Microbial fermentation control system based on intelligent sensor

Abstract

The present application relates to the technical field of microbial fermentation, and particularly relates to a microbial fermentation control system based on intelligent sensors, which comprises: a data acquisition module, which is used to periodically acquire environmental information and reactant information inside a fermentation tank; a data analysis module, which is used to construct a fermentation coupling model based on the change of the reactant information inside the fermentation tank in a target time period, construct a microbial growth curve according to the change of the environmental information, and determine a microbial growth key area based on the microbial growth curve; a data judgment module, which is used to determine a microbial fermentation process based on the fermentation coupling model and the microbial growth key area, and determine whether the microbial fermentation meets preset standards according to the microbial fermentation process; and a control adjustment module, which is used to determine a control adjustment mode of the fermentation tank based on the determination result of whether the microbial fermentation process meets the preset standards. The present application can accurately control the microbial fermentation environment, thereby improving the fermentation effect and the fermentation efficiency.

Description

Technical Field

[0001] This invention relates to the field of microbial fermentation technology, and in particular to a microbial fermentation control system based on intelligent sensors. Background Technology

[0002] Microbial fermentation refers to the process by which microorganisms, under suitable conditions, transform raw materials into products needed by humans through specific metabolic pathways. The production level of microbial fermentation mainly depends on the genetic characteristics of the microbial strain and the cultivation conditions. Automated control of bio-fermentation projects not only improves workshop production efficiency but also ensures high product quality. The fermentation process itself is crucial to the success of the entire fermentation production process and is also the most complex and susceptible to contamination. Therefore, before production, the fermenter and its process pipelines must be cleaned and sterilized. After adding the culture medium, sterilization is repeated. Simultaneously with sterilization, the control of process parameters such as pressure and temperature within the fermenter must be initiated to ensure sterility. These process parameters not only affect fermentation efficiency but also determine the fermentation effect. However, traditional microbial fermentation control relies heavily on manual monitoring and control. When changes in the fermentation environment are detected, manual judgment is used to determine whether process parameter adjustments are necessary. However, the complexity and uncertainty of the parameters within the fermenter during fermentation make precise control of the fermentation process difficult, resulting in low fermentation efficiency and poor fermentation effects.

[0003] Chinese Patent Publication No. CN116144489B discloses an automatic control system for microbial fermentation, comprising: acquiring time-series temperature data of a fermenter to form a temperature curve, and obtaining several target curve segments; selecting a first temperature curve and a first target curve segment, and obtaining distance parameters between data points of the first target curve segment and data points of the other target curve segments; performing shortest path matching on the first target curve segment and the other target curve segments based on the distance parameters, obtaining matching results, and obtaining the net heat of the fermenter in the time sequence of the first target curve segment based on the matching results; clustering the first target curve segment to obtain clusters; obtaining the compensation amount of the adjustment amount; and adjusting and compensating the heat of the fermenter based on the compensation amount of the adjustment amount to complete the control of microbial fermentation.

[0004] The existing technology has the following problems: relying solely on analyzing the temperature data inside the fermenter to adjust the heat of the fermenter makes it difficult to guarantee the accuracy of the adjustment, thereby reducing fermentation efficiency and fermentation effect. Summary of the Invention

[0005] Therefore, the present invention provides a microbial fermentation control system based on intelligent sensors to overcome the problems of low fermentation efficiency and poor fermentation effect caused by the difficulty in achieving precise control of the fermentation process through artificial regulation and the consideration of only the influence of temperature factors in the prior art.

[0006] To achieve the above objectives, the present invention provides a microbial fermentation control system based on intelligent sensors, comprising: The data acquisition module is used to periodically collect environmental information and reactant information inside the fermenter. The environmental information includes air pressure and temperature, and the reactant information includes substrate concentration, pH, and cell concentration. The data analysis module, which is connected to the data acquisition module, is used to construct a fermentation coupling model based on the changes in reactant information inside the fermenter within a target time period, construct a microbial growth curve based on changes in environmental information, and determine the key microbial growth zone based on the microbial growth curve. The data determination module, which is connected to the data analysis module, is used to determine the microbial fermentation process based on the fermentation coupling model and the key microbial growth zone, and to determine whether the microbial fermentation meets the preset standards based on the microbial fermentation process. A control and adjustment module, connected to both the data determination module and the data analysis module, is used to determine the control and adjustment method of the fermenter based on the determination result that the microbial fermentation process does not meet a preset standard. This includes... The environmental information adjustment amount is determined based on the key environmental information corresponding to the critical microbial growth zone. Alternatively, the predicted reactant information can be determined based on the fermentation coupling model, and the nutrient solution addition parameters, including the amount of nutrient solution added and the addition time, can be determined based on the predicted reactant information.

[0007] Furthermore, the data acquisition module includes: The environmental information acquisition submodule includes several environmental monitoring components set at several first preset positions in the monitoring area inside the fermenter; The reactant information acquisition submodule includes several reactant monitoring components set at several second preset locations in the fermentation area inside the fermenter.

