Microalgae culture carbon dioxide intelligent feeding system based on multi-parameter linkage

Abstract

The invention relates to the technical field of microalgae culture process control, in particular to a microalgae culture carbon dioxide intelligent feeding system based on multi-parameter linkage. Comprising a photobioreactor, a temperature sensor, a pH sensor, a dissolved oxygen sensor, a conductivity sensor, an optical density or turbidity sensor, a photosynthetically active radiation intensity sensor, a circulating flow sensor, a carbon dioxide and air supply module and an embedded controller. The controller sequentially completes multi-sensor access and calibration, culture operation data acquisition and calculation of growth carbon demand parameters formed by illumination, frond concentration, temperature and the like and aeration safety parameters formed by dissolved oxygen, gas-liquid ratio and circulation flow. The volume fraction of carbon dioxide, the total gas flow and the gas distribution proportion of each aeration branch are optimized by integrating the two types of parameters, and intelligent linkage control over carbon dioxide feeding in the microalgae culture process is achieved.

Description

Technical Field

[0001] This invention relates to the field of microalgae cultivation process control technology, specifically a microalgae cultivation carbon dioxide intelligent delivery system based on multi-parameter linkage. Background Technology

[0002] In existing technologies, microalgae cultivation, as an important pathway for carbon dioxide capture and the preparation of high-value-added products, has evolved into various engineering equipment forms, including open algae ponds, raceway ponds, and various closed photobioreactors (tubular, flat-plate, etc.). The typical process configuration involves: on the gas side, high-purity carbon dioxide or flue gas, after depressurization, mixing, and flow control, is introduced into the culture medium through aeration heads or microporous aeration discs to achieve carbon dioxide dissolution and mass transfer; on the liquid side, sensors for temperature, pH, dissolved oxygen, conductivity, and turbidity are installed to upload process signals to a local control cabinet or host computer for basic environmental condition control and data recording. While these systems are relatively mature at the hardware level, certain limitations remain in terms of carbon dioxide delivery strategies and control mechanisms.

[0003] First, most engineering and commercial photobioreactors still rely primarily on pH as a single-parameter feedback mechanism for carbon dioxide supply control. This involves setting a fixed pH target range, increasing or turning on the carbon dioxide flux when the pH exceeds the upper limit, and decreasing or turning off the carbon dioxide supply when the pH falls below the lower limit. This control method fails to explicitly consider key process variables such as photosynthetically active radiation intensity, algal concentration, dissolved oxygen level, nutrient concentration, and circulation flow rate. It also fails to differentiate the varying carbon requirements of different growth stages, such as the adaptation phase, logarithmic growth phase, stationary phase, and decline phase, leading to a deviation between carbon dioxide input and the actual carbon requirements of microalgae. During periods of significant light fluctuations, gradual nutrient depletion, or transitions between growth stages, single pH control struggles to reflect changes in carbon demand in a timely manner, easily resulting in problems such as insufficient early carbon source supply, ineffective later additions, and drastic pH fluctuations.

[0004] Secondly, existing control strategies generally lack quantitative separation and unified measurement of growth carbon demand and aeration safety margin. On the one hand, carbon dioxide introduction not only provides a carbon source but also affects dissolved oxygen stripping and shear conditions; on the other hand, gas-liquid ratio, bubble size, and fluid circulation directly affect the mass transfer limit and equipment safety. Existing systems typically set a constant total aeration flow rate based on experience, or manually set a maximum flow rate limit based on pH control, without constructing explicit indicators to characterize the target carbon dioxide supply intensity per unit time and the allowable addition space under current dissolved oxygen and gas-liquid conditions. When flue gas composition fluctuates, external temperature changes, or algae species are changed, operators often need to repeatedly try and adjust the limits, resulting in poor adaptability and portability, and is not conducive to fully improving carbon dioxide utilization while ensuring dissolved oxygen is not oversaturated and avoiding excessive shear.

[0005] Furthermore, although some systems are equipped with multiple online sensors for dissolved oxygen, light density, and light intensity, and even possess IoT and cloud-based data analysis capabilities, most of this data is used for monitoring and post-event analysis, and has not yet formed an online decision-making logic based on multi-parameter linkage. In existing technologies, a few attempts have been made to introduce improved proportional-integral control or simple algorithms to optimize pH fluctuations, but overall, they remain at the level of real-time feedback control driven by single-point errors, lacking predictive capabilities and parameterized optimization frameworks for the future. They cannot simultaneously consider the sufficiency of carbon dioxide supply, the safety boundaries of dissolved oxygen and gas-liquid ratio, and the smoothness of control actions within a certain time window. In addition, existing systems lack a unified characterization of the coupling relationships between multiple parameters; the degree of matching between carbon dioxide release intensity and light load, algal load, cannot be reflected by calculable indicators. Summary of the Invention

[0006] The purpose of this invention is to provide a smart carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage, so as to solve the technical problems mentioned in the background art.

[0007] Based on the above ideas, the present invention provides the following technical solution:

[0008] A smart carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage, comprising:

[0009] Microalgae photobioreactor, used to contain microalgae culture medium and provide stirring device and circulation pump;

[0010] Temperature sensor, pH electrode, dissolved oxygen electrode, conductivity sensor, optical density or turbidity sensor for characterizing algal concentration, photosynthetically active radiation intensity sensor, and circulating flow meter;

[0011] The gas supply module includes a carbon dioxide gas source, an air source, a gas mixing module, a mass flow controller, and a solenoid valve, used to introduce a mixture of carbon dioxide and air into the microalgae photobioreactor.

[0012] An embedded controller or a host computer, wherein the embedded controller is electrically connected to the sensor and the gas supply module;

[0013] The embedded controller is configured to perform the following steps during system operation:

[0014] S1. Connect the temperature sensor, pH electrode, dissolved oxygen electrode, conductivity sensor, optical density or turbidity sensor, photosynthetically active radiation intensity sensor and circulating flow meter to the embedded controller, and connect the carbon dioxide gas source and air source to the gas mixing module and the mass flow controller respectively.

[0015] S2, before inoculation, calibrate each of the sensors, start the stirring device and the circulating pump, establish a stable circulating flow field, and collect real-time data of temperature, pH, dissolved oxygen, conductivity, turbidity, photosynthetically active radiation intensity and circulating flow rate at a preset sampling period to form a multi-parameter operating condition vector.

[0016] S3. Introduce a mixture of carbon dioxide and air into the microalgae photobioreactor at a constant total aeration flow rate. In the initial stage, operate according to the empirically set carbon dioxide volume fraction and pH target range to obtain historical operating data representing the reactor under different operating conditions.

[0017] S4. During the cultivation process, based on the photosynthetically active radiation intensity, algal concentration, temperature and historical growth curve, identify the current growth stage of the microalgae, and calculate the growth carbon demand parameter to characterize the carbon demand intensity per unit time according to the growth stage and environmental conditions.

[0018] S5. During the cultivation process, based on dissolved oxygen concentration, gas-liquid ratio, circulation flow rate and the aforementioned growth stage information, calculate the aeration safety parameters used to characterize the gas-side safety margin and the upper limit of carbon dioxide mass transfer.

[0019] S6. Based on the combined growth carbon requirement parameters and aeration safety parameters, determine the optimal combination of carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch, and adjust the mass flow controller and the solenoid valve accordingly to achieve intelligent linkage control of carbon dioxide injection.

[0020] By integrating a microalgae photobioreactor, multiple online sensors, a gas supply module, and an embedded controller into a single system, and driving carbon dioxide delivery with a multi-parameter operating condition vector, this system no longer relies on a single pH feedback but simultaneously considers state variables such as temperature, pH, dissolved oxygen, algal concentration, photosynthetically active radiation intensity, and circulation flow rate. This approach allows for a more accurate reflection of the actual physiological needs of the cultivation process, reducing efficiency losses caused by insufficient or excessive carbon dioxide supply. Furthermore, it offers good hardware compatibility with existing photobioreactors and gas supply equipment, enabling upgrades to existing systems by adding sensors and controllers, reducing modification costs and facilitating widespread application in experimental, pilot-scale, and industrial-scale facilities. Simultaneously, the combination of historical and real-time operating data enables the control strategy to adaptively adjust, reducing frequent manual trial-and-error settings and improving long-term operational stability and repeatability.

