Microorganism consumption and carbon dioxide ventilation balance-based prediction model and prediction method for carbon dioxide concentration field in photobioreactor

Abstract

The invention discloses a prediction model and a prediction method for a carbon dioxide concentration field in a photobioreactor based on microbial consumption and carbon dioxide ventilation balance. The model comprises a pretreatment module, a fluid dynamics calculation module, an illumination transmission module, a microbial growth rate mechanism module and a mass transfer module, the method comprises the following steps: firstly, obtaining a physical model of the photobioreactor based on the appearance and operation parameter information of the photobioreactor; secondly, according to the obtained physical model and the calculation area, grid division is carried out, and a pretreatment module used for predicting and calculating the growth rate of microorganisms in the photobioreactor is constructed; then, based on the pretreatment module, describing flow field calculation in the photobioreactor by using an Euler method, describing a growth rate and a flow field flow velocity of microbial particles by using a Lagrange method, calculating a carbon dioxide consumption rate based on the growth rate, and constructing a carbon dioxide consumption matrix under an Euler framework from a Lagrange framework; the method is used for subsequent mass transfer equation coupling; finally, the constructed carbon dioxide consumption rate matrix is coupled with the flow velocity of the flow field to construct a mass transfer equation which is used for predicting the distribution of the carbon dioxide concentration field in the photobioreactor. The method has the advantages of high accuracy and good universality.

Description

Technical Field

[0001] The invention relates to the technical field of microbial culture photobioreactor design and model calculation, and in particular to a carbon dioxide concentration field prediction model and prediction method in a photobioreactor based on microbial consumption and carbon dioxide ventilation balance. Background Art

[0002] Currently, people are seeking integrated technologies for the cultivation of microalgae, aiming to separate various bioactive substances from biomass in order to improve the profitability of algae production. To achieve this development, the high productivity of industrial cultivation systems must be accompanied by the ability to control the biosynthesis of biologically valuable compounds under intensive cultivation growth conditions. During the cultivation process, carbon dioxide occupies the main source of nutrients, so the supply method and strategy of carbon dioxide are very important for the cultivation efficiency and carbon dioxide fixation efficiency. The current mainstream carbon dioxide supply method is aeration mass transfer, but the existing aeration strategy is rough, wastes a lot of energy and materials, causes the cultivation to be imprecise, produces a large amount of carbon escape, and greatly reduces the carbon fixation efficiency. Therefore, a general carbon dioxide aeration strategy prediction method based on material flow balance needs to be studied.

[0003] In order to capture carbon, aeration is necessary whether it is cultivated in an open or closed photobioreactor. The introduction of excess CO2 without considering carbon consumption will not only lead to CO2 overflow and reduced carbon fixation efficiency, but also reduce the pH of the suspension, affecting the growth of microorganisms in the photobioreactor. When the CO2 input rate is lower than the CO2 consumption rate, the solution is not mixed evenly, and the growth rate in the photobioreactor is reduced due to insufficient carbon source supply, thereby reducing the carbon fixation rate. However, there is currently a lack of guidance on determining the optimal and balanced aeration strategy to determine the balance between the two. Therefore, progress in the prediction of aeration strategies for photobioreactors is a key leap for future applications in large-scale carbon capture. Most of the existing aeration strategy studies are based on empirical experiments, treating the culture as a process system, that is, considering the photobioreactor as a uniformly distributed system without large gradients and low versatility. However, in large-scale photobioreactors, even in small-scale photobioreactors, this assumption does not conform to the actual situation. Some researchers have conducted numerical simulation studies on carbon dioxide mass transfer. Most of these studies only considered the effect of ventilation supplementation, without considering the effect of consumption. Only a few researchers considered the effects of both ventilation and consumption. In these studies that considered the effect of microbial consumption, almost all of them approximated the microbial consumption as a uniform and stable field quantity, which is inconsistent with the high gradient in the photobioreactor during large-scale culture. In addition, due to the existence of light gradients, the growth rate of microorganisms at different positions in the suspension is different, so the consumption rate of carbon dioxide is also different, which means that consumption cannot be regarded as a constant. Summary of the invention

[0004] In order to fill the gap in this field, the purpose of the present invention is to provide a prediction model and method for the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation. On the basis of CO2 gas-liquid mass transfer, a universal growth rate prediction model for microorganisms is added, so it not only includes gas-liquid mass transfer, but also includes the consumption of microbial particles, and the optimal ventilation strategy is determined by the balance between the two; the CO2 consumption of the microorganisms is calculated according to the growth rate of the algae particles, and the growth rate is related to the light intensity received by the particles, and the light intensity received by the particles is related to the light intensity transmission and the particle flow. Through the universal growth rate prediction model for microorganisms, photobioreactors of various shapes, lighting parameters and aeration strategies are supported, and the CO2 concentration distribution can be intuitively observed.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] A prediction model for carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation, comprising:

[0007] Pre-processing module: used to describe the physical model, calculation area, initial value conditions, boundary conditions, ventilation conditions, and grid division conditions of the photobioreactor;

[0008] Fluid dynamics calculation module: used to calculate the flow distribution of gas, liquid and solid phases in the photobioreactor, so as to obtain the accurate flow distribution of solid phase microorganisms in the photobioreactor. The gas and liquid phases are described as continuous phases by Euler method, and the solid phase is described as discrete phase by Lagrangian method.