[0008] Furthermore, the data analysis module includes: An environmental information analysis submodule, which is connected to the environmental information acquisition submodule, is used to determine the environmental change curve corresponding to each first preset location and the environmental characterization curve corresponding to each acquisition time point based on the environmental information change at each first preset location, and to determine the target time period and key preset location based on the environmental change curve and the environmental characterization curve.

[0009] Furthermore, the data analysis module also includes: The curve construction submodule, which is connected to the environmental information analysis submodule, is used to determine the fermentation environment influence coefficient based on the changes in environmental information at key preset locations within the target time period, and to construct a microbial growth curve based on the fermentation environment influence coefficient.

[0010] Furthermore, the data analysis module also includes: The model construction submodule, which is connected to the reactant information acquisition submodule, is used to determine several reactant fermentation factors based on the changes in reactant information at each of the second preset locations, and to construct a fermentation coupling model based on each reactant fermentation factor.

[0011] Furthermore, the data analysis module also includes: The curve analysis submodule, which is connected to the curve construction submodule, is used to determine the critical zone of microbial growth based on the curve change rate of the microbial growth curve.

[0012] Furthermore, the data determination module includes: The fermentation process determination submodule is connected to the model construction submodule and the curve analysis submodule, respectively. It is used to determine the key time period based on the duration of the key growth zone of the microorganism, and to determine the microbial fermentation process based on the changes in reactant information within the key time period and the fermentation coupling model.

[0013] Furthermore, the data determination module also includes: The fermentation determination submodule, which is connected to the fermentation process determination submodule, is used to determine standard fermentation indicators based on the microbial fermentation process, and to determine whether the microbial fermentation meets the preset standards based on the comparison results between the current environmental information and the current reactant information and the standard fermentation indicators. If both the current environmental information and the current reactant information meet the standard fermentation indicators, then the microbial fermentation is determined to meet the preset standards.

[0014] Furthermore, the control adjustment module determines the control adjustment mode of the fermenter as the first adjustment mode based on the comparison results of the current environmental information not meeting the standard fermentation indicators; The first adjustment method involves determining the environmental information adjustment amount based on the key environmental information corresponding to the critical microbial growth zone.

[0015] Furthermore, the control adjustment module determines the control adjustment mode of the fermenter as the second adjustment mode based on the comparison results of the current reactant information not meeting the standard fermentation index; The second adjustment method involves determining the predicted reactant information based on the fermentation coupling model and determining the nutrient solution addition parameters based on the predicted reactant information.

[0016] Compared with existing technologies, the advantages of this invention are as follows: By setting up a data acquisition module to periodically collect environmental and reactant information within the fermenter, real-time dynamic monitoring of the fermentation process can be achieved, providing multi-dimensional data support for subsequent data analysis. By setting up a data analysis module, a fermentation coupling model can be constructed based on changes in reactant information within the fermenter over a target time period, coupling the product synthesis and inherent laws during the fermentation process. Environmental information has a direct impact on microbial fermentation; by constructing microbial growth curves, the growth characteristics of microorganisms can be analyzed, providing a basis for fermentation control during subsequent microbial fermentation and improving the accuracy of control. By setting up a data judgment module, the fermentation process can be comprehensively determined based on the fermentation coupling model and key microbial growth zones, and whether it meets preset standards can be accurately analyzed, preventing fermentation anomalies and further improving fermentation efficiency and effect. By setting up a control adjustment module, the control adjustment method of the fermenter can be determined based on the judgment results of the data judgment module, enabling precise control of the microbial fermentation process. The control and adjustment methods include adjusting environmental information and nutrient solution addition, which can flexibly adjust the environmental conditions or nutrient supply of the fermenter to adapt to different fermentation conditions and needs, improve fermentation efficiency and fermentation effect, and ensure the stability of the fermentation process.

[0017] Furthermore, the data acquisition module of this invention, by setting up an environmental information acquisition submodule and installing environmental monitoring components at several first preset locations within the monitoring area of ​​the fermenter, can more accurately acquire environmental information at different locations within the fermenter. Multi-point monitoring can provide more comprehensive data, reflecting subtle changes and distribution of the environment within the fermenter. By setting up a reactant information acquisition submodule and installing reactant monitoring components at several second preset locations within the fermentation area of ​​the fermenter, relevant reactant information can be comprehensively monitored. Multi-point monitoring helps to pinpoint the specific locations of reactant changes during fermentation and analyze reactant dynamics in different areas within the fermenter.

[0018] Furthermore, the data analysis module of this invention, by setting up an environmental information analysis submodule, determines the corresponding environmental change curves by analyzing the changes in environmental information at each first preset location. This allows for the determination of the dynamic trend of environmental information at each collection point (first preset location) over time. By analyzing the changes in environmental information at each collection point within the fermenter at each collection time point, corresponding environmental characterization curves are determined. This enables a comprehensive analysis of the correlation between environmental information at different locations within the fermenter, thus determining the overall environmental dynamics within the fermenter. Through the analysis of environmental change curves and environmental characterization curves, the important time periods for microbial growth and product synthesis during fermentation, as well as the determination of target time periods and key preset locations for important collection points, can be identified. This reduces the amount of data processing and improves the accuracy of data analysis, thereby further enhancing the precise monitoring and control of the fermentation process.