[0021] Preferably, S4 further includes:

[0022] Within each sampling period, the current photosynthetically active radiation intensity, algal concentration, temperature, nutrient concentration, and the rate of change of each of the above parameters within a preset time window are used as input variables.

[0023] The cultivation process is divided into at least one growth stage, namely, adaptation period, logarithmic growth period, stationary period and decline period, based on the absolute value of algal concentration and growth rate, and a target carbon conversion rate range is preset for each growth stage.

[0024] By utilizing the photosynthetic response relationship between photosynthetically active radiation intensity and algal concentration, combined with the effect of temperature on photosynthetic efficiency correction and the limiting effect of nutrient concentration on growth rate, the theoretical carbon dioxide consumption rate per unit volume of culture medium in the current time period is estimated, and this theoretical carbon dioxide consumption rate is converted into the growth carbon requirement parameter by culture volume and safety factor.

[0025] By explicitly introducing growth stage identification and theoretical carbon dioxide consumption estimation based on photosynthetic response, algal concentration, temperature, and nutrients into the controller, the traditional extensive control centered on a fixed pH range can be upgraded to refined control that takes into account process kinetics. Different growth stages exhibit significant differences in carbon dioxide demand and sensitivity to environmental disturbances. This scheme divides the cultivation process into adaptation, logarithmic growth, stationary, and decline phases, allowing for differentiated setting of target carbon conversion rates, supply intensity, and safety margins for each stage. This facilitates accelerated cell proliferation in the early stages, improved target product yields in the mid-to-late stages, and avoids ineffective aeration. Furthermore, by incorporating time-dimensional information using parameter change rates, changes in growth trends can be identified early, allowing for timely adjustments to control strategies and reducing significant fluctuations in pH and dissolved oxygen.

[0026] Preferably, the calculation of the growth carbon requirement parameter includes:

[0027] Pre-determine the maximum specific carbon dioxide consumption rate constant per unit algal cell under optimal growth conditions;

[0028] Calculate the light response factor, which is used to characterize the effect of photosynthetically active radiation intensity on the photosynthetic rate per unit cell. The light response factor increases with the increase of photosynthetically active radiation intensity when the photosynthetically active radiation intensity is less than a preset light response characteristic intensity, and enters the response saturation region after the photosynthetically active radiation intensity reaches the light response characteristic intensity.

[0029] Calculate the biomass factor, which is used to characterize the contribution of algal concentration to the overall carbon demand. When the algal concentration is less than a preset concentration characteristic value, it increases with the increase of algal concentration. When the algal concentration reaches or exceeds the concentration characteristic value, it enters the contribution saturation region.

[0030] Calculate a temperature correction factor, which is used to characterize the attenuation effect of the culture temperature on photosynthetic efficiency when it deviates from the optimal growth temperature. The temperature correction factor reaches its maximum value when the culture temperature is consistent with the optimal growth temperature, and decreases according to a preset symmetry law when the culture temperature deviates from the optimal growth temperature.

[0031] The embedded controller combines the maximum specific carbon dioxide consumption rate constant with the light response factor, biomass factor, and temperature correction factor to obtain the growth carbon demand parameter, so that the growth carbon demand parameter can reflect the target carbon dioxide demand intensity per unit time under the current light conditions, algal load, and temperature conditions.

[0032] By multiplicatively coupling the maximum specific carbon dioxide consumption rate with the light response, algal concentration contribution, and temperature correction factor, a growth carbon demand parameter is constructed to represent the intensity of carbon demand per unit time under current operating conditions. This more realistically reflects the combined effects of multiple factors on photosynthesis and carbon fixation rates. Specifically, the light response reflects the saturation effect of photosynthetically active radiation intensity, avoiding the misconception of unlimited demand increases in high-light regions; the algal concentration contribution reflects the characteristic that biomass gradually approaches saturation with increasing concentration, preventing overestimation of demand when biomass is extremely high; and the temperature correction factor reflects the symmetrical decay when temperature deviates from the optimum value, enabling the control algorithm to automatically reduce the ideal demand expectation during temperature fluctuations. Through the joint calculation of these three factors, the growth carbon demand parameter exhibits a continuous, smooth, and biologically consistent response to changes in operating conditions. This facilitates the controller's accurate determination of the appropriate carbon dioxide supply level in complex environments, reducing over-addition or under-supply caused by estimation errors.

[0033] Preferably, step S5 further includes:

[0034] Within each sampling period, acquire the current dissolved oxygen concentration, dissolved oxygen saturation concentration, total gas volumetric flow rate, culture medium volume, and circulation flow rate;

[0035] The gas flux per unit volume of culture medium is estimated based on the ratio of total gas volumetric flow rate to culture medium volume, and the corresponding total volumetric mass transfer coefficient is estimated by combining the reactor structure, bubble characteristics and circulation flow rate.

[0036] Based on the proximity of the current dissolved oxygen concentration to the dissolved oxygen saturation concentration, a dissolved oxygen inhibition factor is constructed to represent the risk of dissolved oxygen oversaturation. When the dissolved oxygen concentration is in the low range of the preset allowable range, the dissolved oxygen inhibition factor takes a lower level, and when the dissolved oxygen concentration is close to the preset allowable upper limit, the dissolved oxygen inhibition factor takes a higher level.

[0037] The dissolved oxygen inhibitor is combined with gas flux, total volumetric mass transfer coefficient, growth stage information, circulation flow rate, and reactor structural parameters. The aeration safety parameters are determined by a preset calculation rule. The aeration safety parameters represent the space available for increasing the carbon dioxide dosage under the current operating conditions.

[0038] By incorporating information such as current dissolved oxygen concentration, dissolved oxygen saturation level, total gas volumetric flow rate, culture medium volume, and circulation flow rate into the aeration safety assessment, and estimating the gas flux per unit volume and overall volumetric mass transfer coefficient, a dissolved oxygen inhibition factor is constructed to measure the risk of oversaturation. This allows for a quantitative judgment on whether further aeration and carbon addition are permissible. Compared to simply limiting the aeration based on empirical gas-liquid ratios or dissolved oxygen upper limits, this approach dynamically adjusts the safety margin according to actual operating conditions: when dissolved oxygen is far from saturation and the gas flux is within a suitable range, the system provides a relatively lenient allowable addition intensity; when dissolved oxygen is close to saturation and the gas-liquid ratio is close to the equipment's upper limit, the system automatically tightens the allowable addition space. This avoids severe dissolved oxygen oversaturation and shear damage caused by further carbon dioxide addition, and also reduces the risk of insufficient effective carbon source due to overly conservative safety boundary settings, thus achieving a balance between safety and efficiency.

[0039] Preferably, when calculating the aeration safety parameters:

[0040] The ratio of total gas volumetric flow rate to reactor working volume is used as the basic quantity characterizing the gas-liquid ratio. Based on the empirical law of the influence of gas-liquid ratio on mass transfer load, the basic quantity is nonlinearly converted to reflect the comprehensive influence of mass transfer capacity and shear effect under different gas-liquid ratios.

[0041] The dissolved oxygen inhibition factor is used as a safety mitigation factor to reflect the degree to which dissolved oxygen approaches the upper limit of the process. When the dissolved oxygen concentration is in the high range of the preset allowable range, the mitigation effect of the dissolved oxygen inhibition factor increases, thereby reducing the available aeration margin.

[0042] Set equipment constants related to reactor structure and bubble characteristics to comprehensively consider the effects of bubble residence time, bubble size and distribution on gas-liquid mass transfer and mixing;

[0043] The light load information and algal load information corresponding to the light response factor and biomass factor are introduced into the aeration safety parameters so that the current light conditions, algal concentration, gas-liquid ratio and dissolved oxygen risk are considered when determining the aeration safety parameters.