[0009] Light transmission module: used to quantitatively calculate the precise distribution of light intensity in the photobioreactor;

[0010] Microbial growth rate mechanism module: accurately calculate the growth rate of microorganisms through the light intensity received by the microorganisms;

[0011] Mass transfer module: used to quantitatively calculate the carbon dioxide concentration field under the combined effects of gas-liquid mass transfer supplement caused by ventilation and microbial consumption under flow conditions.

[0012] Based on the constructed pre-processing module, the precise flow distribution of solid-phase microorganisms in the photobioreactor is obtained through the fluid dynamics calculation module, and the precise distribution of light intensity in the photobioreactor is obtained through the light transmission module, thereby obtaining the precise light intensity received by each solid-phase microorganism in the photobioreactor; the precise microbial growth rate is calculated through the microbial growth rate mechanism module, the amount of carbon dioxide consumed in the growth process is calculated according to the growth rate, and the carbon dioxide concentration matrix consumed by the microalgae growth is constructed, and then the matrix is ​​added as a source term to the carbon dioxide mass transfer equation of the mass transfer module to calculate the carbon dioxide concentration field in the microbial photobioreactor.

[0013] A method for predicting the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation, the specific steps are as follows:

[0014] Step 1, based on the appearance and operation parameter information of any photobioreactor, obtain the physical model of the photobioreactor, the fluid calculation area, the velocity, pressure, phase volume fraction, and initial value of carbon dioxide concentration in the fluid calculation area, the velocity, pressure, phase volume fraction, and carbon dioxide concentration boundary value at the boundary of the fluid calculation area, the initial value condition of microorganism concentration in the fluid, the light intensity boundary condition, and the ventilation condition during the operation of the photobioreactor;

[0015] Step 2: Grid division is performed according to the physical model and fluid calculation area obtained in step 1. Based on the divided grids, the ventilation conditions during the operation of the photobioreactor and the velocity, pressure, phase volume fraction, initial value and boundary value of carbon dioxide, initial value conditions of microbial concentration in the fluid, light intensity boundary conditions, and initial value of the light intensity field calculated by the light transmission module are obtained in step 1, and a pre-processing module for predicting the growth rate of microorganisms in the photobioreactor is constructed;

[0016] Step 3: Based on the pre-processing module constructed in step 2, the flow field in the photobioreactor is described by the Euler method through the fluid dynamics calculation module, the flow field velocity of the microbial particles is described by the Lagrangian method, and the growth rate of the microorganisms is calculated through the microbial growth rate mechanism module. The carbon dioxide consumption rate is calculated based on the growth rate, and the carbon dioxide consumption matrix under the Euler framework is constructed from the Lagrangian framework. Used for subsequent coupling of carbon dioxide mass transfer equation;

[0017] Step 4: Construct the carbon dioxide mass transfer equation based on the pre-treatment module in step 2, and convert the carbon dioxide consumption matrix of microorganisms constructed in step 3 into By adding source terms to the carbon dioxide mass transfer equation, a mass transfer module is constructed to predict the distribution of carbon dioxide concentration field in the photobioreactor.

[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 boundary area and internal area; 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 grid supplement

[0023] Based on the grid divided in step 2.1, the air inlet and outlet boundary areas of the photobioreactor are divided based on the ventilation parameters of the photobioreactor obtained in step 1 to obtain multiphase grid partition information; at the boundary air inlet and air outlet, the corresponding areas are 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 velocity, pressure, phase volume fraction, initial value of carbon dioxide concentration in the fluid calculation area and the velocity, pressure, phase volume fraction, and boundary value of carbon dioxide concentration at the boundary of the fluid calculation area obtained in step 1, the parameters of the boundary area and the internal area of ​​the grid are directly set; according to the initial value conditions of microbial concentration in the fluid and the light intensity boundary conditions obtained in step 1, the initial value of the light intensity field is calculated through the light transmission module, and the method is as follows:

[0026]

[0027] Where 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 velocity, pressure, phase volume fraction, and initial value of carbon dioxide concentration in the fluid calculation area; the velocity, pressure, phase volume fraction, carbon dioxide concentration boundary value 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 Calculate the growth rate of microbial particles in the photobioreactor:

[0033] The coupling computational fluid dynamics, light intensity transmission law, and steady-state microbial growth mechanism are used as a coupling model; the gas-liquid-solid three-phase model in computational fluid dynamics calculates the flow of culture medium (liquid phase), gas (gas phase), and microorganism (solid phase) in the photobioreactor, where the gas phase and liquid phase are continuous phases and the solid phase is a discrete phase; the position of each microbial particle in the photobioreactor calculated by the fluid dynamics calculation module is used to obtain the light intensity at the local grid where it is located, and the growth rate of each microbial particle is calculated by the Lagrangian method according to the steady-state growth rate calculation formula; the average growth rate of all microbial particles in the photobioreactor is averaged to obtain the average growth rate of microbial particles in the photobioreactor under the steady-state assumption. The specific steps are as follows:

[0034] Step 3.1.1 Solve the gas and liquid continuous phase flow conditions according to the NS equations using the Euler method to obtain the gas and liquid continuous phase velocity and pressure flow information:

[0035]

[0036] ρ=αρ1+(1-α)ρ2 (5)

[0037] μ m =αμ1+(1-α)μ2 (6)

[0038]

[0039] 0<α<1 (8)

[0040] 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; fσ 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;

[0041] 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 by Newton's second law using the Lagrangian method:

[0042]

[0043] 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;

[0044] Step 3.1.3 Obtain the light intensity field distribution calculated based on the light intensity transmission law

[0045] 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.

