CN116013411B - Quantitative relation model, application, fermentation monitoring method, device, system and equipment - Google Patents
Quantitative relation model, application, fermentation monitoring method, device, system and equipment Download PDFInfo
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Abstract
The invention relates to the field of fermentation monitoring, and particularly provides a quantitative relation model, an application thereof, a fermentation monitoring method, a device, a system and equipment, wherein the microbial fermentation monitoring method comprises the following steps: collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption; inputting the online parameters to a quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model; the target parameters include at least the current yield of the target product and the current net biomass. The invention can provide rapid and accurate real-time target parameters, thereby ensuring the normal operation of the fermentation process and providing a theoretical basis for the evaluation of the subsequent fermentation condition and the improvement and promotion of the fermentation process.
Description
Technical Field
The invention relates to the field of microbial fermentation and fermentation monitoring, in particular to a quantitative relation model, application, and fermentation monitoring method, device, system and equipment.
Background
Polyhydroxyalkanoate (PHA) is mainly produced by fermentation of saccharides or lipid substances as a carbon source, and is mainly produced by fermentation of microorganisms, and a traditional detection method for monitoring fermentation products adopts a gas chromatography method, wherein fermentation liquor of the method is subjected to centrifugal collection of thalli, washing, centrifugation and drying, then reacts with methanol-chloroform for 4 hours at an acid condition and a temperature of 100 ℃, and then is subjected to extraction to remove the methanol, and then is detected by the gas chromatography method.
Disclosure of Invention
The invention provides a quantitative relation model, application, a fermentation monitoring method, a device, a system and equipment, which are used for solving the technical defects of time consumption, labor consumption and high cost of the existing PHA content detection method.
In a first aspect, a quantitative relationship model is provided, and the method for constructing the quantitative relationship model includes:
performing linear fitting on all first historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states to determine a first target coefficient;
performing linear fitting on all second historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states to determine a second target coefficient;
the first historical sample parameters comprise historical alkali consumption and historical net biomass, and the second historical sample parameters comprise historical substrate consumption and historical target product yield;
determining a first quantitative model according to the first target coefficient, determining a second quantitative model according to the second target coefficient, and constructing a quantitative relation model according to the first quantitative model and the second quantitative model;
the first quantitative model is used for determining the current net biomass according to the current alkali consumption and a first target coefficient, and the second quantitative model is used for determining the yield of the current target product according to the current substrate consumption and a second target coefficient.
According to the quantitative relation model provided by the invention, the second target coefficient comprises a first fermentation stage coefficient and a second fermentation stage coefficient;
the first fermentation stage coefficient is determined by linear fitting according to all second historical sample parameters corresponding to the fermentation starting time to the preset time;
the second fermentation stage coefficient is determined by performing linear fitting according to all second historical sample parameters corresponding to the preset time to the fermentation ending time.
The quantitative relation model provided by the invention further comprises:
validating the first target coefficient according to a first historical validation parameter;
validating the second target coefficient according to a second historical validation parameter;
the first historical verification parameters comprise historical alkali consumption and historical net biomass under different fermentation working conditions, different production strains and different fermentation time states;
the second historical verification parameters comprise historical substrate consumption under different fermentation working conditions, different production strains and different fermentation time states and the yield of a historical target product.
According to the quantitative relation model provided by the invention, when the current tank parameters are known, the quantitative relation model can be used for outputting the current biomass concentration and the content of the current target product;
The quantitative relationship model is also used for determining the current biomass concentration according to the yield of the current target product, the current net biomass and the current tank parameters;
the quantitative relation model is also used for determining the content of the current target product according to the yield of the current target product and the current net biomass;
the current tank parameters comprise the volume of fermentation liquor, the weight of fermentation liquor, the fermentation liquid level and the total weight of fermentation liquor and fermentation liquor in the current fermentation tank.
In a second aspect, there is provided the use of a quantitative relationship model for determining a target parameter of a current target product produced by intracellular fermentation of a microbial strain by using the quantitative relationship model;
the current target product comprises PHA, collagen or astaxanthin;
the target parameters include at least the current yield of the target product and the current net biomass.
Preferably, the microorganism includes microorganisms of the following genera: aeromonas, alcaligenes, azotobacter, bacillus, clostridium, salinomyces, nocardia, rhodospirillum, pseudomonas, rocentrum, and zoogloea.
Preferably, the PHA comprises: at least one selected from the group consisting of poly (3-hydroxybutyrate), poly (3-hydroxybutyrate-co-3-hydroxypropionate), poly (3-hydroxybutyrate-co-3-hydroxyvalerate), poly (3-hydroxybutyrate-co-3-hydroxyhexanoate), poly (3-hydroxybutyrate-co-3-hydroxyheptanoate), poly (3-hydroxybutyrate-co-3-hydroxyoctanoate), poly (3-hydroxybutyrate-co-3-hydroxynonanoate), poly (3-hydroxybutyrate-co-3-hydroxydecanoate), poly (3-hydroxybutyrate-co-3-hydroxyundecanoate), and poly (3-hydroxybutyrate-co-4-hydroxybutyrate).
In a third aspect, the present invention provides a method for monitoring microbial fermentation, comprising:
collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption;
inputting the online parameters to a quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model;
the target parameters include at least the current yield of the target product and the current net biomass.
According to the microbial fermentation monitoring method provided by the invention, the quantitative relation model comprises the following steps:
a linear relationship of the current alkali consumption and the current net biomass;
and, a linear relationship of the current substrate consumption to the yield of the current target product.
According to the microbial fermentation monitoring method provided by the invention, the current target product is PHA; the linear relation between the current alkali consumption and the current net biomass is determined by a first target coefficient, and the value range of the first target coefficient is 1.3-1.6;
the linear relation between the current substrate consumption and the current target product yield is determined by a first fermentation stage coefficient and a second fermentation stage coefficient, the value range of the first fermentation stage coefficient is 0.45-0.6, and the value range of the second fermentation stage coefficient is 0.95-1.05.