[0044] The aeration safety parameters are obtained through comprehensive calculation of the above-mentioned gas-liquid ratio, dissolved oxygen inhibition factor, equipment constant, and light and biomass information. They are used to provide quantitative constraints on the allowable carbon dioxide dosage intensity. The allowable dosage intensity threshold corresponding to the dissolved oxygen near the upper limit and the gas-liquid ratio in the high load range is lower than the allowable dosage intensity threshold when the dissolved oxygen is in the low load range and the gas-liquid ratio is in the low load range.

[0045] By incorporating gas-liquid ratio, dissolved oxygen inhibition factor, and equipment constants related to structure and bubble characteristics into the aeration safety parameters, and further integrating light response and algal concentration contribution information into the same evaluation formula, the gas-side safety margin not only reflects the amount of gas applied and the level of dissolved oxygen, but also considers the matching relationship between the current light load and biomass load. This design avoids the inefficient operation of maintaining high aeration and high shear when algal concentration is low and light utilization is limited, and also avoids prematurely tightening the aeration limit when biomass is high and has strong fixation capacity, leading to insufficient carbon source. Through the above multi-factor comprehensive calculation, the aeration safety parameters have the ability to differentiate between different combinations of operating conditions, and can provide a safe upper limit for addition corresponding to specific growth loads, thereby releasing as much available carbon dioxide supply space as possible while ensuring safety and improving gas utilization efficiency.

[0046] Preferably, step S6 further includes:

[0047] The growth carbon demand parameter is used as a driving variable to increase carbon dioxide supply, and the aeration safety parameter is used as a constraint variable to limit the upper limit of carbon dioxide supply. A comprehensive optimization objective is constructed that includes a penalty term for insufficient carbon dioxide supply and a penalty term for exceeding the limit of dissolved oxygen or aeration conditions.

[0048] Using carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch as decision variables to be determined, within a preset time prediction window, the comprehensive optimization target is predicted and evaluated based on the impact of different combinations of decision variables on pH evolution trend, dissolved oxygen change, and carbon dioxide utilization rate.

[0049] Under the premise of ensuring that dissolved oxygen does not exceed the set upper limit, gas-liquid ratio does not exceed the equipment safety range, and reactor operation is stable, the combination of decision variables that minimizes the comprehensive optimization objective value is selected from multiple candidate combinations as the optimal setting for the current control cycle.

[0050] The optimal settings are converted into the target flow value of the mass flow controller, the carbon dioxide to air ratio setting value, and the opening, closing or adjustment commands of each aeration branch, thereby dynamically updating the carbon dioxide dosing strategy during continuous operation.

[0051] By using the carbon demand parameter as the driving term and the aeration safety parameter as the constraint term, a comprehensive optimization objective including penalties for insufficient supply and penalties for exceeding limits is introduced. Within a certain time prediction window, rolling optimization is performed using carbon dioxide volume fraction, total gas mass flow rate, and the gas distribution ratio of each aeration branch as decision variables. This enables the provision of an optimal dosing strategy that balances current demand and future trends within each control cycle. Compared to traditional simple feedback control based on single-moment deviations, this scheme can predict the impact of different setting combinations on pH, dissolved oxygen, and carbon dioxide utilization in advance, thereby suppressing overshoot and oscillations caused by control lag, and reducing frequent start-stops and large setting changes. Simultaneously, safety constraints and equipment operating boundaries are explicitly embedded in the comprehensive optimization objective. The controller will not exceed key safety indicators such as dissolved oxygen and gas-liquid ratio when searching for the optimal solution, which is conducive to maintaining long-term operational stability and extending equipment life.

[0052] Preferably, the method for determining the comprehensive control intensity parameter is as follows:

[0053] Calculate the ratio between the growth carbon demand parameter and the normalization constant related to the growth stage, and use this ratio as a demand intensity indicator that reflects the current carbon dioxide demand relative to the typical demand level of that growth stage.

[0054] Calculate the ratio between the aeration safety parameter and the reference value used to measure the aeration safety margin, and use this ratio as a safety tension index reflecting how close the current aeration conditions are to the safety boundary;

[0055] Based on the pre-defined rule that an increase in the demand intensity index amplifies the comprehensive control intensity parameter, and an increase in the safety tension index inhibits the comprehensive control intensity parameter, the comprehensive control intensity parameter is calculated and updated in conjunction with the comprehensive amplification coefficient.

[0056] When the demand intensity index is not less than the first threshold and the safety tension index is not greater than the second threshold, the embedded controller sets the carbon dioxide volume fraction and total gas mass flow rate obtained by mapping the comprehensive control intensity parameters within the pre-stored first control range.

[0057] When the demand intensity index is lower than the third threshold or the safety tension index is higher than the fourth threshold, the embedded controller sets the carbon dioxide volume fraction and total gas mass flow rate obtained by mapping the comprehensive control intensity parameters within a pre-stored second control interval. The upper limit of the carbon dioxide volume fraction and the upper limit of the total gas mass flow rate in the second control interval are both lower than the upper limit corresponding to the first control interval.

[0058] The first control interval and the second control interval are linked to the comprehensive control intensity parameter through a monotonic mapping relationship, so that the system can achieve comprehensive optimization and adjustment of the carbon dioxide delivery strategy under different growth stages while meeting the aeration safety constraints.

[0059] By introducing a comprehensive control intensity parameter into the overall optimization objective and decomposing it into demand intensity and safety stress indices, and then mapping the results to pre-defined first and second control intervals using different thresholds, the complex continuous control strategy can be abstracted into a hierarchical control logic that is easy to understand and tune in engineering: when demand intensity is high and safety stress is low, the system automatically enters a control interval that allows for higher carbon dioxide volume fraction and higher total gas mass flow rate; when demand intensity is low or safety stress is high, the system switches to a conservative control interval with a lower upper limit. This continuous calculation and partitioned mapping approach preserves the precision of parameter entanglement calculations, accurately reflecting the impact of X and Y changes on control intensity; it also provides clear intervals and thresholds for engineering commissioning, facilitating rapid tuning and migration applications based on different algal species, reactor sizes, and process objectives, thus reducing the barrier to field implementation due to model complexity.

[0060] The technical solution of the present invention may include the following beneficial effects:

[0061] This invention integrates multiple online sensors, gas supply modules, and embedded controllers on a single platform. Using multiple parameters such as temperature, pH, dissolved oxygen, algal concentration, photosynthetically active radiation intensity, nutrient concentration, and circulation flow rate as common inputs, it constructs a coordinated control chain for carbon dioxide release, moving beyond simple on / off control based on a single pH index. By introducing growth stage identification and carbon demand intensity calculation per unit time, the system can distinguish the carbon source demand characteristics of different stages, such as the adaptation period, logarithmic growth period, stationary period, and decline period. It configures differentiated target ranges and supply strategies for each stage, achieving precise carbon supply according to stage and load. This helps to balance rapid early proliferation with product accumulation in the later stages, reducing efficiency losses caused by blind carbon addition and insufficient supply.

[0062] This invention constructs an aeration safety evaluation system on the gas side, incorporating gas-liquid ratio, dissolved oxygen state, circulation flow rate, and reactor structural parameters. It provides quantitative safety margin parameters and integrates light load and algal load information, ensuring that the upper limit of carbon dioxide addition is constrained by dissolved oxygen and mass transfer conditions while also matching current growth capacity. By establishing optimization targets through the integration of carbon demand parameters and aeration safety parameters, and jointly optimizing the carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch within the prediction window, this system can improve carbon dioxide utilization efficiency, reduce ineffective emissions and energy consumption, and suppress drastic fluctuations in pH and dissolved oxygen, thereby enhancing the long-term stability and reliability of the system.