[0046] 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:

[0047] 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;

[0048] 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:

[0049]

[0050] A+B+C=1 (12)

[0051] μ=kγB-M (13)

[0052] Under the steady-state assumption, the microbial growth mechanism also satisfies the following assumptions:

[0053]

[0054] 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:

[0055]

[0056] 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;

[0057] 3.2 Solving the CO2 consumption rate based on growth rate and microbial concentration

[0058] First, the mass concentration c of the solution is obtained according to the corresponding relationship between the absorbance value of the solution and the absorbance and concentration in formula (1). Then, the mass concentration of the microorganisms that grow within the time dt is calculated according to the specific growth rate μ as dc = μc·dt. According to the carbon content C per unit mass of the microorganisms, m The amount of carbon consumed in time dt can be obtained as C consumption =dc·C m , through the equation The amount of carbon consumed is converted into the amount of CO2 consumed, where is the relative molecular weight of CO2, Ar C is the relative atomic weight of carbon; the fluid concentration field is described using the Euler framework, and the data in the Lagrangian framework is constructed as a matrix under the Euler framework, as follows:

[0059] First, construct a carbon dioxide consumption matrix with the same scale as the number of grids. And initialize each element in the matrix to 0; then traverse all particles, for each traversed particle, search the grid where it is located, and then add the carbon dioxide concentration value consumed by its growth to the carbon dioxide consumption matrix under the grid index; when the traversal is completed, all carbon dioxide consumption values ​​are added to the carbon dioxide consumption matrix.

[0060] Furthermore, the carbon dioxide mass transfer equation in step 4 is described as follows: wherein the carbon dioxide consumption matrix of microorganisms is for:

[0061]

[0062] D=αD L +(1-α)D G (18)

[0063]

[0064] in, is the concentration of carbon dioxide in the gas phase, is the concentration of carbon dioxide in the liquid phase, is the average carbon dioxide concentration, U is the fluid velocity, D is the average diffusion coefficient, and D L is the liquid phase diffusion coefficient, D G is the gas phase diffusion coefficient, α is the liquid phase volume fraction, and H is the Henry constant.

[0065] Among them, the flux J of gas-liquid transfer is determined by the following formula:

[0066]

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] The modeling of the present invention is based on the laws of physics and has strong versatility. It combines computational fluid dynamics, the law of light intensity transmission, the microbial growth mechanism and the law of mass transfer to expand the versatility of growth rate and carbon dioxide concentration prediction, so that the model can more accurately predict the microbial growth rate and carbon dioxide concentration field in any photobioreactor. Specifically, the simulation method based on computational fluid dynamics, the law of light intensity transmission, the microbial growth mechanism and the law of mass transfer has important advantages and beneficial effects in the following aspects:

[0069] 1. High-precision and high-versatility prediction capability: By adopting a model method based on computational fluid dynamics, light intensity transmission, microbial growth mechanism, and mass transfer, the model as a whole is described by a physical model, which can more accurately and universally predict the microbial growth rate and carbon dioxide replenishment and consumption rate in the photobioreactor; and the carbon dioxide consumption distribution and carbon dioxide concentration distribution can be intuitively seen, which is of great significance for the scale-up design of the photobioreactor and the dynamic regulation of operating parameters during operation: Based on the model, the accurate prediction of the carbon dioxide consumption distribution and carbon dioxide concentration distribution in the photobioreactor can provide targeted improvements in the scale-up design of the photobioreactor, making the carbon dioxide consumption distribution and carbon dioxide concentration distribution in the photobioreactor more uniform, significantly reducing the cost and time of traditional methods for experimental attempts on reactors of different shapes and sizes; at the same time, based on the model's balanced prediction of carbon dioxide ventilation replenishment and microbial growth consumption, the ventilation rate and ventilation concentration with the highest carbon dioxide ventilation replenishment efficiency can be targetedly predicted under different microbial concentrations and surface light intensities during the operation of the reactor, so as to achieve the purpose of reducing costs and increasing efficiency.

[0070] 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 microbial growth rate, carbon dioxide consumption rate and carbon dioxide concentration distribution in photobioreactors and factors such as shape, size, flow boundary conditions, and light intensity boundary conditions, and provide a deeper understanding for the scale-up design of photobioreactors and the dynamic regulation of operating parameters: the model conducts a refined analysis of flow, light intensity, growth, and carbon dioxide mass transfer, and transforms the photobioreactor from the traditional overall one-dimensional black box mode to a local multi-dimensional refined distribution mode; therefore, the local flow, light intensity, growth, and carbon dioxide mass transfer in the photobioreactor can be accurately predicted and analyzed. At the same time, the description based on physics and mechanism can provide more intuitive and realistic analysis details, thereby providing theoretical guidance for the scale-up design of photobioreactors and the dynamic regulation of operating parameters.