[0019] Furthermore, the data analysis module of this invention determines the fermentation environment influence coefficient based on the changes in environmental information at key preset locations within a target time period by setting a curve construction sub-module. This can quantify the comprehensive influence of environmental information on microbial growth and characterize the growth status of microorganisms in the fermenter.

[0020] Furthermore, the data analysis module of this invention, by setting up a model construction sub-module, determines several reactant fermentation factors based on the changes in reactant information at each second preset position. This can characterize the correlation between reactant information and product synthesis during the fermentation process, thereby constructing a fermentation coupling model that can accurately couple the dynamic changes of the fermentation process, achieve accurate prediction and simulation, and thus improve the fermentation effect.

[0021] Furthermore, the data determination module of this invention, by setting a fermentation process determination submodule, determines the critical time period based on the duration of the critical microbial growth zone, enabling precise positioning of the stages in the fermentation process that have a decisive impact on microbial growth and product synthesis. By combining the changes in reactant information within the critical time period with the fermentation coupling model, and comprehensively analyzing multi-source data, the microbial fermentation process can be determined more comprehensively and accurately, avoiding the errors and uncertainties caused by single data analysis, thereby further improving fermentation efficiency and fermentation effect.

[0022] Furthermore, the data judgment module of the present invention, by setting a fermentation judgment submodule, determines standard fermentation indicators based on the microbial fermentation process, which can accurately determine whether the fermentation process meets the preset standards. Through real-time comparison and judgment, abnormal situations in the fermentation process can be detected in a timely manner, thereby improving fermentation efficiency and fermentation effect.

[0023] Furthermore, the control and adjustment module of this invention determines different control and adjustment methods based on the comparison results between the current environmental information and the current reactant information with standard fermentation indicators. It determines the environmental information adjustment amount based on the key environmental information corresponding to the critical microbial growth zone, enabling precise adjustments to be made when the current environmental information does not meet the standard fermentation indicators. This helps to quickly restore the stability of the fermentation environment and ensures that microorganisms grow under optimal fermentation conditions. Based on the fermentation coupling model, the predicted reactant information is determined, and the nutrient solution addition parameters are used to optimize the nutrient solution supply. By rationally adding nutrient solution, the degradation of product quality caused by insufficient or excessive nutrients can be avoided, thereby improving fermentation efficiency and fermentation effect. Attached Figure Description

[0024] Figure 1 This is a structural block diagram of a microbial fermentation control system based on intelligent sensors, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of the data acquisition module according to an embodiment of the present invention; Figure 3 This is a structural block diagram of the data analysis module in an embodiment of the present invention; Figure 4 This is a structural block diagram of the data determination module in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0026] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0027] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0028] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] Please see Figures 1-4 As shown, Figure 1 This is a structural block diagram of a microbial fermentation control system based on intelligent sensors, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of the data acquisition module according to an embodiment of the present invention; Figure 3 This is a structural block diagram of the data analysis module in an embodiment of the present invention; Figure 4 This is a structural block diagram of the data determination module in an embodiment of the present invention; an embodiment of the present invention provides a microbial fermentation control system based on intelligent sensors, including: The data acquisition module is used to periodically collect environmental information and reactant information inside the fermenter. The environmental information includes air pressure and temperature, and the reactant information includes substrate concentration, pH, and cell concentration. Specifically, the data acquisition module includes: The environmental information acquisition submodule includes several environmental monitoring components set at several first preset positions in the monitoring area inside the fermenter; The reactant information acquisition submodule includes several reactant monitoring components set at several second preset locations in the fermentation area inside the fermenter.

[0030] In implementation, the specific structure of the environmental monitoring component and the reactant monitoring component is not limited. The environmental monitoring component shall include at least a pressure sensor and a temperature sensor, and the reactant monitoring component shall include at least a biosensor (to detect substrate concentration), a pH sensor (to detect acidity and alkalinity), and a capacitance sensor (to detect cell concentration by detecting changes in the capacitance of the fermentation broth).

[0031] Understandably, inside the fermenter, the reactant liquid level serves as the dividing line, with the area above the liquid level being the monitoring area and the area below the liquid level being the fermentation area. Several first preset positions are evenly distributed on the fermenter wall in the monitoring area (the area of ​​the fermenter wall in the monitoring area is positively correlated with the number of first preset positions), and each first preset position is equipped with a corresponding environmental monitoring component. Several second preset positions are evenly distributed on the fermenter wall and bottom of the fermenter in the fermentation area (the area of ​​the fermenter wall and bottom of the fermentation area is positively correlated with the number of second preset positions), and each second preset position is equipped with a corresponding reactant monitoring component.