[0063] This invention employs a control approach combining continuous calculation and hierarchical interval mapping. It summarizes the complex results of multi-parameter coupled calculations into demand intensity and safety stress indices. By setting upper limits for different control intervals, it manages the carbon dioxide volume fraction and total gas mass flow rate in tiers. This approach retains the fine-grained response of model-driven control while providing a clear parameter tuning window for engineering implementation. The system exhibits good hardware compatibility with existing photobioreactors and gas supply devices, allowing for smooth retrofitting through the addition of sensors and upgrades to the control software. It is suitable for widespread application from laboratory-scale to pilot-scale and some industrial installations, facilitating a comprehensive improvement in microalgae carbon fixation efficiency, energy utilization efficiency, and operational safety in practical engineering projects. Attached Figure Description

[0064] Figure 1 This is a flowchart of a microalgae cultivation carbon dioxide intelligent dispensing system based on multi-parameter linkage, according to the present invention. Detailed Implementation

[0065] Example 1

[0066] like Figure 1 ,

[0067] A smart carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage, comprising:

[0068] Microalgae photobioreactor, used to contain microalgae culture medium and provide stirring device and circulation pump;

[0069] Temperature sensor, pH electrode, dissolved oxygen electrode, conductivity sensor, optical density or turbidity sensor for characterizing algal concentration, photosynthetically active radiation intensity sensor, and circulating flow meter;

[0070] The gas supply module includes a carbon dioxide gas source, an air source, a gas mixing module, a mass flow controller, and a solenoid valve, used to introduce a mixture of carbon dioxide and air into the microalgae photobioreactor.

[0071] An embedded controller or a host computer, wherein the embedded controller is electrically connected to the sensor and the gas supply module;

[0072] The embedded controller is configured to perform the following steps during system operation:

[0073] S1. Connect the temperature sensor, pH electrode, dissolved oxygen electrode, conductivity sensor, optical density or turbidity sensor, photosynthetically active radiation intensity sensor and circulating flow meter to the embedded controller, and connect the carbon dioxide gas source and air source to the gas mixing module and the mass flow controller respectively.

[0074] S2, before inoculation, calibrate each of the sensors, start the stirring device and the circulating pump, establish a stable circulating flow field, and collect real-time data of temperature, pH, dissolved oxygen, conductivity, turbidity, photosynthetically active radiation intensity and circulating flow rate at a preset sampling period to form a multi-parameter operating condition vector.

[0075] S3. Introduce a mixture of carbon dioxide and air into the microalgae photobioreactor at a constant total aeration flow rate. In the initial stage, operate according to the empirically set carbon dioxide volume fraction and pH target range to obtain historical operating data representing the reactor under different operating conditions.

[0076] S4. During the cultivation process, based on the photosynthetically active radiation intensity, algal concentration, temperature and historical growth curve, identify the current growth stage of the microalgae, and calculate the growth carbon demand parameter to characterize the carbon demand intensity per unit time according to the growth stage and environmental conditions.

[0077] S5. During the cultivation process, based on dissolved oxygen concentration, gas-liquid ratio, circulation flow rate and the aforementioned growth stage information, calculate the aeration safety parameters used to characterize the gas-side safety margin and the upper limit of carbon dioxide mass transfer.

[0078] S6. Based on the combined growth carbon requirement parameters and aeration safety parameters, determine the optimal combination of carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch, and adjust the mass flow controller and the solenoid valve accordingly to achieve intelligent linkage control of carbon dioxide injection.

[0079] In one specific embodiment, the microalgae cultivation device adopts a vertical cylindrical closed photobioreactor with an effective volume of 500L. LED light panels are arranged on the outer wall of the reactor to provide a range of adjustable photosynthetically effective radiation intensity of 100 to 300 μmol·m⁻²·s⁻¹. The reactor is equipped with a paddle stirrer and a bypass circulation pump, and the circulation flow rate can be adjusted within the range of 2 to 10 m³ / h.

[0080] The sensor module includes:

[0081] A platinum resistance temperature sensor with a measuring range of 0–50 °C;

[0082] An industrial pH composite electrode with a pH range of 4–10 and automatic temperature compensation;

[0083] A polarographic dissolved oxygen electrode with a measurement range of 0–25 mg / L;

[0084] A conductivity sensor is used to indirectly monitor changes in nutrient concentration;

[0085] An online turbidity / optical density sensor, employing a bypass flow cell structure, supports dual-wavelength measurements at 680nm and 750nm for estimating algal concentration;

[0086] A photosynthetically active radiation sensor is installed on the outside of the reactor to measure the intensity of incident light.

[0087] An electromagnetic flow meter is installed in the circulation loop to measure the circulation flow rate;

[0088] The gas supply module consists of two mass flow controllers, a mixing manifold, and multiple microporous aerators: one mass flow controller connects to high-purity or flue gas source carbon dioxide, with an adjustment range of, for example, 0.1–10 L / min; the other mass flow controller connects to air or purified exhaust gas, with an adjustment range of 0.5–30 L / min. After mixing in the mixing manifold, the gas enters the bottom of the reactor via the main pipe. Four to eight branches branch off from the main pipe, each connecting to a microporous aerator. Small regulating valves are installed on the branches for zoned aeration.

[0089] The controller uses an industrial embedded controller or soft PLC, and the host computer is an industrial computer. The controller collects data from various sensors through a 4-20mA / Modbus interface and controls the mass flow controller and solenoid valve through analog or communication interfaces.

[0090] During runtime, the controller executes the following sequentially:

[0091] For S1: Connect temperature, pH, dissolved oxygen, conductivity, turbidity / optical density, photosynthetically active radiation intensity and circulation flow rate to the controller, and complete channel configuration and range calibration in the engineering screen; connect carbon dioxide and air source to the gas mixing module and mass flow controller respectively, and set the basic communication baud rate and control mode.

[0092] For S2: Before inoculation, pH, conductivity, and dissolved oxygen were calibrated using standard buffer and saturated air-water; the flow meter was calibrated by circulating clean water; the stirring and circulation pump were run under no-load conditions, and the sampling period was set to 10-30s. The samples were continuously collected for a period of time to confirm signal stability and generate an initial multi-parameter operating condition vector.

[0093] For S3: Start a constant total aeration flow rate, for example, set the total gas volume flow rate to 5L / min, the carbon dioxide volume fraction to 2%, and the pH to 7.5-8.0. Collect historical data through a period of operation to provide a basis for subsequent model parameter tuning.

[0094] By integrating a microalgae photobioreactor, multiple online sensors, a gas supply module, and an embedded controller into a single system, and driving carbon dioxide delivery with a multi-parameter operating condition vector, this system no longer relies on a single pH feedback but simultaneously considers state variables such as temperature, pH, dissolved oxygen, algal concentration, photosynthetically active radiation intensity, and circulation flow rate. This approach allows for a more accurate reflection of the actual physiological needs of the cultivation process, reducing efficiency losses caused by insufficient or excessive carbon dioxide supply. Furthermore, it offers good hardware compatibility with existing photobioreactors and gas supply equipment, enabling upgrades to existing systems by adding sensors and controllers, reducing modification costs and facilitating widespread application in experimental, pilot-scale, and industrial-scale facilities. Simultaneously, the combination of historical and real-time operating data enables the control strategy to adaptively adjust, reducing frequent manual trial-and-error settings and improving long-term operational stability and repeatability.

[0095] Specifically, S4 further includes:

[0096] Within each sampling period, the current photosynthetically active radiation intensity, algal concentration, temperature, nutrient concentration, and the rate of change of each of the above parameters within a preset time window are used as input variables.

[0097] The cultivation process is divided into at least one growth stage, namely, adaptation period, logarithmic growth period, stationary period and decline period, based on the absolute value of algal concentration and growth rate, and a target carbon conversion rate range is preset for each growth stage.

[0098] By utilizing the photosynthetic response relationship between photosynthetically active radiation intensity and algal concentration, combined with the effect of temperature on photosynthetic efficiency correction and the limiting effect of nutrient concentration on growth rate, the theoretical carbon dioxide consumption rate per unit volume of culture medium in the current time period is estimated, and this theoretical carbon dioxide consumption rate is converted into the growth carbon requirement parameter by culture volume and safety factor.

[0099] The algal concentration CX(t) was obtained by converting optical density or dry weight, and the instantaneous growth rate μ(t) was obtained by linear or exponential fitting of the data over the past 4 to 6 hours.

[0100] By a preset threshold:

[0101] μ(t) is close to zero and C X The lower threshold is considered the adaptation period;

[0102] μ(t) is greater than the set growth rate threshold μ log C X If the upper limit is not approached, it is considered to be in the logarithmic growth phase.

[0103] μ(t) is close to zero and C X The period is considered to be in a stable phase when it is close to the historical high.