[0071] 3. Low cost: Compared with the traditional empirical coupling model, the model parameters need to be experimentally measured for different photobioreactors, and the experiment needs to be carried out under photobioreactors of the same size and shape. This method only needs to obtain the microbial growth mechanism parameters, and can predict the growth rate, carbon dioxide consumption rate and carbon dioxide concentration distribution of photobioreactors of any size, any lighting conditions and any flow conditions. It greatly reduces the experimental cost.

[0072] 4. Flexibility and scalability: This method consists of four independent modules coupled to 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 Bill-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 single-factor full transient mechanism of light intensity to a two-factor model, or modified to a single-factor model affected by other factors as needed.

[0073] In summary, the simulation method constructed based on computational fluid dynamics, light intensity transfer, microbial growth mechanism, and mass transfer can effectively provide important data support, theoretical guidance, and decision-making basis for the scale-up design of photobioreactors, the prediction of operation parameter regulation, the prediction of growth rate, and the prediction of carbon dioxide consumption and supplement balance. The model conducts a refined analysis of flow, light intensity, growth, and carbon dioxide mass transfer, and can accurately predict and analyze the local flow, light intensity, growth, and carbon dioxide mass transfer in the photobioreactor; at the same time, the description based on physics and mechanism can provide more intuitive and closer to the actual law of analysis details, and provide theoretical guidance for the scale-up design of photobioreactors and the dynamic regulation of operation parameters. Based on the model's prediction of the balance of carbon dioxide ventilation supplement and microbial growth consumption, the ventilation rate and ventilation concentration with the highest carbon dioxide ventilation supplement efficiency can be specifically predicted under different microbial concentrations and surface light intensities during the operation of the reactor, so as to achieve the purpose of reducing costs and increasing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The figure is a flow chart of the method of the present invention.

[0075] Figure 2 Schematic diagram of a physical model of an embodiment of the present invention.

[0076] Figure 3 This is the light intensity distribution diagram of the present invention.

[0077] Figure 4 This is a graph of the carbon dioxide consumption rate of the present invention.

[0078] Figure 5 This is a heat map of the carbon dioxide concentration field distribution during the ventilation process of an embodiment of the present invention.

[0079] Figure 6 This is a graph showing the change in the dimensionless concentration of dissolved carbon dioxide in water over time under various ventilation conditions according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the cases 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 them. 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.

[0081] The overall process algorithm is as follows Figure 1 shown.

[0082] A prediction model for carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation, comprising:

[0083] Pre-processing module: used to describe the physical model, calculation area, initial value conditions, boundary conditions, ventilation conditions, and grid division conditions of the photobioreactor;

[0084] Fluid dynamics calculation module: used to calculate the flow distribution of gas, liquid and solid phases in the photobioreactor, so as to obtain the accurate flow distribution of solid phase microorganisms in the photobioreactor. The gas and liquid phases are described as continuous phases by Euler method, and the solid phase is described as discrete phase by Lagrangian method.

[0085] Light transmission module: used to quantitatively calculate the precise distribution of light intensity in the photobioreactor;

[0086] Microbial growth rate mechanism module: accurately calculate the growth rate of microorganisms through the light intensity received by the microorganisms;

[0087] Mass transfer module: used to quantitatively calculate the carbon dioxide concentration field under the combined effects of gas-liquid mass transfer supplement caused by ventilation and microbial consumption under flow conditions.

[0088] Based on the constructed pre-processing module, the precise flow distribution of solid-phase microorganisms in the photobioreactor is obtained through the fluid dynamics calculation module, and the precise distribution of light intensity in the photobioreactor is obtained through the light transmission module, thereby obtaining the precise light intensity received by each solid-phase microorganism in the photobioreactor; the precise microbial growth rate is calculated through the microbial growth rate mechanism module, the amount of carbon dioxide consumed in the growth process is calculated according to the growth rate, and the carbon dioxide concentration matrix consumed by the microalgae growth is constructed, and then the matrix is ​​added as a source term to the carbon dioxide mass transfer equation to calculate the carbon dioxide concentration field in the microbial photobioreactor.

[0089] A general method for predicting the growth rate of microorganisms in a photobioreactor, the specific steps are as follows: step 1, based on the appearance and operation parameter information of any photobioreactor, a physical model of the photobioreactor is obtained; a fluid calculation region; a velocity, pressure, phase volume fraction, and initial values ​​of carbon dioxide concentration in the fluid calculation region; a velocity, pressure, phase volume fraction, and carbon dioxide concentration boundary value at the boundary of the fluid calculation region; initial value conditions of microorganism concentration in the fluid; light intensity boundary conditions and ventilation conditions during the operation of the photobioreactor;

[0090] Step 2: Grid division is performed according to the physical model and fluid calculation area obtained in step 1. Based on the divided grids, the ventilation conditions during the operation of the photobioreactor and the velocity, pressure, phase volume fraction, initial value and boundary value of carbon dioxide, initial value conditions of microbial concentration in the fluid, light intensity boundary conditions, and initial value of the light intensity field calculated by the light transmission module are obtained in step 1, and a pre-processing module for predicting the growth rate of microorganisms in the photobioreactor is constructed;

[0091] Step 2.1 Basic mesh construction

[0092] Based on the optimal principle of meshing, mesh the physical model obtained in step 1;