The microbial fermentation monitoring method provided by the invention further comprises the following steps: when the online parameters comprise current tank parameters, the yield of the current PHA and the current net biomass can also be input as online parameters, the online parameters are input into the quantitative relation model for data analysis, and the quantitative relation model outputs the current biomass concentration and the current PHA content;
wherein the quantitative relationship model is used to determine a current biomass concentration based on the current PHA yield, the current net biomass, and the current tank parameters;
wherein the quantitative relation model is used for determining the content of the current PHA according to the yield of the current PHA and the current net biomass
The current tank parameters comprise the volume of fermentation liquor, the weight of fermentation liquor, the fermentation liquid level and the total weight of fermentation liquor and fermentation liquor in the current fermentation tank.
According to the microbial fermentation monitoring method provided by the invention, the microorganisms comprise microorganisms of the following bacteria: aeromonas, alcaligenes, azotobacter, bacillus, clostridium, salinomyces, nocardia, rhodospirillum, pseudomonas, rocentrum, and zoogloea.
In a fourth aspect, the present invention also provides a microbial fermentation monitoring apparatus comprising:
The acquisition unit: the method comprises the steps of collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption;
an output unit: the online parameter analysis module is used for inputting the online parameter to a quantitative relation model for data analysis, and outputting a target parameter by the quantitative relation model;
the target parameters include at least the current yield of the target product and the current net biomass.
In a fifth aspect, the invention further provides a microbial fermentation monitoring system, which comprises a data acquisition module, a data processing module and a data visualization module, wherein the data processing module comprises the microbial fermentation monitoring device and is used for acquiring target parameters;
the data acquisition module comprises:
the feed supplement sensor is used for acquiring the current alkali consumption and the current substrate consumption;
a tank parameter sensor for acquiring a current tank parameter;
the data visualization module comprises:
an image drawing unit: for displaying the target parameters.
According to the microbial fermentation monitoring system provided by the invention, the data processing module further comprises: a data comparison unit; the data comparison unit is used for generating abnormal information under the condition that the target parameters exceed a preset range;
The microbial fermentation monitoring system further comprises: an abnormality prompting unit; the abnormality prompting unit: for displaying the anomaly information.
According to the microbial fermentation monitoring system provided by the invention, the microorganism is a microorganism capable of accumulating polyhydroxyalkanoate in cells.
In a sixth aspect, there is also provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the microbial fermentation monitoring method when executing the program.
In a seventh aspect, there is also provided a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor implements the microbial fermentation monitoring method.
The invention provides a quantitative relation model and application, fermentation monitoring method, device, system and equipment, the online parameters of the fermentation process of microorganisms are collected and input into the quantitative relation model for data analysis, the target parameters in the fermentation process are predicted in real time, the target parameters at least comprise the yield of the current target product and the current net biomass, an off-line measurement method adopted in the prior art is abandoned, and the rapid and accurate real-time target parameters are provided, so that the normal operation of the fermentation process is ensured, the theoretical basis is provided for the evaluation of the subsequent fermentation condition and the improvement and promotion of the fermentation process, and the average deviation of the model prediction result of the net biomass can be controlled within 2.5 percent, even less than 2 percent; the average deviation of the model predictions of PHA production is controlled to be within 1% or even below 0.7%.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for monitoring microbial fermentation provided by the invention;
FIG. 2 is a schematic structural diagram of a microorganism fermentation monitoring device provided by the invention;
FIG. 3 is a schematic diagram of a system for monitoring fermentation of microorganisms according to the present invention
FIG. 4 is a schematic diagram of another system for monitoring microbial fermentation according to the present invention;
fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The microorganisms referred to in the present invention include microorganisms capable of accumulating polyhydroxyalkanoates in cells, and specifically include microorganisms of the following genera: for more accurate description of specific embodiments of the present invention, aeromonas, alcaligenes, azotobacter, bacillus, clostridium, halophilum, nocardia, rhodospirillum, pseudomonas, rocentre, zoogloea, the present invention is exemplified by microorganisms producing Polyhydroxyalkanoates (PHA), and all fermentation control methods in the examples described below are exemplified by microorganisms producing Polyhydroxyalkanoates (PHA), but should not be construed as the present invention only fermentation control of Polyhydroxyalkanoates (PHA), and are not described herein.
The invention adopts a soft sensing technology to realize real-time online monitoring of fermentation parameters, and the soft sensing technology predicts variables which cannot be directly detected by a hard sensing instrument by detecting certain process variables which can be directly acquired and according to a mathematical model between the process variables and the variables to be detected. Soft sensors can be broadly divided into two categories, a mechanism model that requires detailed knowledge of the fermentation process mechanism, thus creating differential equations to describe the fermentation process, and a data-driven model that statistically analyzes historical data to describe the correlation of process variables.
When the mechanism model can accurately describe the mechanism of the fermentation process, the accuracy, stability and interpretability of the mechanism model are all first-aid, but the modeling mode also has certain drawbacks, the established mechanism model is often limited, the mobility is poor, and meanwhile, some process mechanisms are difficult to clarify. Under the condition that the mechanism model is not clear, a data-driven modeling mode can be adopted to establish a soft sensing model, parameters in the model are adjusted according to deviation between a predicted result and a true value through training existing historical data, and the relationship between a directly acquired process variable and a variable to be detected is established, so that the effect that the mechanism model cannot be explained is achieved, however, the data-driven model is also easy to cause problems, such as prediction accuracy reduction caused by abnormal data in monitored data. Because in actual production, the requirements on the stability and the robustness of the process are higher, the requirements cannot be completely met by only relying on a data driving model.
According to one aspect of the present invention, there is provided a quantitative relationship model for outputting target parameters in microbial fermentation monitoring, comprising:
performing linear fitting on all first historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states to determine a first target coefficient;
Performing linear fitting on all second historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states to determine a second target coefficient;
determining a first quantitative model according to the first target coefficient, determining a second quantitative model according to the second target coefficient, and constructing a quantitative relation model according to the first quantitative model and the second quantitative model;
the first historical sample parameters include historical alkali consumption and historical net biomass, and the second historical sample parameters include historical substrate consumption and historical yield of target product.