[0104] μ(t) is negative and C X A downward trend indicates a recession.

[0105] Different target carbon conversion rate ranges and safety factors are associated with different stages in the parameter table for use in subsequent calculations.

[0106] The controller adjusts the current light intensity I and algal concentration C accordingly. X Temperature T and nutrient level N, represented by electrical conductivity, are used to apply the photosynthetic response model:

[0107] The effects of light intensity and algal concentration on photosynthetic rate per unit biomass were described using functions similar to rectangular hyperbolas or Monod types.

[0108] Applying Gaussian or exponential correction to temperature reflects the decrease in efficiency when deviating from the optimal temperature.

[0109] A saturation-type correction is used for nutrients, and the theoretical consumption rate is reduced when nutrients are insufficient.

[0110] After obtaining the theoretical carbon dioxide consumption rate per unit biomass, multiply it by the current algal concentration, and then multiply it by the culture volume to obtain the theoretical carbon dioxide consumption per unit time.

[0111] Multiplying this by the safety factor corresponding to each growth stage (e.g., a slightly lower safety factor for the logarithmic phase and a slightly higher safety factor for the stationary phase) yields the engineering significance value of the growth carbon demand parameter X, which is used to drive subsequent control decisions.

[0112] In practical engineering, the photosynthetic response relationship and various correction factors can be obtained through small-scale calibration experiments. For example, the carbon dioxide uptake rate per unit biomass can be measured under multiple light intensities, different temperatures, and nutrient concentrations, and then the model parameters can be obtained through nonlinear fitting. Those skilled in the art can use existing growth kinetic models as replacements, as long as the final output can represent a numerical value representing the intensity of carbon demand.

[0113] By explicitly introducing growth stage identification and theoretical carbon dioxide consumption estimation based on photosynthetic response, algal concentration, temperature, and nutrients into the controller, the traditional extensive control centered on a fixed pH range can be upgraded to refined control that takes into account process kinetics. Different growth stages exhibit significant differences in carbon dioxide demand and sensitivity to environmental disturbances. This scheme divides the cultivation process into adaptation, logarithmic growth, stationary, and decline phases, allowing for differentiated setting of target carbon conversion rates, supply intensity, and safety margins for each stage. This facilitates accelerated cell proliferation in the early stages, improved target product yields in the mid-to-late stages, and avoids ineffective aeration. Furthermore, by incorporating time-dimensional information using parameter change rates, changes in growth trends can be identified early, allowing for timely adjustments to control strategies and reducing significant fluctuations in pH and dissolved oxygen.

[0114] Specifically, the calculation of the growth carbon requirement parameter includes the following formula:

[0115]

[0116] q max This represents the maximum specific carbon dioxide consumption rate constant per unit algal cell under optimal growth conditions.

[0117] a is the light response factor, defined as:

[0118]

[0119] I represents the current photosynthetically active radiation intensity, K I Let I be the light intensity parameter of the light response characteristic, such that when I is less than K I When 'a' increases significantly with increasing I, and when I is greater than or equal to K... I When a is close to 1.

[0120] b is a biomass factor, defined as:

[0121]

[0122] C X K represents the current algal concentration. X Let C be a characteristic parameter of algal concentration, so that in C X Less than K X b follows C X Increase significantly, in C X Greater than or equal to K X When b is close to 1.

[0123] c is a temperature correction factor, used to characterize the attenuation effect of the culture temperature on photosynthetic efficiency when it deviates from the optimal growth temperature, and is defined as:

[0124]

[0125] T represents the current incubation temperature. opt The optimal growth temperature is given by αT, which is a temperature sensitivity constant.

[0126] Through the above relationship, the carbon demand parameter X for growth changes simultaneously with the changes in light response factor a, biomass factor b, and temperature correction factor c, which is used to reflect the target carbon dioxide demand intensity per unit time under the current light conditions, algal load, and temperature conditions.

[0127] In one specific embodiment, the carbon requirement parameter X for growth is implemented according to the following process:

[0128] In a 5L plate-mounted photobioreactor in the laboratory, the same algal species was cultured under different light intensities (e.g., 50, 100, 200, and 300 μmol·m⁻²·s⁻¹). The carbon dioxide uptake rate per unit biomass was measured under nutrient-sufficient and optimal temperature conditions, and curves showing the change with light intensity were obtained. By fitting the data of I and the uptake rate, the characteristic light intensity K_I and the maximum specific carbon dioxide consumption rate constant q were estimated. max .

[0129] Under constant light intensity and temperature, the carbon dioxide consumption rate per unit volume and C were obtained by varying the initial inoculum concentration and culture time at different algal concentrations. X The relationship was used to fit the algal concentration characteristic parameter K. X .

[0130] Under conditions of constant light intensity and sufficient nutrients, the carbon dioxide uptake rate per unit biomass was measured by setting the temperature at a series of values ​​(e.g., 20, 25, 30℃). 25℃ was identified as the optimum temperature, and the temperature sensitivity constant α was obtained through fitting. T .

[0131] In the software implementation, the controller can... I K X α T T opt and q max The formula table stores the values ​​as configurable parameters. During runtime, the numerical value X is calculated according to the formula above, and X is output to the subsequent optimization and control module.

[0132] By multiplicatively coupling the maximum specific carbon dioxide consumption rate with the light response, algal concentration contribution, and temperature correction factor, a growth carbon demand parameter is constructed to represent the intensity of carbon demand per unit time under current operating conditions. This more realistically reflects the combined effects of multiple factors on photosynthesis and carbon fixation rates. Specifically, the light response reflects the saturation effect of photosynthetically active radiation intensity, avoiding the misconception of unlimited demand increases in high-light regions; the algal concentration contribution reflects the characteristic that biomass gradually approaches saturation with increasing concentration, preventing overestimation of demand when biomass is extremely high; and the temperature correction factor reflects the symmetrical decay when temperature deviates from the optimum value, enabling the control algorithm to automatically reduce the ideal demand expectation during temperature fluctuations. Through the joint calculation of these three factors, the growth carbon demand parameter exhibits a continuous, smooth, and biologically consistent response to changes in operating conditions. This facilitates the controller's accurate determination of the appropriate carbon dioxide supply level in complex environments, reducing over-addition or under-supply caused by estimation errors.

[0133] Specifically, S5 further includes:

[0134] Within each sampling period, acquire the current dissolved oxygen concentration, dissolved oxygen saturation concentration, total gas volumetric flow rate, culture medium volume, and circulation flow rate;

[0135] The gas flux per unit volume of culture medium is estimated based on the ratio of total gas volumetric flow rate to culture medium volume, and the corresponding total volumetric mass transfer coefficient is estimated by combining the reactor structure, bubble characteristics and circulation flow rate.

[0136] Based on the proximity of the current dissolved oxygen concentration to the dissolved oxygen saturation concentration, a dissolved oxygen inhibition factor is constructed to represent the risk of dissolved oxygen oversaturation. When the dissolved oxygen concentration is in the low range of the preset allowable range, the dissolved oxygen inhibition factor takes a lower level, and when the dissolved oxygen concentration is close to the preset allowable upper limit, the dissolved oxygen inhibition factor takes a higher level.

[0137] The dissolved oxygen inhibitor is combined with gas flux, total volumetric mass transfer coefficient, growth stage information, circulation flow rate, and reactor structural parameters. The aeration safety parameters are determined by a preset calculation rule. The aeration safety parameters represent the space available for increasing the carbon dioxide dosage under the current operating conditions.

[0138] The controller calculates the current total gas volumetric flow rate Q from the mass flow controller and gas density parameters. g (For example, in units of L / min or m³ / h), then divide by the reactor working volume V to obtain the gas flux per unit volume Q. g / V.

[0139] The overall volumetric mass transfer coefficient k is estimated using empirical formulas or pre-calibrated curves, taking into account the reactor type (e.g., vertical tubular or flat-plate), aeration method (bottom micropore aeration), and circulation flow rate. La For engineering applications, it can be simplified to k. La Follow Q g The power function growth of / V, with its power exponent related to reactor geometry and bubble size, is reflected by equipment constants.