[0093] The physical model dimensions of the photobioreactor are calibrated according to the mesh size, mesh skewness, mesh aspect ratio, mesh boundary area and internal area; 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;

[0094] Step 2.2 Multiphase grid supplement

[0095] Based on the grid divided in step 2.1, the air inlet and outlet boundary areas of the photobioreactor are divided based on the ventilation parameters of the photobioreactor obtained in step 1 to obtain multiphase grid partition information; at the boundary air inlet and air outlet, the corresponding areas are divided into gas inlet and outlet area boundaries according to the physical model obtained in step 1;

[0096] Step 2.3 Calculation of initial conditions of light intensity field

[0097] According to the velocity, pressure, phase volume fraction, initial value of carbon dioxide concentration in the fluid calculation area and the velocity, pressure, phase volume fraction, and boundary value of carbon dioxide concentration at the boundary of the fluid calculation area obtained in step 1, the parameters of the boundary area and the internal area of ​​the grid are directly set; according to the initial value conditions of microbial concentration in the fluid and the light intensity boundary conditions obtained in step 1, the initial value of the light intensity field is calculated through the light transmission module, and the method is as follows:

[0098]

[0099] Where 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:

[0100]

[0101] 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;

[0102] At this point, the pre-processing module is constructed, which includes the grid information of the photobioreactor; the velocity, pressure, phase volume fraction, and initial value of carbon dioxide concentration in the fluid calculation area; the velocity, pressure, phase volume fraction, carbon dioxide concentration boundary value and light intensity boundary conditions at the boundary of the fluid calculation area.

[0103] Step 3: Based on the pre-processing module constructed in step 2, the Euler method is used to describe the flow field calculation in the photobioreactor, the Lagrangian method is used to describe the growth rate of the microbial particles and the flow field velocity, and the carbon dioxide consumption rate is calculated based on the growth rate. At the same time, the carbon dioxide consumption matrix under the Euler framework is constructed from the Lagrangian framework for subsequent mass transfer equation coupling;

[0104] The specific steps are as follows:

[0105] Step 3.1 Calculate the growth rate of microbial particles in the photobioreactor under the steady-state assumption:

[0106] The coupling computational fluid dynamics, light intensity transmission law, and steady-state microbial growth mechanism are used as a coupling model; the gas-liquid-solid three-phase model in computational fluid dynamics calculates the flow of culture medium (liquid phase), gas (gas phase), and microorganism (solid phase) in the photobioreactor, where the gas phase and liquid phase are continuous phases and the solid phase is a discrete phase; the position of each microbial particle in the photobioreactor calculated by the fluid dynamics calculation module is used to obtain the light intensity at the local grid where it is located, and the growth rate of each microbial particle is calculated by the Lagrangian method according to the steady-state growth rate calculation formula; the average growth rate of microbial particles in the photobioreactor under the steady-state assumption can be obtained by averaging the growth rates of all microbial particles in the photobioreactor, and the specific steps are as follows:

[0107] Step 3.1.1 Solve the gas and liquid continuous phase flow conditions according to the NS equations using the Euler method to obtain the gas and liquid continuous phase velocity and pressure flow information:

[0108]

[0109] ρ=αρ1+(1-α)ρ2 (5)

[0110] μ m =αμ1+(1-α)μ2 (6)

[0111]

[0112] 0<α<1 (8)

[0113] 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;

[0114] 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 by Newton's second law using the Lagrangian method:

[0115]

[0116] 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;

[0117] Step 3.1.3 Obtain the light intensity field distribution calculated based on the light intensity transmission law

[0118] 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.

[0119] Step 3.1.4 Calculate the local growth rate of each discrete phase microbial particle by describing the steady-state microbial growth mechanism through the photosynthetic factory (PSF);

[0120] 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;

[0121] 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:

[0122]

[0123] A+B+C=1 (12)

[0124] μ=kγB-M (13)

[0125] Under the steady-state assumption, the microbial growth mechanism also satisfies the following assumptions:

[0126]

[0127] 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:

[0128]

[0129] 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;

[0130] 3.2 Solving the CO2 consumption rate based on growth rate and microbial concentration

[0131] First, the mass concentration c of the solution is obtained according to the corresponding relationship between the absorbance value of the solution and the absorbance and concentration in formula (1). Then, the mass concentration of the microorganisms that grow within the time dt is calculated according to the specific growth rate μ as dc = μc·dt. According to the carbon content C per unit mass of the microorganisms, m The amount of carbon consumed in time dt can be obtained as C consumption =dc·C m , through the equation The amount of carbon consumed is converted into the amount of CO2 consumed, where is the relative molecular weight of CO2, Ar C is the relative atomic weight of carbon; the fluid concentration field is described using the Euler framework, and the data in the Lagrangian framework is constructed as a matrix under the Euler framework, as follows:

[0132] First, construct a carbon dioxide consumption matrix with the same scale as the number of grids. And initialize each element in the matrix to 0; then traverse all particles, for each traversed particle, search the grid where it is located, and then add the carbon dioxide concentration value consumed by its growth to the carbon dioxide consumption matrix under the grid index; when the traversal is completed, all carbon dioxide consumption values ​​are added to the carbon dioxide consumption matrix.

