of the fluid calculation region; initial values ​​of microbial concentration in the fluid; light intensity boundary conditions and ventilation / agitation mixing conditions during the operation of the photobioreactor; Step 2: Grid division is performed according to the physical model and fluid calculation area obtained in step 1, and a pre-processing module for predicting the growth rate of microorganisms in the photobioreactor is constructed based on the divided grids, the mixing conditions of aeration / agitation during the operation of the photobioreactor obtained in step 1, the velocity, pressure, and initial values ​​of phase volume fraction in the fluid calculation area, the velocity, pressure, and phase volume fraction boundary values ​​at the boundary of the fluid calculation area, the initial value of the concentration of microorganisms in the fluid, and the light intensity boundary conditions; Step 3: Based on the pre-processing module constructed in step 2, the initial values ​​of the growth parameters of the microbial particles in the photobioreactor are calculated; Step 4: Based on the pre-processing module constructed in step 2 and the initial values ​​of the microbial particle growth parameters calculated in step 3, the microbial growth rate in the photobioreactor is calculated and predicted by computational fluid dynamics, light intensity transmission law and microbial growth mechanism coupling model.

3. A general method for predicting the growth rate of microorganisms in a photobioreactor according to claim 2, characterized in that: The specific method of step 2 is: Step 2.1 Basic mesh construction Based on the optimal principle of meshing, mesh the physical model obtained in step 1: The physical model dimensions of the photobioreactor are calibrated according to the mesh size, mesh skewness, mesh aspect ratio, mesh dynamic and static partitions, boundary areas and internal areas; the physical model of the photobioreactor is meshed based on the optimal meshing principle; meshes with high mesh skewness and aspect ratio, i.e., poor quality, and mesh flow boundary areas are meshed and re-segmented to improve the meshing quality, and then the nodes, boundaries, sizes, volumes, and topological relationship information of the mesh are extracted to obtain the physical mesh information of the photobioreactor; Step 2.2 Multiphase, stirring grid supplementation On the basis of the grid divided in step 2.1, based on the ventilation / stirring operation parameters of the photobioreactor obtained in step 1, the air inlet and outlet boundaries and / or stirring areas of the photobioreactor are divided to obtain multiphase and / or dynamic grid partition information; when there is a stirring operation, the grid needs to be divided into dynamic and static areas, wherein the dynamic area includes the stirring components of the photobioreactor, and the static area is other areas, and the dynamic and static areas are coupled at the contact interface. In the subsequent calculation process, the static area grid always remains stationary, and the dynamic area grid actively rotates with the stirring components; when there is a ventilation condition, at the boundary air inlet and outlet, the corresponding area is divided into gas inlet and outlet area boundaries according to the physical model obtained in step 1; Step 2.3 Calculation of initial conditions of light intensity field According to the initial values ​​of velocity, pressure, phase volume fraction in the fluid calculation area obtained in step 1 and the boundary values ​​of velocity, pressure, phase volume fraction at the boundary of the fluid calculation area, the parameters of the boundary area and the internal area of ​​the grid are directly set; according to the initial concentration conditions and light intensity boundary conditions obtained in step 1, the initial light intensity field conditions in the photobioreactor are calculated as follows: Wherein, A0 is absorbance, I0 is incident light intensity, I is local light intensity, K is a constant coefficient related to light wavelength, b is the distance from the incident point to the local point, also known as light path, and c is the concentration of the solution where the light is incident. Formula (1) is the description equation of the Bill-Lambert law, which describes the relationship between the incident light intensity and the outgoing light intensity under different concentrations and light paths. When the incident light is not superimposed on each other, that is, the incident light direction is parallel or the solution concentration is too high for the light to penetrate, then formula (1) is directly used to calculate the light intensity field distribution. When the incident light is superimposed, the basic Bill-Lambert law is improved, and the light intensity field in the photobioreactor is calculated by the following discrete summation method: Among them, I j represents the local light intensity at the jth grid, n represents a total of n incident light points, I i represents the incident light intensity at the i-th incident point, A0 is the absorbance, and l is the distance from the i-th incident point to the j-th grid, i.e., the light path; At this point, the pre-processing module is constructed, which includes the grid information of the photobioreactor; the initial values ​​of velocity, pressure, and phase volume fraction in the fluid calculation area; and the boundary values ​​of velocity, pressure, and phase volume fraction and light intensity boundary conditions at the boundary of the fluid calculation area.

4. A general method for predicting the growth rate of microorganisms in a photobioreactor according to claim 2, characterized in that: The specific steps of step 3 are as follows: Step 3.1 Based on the pre-processing module constructed in step 2, calculate the growth rate of microbial particles in the photobioreactor under the steady-state assumption: that is, couple computational fluid dynamics, light intensity transmission law and steady-state microbial growth mechanism as a coupling model, and evolve the grid according to the rotating grid method in the dynamic change of the grid: after each time step, the dynamic area grid will rotate according to a predetermined angle, that is, the grid is iterated at each time step iteration, the dynamic area grid is changed, and the static area grid is not moved, and the connection surface between the dynamic area grid and the static area grid is coupled by an arbitrary grid interface (AMI) surface to avoid grid deformation; Subsequently, the flow conditions of the culture medium (liquid phase), gas (gas phase), and microorganism (solid phase) in the photobioreactor are calculated by the gas-liquid-solid three-phase model in computational fluid dynamics, where the gas phase and liquid phase are continuous phases and the solid phase is a discrete phase; the light intensity at the local grid where each microbial particle is located is obtained according to the position of each microbial particle in the photobioreactor calculated by fluid dynamics, and the growth rate of each microbial particle is calculated according to the steady-state growth rate calculation formula; the average growth rate of the microbial particles in the reactor under the steady-state assumption is obtained by averaging the growth rates of all microbial particles in the reactor; Step 3.2 solves the average growth parameter as the initial value of the transient solution.