The first quantitative model is used for determining the current net biomass according to the current alkali consumption and a first target coefficient, and the second quantitative model is used for determining the yield of the current target product according to the current substrate consumption and a second target coefficient.
For the first quantitative model, which has a corresponding relation between an online parameter and a target parameter, the corresponding relation is a corresponding relation between alkali consumption and net biomass, in order to accurately analyze a first target coefficient corresponding to the corresponding relation, the invention carries out linear fitting on all first historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states, wherein the first historical sample parameters comprise historical alkali consumption and historical net biomass, the historical alkali consumption can be obtained through a feed supplement sensor, the historical net biomass can be obtained through offline experiments, the first target coefficient is obtained after the linear fitting, and then the current net biomass is determined according to the current alkali consumption and the first target coefficient in combination with the first quantitative model in the actual microbial fermentation monitoring process under the condition that the current alkali consumption is obtained.
For the second quantitative model, which also has a corresponding relation between an online parameter and a target parameter, the corresponding relation in the second quantitative model is a corresponding relation between the substrate consumption and the current PHA yield, in order to accurately analyze a second target coefficient corresponding to the corresponding relation, the invention carries out linear fitting on all second historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states, wherein the second historical sample parameters comprise the historical substrate consumption and the historical PHA yield, the historical substrate consumption can be obtained through a feed supplement sensor, the historical PHA yield can be obtained through offline experiments, for example, a gas chromatography method, after the linear fitting, the second target coefficient is obtained, and then in combination with the second quantitative model, the current PHA yield is determined according to the current substrate consumption and the second target coefficient under the condition that the current substrate consumption is obtained in the actual microbial fermentation monitoring process.
Optionally, the second target coefficient comprises a first fermentation stage coefficient and a second fermentation stage coefficient; the first fermentation stage coefficient is determined by linear fitting according to all second historical sample parameters corresponding to the fermentation starting time to the preset time; the second fermentation stage coefficient is determined by performing linear fitting according to all second historical sample parameters corresponding to the preset time to the fermentation ending time.
In the microbial fermentation process, according to the finding that after linear fitting is performed on all the second historical sample parameters, two second target coefficients exist, wherein the two second target coefficients correspond to different stages in the microbial fermentation process respectively, and in order to effectively distinguish different coefficients corresponding to the second target coefficients in different stages, the second target coefficients comprise a first fermentation stage coefficient and a second fermentation stage coefficient, optionally, the quantitative relation between the substrate consumption and the PHA yield has obvious stage characteristics, the fermentation time corresponding to the first fermentation stage coefficient is 0-24 h, and the fermentation time corresponding to the second fermentation stage coefficient is 24h to the end of fermentation.
Optionally, after completing the construction of the quantitative relation model and determining the first target coefficient and the second target coefficient, the method further includes:
validating the first target coefficient according to a first historical validation parameter;
validating the second target coefficient according to a second historical validation parameter;
the first historical verification parameters comprise historical alkali consumption and historical net biomass under different fermentation working conditions, different production strains and different fermentation time states;
The second historical verification parameters include historical substrate consumption and historical PHA yield for different fermentation conditions, different production strains and different fermentation time conditions.
In such an embodiment, the present invention may divide all the first historical sample parameters and the second historical sample parameters under different fermentation conditions, different production strains, and different fermentation time conditions, divide the first historical sample parameters into a first history fit parameter and a first history verification parameter, where the first history verification parameter includes a historical alkali consumption and a historical net biomass under different fermentation conditions, different production strains, and different fermentation time conditions, linearly fit the first target coefficient according to the first history fit parameter, and verify the obtained first target coefficient by the first history verification parameter to verify the accuracy of the first target coefficient.
Further, the second historical sample parameters are divided into second historical fitting parameters and second historical verification parameters, the second historical verification parameters comprise historical substrate consumption and historical PHA yield under different fermentation working conditions, different production strains and different fermentation time states, the second target coefficient is linearly fitted according to the second historical fitting parameters, and the obtained second target coefficient is verified through the second historical verification parameters so as to verify the accuracy of the second target coefficient.
The quantitative relation model constructed by the invention can also be regarded as a soft sensor, and is mainly used for measuring fermentation parameters in real time, and specifically comprises the measurement of target parameters of a current target product produced by intracellular fermentation of a microbial strain;
the microorganism is a microorganism capable of accumulating polyhydroxyalkanoate in a cell, and includes microorganisms of the following genera: aeromonas, alcaligenes, azotobacter, bacillus, clostridium, salinomyces, nocardia, rhodospirillum, pseudomonas, rocentrum, and zoogloea.
Specifically, in the construction of a soft sensor for measuring fermentation parameters in real time, firstly, acquiring on-line parameter data and off-line parameter data under different working conditions, different PHA production strains and different fermentation times in a sufficient quantity, and dividing the on-line parameter data and the off-line parameter data into training data and test data; then, constructing a corresponding relation between the online parameters and the target parameters by utilizing the training data, so as to establish a prediction model of the PHA fermentation process, and predicting the target parameters by utilizing the online parameters; and then using a PHA fermentation process prediction model to test data under different fermentation conditions, different PHA production strains and different fermentation times, wherein the construction of the soft sensor can be suitable for the real-time measurement of target parameters of products produced by intracellular fermentation of the strains, such as intracellular products: PHA, collagen, astaxanthin, and the like.
Alternatively, the construction method may be adapted for constructing a real-time measurement soft sensor for a target parameter of a different intracellular product produced by intracellular fermentation of a strain, such as an intracellular product: PHA, collagen, astaxanthin and the like are used for measuring PHA fermentation parameters in real time, and can be particularly used for measuring PHA fermentation parameters produced under different fermentation conditions, different production strains and different fermentation times.
Specifically, the PHA (polyhydroxyalkanoate) is a polymer containing a structural unit represented by the following general formula:
[CHR 1 CH 2 COO] I
in the general formula I, R 1 Represents an alkyl group represented by CpH2p+1, and p represents an integer of 1 to 15; preferably an integer of 1 to 10, more preferably an integer of 1 to 8.