[0032] The data acquisition module of this invention, by setting up an environmental information acquisition submodule and installing environmental monitoring components at several first preset locations within the monitoring area of ​​the fermenter, can more accurately acquire environmental information at different locations within the fermenter. Multi-point monitoring can provide more comprehensive data, reflecting subtle changes and distribution of the environment within the fermenter. Similarly, by setting up a reactant information acquisition submodule and installing reactant monitoring components at several second preset locations within the fermentation area of ​​the fermenter, relevant reactant information can be comprehensively monitored. Multi-point monitoring helps to pinpoint the specific locations of reactant changes during fermentation and analyze reactant dynamics in different areas within the fermenter.

[0033] The data analysis module, which is connected to the data acquisition module, is used to construct a fermentation coupling model based on the changes in reactant information inside the fermenter within a target time period, construct a microbial growth curve based on changes in environmental information, and determine the key microbial growth zone based on the microbial growth curve. Specifically, the data analysis module includes: An environmental information analysis submodule, which is connected to the environmental information acquisition submodule, is used to determine the environmental change curve corresponding to each first preset location and the environmental characterization curve corresponding to each acquisition time point based on the environmental information change at each first preset location, and to determine the target time period and key preset location based on the environmental change curve and the environmental characterization curve.

[0034] In implementation, for each first preset location, the corresponding environmental characteristic value is determined based on the collected air pressure and temperature (environmental characteristic value HT=(QY / BQY+WD / BWD)) / 2, where QY is the air pressure, BQY is the preset air pressure, WD is the temperature, and BWD is the preset temperature. The implementers can set the preset air pressure based on actual conditions or the average fermentation air pressure that has passed qualification tests in historical data, and the preset temperature can be set based on actual conditions or the average fermentation temperature that has passed qualification tests in historical data. The collection time point is used as the independent variable, and each collection time point is used as the first preset location. Let the environmental characteristic value of the location be the dependent variable, and construct the environmental change curve corresponding to each first preset location. For each collection time point, each first preset location is labeled (it can be sorted from bottom to top in the monitoring area, with any first preset location at the same height at the bottom (bottom layer) as label 1, and the first preset location on the same axis as label 1 in clockwise or counterclockwise ascending order is used as the starting point of the label for each layer, and the labeling direction is the same as the bottom layer). With the label of the first preset location as the independent variable and the environmental characteristic value determined by the first preset location as the dependent variable, construct the environmental characterization curve corresponding to each collection time point.

[0035] Understandably, the curve change rate corresponding to each collection time point in each environmental change curve is calculated. If the duration of the curve change rate greater than the preset environmental change rate is greater than the preset duration, then the corresponding time period is determined as the target time period. The target time period includes several collection time points. Practitioners can determine the preset duration based on actual conditions or the average duration of successful compliance checks from historical data. Preferably, the preset duration is set to 1 / 5 to 1 / 6 of the total fermentation time. Practitioners can also set the preset environmental change rate based on actual conditions. Preferably, the preset environmental change rate is set to 0.9 to 1.5.

[0036] Understandably, the environmental characterization curves corresponding to each collection time point within the target time period are determined as key environmental characterization curves. Key preset location groups are determined based on the comparison results of the maximum air pressure difference and the preset air pressure difference in each preset location group within each key environmental characterization curve, as well as the comparison results of the maximum temperature difference and the preset temperature difference. Each labeled layer is considered a preset location group. The air pressure difference and temperature difference collected at any two first preset locations at each collection time point within each preset location group are calculated. If the maximum air pressure difference of any preset location group at any collection time point is greater than the preset air pressure difference, and the temperature difference between the maximum air pressure location and the minimum temperature in that preset location group is greater than the preset temperature difference; or, if the maximum temperature difference of any preset location group at any collection time point is greater than the preset temperature difference, and the air pressure difference between the maximum temperature location and the minimum air pressure in that preset location group is greater than the preset air pressure difference, then the first preset location corresponding to the maximum air pressure and maximum temperature is designated as a key preset location. The implementers can set a preset pressure difference based on the actual situation or the maximum pressure difference in the fermentation tank during the fermentation process that has passed the qualification test in historical data. The implementers can also set a preset temperature difference based on the actual situation or the maximum temperature difference in the fermentation tank during the fermentation process that has passed the qualification test in historical data. Preferably, the preset pressure difference is set to a range of 0.01MPa to 0.02MPa, and the preset temperature difference is set to a range of 1℃ to 2℃.

[0037] The mean value of the environmental information difference is set to the preset environmental information difference.

[0038] The data analysis module of this invention, by setting up an environmental information analysis submodule, determines the corresponding environmental change curves by analyzing the changes in environmental information at each first preset location. This allows for the determination of the dynamic trend of environmental information at each collection point (first preset location) over time. By analyzing the changes in environmental information at each collection point within the fermenter at each collection time point, corresponding environmental characterization curves are determined. This enables a comprehensive analysis of the correlation between environmental information at different locations within the fermenter, determining the overall environmental dynamics within the fermenter. Through the analysis of environmental change curves and environmental characterization curves, the critical time periods for microbial growth and product synthesis during fermentation, as well as the determination of target time periods and key preset locations for important collection points, can be identified. This reduces data processing volume, improves the accuracy of data analysis, and thus further enhances the precise monitoring and control of the fermentation process.