5. A general method for predicting the growth rate of microorganisms in a photobioreactor according to claim 4, characterized in that: The specific process of step 3.1 is as follows: Step 3.1.1 Solve the gas and liquid continuous phase flow conditions according to the NS equation to obtain the gas and liquid continuous phase velocity and pressure flow information: ρ=αρ1+(1-α)ρ2 (5) m m =am1+(1-a)m2 (6) 0<α<1 (8) in, is the velocity of the fluid; ρ is the density of the fluid; ρ1, ρ2 are the densities of each phase; p is the pressure; τ is the shear stress; g is the gravity; f σ is the surface tension; s is the source term; μ m is the viscosity of the fluid; μ1, μ2 are the viscosities of each phase; α is the volume fraction of one of the phases; Step 3.1.2 After obtaining the velocity and pressure flow information of the gas and liquid continuous phases, the velocity of the discrete phase microbial particles is solved using Newton's second law: in, is the particle velocity, m is the particle mass; is the force exerted on the particles by the fluid; is the drag force; It is gravity; is the pressure gradient; is the virtual mass force; It is other forces; Step 3.1.3 Obtain the light intensity field distribution calculated based on the light intensity transmission law After solving the velocity of the discrete phase microbial particles in step 3.1.2, the position of the discrete phase microbial particles is known. According to formula (2), the local light intensity basis of each discrete phase microbial particle is obtained to calculate the growth rate of each discrete phase microbial particle in the next step. Step 3.1.4 describes the steady-state microbial growth mechanism through the photosynthetic factory (PSF) to calculate the local growth rate of each discrete phase microbial particle: The photosynthetic factory (PSF) is the sum of the light-harvesting system, reaction center and related devices, which produces corresponding photosynthetic products under the action of given light energy; PSF has three states: resting state, activated state and inhibited state; the probability of PSF being in resting state, activated state and inhibited state is represented by growth mechanism parameters A, B and C respectively; The steady-state microbial growth mechanism is derived by adopting the steady-state assumption for the transient microbial growth mechanism, where the transient microbial growth mechanism equation is described as follows: A+B+C=1 (12) μ=kγB-M (13) Under the steady-state assumption, the microbial growth mechanism also satisfies the following assumptions: Substituting equation (14) into equations (10)-(13) can obtain the following steady-state microbial growth mechanism control equation, and the growth rate of the particle at this time is calculated according to the following equation: Among them, μ is the growth rate, α, β, δ, γ, k, and M are all model parameters, I represents the light intensity received by the microorganism, and the average growth rate of the photobioreactor is obtained by averaging all the solid particles representing the microorganisms.

6. A general method for predicting the growth rate of microorganisms in a photobioreactor according to claim 4, characterized in that: The specific process of step 3.2 is as follows: According to the average growth rate of all microorganisms in the photobioreactor Solving for the average growth parameter Then, according to the average growth parameters Solving for the average light intensity in the reactor Among them, formula (17) is about the independent variable The quadratic equation has two solutions. The average light intensity value to be solved is constrained according to the incident light boundary conditions; then another average growth parameter of the microorganism is calculated The average value of the growth parameter obtained by solving is used as the initial value of the transient growth parameter, and thus the initial values ​​of the growth mechanism parameters A and B of the microorganisms in the photobioreactor are obtained.

7. A universal method for predicting the growth rate of microorganisms in a photobioreactor according to claim 2, characterized in that: The specific steps of step 4 are as follows: According to the coupled model of coupled computational fluid dynamics, light intensity transmission law and steady-state microbial growth mechanism described in step 3.1, the growth parameters of each microbial particle are updated based on the historical growth characteristics of each microbial particle, i.e., the growth mechanism parameters A, B, C at the past moment and the light intensity value obtained in step 3.1.3, as well as the discrete time step set by the coupled model calculation: A+B+C=1 (21) μ=kγB-M (22) Discretize it explicitly with respect to time: A t+1 -A t =Δt(-αI t A t +γB t +δC t ) (23) B t+1 -B t =Δt(αI t A t -γB t -βI t B t ) (24) C t+1 =1-A t -B t (25) Among them, A, B, and C are stored separately with each particle, and the superscript t+1 and t are the values ​​of the current time step and the previous time step respectively, and Δt is the time step length; after updating the growth mechanism parameters A, B, and C at the current moment, the growth rate of the particle can be expressed by the following formula: m t+1 =kγB t+1 -M (26) After traversing all discrete phase particles in the photobioreactor, the growth rates of all particles are averaged to obtain the overall growth rate of the photobioreactor.

Images