[0140] By incorporating information such as current dissolved oxygen concentration, dissolved oxygen saturation level, total gas volumetric flow rate, culture medium volume, and circulation flow rate into the aeration safety assessment, and estimating the gas flux per unit volume and overall volumetric mass transfer coefficient, a dissolved oxygen inhibition factor is constructed to measure the risk of oversaturation. This allows for a quantitative judgment on whether further aeration and carbon addition are permissible. Compared to simply limiting the aeration based on empirical gas-liquid ratios or dissolved oxygen upper limits, this approach dynamically adjusts the safety margin according to actual operating conditions: when dissolved oxygen is far from saturation and the gas flux is within a suitable range, the system provides a relatively lenient allowable addition intensity; when dissolved oxygen is close to saturation and the gas-liquid ratio is close to the equipment's upper limit, the system automatically tightens the allowable addition space. This avoids severe dissolved oxygen oversaturation and shear damage caused by further carbon dioxide addition, and also reduces the risk of insufficient effective carbon source due to overly conservative safety boundary settings, thus achieving a balance between safety and efficiency.

[0141] Specifically, the calculation of the aeration safety parameters includes the following formula:

[0142]

[0143] Q g , where V is the current total gas volume flow rate, β is the working volume of the reactor, β is the exponential coefficient reflecting the influence of the gas-liquid ratio on the mass transfer load, and γ is the equipment constant related to the reactor structure and bubble characteristics.

[0144] d is the dissolved oxygen inhibition factor, used to characterize the degree to which dissolved oxygen approaches the process upper limit, and is defined as:

[0145]

[0146] C DO C represents the current dissolved oxygen concentration. DO,min C is the lower limit of dissolved oxygen allowed by the process. DO,max The upper limit of dissolved oxygen allowed by the process; a and b are the light response factor and biomass factor, respectively, used to incorporate the current light load and algal load into the evaluation of aeration safety;

[0147] Through the above relationships, the aeration safety parameter Y changes together with the gas-liquid ratio, dissolved oxygen inhibition factor, and light and biomass status, which is used to provide quantitative constraints on the allowable carbon dioxide dosage.

[0148] In one embodiment, the aeration safety parameter Y is implemented as follows:

[0149] The working volume V of the reactor is determined by the equipment size, for example, 500L.

[0150] Total gas volume flow rate Q g Provided in real time by the mass flow controller.

[0151] The exponential coefficient β is obtained by fitting the test results of dissolved oxygen and mass transfer performance under different gas-liquid ratios. For example, β is taken as 0.7 to 1.2.

[0152] The device constant γ is used to scale up the dimensionless ratio to the same order of magnitude as X, so that it can be easily compared during optimization, for example, by setting it empirically or by regression.

[0153] The controller performs the following actions during each sampling cycle: Calculates the gas-liquid ratio, which increases as gas flux increases; calculates (1-d) as the dissolved oxygen safety margin, where d approaches 1 and (1-d) approaches 0 when dissolved oxygen concentration is close to the upper limit, thus suppressing available space in the safety parameters; calculates a / b as the coupling term between light load and biomass load, where a is larger and b is smaller when the system is in a high light, low biomass state, resulting in a higher a / b ratio, indicating that additional aeration under these conditions is more likely to cause energy waste and shear risk; and multiplies the above terms by the equipment constant γ to obtain Y.

[0154] The controller uses Y as an indicator of the amount of space that can be further increased for aeration and carbon dioxide addition under the current light, algal concentration and dissolved oxygen conditions. The larger Y is, the closer it is to the safety boundary under the current operating conditions.

[0155] In subsequent optimization control, Y will be compared with the preset reference value Y. ref By comparison, a safety tension index is obtained, which is used to limit the upper limit when demand is high and to forcibly tighten injection when safety is tense.

[0156] By incorporating gas-liquid ratio, dissolved oxygen inhibition factor, and equipment constants related to structure and bubble characteristics into the aeration safety parameters, and further integrating light response and algal concentration contribution information into the same evaluation formula, the gas-side safety margin not only reflects the amount of gas applied and the level of dissolved oxygen, but also considers the matching relationship between the current light load and biomass load. This design avoids the inefficient operation of maintaining high aeration and high shear when algal concentration is low and light utilization is limited, and also avoids prematurely tightening the aeration limit when biomass is high and has strong fixation capacity, leading to insufficient carbon source. Through the above multi-factor comprehensive calculation, the aeration safety parameters have the ability to differentiate between different combinations of operating conditions, and can provide a safe upper limit for addition corresponding to specific growth loads, thereby releasing as much available carbon dioxide supply space as possible while ensuring safety and improving gas utilization efficiency.

[0157] Specifically, S6 further includes:

[0158] The growth carbon demand parameter is used as a driving variable to increase carbon dioxide supply, and the aeration safety parameter is used as a constraint variable to limit the upper limit of carbon dioxide supply. A comprehensive optimization objective is constructed that includes a penalty term for insufficient carbon dioxide supply and a penalty term for exceeding the limit of dissolved oxygen or aeration conditions.

[0159] Using carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch as decision variables to be determined, within a preset time prediction window, the comprehensive optimization target is predicted and evaluated based on the impact of different combinations of decision variables on pH evolution trend, dissolved oxygen change, and carbon dioxide utilization rate.

[0160] Under the premise of ensuring that dissolved oxygen does not exceed the set upper limit, gas-liquid ratio does not exceed the equipment safety range, and reactor operation is stable, the combination of decision variables that minimizes the comprehensive optimization objective value is selected from multiple candidate combinations as the optimal setting for the current control cycle.

[0161] The optimal settings are converted into the target flow value of the mass flow controller, the carbon dioxide to air ratio setting value, and the opening, closing or adjustment commands of each aeration branch, thereby dynamically updating the carbon dioxide dosing strategy during continuous operation.

[0162] The control period was set to 1 minute, and the prediction window was set to the next 10 periods (10 minutes).

[0163] Decision variables include: carbon dioxide volume fraction f CO2 Total gas mass flow rate Q m The distribution ratio vector P of the total gas among the aeration branches.

[0164] The overall optimization objective function J consists of three superimposed parts:

[0165] The carbon supply deviation term, which is related to the carbon demand parameter X for growth, measures whether the carbon dioxide supply under the current setting meets the target value corresponding to X.

[0166] The soft penalty for safety constraints related to the aeration safety parameter Y increases when Y is predicted to exceed the reference range.

[0167] Penalty for variation magnitude related to control smoothness, limiting f CO2 Q m The variation of P between adjacent periods is too large;

[0168] Predict changes in pH and dissolved oxygen over the next 10 minutes using a simplified first-order dynamic model:

[0169] pH prediction is a rough estimate based on the current alkalinity, dissolved inorganic carbon concentration and total carbon dioxide flux.

[0170] Dissolved oxygen prediction combines current dissolved oxygen concentration and kJ / kJ. La The estimated rates of photosynthetic oxygen production and respiratory oxygen consumption were calculated.

[0171] The aforementioned formula for calculating X is used to estimate whether the carbon demand in the next 10 minutes will be met under different decision variables; the formula for Y is used to estimate the aeration safety index at each time point.

[0172] To reduce computational load, f_CO2 and Q_m can be discretized into a finite number of candidate values ​​(such as CO2 volume fraction of 0.5%, 1%, 2%, 3%, 4%, and total flow rate of 2, 4, 6, 8 L / min), and P can be selected from a finite number of distribution ratio combinations on each branch.

[0173] By enumerating these candidate combinations, the objective function J within the prediction window is calculated for each combination, and the combination with the smallest J or less than a preset threshold is selected as the optimal solution for the current control cycle.

[0174] The controller will optimize f CO2 Q m P is converted into the mass flow controller setpoint and the branch regulating valve opening command.