[0039] Specifically, the data analysis module also includes: The curve construction submodule, which is connected to the environmental information analysis submodule, is used to determine the fermentation environment influence coefficient based on the changes in environmental information at key preset locations within the target time period, and to construct a microbial growth curve based on the fermentation environment influence coefficient.

[0040] In implementation, the first pressure ratio is determined based on the ratio of the minimum to the maximum air pressure collected at key preset locations within the target time period, and the first temperature ratio is determined based on the ratio of the minimum to the maximum temperature collected at key preset locations within the target time period. The product of the first pressure ratio and the first temperature ratio is determined as the fermentation environment influence coefficient.

[0041] It is understood that an initial growth curve model is trained based on historical microbial growth data and fermentation environment influence coefficients, and a microbial growth curve is constructed based on the trained growth curve model. Those skilled in the art know that any growth curve model in the prior art that can characterize the microbial growth curve falls within the protection scope of this invention, such as the Logistic model, the Gompertz model, etc., which will not be elaborated here.

[0042] The data analysis module of this invention determines the fermentation environment influence coefficient based on the changes in environmental information at key preset locations within a target time period by setting a curve construction submodule. This can quantify the comprehensive influence of environmental information on microbial growth and characterize the growth status of microorganisms in the fermenter.

[0043] Specifically, the data analysis module also includes: The model construction submodule, which is connected to the reactant information acquisition submodule, is used to determine several reactant fermentation factors based on the changes in reactant information at each of the second preset locations, and to construct a fermentation coupling model based on each reactant fermentation factor.

[0044] In implementation, for each second preset location, a substrate concentration change curve is constructed based on the substrate concentration change at adjacent sampling time points, a reactant change curve is constructed based on the pH change at adjacent sampling time points, and a cell concentration change curve is constructed based on the cell concentration change at adjacent sampling time points. The rate of change of the substrate concentration change curve, pH change curve, and cell concentration change curve at each location is calculated. If any substrate concentration change rate is greater than the preset substrate concentration change rate, or any pH change rate is greater than the preset pH change rate, or any cell concentration change rate is greater than the preset cell concentration change rate, then the corresponding substrate concentration change rate, pH change rate, and cell concentration change rate are determined as reactant fermentation factors.

[0045] It is understandable that the fermentation factor of the reactants is used as the input variable, and the corresponding degree of fermentation (determined based on the ratio of products to reactants in the fermenter) is used as the output variable to train the initial neural network model in order to obtain the fermentation coupling model.

[0046] The data analysis module of this invention, by setting up a model construction sub-module, determines several reactant fermentation factors based on the changes in reactant information at each second preset position. This can characterize the correlation between reactant information and product synthesis during the fermentation process, thereby constructing a fermentation coupling model that can accurately couple the dynamic changes of the fermentation process, achieve accurate prediction and simulation, and thus improve the fermentation effect.

[0047] Specifically, the data analysis module also includes: The curve analysis submodule, which is connected to the curve construction submodule, is used to determine the critical zone of microbial growth based on the curve change rate of the microbial growth curve.

[0048] In implementation, the period during which the rate of change of the microbial growth curve is greater than the preset growth rate of change is defined as the critical zone for microbial growth. Practitioners can set the preset growth rate of change based on the actual situation. Preferably, the preset growth rate of change is set to a range of 0.5 to 1.0.

[0049] It is understandable that the rate of change of the curve can be calculated based on numerical differentiation or mathematical fitting methods, which are existing technologies and will not be elaborated here.

[0050] The data determination module, which is connected to the data analysis module, is used to determine the microbial fermentation process based on the fermentation coupling model and the key microbial growth zone, and to determine whether the microbial fermentation meets the preset standards based on the microbial fermentation process. Specifically, the data determination module includes: The fermentation process determination submodule is connected to the model construction submodule and the curve analysis submodule, respectively. It is used to determine the key time period based on the duration of the key growth zone of the microorganism, and to determine the microbial fermentation process based on the changes in reactant information within the key time period and the fermentation coupling model.

[0051] In implementation, the substrate concentration coefficient is determined based on the ratio of the minimum to the maximum substrate concentration collected at each second preset location within the key time period; the pH coefficient is determined based on the ratio of the minimum to the maximum pH collected at each second preset location within the key time period; the cell concentration coefficient is determined based on the ratio of the minimum to the maximum cell concentration collected at each second preset location within the key time period; the target comprehensive reactant coefficient is determined based on the average of the substrate concentration coefficient, pH coefficient, and cell concentration coefficient; and the target fermentation degree is determined based on the output of the fermentation coupling model.