Alternatively, R 1 Represents a C1-C6 linear or branched alkyl group. For example, methyl, ethyl, propyl, butyl, isobutyl, t-butyl, pentyl, hexyl, and the like.
Optionally, the polyhydroxyalkanoate comprises at least one poly (3-hydroxyalkanoate).
Optionally, the poly (3-hydroxyalkanoate) comprises 3-hydroxybutyrate structural units and at least one of other hydroxyalkanoate structural units (e.g., 4-hydroxyalkanoate structural units, etc.).
Alternatively, the polyhydroxyalkanoate is at least one selected from the group consisting of poly (3-hydroxybutyrate), poly (3-hydroxybutyrate-co-3-hydroxypropionate), poly (3-hydroxybutyrate-co-3-hydroxyvalerate), poly (3-hydroxybutyrate-co-3-hydroxyhexanoate), poly (3-hydroxybutyrate-co-3-hydroxyheptanoate), poly (3-hydroxybutyrate-co-3-hydroxyoctanoate), poly (3-hydroxybutyrate-co-3-hydroxynonanoate), poly (3-hydroxybutyrate-co-3-hydroxydecanoate), poly (3-hydroxybutyrate-co-3-hydroxyundecanoate), and poly (3-hydroxybutyrate-co-4-hydroxybutyrate).
In another aspect, in order to improve accuracy and stability of fermentation target parameter prediction, the invention further provides a fermentation parameter real-time on-line monitoring method with high mobility and robustness by utilizing the quantitative relation model and utilizing a soft sensing technology. Fig. 1 is a schematic flow chart of a method for monitoring microbial fermentation provided by the invention, which comprises the following steps:
102, inputting the online parameters into a quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model;
the target parameters include at least the current yield of the target product and the current net biomass.
In step 101, during the fermentation of the microorganism, the previous parameters at each stage of the fermentation, optionally parameters directly obtainable by the respective sensors, may be obtained in real time using different types of sensors, which in the present invention include the current alkali consumption, the current substrate consumption and the current tank parameters, for example, obtained by the feed sensor, and also obtained by the tank parameter sensor.
Alternatively, the alkali in the current alkali consumption in the present invention includes, but is not limited to, sodium hydroxide, ammonia water and calcium hydroxide, wherein the concentration of OH ions ranges from 5.7mol/L to 8.6mol/L, and preferably, the concentration of OH ions is 7.4mol/L.
Alternatively, the substrate in the present substrate consumption of the present invention is primarily a fat and oil, including edible vegetable oils such as one or more of soybean oil, palm oil, peanut oil, corn oil, rapeseed oil, peanut oil, sesame oil, canola oil, refined coconut oil, crude rice bran oil, crude cottonseed oil, rice oil, refined safflower oil, linseed oil, pricklyash peel oil, and animal oils such as one or more of fish oil, chicken oil, beef tallow, mutton tallow, and lard.
In step 102, the real-time monitoring of the target parameters is realized mainly through the acquired on-line parameters of the fermentation process of the microorganism, and the invention rapidly and accurately realizes the calculation of the yield of the current target product and the current net biomass in a quantitative model mode, thereby meeting the requirement of large-scale real-time quantitative monitoring.
Those skilled in the art understand that the current target product is PHA, polyhydroxyalkanoate (PHA) is a generic term for completely synthesizing high molecular polyester by microorganisms, which is widely present in microbial cells as an energy reserve substance, and has very wide application prospects in agriculture, food, medical treatment and pharmaceutical industry due to its excellent biodegradability and plasticity. Currently, PHA is mainly prepared by means of microbial fermentation, the fermentation process is a time-varying, nonlinear and strongly coupled system, and in order to ensure the normal operation of the fermentation, a large number of parameters are monitored in the process, and these parameters are divided into online parameters and offline parameters, wherein the online parameters are collected and monitored by suitable sensors, such as temperature, hydrogen ion concentration index pH, rotation speed, etc., corresponding to the present invention, i.e. the present alkali consumption, the present substrate consumption and the present tank parameters obtained in step 101, and the offline parameters usually need to be manually sampled and then measured by external analysis and detection devices, such as OD600, PHA concentration, etc., wherein the PHA concentration is critical to the evaluation of the fermentation condition.
The traditional PHA detection method adopts gas chromatography, fermentation liquor is firstly subjected to centrifugation to collect thalli, then subjected to washing, centrifugation and drying, then subjected to reaction with methanol-chloroform for 4 hours at an acid condition and at a temperature of 100 ℃, and then subjected to extraction to remove methanol, and then subjected to detection by gas chromatography.
The different fermentation conditions optionally include different fermentation systems, such as a 2L fermenter, a 50L fermenter, a 200L fermenter, or a 1500L fermenter.
As for intracellular PHA, the different production strains refer to microorganisms capable of accumulating PHA in cells, including Aeromonas, alcaligenes, azotobacter, bacillus, clostridium, salmonella, nocardia, rhodospirillum, pseudomonas, ralstonia, acinetobacter, etc. Alternatively, such as Alcaligenes autolycum (Alcaligenes lipolytica), alcaligenes extenis (Alcaligenes latus), eutrophic bacteria (Ralstonia eutropha), pseudomonas aeruginosa (Pseudomonas aeruginosa), rhodococcus (Rhodococcus opacus) and Bacillus subtilis.
All first historical sample parameters in different fermentation time states refer to all first historical sample parameters obtained from any moment in the fermentation starting moment to the fermentation ending moment of the microorganism, and all second historical sample parameters in different fermentation time states refer to all second historical sample parameters obtained from any moment in the fermentation starting moment to the fermentation ending moment of the microorganism.
By combining three state variable conditions of different fermentation working conditions, different production strains and different fermentation time, the method can obtain a sufficient number of first historical sample parameters and second historical sample parameters from the process of fermenting the historical microorganisms, and the total sample number can be 10-30 batches.