[0133] Step 4: Construct the carbon dioxide mass transfer equation based on the pre-treatment module in step 2, and convert the carbon dioxide consumption matrix of microorganisms constructed in step 3 into By adding the source term to the carbon dioxide mass transfer equation, a mass transfer module is constructed to predict the distribution of carbon dioxide concentration field in the photobioreactor:

[0134] The mass transfer control equation is described as follows, where the carbon dioxide consumption matrix of microorganisms is Add to the mass transfer equation via the source term:

[0135]

[0136] D=αD L +(1-α)D G (18)

[0137]

[0138] in, is the concentration of carbon dioxide in the gas phase, is the concentration of carbon dioxide in the liquid phase, is the average carbon dioxide concentration, U is the fluid velocity, D is the average diffusion coefficient, and D L is the liquid phase diffusion coefficient, D G is the gas phase diffusion coefficient, α is the liquid phase volume fraction, and H is the Henry constant.

[0139] The flux J of gas-liquid transfer is determined by the following formula:

[0140]

[0141] Example

[0142] First, for any photobioreactor, this example uses the fluid calculation region as Figure 2 The flat-plate photobioreactor shown has a gas inlet at the bottom center.

[0143] Secondly, according to the physical conditions and operating conditions, the velocity, pressure, phase volume fraction, and initial value of carbon dioxide concentration in the actual fluid calculation area are obtained; the velocity, pressure, phase volume fraction, and boundary value of carbon dioxide concentration at the boundary of the fluid calculation area are obtained; and since the photobioreactor has ventilation settings, the gas inlet is set as the velocity inlet, the top outlet is set as the pressure outlet, and the wall is set as a no-slip boundary condition. The inlet concentration is set according to the concentration value of the gas. The inlet liquid phase volume fraction is set to 0, and the gas phase volume fraction is set to 1. The initial value conditions of the internal initial phase volume fraction are set, and the half height of the photobioreactor is set as the phase interface, with the liquid phase below the interface and the gas phase above the interface.

[0144] The initial concentration value is given as absorbance A0, and the illumination boundary condition is parallel illumination with light intensity I0 from the right side of the photobioreactor. Then the light intensity distribution in the photobioreactor is calculated. Due to the parallel illumination, the light intensity field distribution method is calculated according to the Beer-Lambert law:

[0145]

[0146] Light intensity distribution Figure 3 shown.

[0147] After obtaining the above model pre-processing module, the time step cycle calculation begins. The calculation process is as follows Figure 1 As shown, the PIMPLE velocity-pressure loop iterative algorithm is first used to solve the following set of equations. The inner loop includes calculating the volume fractions of the gas-liquid two-phase phases, and then iteratively solving the velocity field and pressure field of the continuous phase, and the inner loop ends.

[0148]

[0149] ρ=αρ1+(1-α)ρ2

[0150] μ m =αμ1+(1-α)μ2

[0151]

[0152] 0<α<1

[0153] Then, the following formula is used to solve the grid where each particle is located to calculate the force on each particle. After obtaining the force, the particle acceleration is calculated, and then the velocity and coordinates are updated.

[0154]

[0155] That is, the position of each discrete phase microbial particle is known, the local light intensity of each discrete phase microbial particle is obtained, and the growth rate of each particle is updated by the following formula:

[0156]

[0157] Then all particles are traversed, and for each particle, the concentration growth value dc = μc·dt at this time step is calculated based on the calculated growth rate μ and the concentration c. Then, the carbon consumption at this time step is calculated as C according to the relationship between biomass and carbon content. consumption =dc·C m According to the relative molecular weight between carbon and carbon dioxide, the carbon dioxide consumption rate at this time step is Then construct a carbon dioxide consumption value matrix with the same scale as the number of grids And initialize each element in the matrix to 0. Then traverse all particles, search the grid where each particle is located, and then add the carbon dioxide concentration value consumed by its growth to the carbon dioxide consumption value matrix under the grid index. When the traversal is completed, all carbon dioxide consumption values ​​are added to the matrix.

[0158] The carbon dioxide consumption rate is Figure 4 shown.

[0159] Then solve the mass balance equation, i.e. the mass transfer equation:

[0160]

[0161] D=αD L +(1-α)D G

[0162]

[0163] The relationship between flow field velocity, pressure, growth rate, gas-liquid distribution and carbon dioxide concentration field distribution at any time can be obtained.

[0164] Among them, we selected several common ventilation strategies for calculation results. Figure 6As shown, it can be seen that under the three ventilation conditions of 5% 0.1vvm, 5% 0.02vvm, and 3% 0.1vvm, the concentration of dissolved carbon dioxide in the solution is the saturated concentration under the corresponding gas concentration, which means that the ventilation strategy at this time causes the dissipation of carbon dioxide and seriously reduces the utilization rate. In addition, under the ventilation condition of air concentration 0.1vvm, the concentration of carbon dioxide in the solution decreases rapidly, which means that the amount of carbon dioxide supplemented by ventilation at this time cannot meet the growth consumption of microorganisms. At the same time, for the two examples of 0.1vvm and 0.05vvm at 1% concentration, it can be seen that when the gas concentration is the same, the ventilation rate affects the concentration of dissolved carbon dioxide in the solution at equilibrium. The higher the rate, the closer the concentration of dissolved carbon dioxide in the solution at equilibrium is to the saturated concentration.