[0052] In one specific embodiment, a training sample set is constructed based on the comprehensive reactant coefficient and fermentation degree of the pass qualification test in historical data, and the neural network model is trained based on the training sample set to obtain a microbial fermentation process model. The target comprehensive reactant coefficient and the target fermentation degree are input into the microbial fermentation process model to obtain the microbial fermentation process output by the microbial fermentation process model.

[0053] In another specific embodiment, a microbial fermentation process reference table can be set based on actual conditions, and a corresponding comprehensive reactant coefficient range and fermentation degree can be set for each microbial fermentation process. The fermentation degree includes the initial fermentation stage (product to reactant ratio of [0-0.3]), the middle fermentation stage (product to reactant ratio of [0.3-0.7]), and the final fermentation stage (product to reactant ratio of [0.7-1.0]). In the initial fermentation stage, the substrate concentration and pH are relatively high, while the cell concentration is relatively low, and the degree of change is relatively small. Therefore, the corresponding comprehensive reactant coefficient is relatively small. Preferably, The comprehensive reactant coefficient is set to a range of 0.1 to 0.5. In the middle stage of fermentation, as the fermentation reaction occurs, the substrate concentration and pH gradually decrease, while the cell concentration increases, resulting in a large degree of change. Therefore, the corresponding comprehensive reactant coefficient is relatively large. Preferably, the comprehensive reactant coefficient is set to a range of 0.8 to 1.0. In the late stage of fermentation, the substrate concentration and pH are relatively low, with a small degree of change, while the cell concentration gradually increases, resulting in a large degree of change. Therefore, the corresponding comprehensive reactant coefficient is larger than that in the early stage of fermentation but smaller than that in the middle stage of fermentation. Preferably, the comprehensive reactant coefficient is set to a range of 0.5 to 0.8.

[0054] The data determination module of this invention, by setting a fermentation process determination submodule, determines the critical time period based on the duration of the critical microbial growth zone. This allows for precise positioning of the stages in the fermentation process that have a decisive impact on microbial growth and product synthesis. By combining reactant information changes within the critical time period with the fermentation coupling model, and comprehensively analyzing multi-source data, the microbial fermentation process can be determined more comprehensively and accurately. This avoids the errors and uncertainties caused by single-data analysis, thereby further improving fermentation efficiency and fermentation effect.

[0055] Specifically, the data determination module also includes: The fermentation determination submodule, which is connected to the fermentation process determination submodule, is used to determine standard fermentation indicators based on the microbial fermentation process, and to determine whether the microbial fermentation meets the preset standards based on the comparison results between the current environmental information and the current reactant information and the standard fermentation indicators. If both the current environmental information and the current reactant information meet the standard fermentation indicators, then the microbial fermentation is determined to meet the preset standards.

[0056] During implementation, if the current environmental information and / or the current reactant information do not meet the standard fermentation indicators, the microbial fermentation is deemed not to meet the preset standards.

[0057] Understandably, practitioners can set standard pressure ranges based on the average maximum and minimum atmospheric pressure values ​​corresponding to each microbial fermentation process that passed qualification tests in historical data. Similarly, practitioners can set standard temperature ranges based on the average maximum and minimum temperature values ​​corresponding to each microbial fermentation process that passed qualification tests in historical data. They can also set standard substrate concentration ranges based on the average maximum and minimum substrate concentration values ​​corresponding to each microbial fermentation process that passed qualification tests in historical data. Furthermore, practitioners can set standard pH ranges based on the average maximum and minimum pH values ​​corresponding to each microbial fermentation process that passed qualification tests in historical data. Finally, practitioners can set standard cell concentration ranges based on the average maximum and minimum cell concentration values ​​corresponding to each microbial fermentation process that passed qualification tests in historical data. Standard fermentation indicators include standard pressure ranges, standard temperature ranges, standard substrate concentration ranges, standard pH ranges, and standard cell concentration ranges.

[0058] The data judgment module of this invention, by setting up a fermentation judgment sub-module, determines standard fermentation indicators based on the microbial fermentation process, which can accurately determine whether the fermentation process meets the preset standards. Through real-time comparison and judgment, abnormal situations in the fermentation process can be detected in a timely manner, thereby improving fermentation efficiency and fermentation effect.

[0059] A control and adjustment module, connected to both the data determination module and the data analysis module, is used to determine the control and adjustment method of the fermenter based on the determination result that the microbial fermentation process does not meet a preset standard. This includes... The environmental information adjustment amount is determined based on the key environmental information corresponding to the critical microbial growth zone. Alternatively, the predicted reactant information can be determined based on the fermentation coupling model, and the nutrient solution addition parameters, including the amount of nutrient solution added and the addition time, can be determined based on the predicted reactant information.

[0060] Specifically, the control adjustment module determines the control adjustment mode of the fermenter as the first adjustment mode based on the comparison results of the current environmental information not meeting the standard fermentation indicators; The first adjustment method involves determining the environmental information adjustment amount based on the key environmental information corresponding to the critical microbial growth zone.