After quantitatively analyzing the historical parameter data, the invention determines the corresponding relation between the online parameter and the target parameter, mainly refers to the quantitative relation between the alkali consumption and cells (net biomass) and the quantitative relation between the substrate consumption and PHA yield. Wherein, the quantitative relation between the alkali consumption and the cells (net biomass) is as follows:
in the formula (1), the components are as follows,
is the ratio of the growth speed of cells to the alkali consumption speed，/>
For net biomass, <' > for example>
The alkali consumption is shown, and t is the fermentation time.
Alternatively, the quantitative relationship of substrate consumption to PHA yield is:
in the formula (2), the amino acid sequence of the compound,
for the ratio of PHA production rate to substrate consumption rate, < >>
For PHA production,/->
Is the consumption of the substrate.
Optionally, the quantitative relationship model includes:
a linear relationship of the current alkali consumption and the current net biomass;
and, a linear relationship of the current substrate consumption to the yield of the current target product.
It is understood by those skilled in the art that the quantitative relationship model includes a first quantitative model for determining a current net biomass from a current alkali consumption and a first target coefficient, and a second quantitative model for determining a current PHA yield from a current substrate consumption and a second target coefficient.
The linear relationship of the current alkali consumption and the current net biomass depends on a first target coefficient, and the first target coefficient is determined by linear fitting all first historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states, wherein the first historical sample parameters comprise the historical alkali consumption and the historical net biomass.
The linear relationship of the current substrate consumption to the current target product yield is dependent on a second target coefficient determined by linear fitting all second historical sample parameters for different fermentation conditions, different production strains, and different fermentation time states, including historical substrate consumption and historical PHA yield.
Optionally, the value range of the first target coefficient is 1.3-1.6, and it is understood by those skilled in the art that, since the first target coefficient is determined by linearly fitting all the first historical sample parameters under different fermentation conditions, different production strains and different fermentation time states, the first target coefficient will change according to different fermentation conditions, different production strains and different fermentation time states, but the change interval is 1.3-1.6.
Optionally, the value range of the first fermentation stage coefficient is 0.45-0.6; the value range of the second fermentation stage coefficient is 0.95-1.05, and the second target coefficient is determined by performing linear fitting on all second historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states.
The quantitative relation model (soft sensing model) is established by combining the obtained quantitative relation with the data-driven modeling mode of the historical sample parameters, so that the problem that the mechanism model cannot be explained can be definitely solved, and the problem that the accuracy and the adaptability are reduced due to the fact that the model is driven by data only can be solved.
Optionally, the online parameters further comprise current tank parameters, and the target parameters further comprise current biomass concentration, current PHA content;
the quantitative relationship model is further used to determine a current biomass concentration based on the current PHA yield, the current net biomass, and the current tank parameters;
the quantitative relationship model is further used to determine a current PHA content based on the current PHA yield and the current net biomass;
the current tank parameters comprise the volume of fermentation liquor, the weight of fermentation liquor, the fermentation liquid level and the total weight of fermentation liquor and fermentation liquor in the current fermentation tank.
The microbial fermentation monitoring in the invention not only can realize the monitoring of the current PHA yield and the current net biomass, but also can realize the monitoring of the current biomass concentration and the current PHA content, and the monitoring of the current biomass concentration and the current PHA content is determined by further calculation after the monitoring of the current PHA yield and the current net biomass is completed.
Alternatively, the calculation of the current biomass concentration may refer to the following formula:
D=（J+C）/G*1.03 （3）
in the formula (3), D is the current biomass concentration, J is the current net biomass, G is the total weight of the fermentation tank and the fermentation broth in the current tank parameters, and C is the current PHA yield.
Alternatively, the calculation of the current PHA content may refer to the following formula:
H=C/(C+J) （4）
in formula (4), H is the current PHA content, C is the current PHA yield, and J is the current net biomass.
Aiming at the defects existing in the prior art, the invention establishes a real-time online monitoring method for the PHA fermentation parameters, establishes a soft sensor model which is driven by data and is assisted by mechanism calibration by selecting specific online parameters from a historical data set, and can predict the target parameters of the fermentation process in real time based on the established software sensor model, thereby providing quick and accurate online values without offline measurement.
The quantitative relation model provided by the invention can predict the target parameters including the yield of the current target product, the current net biomass and the like in the fermentation process in real time, abandons the offline measurement method adopted in the prior art, provides quick and accurate real-time target parameters, ensures the normal operation of the fermentation process, provides a theoretical basis for the evaluation of the subsequent fermentation condition and the improvement and promotion of the fermentation process, and can control the average deviation of the model prediction result of the net biomass to be within 2.5%, even less than 2%; the average deviation of the model predictions of PHA production is controlled to be within 1% or even below 0.7%.
Fig. 2 is a schematic structural diagram of a microbial fermentation monitoring device provided by the invention, which comprises an acquisition unit 1: the working principle of the collecting unit 1 may refer to the foregoing step 101, and will not be described herein.
Wherein, the microorganism fermentation monitoring device further comprises an analysis unit 2: the working principle of the analysis unit 2 may refer to the foregoing step 102, and will not be described herein.
The fermentation monitoring device provided by the invention can be used for acquiring target parameters including the yield of the current target product and the current net biomass, and the device can be used for discarding an off-line measuring method adopted in the prior art and providing quick and accurate real-time target parameters, so that the normal operation of a fermentation process is ensured, and a theoretical basis is provided for the evaluation of the subsequent fermentation condition and the improvement and promotion of the fermentation process.
FIG. 3 is a schematic diagram of a structure of a microorganism fermentation monitoring system provided by the present invention, where the microorganism fermentation monitoring system shown in FIG. 3 includes a data acquisition module, a data processing module, and a data visualization module, and the data processing module includes a microorganism fermentation monitoring device provided by the present embodiment, and is configured to obtain a target parameter;
the data acquisition module comprises:
the feed supplement sensor is used for acquiring the current alkali consumption and the current substrate consumption;
tank parameter sensors, such as a tank weight sensor, for acquiring a current tank weight;
the data visualization module comprises:
an image drawing unit: for displaying the target parameter;
in the monitoring system provided in this example, the microorganism to be monitored is a microorganism capable of accumulating polyhydroxyalkanoate in a cell.