[0165] It can be seen that under the conditions of this microbial concentration and light, the combination of 5% carbon dioxide in the gas and a ventilation rate of 0.1vvm is the most common aeration strategy, which is balanced at the saturated concentration and provides excessive carbon dioxide supplementation, thus causing a large amount of carbon escape and greatly reducing the carbon fixation efficiency. When the gas contains 5% carbon dioxide and the ventilation rate is 0.01vvm, although it can meet the consumption of carbon dioxide, it is not balanced at the saturated position. Here, we use 1D process simulation to predict this case and find that under this growth condition, at a carbon dioxide concentration of 5%, only a ventilation rate of 0.003vvm is required to meet the solution's equilibrium at the saturated concentration. The error between the two methods is enough to show that the 1D process simulation is not enough to provide accurate results for the prediction of this physical process.

[0166] The distribution of carbon dioxide concentration field at a certain moment is as follows Figure 5 As shown, it can be seen that ventilation and culture are both in the initial stage. There are multiple bubbles in the center of the photobioreactor, and the carbon dioxide concentration in the bubbles is significantly higher than that in the surrounding liquid. At the same time, the carbon dioxide concentration on the right side of the photobioreactor is significantly lower because the light intensity here is greater, and the growth and consumption of microorganisms is obvious.

[0167] The present invention combines computational fluid dynamics, light intensity transmission, microbial growth mechanism and mass transfer law to work together to enhance the versatility of the model and accurately predict the distribution of carbon dioxide concentration field. At the same time, the consumption of carbon dioxide by microorganisms and the supplement of carbon dioxide by ventilation are considered, so that the study of the dynamic balance between the two can be realized. The workflow of computational fluid dynamics, light intensity transmission law, microbial growth mechanism coupling model and mass transfer law, in the fluid dynamics calculation module, 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, the continuous phase gas-liquid two phases described by the Euler method are described by the VOF method, the microorganisms are described by the discrete phase particles described by the Lagrangian method, and the gas-liquid mass transfer is described by the mass transfer law. The consumption of carbon dioxide by discrete phase microorganisms described by the Lagrangian method is added to the mass transfer equation through the source term. The light intensity distribution in the photobioreactor is calculated by the light intensity transmission law. As the time step advances, the growth rate of each microbial particle is updated, and the carbon dioxide consumption matrix is ​​updated, which is coupled with the flow mass transfer equation. It effectively provides important data support, theoretical guidance, and decision-making basis for the scale-up design of photobioreactors, the prediction of operating parameter regulation, the prediction of growth rate, and the prediction of carbon dioxide consumption and replenishment balance.

Claims

1. A prediction model for carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation, characterized in that: The model includes: Pre-processing module: used to describe the physical model, calculation area, initial value conditions, boundary conditions, ventilation conditions, and grid division conditions of the photobioreactor; Fluid dynamics calculation module: used to calculate the flow distribution of gas, liquid and solid phases in the photobioreactor, so as to obtain the accurate flow distribution of solid phase microorganisms in the photobioreactor. The gas and liquid phases are described as continuous phases by Euler method, and the solid phase is described as discrete phase by Lagrangian method. 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; Mass transfer module: used to quantitatively calculate the carbon dioxide concentration field under the combined effects of gas-liquid mass transfer supplementation caused by ventilation and microbial consumption under flow conditions; Based on the constructed pre-processing module, the precise flow distribution of solid-phase microorganisms in the photobioreactor is obtained through the fluid dynamics calculation module, and the precise distribution of light intensity in the photobioreactor is obtained through the light transmission module, thereby obtaining the precise light intensity received by each solid-phase microorganism in the photobioreactor; the precise microbial growth rate is calculated through the microbial growth rate mechanism module, the amount of carbon dioxide consumed in the growth process is calculated according to the growth rate, and the carbon dioxide concentration matrix consumed by the microalgae growth is constructed, and then the matrix is ​​added as a source term to the carbon dioxide mass transfer equation of the mass transfer module to calculate the carbon dioxide concentration field in the microbial photobioreactor.

2. A method for predicting the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation, characterized in that: The specific steps are as follows: Step 1, based on the appearance and operation parameter information of any photobioreactor, obtain the physical model of the photobioreactor, the fluid calculation area, the velocity, pressure, phase volume fraction, and initial value of carbon dioxide concentration in the fluid calculation area, the velocity, pressure, phase volume fraction, and carbon dioxide concentration boundary value at the boundary of the fluid calculation area, the initial value condition of microorganism concentration in the fluid, the light intensity boundary condition, and the ventilation condition 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. Based on the divided grids, the ventilation conditions during the operation of the photobioreactor and the velocity, pressure, phase volume fraction, initial value and boundary value of carbon dioxide, initial value conditions of microbial concentration in the fluid, light intensity boundary conditions, and initial value of the light intensity field calculated by the light transmission module are obtained in step 1, and a pre-processing module for predicting the growth rate of microorganisms in the photobioreactor is constructed; Step 3: Based on the pre-processing module constructed in step 2, the flow field in the photobioreactor is described by the Euler method through the fluid dynamics calculation module, the flow field velocity of the microbial particles is described by the Lagrangian method, and the growth rate of the microorganisms is calculated by the microbial growth rate mechanism module. The carbon dioxide consumption rate is calculated based on the growth rate, and at the same time, the carbon dioxide consumption matrix under the Euler framework is constructed from the Lagrangian framework for subsequent mass transfer equation coupling; Step 4: Construct the carbon dioxide mass transfer equation based on the pre-treatment module in step 2, and convert the carbon dioxide consumption matrix of microorganisms constructed in step 3 into By adding source terms to the carbon dioxide mass transfer equation, a mass transfer module is constructed to predict the distribution of carbon dioxide concentration field in the photobioreactor.