[0061] In implementation, the average air pressure corresponding to the key area for microbial growth is determined as the key air pressure, the average temperature corresponding to the key area for microbial growth is determined as the key temperature, the difference between the key air pressure and the current air pressure is determined as the air pressure adjustment amount, and the difference between the key temperature and the current temperature is determined as the temperature adjustment amount. The environmental information adjustment amount includes the air pressure adjustment amount and the temperature adjustment amount.

[0062] Specifically, the control adjustment module determines the control adjustment mode of the fermenter as the second adjustment mode based on the comparison results of the current reactant information not meeting the standard fermentation index; The second adjustment method involves determining the predicted reactant information based on the fermentation coupling model and determining the nutrient solution addition parameters based on the predicted reactant information.

[0063] In practice, the fermentation degree output by the fermentation coupling model and the preset fermentation degree comparison table or any prediction model that can output the predicted reactant information are used to determine the predicted reactant information. The implementers can set the preset fermentation degree comparison table based on the actual situation.

[0064] In implementation, the predicted substrate concentration coefficient is determined based on the ratio of the predicted substrate concentration to the current substrate concentration; the predicted pH coefficient is determined based on the ratio of the predicted pH to the current pH; the predicted cell concentration coefficient is determined based on the ratio of the predicted cell concentration to the current cell concentration; the comprehensive adjustment coefficient is determined based on the average of the predicted substrate concentration coefficient, the predicted pH coefficient, and the predicted cell concentration coefficient; the nutrient solution addition amount is determined based on the product of the comprehensive adjustment coefficient and the standard nutrient solution addition amount; and the nutrient solution addition time corresponding to the nutrient solution addition time is determined based on the product of the comprehensive adjustment coefficient and the standard nutrient solution addition time (i.e., the time from the standard nutrient solution addition time to the current time).

[0065] It is understandable that the current environmental information refers to the environmental information collected at the key preset location at the current time. The current reactant information is the average of the reactant information collected at each of the second preset locations at the current time.

[0066] It is understandable that implementers can set the standard nutrient solution addition amount and standard nutrient solution addition time based on the actual situation. Preferably, the standard nutrient solution addition amount is set to a range of 0.1L to 0.5L, and the standard nutrient solution addition time is set to a range of 15min to 30min from the current time.

[0067] This invention's control and adjustment module determines different control and adjustment methods based on comparisons between current environmental information, current reactant information, and standard fermentation indicators. It determines the environmental information adjustment amount based on key environmental information corresponding to critical microbial growth zones. This allows for precise adjustments when current environmental information does not meet standard fermentation indicators, facilitating rapid restoration of fermentation environment stability and ensuring microbial growth under optimal conditions. Furthermore, by determining predicted reactant information based on a fermentation coupling model and using this information to determine nutrient solution addition parameters, the nutrient solution supply can be optimized. Reasonable nutrient addition avoids product quality degradation caused by insufficient or excessive nutrients, thereby improving fermentation efficiency and overall fermentation effect.

[0068] This invention, through a data acquisition module, periodically collects environmental and reactant information within the fermenter, enabling real-time dynamic monitoring of the fermentation process and providing multi-dimensional data support for subsequent data analysis. A data analysis module constructs a fermentation coupling model based on changes in reactant information within the fermenter over a target time period, coupling the product synthesis and inherent patterns during fermentation. Environmental information directly impacts microbial fermentation; by constructing microbial growth curves, the growth characteristics of microorganisms can be analyzed, providing a basis for subsequent fermentation control and improving control precision. A data judgment module, based on the fermentation coupling model and key microbial growth zones, comprehensively determines the microbial fermentation process and judges whether it meets preset standards, enabling precise analysis of the fermentation process, avoiding fermentation anomalies, and further improving fermentation efficiency and effect. A control adjustment module, based on the judgment results of the data judgment module, determines the control adjustment method for the fermenter, enabling precise control of the microbial fermentation process. The control adjustment method includes adjusting environmental information and nutrient solution addition, allowing flexible adjustment of the fermenter's environmental conditions or nutrient supply to adapt to different fermentation conditions and needs, improving fermentation efficiency and effect, and ensuring the stability of the fermentation process.