[0175] By using the carbon dioxide demand parameter as the driving term and the aeration safety parameter as the constraint term, a comprehensive optimization objective including penalties for insufficient supply and penalties for exceeding limits is introduced. Within a certain time prediction window, rolling optimization is performed using carbon dioxide volume fraction, total gas mass flow rate, and the gas distribution ratio of each aeration branch as decision variables. This enables the provision of an optimal dispensing strategy that balances current demand and future trends within each control cycle. Compared to traditional simple feedback control based on single-moment deviations, this scheme can predict the impact of different setting combinations on pH, dissolved oxygen, and carbon dioxide utilization in advance, thereby suppressing overshoot and oscillations caused by control lag and reducing frequent start-stops and large setting changes. Simultaneously, safety constraints and equipment operating boundaries are explicitly embedded in the comprehensive optimization objective. The controller will not exceed key safety indicators such as dissolved oxygen and gas-liquid ratio when searching for the optimal solution, which is conducive to maintaining long-term operational stability and extending equipment life.

[0176] Specifically, the method for determining the comprehensive control intensity parameters is as follows: the target setpoints for carbon dioxide volume fraction and total gas mass flow rate are determined in the following manner:

[0177] The comprehensive control intensity parameter Z is calculated according to the following formula:

[0178]

[0179] in:

[0180]

[0181] R d Here, X is the carbon demand index, K is the carbon demand parameter for growth, and K is the carbon intensity index. Z This is a normalization constant related to the growth stage.

[0182]

[0183] R s Y represents the safety tension index, and Y represents the aeration safety parameter. ref θ1 is a reference value used to measure the aeration safety margin; θ1 is the comprehensive amplification factor; the embedded controller maps the comprehensive control intensity parameter Z according to the following interval conditions: when the demand intensity index R d Greater than or equal to the first threshold and the safety tension index R s When Z is less than or equal to the second threshold, it is mapped to the target values ​​of carbon dioxide volume fraction and total gas mass flow rate within the first control interval; when the demand intensity index R d Less than the third threshold or the safety tension index R sWhen the value exceeds the fourth threshold, Z is mapped to the target values ​​of carbon dioxide volume fraction and total gas mass flow rate within the second control interval. The upper limit values ​​of carbon dioxide volume fraction and total gas mass flow rate in the second control interval are both lower than the corresponding upper limit values ​​in the first control interval. The first control interval and the second control interval are linked to the comprehensive control intensity parameter Z through a monotonic mapping relationship, enabling the system to achieve comprehensive optimization and adjustment of the carbon dioxide delivery strategy under different growth stages while meeting aeration safety constraints.

[0184] Two control ranges are defined in the parameter configuration:

[0185] First, define hard boundaries based on the equipment and process, such as the maximum allowable total aeration rate of the reactor and the upper / lower limits of the carbon dioxide volume fraction commonly used in the process. Then, combine existing production or scale-up experimental data to select a set of high-load, still stable combinations as the upper limit of the first control range (e.g., 4% & 10 L / min), and select a set of conservative combinations with a large safety redundancy as the upper limit of the second control range (e.g., 2% & 5 L / min). The lower limit is taken as the lower limit of normal stable operation or the commonly used value in the start-up phase.

[0186] First control zone: the allowable range for carbon dioxide volume fraction is 1.5% to 4.0%, and the allowable range for total gas mass flow rate is 4 to 10 L / min;

[0187] The second control range is as follows: the allowable range for carbon dioxide volume fraction is 0.5% to 2.0%, and the allowable range for total gas mass flow rate is 2 to 5 L / min.

[0188] Set four thresholds:

[0189] The high (0.7) and low (0.3) thresholds for the demand intensity index are derived from statistics on the normal distribution of X: a level close to or exceeding 70% is considered significantly high demand, while a level below 30% is considered low demand; the low (0.8) and high (1.2) thresholds for the safety tension index are based on the Y / Y ratio in historical safe operation data. ref Distribution selection: Below 0.8, the safety boundary is basically not touched, and above 1.2 indicates that it has obviously approached or exceeded the historical safety limit.

[0190] First threshold R d,high For example, 0.7;

[0191] Second threshold R s,low For example, 0.8;

[0192] Third threshold R d,low For example, 0.3;

[0193] Fourth threshold R s,high For example, 1.2.

[0194] The control logic is as follows:

[0195] When R d ≥R d,high And R s ≤R s,low When selecting the first control interval, determine f for the current period based on the linear or nonlinear mapping relationship of Z from 0 to Z_max. CO2 With Q m，Z The closer to Z max The corresponding f CO2 and Q m The closer to the upper limit of the interval;

[0196] When R d <R d,low or R s >R s,high When selecting the second control interval, based on Z being between 0 and Z... max The mapping relationship is used to determine a more conservative f. CO2 With Q m Upper limit;

[0197] When R d R s When the values ​​fall within the aforementioned threshold ranges, an interpolation strategy can be used to gradually transition between the two control intervals.

[0198] Thresholds and upper and lower limits of the range can be adjusted online in the engineering screen, so that process engineers can set them according to different algae species and production goals;

[0199] The interval mapping module can be placed before or after the optimization module: a rough interval can be given by Z first, and then detailed optimization can be performed within the interval, or the optimization result can be limited to the interval corresponding to Z.

[0200] By introducing a comprehensive control intensity parameter into the overall optimization objective and decomposing it into demand intensity and safety stress indices, and then mapping the results to pre-defined first and second control intervals using different thresholds, the complex continuous control strategy can be abstracted into a hierarchical control logic that is easy to understand and tune in engineering: when demand intensity is high and safety stress is low, the system automatically enters a control interval that allows for higher carbon dioxide volume fraction and higher total gas mass flow rate; when demand intensity is low or safety stress is high, the system switches to a conservative control interval with a lower upper limit. This continuous calculation and partitioned mapping approach preserves the precision of parameter entanglement calculations, accurately reflecting the impact of X and Y changes on control intensity; it also provides clear intervals and thresholds for engineering commissioning, facilitating rapid tuning and migration applications based on different algal species, reactor sizes, and process objectives, thus reducing the barrier to field implementation due to model complexity.

Claims

1. A smart carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage, characterized in that, include: Microalgae photobioreactor, used to contain microalgae culture medium and provide stirring device and circulation pump; Temperature sensor, pH electrode, dissolved oxygen electrode, conductivity sensor, optical density or turbidity sensor for characterizing algal concentration, photosynthetically active radiation intensity sensor, and circulating flow meter; The gas supply module includes a carbon dioxide gas source, an air source, a gas mixing module, a mass flow controller, and a solenoid valve, used to introduce a mixture of carbon dioxide and air into the microalgae photobioreactor. An embedded controller or a host computer, wherein the embedded controller is electrically connected to the sensor and the gas supply module; The embedded controller is configured to perform the following steps during system operation: S1. Connect the temperature sensor, pH electrode, dissolved oxygen electrode, conductivity sensor, optical density or turbidity sensor, photosynthetically active radiation intensity sensor, and circulating flow meter to the embedded controller, and connect the carbon dioxide gas source and air source to the gas mixing module and the mass flow controller, respectively; S2. Before inoculation, calibrate each of the sensors, start the stirring device and circulating pump to establish a stable circulating flow field, and collect real-time data of temperature, pH, dissolved oxygen, conductivity, turbidity, photosynthetically active radiation intensity, and circulating flow rate at a preset sampling period to form a multi-parameter operating condition vector; S3. Introduce a mixture of carbon dioxide and air into the microalgae photobioreactor at a constant total aeration flow rate. In the initial stage, operate according to the empirically set carbon dioxide volume fraction and pH target range to obtain historical operating data representing the reactor under different operating conditions. S4. During the cultivation process, based on the photosynthetically active radiation intensity, algal concentration, temperature and historical growth curve, identify the current growth stage of the microalgae, and calculate the growth carbon demand parameter to characterize the carbon demand intensity per unit time according to the growth stage and environmental conditions. S5. During the cultivation process, based on dissolved oxygen concentration, gas-liquid ratio, circulation flow rate and the aforementioned growth stage information, calculate the aeration safety parameters used to characterize the gas-side safety margin and the upper limit of carbon dioxide mass transfer. S6. Based on the combined growth carbon requirement parameters and aeration safety parameters, determine the optimal combination of carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch, and adjust the mass flow controller and the solenoid valve accordingly to achieve intelligent linkage control of carbon dioxide injection.