Wherein the tank parameters comprise the volume of fermentation liquor in the current fermentation tank, the weight of the fermentation liquor, the fermentation liquid level and the total weight of the fermentation tank and the fermentation liquor; the corresponding tank parameter sensor comprises: level sensor, weight sensor, volume sensor, concentration sensor, etc.
As an alternative embodiment of the present invention, on the basis of the structural schematic diagram of the microorganism fermentation monitoring system shown in fig. 3, fig. 4 provides a structural schematic diagram of another microorganism fermentation monitoring system, such as a second microorganism fermentation monitoring system shown in fig. 4, where the second microorganism fermentation monitoring system includes a data acquisition module, a data processing module, and a data visualization module, and the data processing module includes the microorganism fermentation monitoring device provided by the embodiment, and is used for acquiring a target parameter;
the data acquisition module comprises:
the feed supplement sensor is used for acquiring the current alkali consumption and the current substrate consumption;
tank parameter sensors, such as a tank weight sensor, for acquiring a current tank weight;
the data processing module further comprises:
the data comparison unit is used for generating abnormal information under the condition that the target parameters exceed a preset range;
the data visualization module further includes:
An image drawing unit: for displaying the target parameter;
an abnormality prompting unit: for displaying the abnormality information;
the microorganism is a microorganism capable of accumulating polyhydroxyalkanoate in a cell.
Wherein the tank parameters comprise the volume of fermentation liquor in the current fermentation tank, the weight of the fermentation liquor, the fermentation liquid level and the total weight of the fermentation tank and the fermentation liquor; the corresponding tank parameter sensor comprises: level sensor, weight sensor, volume sensor, concentration sensor, etc.
As shown in fig. 4, the microbial fermentation monitoring system comprises a data acquisition model, a data processing module and a data visualization module, wherein the data acquisition module comprises a feed supplement sensor and a tank weight sensor, the data processing module comprises a data comparison unit and a microbial fermentation monitoring device, the microbial fermentation monitoring device can be a PHA real-time online soft sensor unit, and the data visualization module is display equipment and comprises an image drawing unit and an abnormality prompting unit.
Optionally, the data visualization module receives the data output by the data processing module, and draws a curve and prompts abnormality on the data.
Optionally, the feeding sensor can monitor the weights of the alkali bottle and the feeding bottle in real time, so that the purposes of automatically recording the alkali consumption and the substrate consumption are achieved.
Optionally, the tank weight sensor can monitor the weight of the fermentation tank in real time, so that the purpose of automatically recording the weight of the fermentation liquid can be achieved.
Optionally, the second microbial fermentation monitoring system may calculate the target parameter using the online parameter, and transmit the target parameter to the data visualization module. Meanwhile, the data comparison unit sets a normal floating range of the target parameter, and if the target parameter exceeds the normal floating range, the data processing module transmits an abnormal signal to the data visualization module while transmitting the target parameter.
Optionally, the data visualization module performs drawing display on the received data, and if abnormal data exists, performs data abnormality prompt.
The person skilled in the art understands that the stability of data is poor due to the complexity of fermentation conditions and working conditions, and in addition, even if the mechanism of the fermentation process in the bacterial strain cells is not completely clear, the software sensor model is built by adopting a mode of mixed modeling of half mechanism and half data, so that the prediction accuracy is improved more accurately. In order to more clearly reflect the system construction, the soft sensor application and the microbial fermentation monitoring of the present invention, the following four embodiments are combined, and the technical solution and implementation means of the present invention will be described in more detail:
As a first embodiment of the present invention, a PHA fermentation real-time on-line detection system is provided. Referring to fig. 3 and 4, in order to monitor the target parameters of the PHA fermentation process on line in real time, the PHA real-time on-line monitoring system includes a data acquisition module, a data processing module and a data visualization module. The data acquisition module comprises a feed supplement sensor and a tank parameter sensor, such as a tank weight sensor, wherein the feed supplement sensor and the tank weight sensor are actually devices for converting a quality signal into a measurable electric signal and outputting the measurable electric signal, the feed supplement sensor can acquire the alkali consumption and the substrate consumption of the fermentation process in real time, and the tank weight sensor can acquire the weight of fermentation liquid in real time.
And the data acquisition module transmits acquired data to the data processing module, the PHA real-time online detection soft sensor unit in the module calculates the acquired online parameters to obtain target parameters, the calculated target parameters enter a preset data comparison unit, and the data processing module sends out data abnormal signals and transmits the target parameters and the abnormal signals to the data visualization module according to whether the target parameters are in a normal floating range or not and if the target parameters are out of the normal floating range. In the data visualization module, the image drawing unit performs drawing display on the received data, and the abnormality prompting unit prompts according to the error abnormality signal. The user judges the current fermentation state through the target data value in the visualization module, and when the target data value reaches the expected tank discharging requirement, the fermentation can be ended.
As a second embodiment of the present invention, there is provided a process of parameter acquisition, model construction and model verification of a PHA real-time on-line monitoring soft sensor (200L system), specifically:
in the on-line parameter and off-line parameter collection of the PHA fermentation process, the PHA fermentation process is a typical multi-stage process and mainly comprises a cell rapid growth stage (a first stage) and a PHA rapid accumulation stage (a second stage), and the whole fermentation process lasts for 56-64 h. PHBHHx is fermented by taking a fungus strain of the real culture of Roche as a chassis strain, sampling is started from 8h of fermentation under a 200L fermentation system, sampling is performed every 4h, on-line parameter information including alkali consumption (kg), oil consumption (kg) and tank weight (kg) is recorded, and target parameters including biomass concentration and PHA content in the PHA fermentation process are detected. To improve the accuracy of the model, 30 sets of PHA fermentation process data for different batches were collected and the parameter data was divided into training (80%) and testing (20%).