3. The method for predicting the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation 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 boundary area and internal area; 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 grid supplement Based on the grid divided in step 2.1, the air inlet and outlet boundary areas of the photobioreactor are divided based on the ventilation parameters of the photobioreactor obtained in step 1 to obtain multiphase grid partition information; at the boundary air inlet and air outlet, the corresponding areas are 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 velocity, pressure, phase volume fraction, initial value of carbon dioxide concentration in the fluid calculation area and the velocity, pressure, phase volume fraction, and boundary value of carbon dioxide concentration at the boundary of the fluid calculation area obtained in step 1, the parameters of the boundary area and the internal area of ​​the grid are directly set; according to the initial value conditions of microbial concentration in the fluid and the light intensity boundary conditions obtained in step 1, the initial value of the light intensity field is calculated through the light transmission module, and the method is as follows: Where 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 velocity, pressure, phase volume fraction, and initial value of carbon dioxide concentration in the fluid calculation area; the velocity, pressure, phase volume fraction, carbon dioxide concentration boundary value and light intensity boundary conditions at the boundary of the fluid calculation area.

4. The method for predicting the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation according to claim 2, characterized in that: The step 3 is based on the pre-processing module constructed in step 2, and the Euler method is used to describe the flow field calculation in the photobioreactor, and the Lagrangian method is used to describe the growth rate of the microbial particles and the flow field velocity, and the carbon dioxide consumption rate is calculated based on the growth rate. At the same time, the carbon dioxide consumption matrix under the Euler framework is constructed from the Lagrangian framework for coupling with the mass transfer equation in step 4; the specific steps are as follows: Step 3.1 Calculate the growth rate of microbial particles in the photobioreactor under the steady-state assumption: The coupling model is based on computational fluid dynamics, light intensity transmission law, and steady-state microbial growth mechanism. The gas-liquid-solid three-phase model in computational fluid dynamics is used to calculate the flow of culture medium (liquid phase), gas (gas phase), and microorganisms (solid phase) in the photobioreactor, where the gas phase and liquid phase are continuous phases and the solid phase is a discrete phase. According to the position of each microbial particle in the photobioreactor calculated by the fluid dynamics calculation module, the light intensity at the local grid where it is located is obtained, and the growth rate of each microbial particle is calculated according to the steady-state growth rate calculation formula by the Lagrangian method; by averaging the growth rates of all microbial particles in the photobioreactor, the average growth rate of microbial particles in the photobioreactor under the steady-state assumption is obtained. The specific steps are as follows: Step 3.1.1 Solve the gas and liquid continuous phase flow conditions according to the NS equations using the Euler method 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 by Newton's second law using the Lagrangian method: 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; 3.2 Solving the CO2 consumption rate based on growth rate and microbial concentration First, the mass concentration c of the solution is obtained according to the corresponding relationship between the absorbance value of the solution and the absorbance and concentration in formula (1). Then, the mass concentration of the microorganisms that grow within the time dt is calculated according to the specific growth rate μ as dc = μc·dt. According to the carbon content C per unit mass of the microorganisms, m The amount of carbon consumed in time dt can be obtained as C consumption =dc·C m , through the equation The amount of carbon consumed is converted into the amount of CO2 consumed, where is the relative molecular weight of CO2, Ar C is the relative atomic weight of carbon; the fluid concentration field is described using the Euler framework, and the data in the Lagrangian framework is constructed as a matrix under the Euler framework, as follows: First, construct a carbon dioxide consumption matrix with the same scale as the number of grids. And initialize each element in the matrix to 0; then traverse all particles, for each traversed particle, search the grid where it is located, and then add the carbon dioxide concentration value consumed by its growth to the carbon dioxide consumption matrix under the grid index; when the traversal is completed, all carbon dioxide consumption values ​​are added to the carbon dioxide consumption matrix.

5. The method for predicting the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation according to claim 2, characterized in that: The carbon dioxide mass transfer equation in step 4 is described as follows: Wherein, the carbon dioxide consumption matrix of microorganisms is D=αD L +(1-α)D G (18) in, is the concentration of carbon dioxide in the gas phase, is the concentration of carbon dioxide in the liquid phase, is the average carbon dioxide concentration, U is the fluid velocity, D is the average diffusion coefficient, and D L is the liquid phase diffusion coefficient, D G is the gas phase diffusion coefficient, α is the liquid phase volume fraction, and H is the Henry constant.

6. The method for predicting the carbon dioxide concentration field in a photobioreactor based on the balance between microbial consumption and carbon dioxide ventilation according to claim 2, characterized in that: The flux J of the gas-liquid transfer is determined by the following formula:

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