[0069] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A microbial fermentation control system based on intelligent sensors, characterized in that, include: The data acquisition module is used to periodically collect environmental information and reactant information inside the fermenter. The environmental information includes air pressure and temperature, and the reactant information includes substrate concentration, pH, and cell concentration. The data analysis module, which is connected to the data acquisition module, is used to construct a fermentation coupling model based on the changes in reactant information inside the fermenter within a target time period, construct a microbial growth curve based on changes in environmental information, and determine the key microbial growth zone based on the microbial growth curve. The data determination module, which is connected to the data analysis module, is used to determine the microbial fermentation process based on the fermentation coupling model and the key microbial growth zone, and to determine whether the microbial fermentation meets the preset standards based on the microbial fermentation process. A control and adjustment module, connected to both the data determination module and the data analysis module, is used to determine the control and adjustment method of the fermenter based on the determination result that the microbial fermentation process does not meet a preset standard. This includes... The environmental information adjustment amount is determined based on the key environmental information corresponding to the critical microbial growth zone. Alternatively, the predicted reactant information can be determined based on the fermentation coupling model, and the nutrient solution addition parameters, including the amount of nutrient solution added and the addition time, can be determined based on the predicted reactant information. The data acquisition module includes: The environmental information acquisition submodule includes several environmental monitoring components set at several first preset positions in the monitoring area inside the fermenter; The data analysis module includes: An environmental information analysis submodule, which is connected to the environmental information acquisition submodule, is used to determine the environmental change curve corresponding to each first preset location and the environmental characterization curve corresponding to each acquisition time point based on the environmental information change at each first preset location, and to determine the target time period and key preset location based on the environmental change curve and the environmental characterization curve. For each of the first preset locations, an environmental change curve is constructed using the collection time point as the independent variable and the environmental characteristic value determined at each collection time point at the first preset location as the dependent variable. The environmental characteristic value HT corresponding to the first preset location is defined as HT = (QY / BQY + WD / BWD) / 2, where QY is the air pressure, BQY is the preset air pressure, WD is the temperature, and BWD is the preset temperature. For each collection time point, each of the first preset locations is labeled, and an environmental characterization curve is constructed using the label of the first preset location as the independent variable and the environmental characteristic value determined at the first preset location as the dependent variable. The rate of change of the curve at each collection time point in each environmental change curve is calculated. If the duration of the rate of change of the curve is greater than the preset environmental change rate, then... If a preset duration is set, the corresponding time period is determined as the target time period. The environmental characterization curves corresponding to each collection time point within the target time period are determined as key environmental characterization curves. Each labeled layer is a preset location group. The pressure difference and temperature difference between any two first preset locations at each collection time point within each preset location group are calculated. If the maximum pressure difference of any preset location group at any collection time point is greater than the preset pressure difference and the temperature difference between the maximum pressure location and the minimum temperature in the preset location group is greater than the preset temperature difference, or if the maximum temperature difference of any preset location group at any collection time point is greater than the preset temperature difference and the pressure difference between the maximum temperature location and the minimum pressure in the preset location group is greater than the preset pressure difference, then the first preset location corresponding to the maximum pressure and maximum temperature is taken as the key preset location. The curve construction submodule, which is connected to the environmental information analysis submodule, is used to determine the fermentation environment influence coefficient based on the changes in environmental information at key preset locations within the target time period, and to construct a microbial growth curve based on the fermentation environment influence coefficient.

2. The microbial fermentation control system based on intelligent sensors according to claim 1, characterized in that, The data acquisition module includes: The reactant information acquisition submodule includes several reactant monitoring components set at several second preset locations in the fermentation area inside the fermenter.

3. The microbial fermentation control system based on intelligent sensors according to claim 2, characterized in that, The data analysis module also includes: The model construction submodule, which is connected to the reactant information acquisition submodule, is used to determine several reactant fermentation factors based on the changes in reactant information at each of the second preset locations, and to construct a fermentation coupling model based on each reactant fermentation factor.

4. The microbial fermentation control system based on intelligent sensors according to claim 3, characterized in that, The data analysis module also includes: The curve analysis submodule, which is connected to the curve construction submodule, is used to determine the critical zone of microbial growth based on the curve change rate of the microbial growth curve.

5. The microbial fermentation control system based on intelligent sensors according to claim 4, characterized in that, The data determination module includes: The fermentation process determination submodule is connected to the model construction submodule and the curve analysis submodule, respectively. It is used to determine the key time period based on the duration of the key growth zone of the microorganism, and to determine the microbial fermentation process based on the changes in reactant information within the key time period and the fermentation coupling model.

6. The microbial fermentation control system based on intelligent sensors according to claim 5, characterized in that, The data determination module also includes: The fermentation determination submodule, which is connected to the fermentation process determination submodule, is used to determine standard fermentation indicators based on the microbial fermentation process, and to determine whether the microbial fermentation meets the preset standards based on the comparison results between the current environmental information and the current reactant information and the standard fermentation indicators. If both the current environmental information and the current reactant information meet the standard fermentation indicators, then the microbial fermentation is determined to meet the preset standards.

7. The microbial fermentation control system based on intelligent sensors according to claim 6, characterized in that, The control adjustment module determines the first adjustment mode of the fermenter based on the comparison results of the current environmental information not meeting the standard fermentation indicators. The first adjustment method involves determining the environmental information adjustment amount based on the key environmental information corresponding to the critical microbial growth zone.

8. The microbial fermentation control system based on intelligent sensors according to claim 7, characterized in that, The control adjustment module determines the control adjustment mode of the fermenter to be the second adjustment mode based on the comparison results of the current reactant information not meeting the standard fermentation index. The second adjustment method involves determining the predicted reactant information based on the fermentation coupling model and determining the nutrient solution addition parameters based on the predicted reactant information.

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