2. The intelligent carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage according to claim 1, characterized in that, The S4 further includes: Within each sampling period, the current photosynthetically active radiation intensity, algal concentration, temperature, nutrient concentration, and the rate of change of each of the above parameters within a preset time window are used as input variables. The cultivation process is divided into at least one growth stage, namely, adaptation period, logarithmic growth period, stationary period and decline period, based on the absolute value of algal concentration and growth rate, and a target carbon conversion rate range is preset for each growth stage. By utilizing the photosynthetic response relationship between photosynthetically active radiation intensity and algal concentration, combined with the effect of temperature on photosynthetic efficiency correction and the limiting effect of nutrient concentration on growth rate, the theoretical carbon dioxide consumption rate per unit volume of culture medium in the current time period is estimated, and this theoretical carbon dioxide consumption rate is converted into the growth carbon requirement parameter by culture volume and safety factor.

3. The intelligent carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage according to claim 2, characterized in that, Calculating the carbon requirement parameters for growth includes: Pre-determine the maximum specific carbon dioxide consumption rate constant per unit algal cell under optimal growth conditions; Calculate the light response factor, which is used to characterize the effect of photosynthetically active radiation intensity on the photosynthetic rate per unit cell. The light response factor increases with the increase of photosynthetically active radiation intensity when the photosynthetically active radiation intensity is less than a preset light response characteristic intensity, and enters the response saturation region after the photosynthetically active radiation intensity reaches the light response characteristic intensity. Calculate the biomass factor, which is used to characterize the contribution of algal concentration to the overall carbon demand. When the algal concentration is less than a preset concentration characteristic value, it increases with the increase of algal concentration. When the algal concentration reaches or exceeds the concentration characteristic value, it enters the contribution saturation region. Calculate a temperature correction factor, which is used to characterize the attenuation effect of the culture temperature on photosynthetic efficiency when it deviates from the optimal growth temperature. The temperature correction factor reaches its maximum value when the culture temperature is consistent with the optimal growth temperature, and decreases according to a preset symmetry law when the culture temperature deviates from the optimal growth temperature. The embedded controller combines the maximum specific carbon dioxide consumption rate constant with the light response factor, biomass factor, and temperature correction factor to obtain the growth carbon demand parameter, so that the growth carbon demand parameter can reflect the target carbon dioxide demand intensity per unit time under the current light conditions, algal load, and temperature conditions.

4. The intelligent carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage according to claim 3, characterized in that, The S5 also includes: Within each sampling period, acquire the current dissolved oxygen concentration, dissolved oxygen saturation concentration, total gas volumetric flow rate, culture medium volume, and circulation flow rate; The gas flux per unit volume of culture medium is estimated based on the ratio of total gas volumetric flow rate to culture medium volume, and the corresponding total volumetric mass transfer coefficient is estimated by combining the reactor structure, bubble characteristics and circulation flow rate. Based on the proximity of the current dissolved oxygen concentration to the dissolved oxygen saturation concentration, a dissolved oxygen inhibition factor is constructed to represent the risk of dissolved oxygen oversaturation. When the dissolved oxygen concentration is in the low range of the preset allowable range, the dissolved oxygen inhibition factor takes a lower level, and when the dissolved oxygen concentration is close to the preset allowable upper limit, the dissolved oxygen inhibition factor takes a higher level. The dissolved oxygen inhibitor is combined with gas flux, total volumetric mass transfer coefficient, growth stage information, circulation flow rate, and reactor structural parameters. The aeration safety parameters are determined by a preset calculation rule. The aeration safety parameters represent the space available for increasing the carbon dioxide dosage under the current operating conditions.

5. The intelligent carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage according to claim 4, characterized in that, When calculating the aeration safety parameters: The ratio of total gas volumetric flow rate to reactor working volume is used as the basic quantity characterizing the gas-liquid ratio. Based on the empirical law of the influence of gas-liquid ratio on mass transfer load, the basic quantity is nonlinearly converted to reflect the comprehensive influence of mass transfer capacity and shear effect under different gas-liquid ratios. The dissolved oxygen inhibition factor is used as a safety mitigation factor to reflect the degree to which dissolved oxygen approaches the upper limit of the process. When the dissolved oxygen concentration is in the high range of the preset allowable range, the mitigation effect of the dissolved oxygen inhibition factor increases, thereby reducing the available aeration margin. Set equipment constants related to reactor structure and bubble characteristics to comprehensively consider the effects of bubble residence time, bubble size and distribution on gas-liquid mass transfer and mixing; The light load information and algal load information corresponding to the light response factor and biomass factor are introduced into the aeration safety parameters so that the current light conditions, algal concentration, gas-liquid ratio and dissolved oxygen risk are considered when determining the aeration safety parameters. The aeration safety parameters are obtained through comprehensive calculation of the above-mentioned gas-liquid ratio, dissolved oxygen inhibition factor, equipment constant, and light and biomass information. They are used to provide quantitative constraints on the allowable carbon dioxide dosage intensity. The allowable dosage intensity threshold corresponding to the dissolved oxygen near the upper limit and the gas-liquid ratio in the high load range is lower than the allowable dosage intensity threshold when the dissolved oxygen is in the low load range and the gas-liquid ratio is in the low load range.

6. The intelligent carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage according to claim 5, characterized in that, The S6 further includes: The growth carbon demand parameter is used as a driving variable to increase carbon dioxide supply, and the aeration safety parameter is used as a constraint variable to limit the upper limit of carbon dioxide supply. A comprehensive optimization objective is constructed that includes a penalty term for insufficient carbon dioxide supply and a penalty term for exceeding the limit of dissolved oxygen or aeration conditions. Using carbon dioxide volume fraction, total gas mass flow rate, and gas distribution ratio of each aeration branch as decision variables to be determined, within a preset time prediction window, the comprehensive optimization target is predicted and evaluated based on the impact of different combinations of decision variables on pH evolution trend, dissolved oxygen change, and carbon dioxide utilization rate. Under the premise of ensuring that dissolved oxygen does not exceed the set upper limit, gas-liquid ratio does not exceed the equipment safety range, and reactor operation is stable, the combination of decision variables that minimizes the comprehensive optimization objective value is selected from multiple candidate combinations as the optimal setting for the current control cycle. The optimal settings are converted into the target flow value of the mass flow controller, the carbon dioxide to air ratio setting value, and the opening, closing or adjustment commands of each aeration branch, thereby dynamically updating the carbon dioxide dosing strategy during continuous operation.

7. The intelligent carbon dioxide delivery system for microalgae cultivation based on multi-parameter linkage according to claim 6, characterized in that, The method for determining the comprehensive control intensity parameters is as follows: Calculate the ratio between the growth carbon demand parameter and the normalization constant related to the growth stage, and use this ratio as a demand intensity indicator that reflects the current carbon dioxide demand relative to the typical demand level of that growth stage. Calculate the ratio between the aeration safety parameter and the reference value used to measure the aeration safety margin, and use this ratio as a safety tension index reflecting how close the current aeration conditions are to the safety boundary; Based on the pre-defined rule that an increase in the demand intensity index amplifies the comprehensive control intensity parameter, and an increase in the safety tension index inhibits the comprehensive control intensity parameter, the comprehensive control intensity parameter is calculated and updated in conjunction with the comprehensive amplification coefficient. When the demand intensity index is not less than the first threshold and the safety tension index is not greater than the second threshold, the embedded controller sets the carbon dioxide volume fraction and total gas mass flow rate obtained by mapping the comprehensive control intensity parameters within the pre-stored first control range. When the demand intensity index is lower than the third threshold or the safety tension index is higher than the fourth threshold, the embedded controller sets the carbon dioxide volume fraction and total gas mass flow rate obtained by mapping the comprehensive control intensity parameters within a pre-stored second control interval. The upper limit of the carbon dioxide volume fraction and the upper limit of the total gas mass flow rate in the second control interval are both lower than the upper limit corresponding to the first control interval. The first control interval and the second control interval are linked to the comprehensive control intensity parameter through a monotonic mapping relationship, so that the system can achieve comprehensive optimization and adjustment of the carbon dioxide delivery strategy under different growth stages while meeting the aeration safety constraints.

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