In the quantitative relation model construction, it is assumed that the alkali consumption is proportional to the cell (net biomass), the quantitative relation of the formula (1) is satisfied, in the formula (1),
is the ratio of the cell growth rate to the alkali consumption rate, < > >
For the consumption of alkali (kg), R is the net biomass (kg), t is the fermentation time (h), and the coefficient is 1.4435 by training the ratio of different net biomass to the consumption of alkali, so that the quantitative relation between the net biomass and the consumption of alkali is as follows:
assuming that the substrate consumption is mainly related to cell growth and PHA production, satisfying the quantitative relationship of PHA yield is represented by formula (2), wherein
For the ratio of the PHA production rate to the substrate consumption rate, the yield of the PHA converted from the substrate is different in different stages, so that the PHA production is mainly divided into two stages, the ratio coefficients of the different stages are obtained by training the ratio between the PHA yield and the oil consumption, and the ratio coefficients are 0.57146 and 1.03977 respectively, and the quantitative relationship between the PHA yield and the oil consumption is as follows:
stage one: the quantitative relationship between PHA amount and oil consumption is:
stage two: the quantitative relationship between PHA amount and oil consumption is:
in model verification, the net biomass of cells and PHA yield were used to determine the reliability and performance of the model estimation, and the model prediction results of the net biomass in table 1 had a large deviation, an average deviation of 2.105%, and the PHA yield had a smaller deviation, and an average deviation of 0.225%, from the prediction results of the model. Model verification results under 200L system are shown in the following table 1:
TABLE 1
As a third embodiment of the present invention, there is provided a process of parameter acquisition, model construction and model verification of a PHA real-time on-line monitoring soft sensor (50L system), specifically:
in the on-line and off-line parameter collection of the PHA fermentation process, the data collection method refers to the second embodiment of the invention, except that the third embodiment is that the batch collection number is 10 groups under a 50L fermentation system.
In the quantitative relation model, referring to the first embodiment of the invention, the ratios of different net biomass and alkali consumption are trained to obtain the coefficient 1.3448, and the quantitative relation of the net biomass and the alkali consumption is as follows:
by training the ratio between PHA yield and oil consumption, ratio coefficients of different stages are obtained, namely 0.5373 and 1.0387 respectively, and the quantitative relationship between PHA yield and oil consumption is as follows:
stage one: the quantitative relationship between PHA amount and oil consumption is:
stage two: the quantitative relationship between PHA amount and oil consumption is:
in model verification, the net biomass of cells and the PHA yield were used to determine the reliability and performance of the model estimation, the model prediction results of the net biomass in table 2 showed a large deviation of 1.93% on average from the prediction results of the model, the model prediction results of the PHA yield showed a small deviation of 0.79% on average from the prediction results of the model, and the model verification results under the 50L system were shown in table 2:
TABLE 2
As a fourth embodiment of the present invention, there is provided a process of parameter acquisition, model construction and model verification of a PHA real-time on-line monitoring soft sensor (high performance strain), specifically:
in the acquisition of online parameters and offline parameters in the PHA fermentation process, the data acquisition method is the same as that of the second embodiment, and the difference is that the PHA production strain is the modified high-performance real-raising bacteria of Roche, and the acquisition number of product batches is 15 groups.
In this embodiment, the quantitative relationship model is the same as that of the first embodiment, and the ratio of the net biomass to the alkali consumption is trained to obtain a coefficient 1.4131, so that the quantitative relationship between the net biomass and the alkali consumption is:
by training the ratio between PHA yield and oil consumption, ratio coefficients of different stages are obtained, namely 0.5254 and 1.0418 respectively, and the quantitative relationship between PHA yield and oil consumption is as follows:
stage one: the quantitative relationship between PHA amount and oil consumption is:
stage two: the quantitative relationship between PHA amount and oil consumption is:
in model verification, the net biomass of cells and PHA yield were used to determine the reliability and performance of the model estimation, and the model prediction results of the net biomass in table 3 showed a large deviation of 1.93% on average, and the model prediction results of PHA yield showed a small deviation of 0.79% on average, compared to the net biomass model prediction results, from the prediction results of the model. The results of the high performance strain model validation are shown in table 3 below:
The invention can rapidly, accurately and online monitor the target parameters of the fermentation process, provides reference and guiding functions for the control of the fermentation process, and has at least the following advantages and positive effects compared with the prior art by adopting the technical scheme:
firstly, the method can acquire the process variable in real time only by offline measurement, and omits operations such as offline sampling detection and the like;
in addition, the invention can detect the changes of the net biomass, PHA yield, PHA content and biomass concentration of the fermentation liquid in real time, further improves the working efficiency and reduces the detection workload in the fermentation process. On the premise of utilizing at least 30 groups of PHA fermentation process data of different batches, the average deviation of the model prediction result of the net biomass can be controlled within 2.5%, even less than 2%; average deviation of the model prediction results of PHA yield is controlled to be within 1%, even lower than 0.7%;
finally, through the visualization module provided by the invention, the data obtained by monitoring can be displayed in real time, and the abnormal situation is reminded, so that the staff can find and process the abnormal situation in time.
Through the three advantages, the prediction result obtained by the prediction method is accurate, has high reference value, and can control the fermentation process according to the prediction result.
The invention provides a quantitative relation model and application, fermentation monitoring method, device, system and equipment, the online parameters of the fermentation process of microorganisms are collected and input into the quantitative relation model for data analysis, the target parameters in the fermentation process are predicted in real time, the target parameters at least comprise the yield of the current target product and the current net biomass, an off-line measurement method adopted in the prior art is abandoned, and the rapid and accurate real-time target parameters are provided, so that the normal operation of the fermentation process is ensured, the theoretical basis is provided for the evaluation of the subsequent fermentation condition and the improvement and promotion of the fermentation process, and the average deviation of the model prediction result of the net biomass can be controlled within 2.5 percent, even less than 2 percent; the average deviation of the model predictions of PHA production is controlled to be within 1% or even below 0.7%.
Fig. 5 is a schematic structural diagram of an electronic device provided by the present invention. As shown in fig. 5, the electronic device may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a microbial fermentation monitoring method comprising: collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption; inputting the online parameters to a quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model; the target parameters include at least the current yield of the target product and the current net biomass.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing a method of monitoring fermentation of a microorganism provided by the above methods, the method comprising: collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption; inputting the online parameters to a quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model; the target parameters include at least the current yield of the target product and the current net biomass.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the above methods to provide a microbial fermentation monitoring method, the method comprising: collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption; inputting the online parameters to a quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model; the target parameters include at least the current yield of the target product and the current net biomass.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. A method of constructing a quantitative relationship model, comprising:
performing linear fitting on all first historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states to determine a first target coefficient;
performing linear fitting on all second historical sample parameters under different fermentation working conditions, different production strains and different fermentation time states to determine a second target coefficient;
the first historical sample parameters comprise historical alkali consumption and historical net biomass, and the second historical sample parameters comprise historical substrate consumption and historical target product yield;
determining a first quantitative model according to the first target coefficient, determining a second quantitative model according to the second target coefficient, and constructing a quantitative relation model according to the first quantitative model and the second quantitative model;
the first quantitative model is used for determining the current net biomass according to the current alkali consumption and a first target coefficient, and the second quantitative model is used for determining the yield of the current target product according to the current substrate consumption and a second target coefficient.
2. The method of claim 1, wherein the second target coefficients comprise a first fermentation stage coefficient and a second fermentation stage coefficient;
The first fermentation stage coefficient is determined by linear fitting according to all second historical sample parameters corresponding to the fermentation starting time to the preset time;
the second fermentation stage coefficient is determined by performing linear fitting according to all second historical sample parameters corresponding to the preset time to the fermentation ending time.
3. The method for constructing a quantitative relationship model according to claim 2, further comprising:
validating the first target coefficient according to a first historical validation parameter;
validating the second target coefficient according to a second historical validation parameter;
the first historical verification parameters comprise historical alkali consumption and historical net biomass under different fermentation working conditions, different production strains and different fermentation time states;
the second historical verification parameters comprise historical substrate consumption under different fermentation working conditions, different production strains and different fermentation time states and the yield of a historical target product.
4. A method of constructing a quantitative relationship model according to any one of claims 1-3, wherein the quantitative relationship model is used to output a current biomass concentration, a current target product content when current tank parameters are known;
The quantitative relationship model is also used for determining the current biomass concentration according to the yield of the current target product, the current net biomass and the current tank parameters;
the quantitative relation model is also used for determining the content of the current target product according to the yield of the current target product and the current net biomass;
the current tank parameters comprise the volume of fermentation liquor, the weight of fermentation liquor, the fermentation liquid level and the total weight of fermentation liquor and fermentation liquor in the current fermentation tank.
5. Use of a quantitative relationship model constructed according to any one of claims 1-4, wherein the quantitative relationship model is used to determine a target parameter of a current target product produced by intracellular fermentation of a microbial strain;
the target product comprises PHA, collagen and astaxanthin;
the target parameters include at least the current yield of the target product and the current net biomass.
6. A method for monitoring microbial fermentation, which is characterized by using the quantitative relation model constructed according to any one of claims 1-4, and specifically comprising:
collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption;
inputting the online parameters to the quantitative relation model for data analysis, and outputting target parameters by the quantitative relation model;
The target parameters include at least the current yield of the target product and the current net biomass.
7. The method of claim 6, wherein the quantitative relationship model comprises:
a linear relationship of the current alkali consumption and the current net biomass;
and, a linear relationship of the current substrate consumption to the yield of the current target product.
8. The microbial fermentation monitoring method according to claim 7, wherein the target product is PHA;
the linear relation between the current alkali consumption and the current net biomass is determined by a first target coefficient, and the value range of the first target coefficient is 1.3-1.6;
the linear relation between the current substrate consumption and the current target product yield is determined by a first fermentation stage coefficient and a second fermentation stage coefficient, the value range of the first fermentation stage coefficient is 0.45-0.6, and the value range of the second fermentation stage coefficient is 0.95-1.05.
9. The microbial fermentation monitoring method of claim 8, further comprising: when the online parameters comprise current tank parameters, the yield of the current PHA and the current net biomass are input as online parameters, the online parameters are input into the quantitative relation model for data analysis, and the quantitative relation model outputs the current biomass concentration and the current PHA content;
Wherein the quantitative relationship model is used to determine a current biomass concentration based on the current PHA yield, the current net biomass, and the current tank parameters;
wherein the quantitative relation model is used for determining the content of the current PHA according to the yield of the current PHA and the current net biomass;
the current tank parameters comprise the volume of fermentation liquor, the weight of fermentation liquor, the fermentation liquid level and the total weight of fermentation liquor and fermentation liquor in the current fermentation tank.
10. A microbial fermentation monitoring apparatus, characterized by using the quantitative relationship model constructed according to any one of claims 1 to 4, comprising:
the acquisition unit: the method comprises the steps of collecting online parameters of a fermentation process of microorganisms, wherein the online parameters comprise current alkali consumption and current substrate consumption;
an output unit: the online parameter analysis module is used for inputting the online parameter to a quantitative relation model for data analysis, and outputting a target parameter by the quantitative relation model;
the target parameters include at least the current yield of the target product and the current net biomass.
11. A microbial fermentation monitoring system, comprising a data acquisition module, a data processing module and a data visualization module, wherein the data processing module comprises the microbial fermentation monitoring device of claim 10 for acquiring target parameters;
The data acquisition module comprises:
the feed supplement sensor is used for acquiring the current alkali consumption and the current substrate consumption;
a tank parameter sensor for acquiring a current tank parameter;
the data visualization module comprises:
an image drawing unit: for displaying the target parameters.
12. A microbial fermentation monitoring system according to claim 11, wherein,
the data processing module further comprises: a data comparison unit; the data comparison unit is used for generating abnormal information under the condition that the target parameters exceed a preset range;
the microbial fermentation monitoring system further comprises: an abnormality prompting unit; the abnormality prompting unit: for displaying the anomaly information.
13. The microbial fermentation monitoring system of claim 11 or 12 wherein the microorganisms include microorganisms capable of accumulating PHA in cells.
14. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the microbial fermentation monitoring method of any one of claims 6 to 9 when the computer program is executed.
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