CN116467835B - Beer fermentation tank monitoring system - Google Patents
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- 230000004151 fermentation Effects 0.000 title claims abstract description 90
- 238000000855 fermentation Methods 0.000 title claims abstract description 86
- 235000013405 beer Nutrition 0.000 title claims abstract description 52
- 238000012544 monitoring process Methods 0.000 title claims abstract description 22
- 238000004088 simulation Methods 0.000 claims abstract description 56
- 230000002159 abnormal effect Effects 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000012937 correction Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000010606 normalization Methods 0.000 claims description 12
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 7
- 238000012806 monitoring device Methods 0.000 claims description 7
- SRBFZHDQGSBBOR-IOVATXLUSA-N D-xylopyranose Chemical compound O[C@@H]1COC(O)[C@H](O)[C@H]1O SRBFZHDQGSBBOR-IOVATXLUSA-N 0.000 claims description 6
- 230000005856 abnormality Effects 0.000 claims description 6
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims description 3
- PYMYPHUHKUWMLA-UHFFFAOYSA-N arabinose Natural products OCC(O)C(O)C(O)C=O PYMYPHUHKUWMLA-UHFFFAOYSA-N 0.000 claims description 3
- SRBFZHDQGSBBOR-UHFFFAOYSA-N beta-D-Pyranose-Lyxose Natural products OC1COC(O)C(O)C1O SRBFZHDQGSBBOR-UHFFFAOYSA-N 0.000 claims description 3
- 238000013479 data entry Methods 0.000 claims description 3
- 239000008103 glucose Substances 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 240000004808 Saccharomyces cerevisiae Species 0.000 description 7
- 235000014680 Saccharomyces cerevisiae Nutrition 0.000 description 7
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 239000000498 cooling water Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000796 flavoring agent Substances 0.000 description 2
- 235000019634 flavors Nutrition 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000002503 metabolic effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001954 sterilising effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000010411 cooking Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010790 dilution Methods 0.000 description 1
- 239000012895 dilution Substances 0.000 description 1
- 230000002255 enzymatic effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- 230000000813 microbial effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000003756 stirring Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 235000000346 sugar Nutrition 0.000 description 1
- 150000008163 sugars Chemical class 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Classifications
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12C—BEER; PREPARATION OF BEER BY FERMENTATION; PREPARATION OF MALT FOR MAKING BEER; PREPARATION OF HOPS FOR MAKING BEER
- C12C11/00—Fermentation processes for beer
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention belongs to the technical field of beer fermentation, and particularly relates to a beer fermentation tank monitoring system. Comprising the following steps: the data acquisition device is used for acquiring equipment data of the fermentation tank and real-time component data in beer fermentation; the virtual fermentation simulation device is used for carrying out twice beer fermentation simulation based on the collected equipment data and component data, and specifically comprises the following steps: first fermentation simulation: driving a first virtual beer fermentation tank based on equipment data, and collecting component data in the simulation process as a first simulation result; second fermentation simulation: substituting the real-time component data into a preset inverse push model, calculating to obtain ideal equipment data corresponding to the real-time component data, and according to the difference between the ideal equipment data and the equipment data. According to the invention, through twice fermentation simulation, abnormal connection between equipment data and real-time operation data is found, and the efficiency and accuracy of monitoring and early warning are greatly improved.
Description
Technical Field
The invention belongs to the technical field of beer fermentation, and particularly relates to a beer fermentation tank monitoring system.
Background
The beer fermentation process is that beer yeast performs normal life activities by utilizing fermentable substances in wort under certain conditions, and the metabolic products are the required products, namely beer. The fermentation conditions, product requirements and flavors are different due to different yeast types, and the fermentation modes are also different. Beer can be classified into upper fermented beer and lower fermented beer according to the type of yeast fermentation.
The fermentation process of beer is a microbial metabolic process. It converts fermentable sugars into alcohol and CO2, as well as other metabolites affecting quality and taste, by a variety of enzymatic actions of a variety of yeasts. During fermentation, the variables that are primarily controlled in the process are temperature and time. The beer fermentation object has time variability, time lag and uncertainty, each fermentation tank has individual difference, and the object characteristics are different under different fermentation strains under different process conditions. It is difficult to find or build an exact mathematical model to perform simulation and predictive control. At present, some beer manufacturers in China still adopt conventional meters for control, manually monitor various parameters and have more human factors. The control mode is difficult to ensure the correct execution of the production process, so that the beer quality is unstable, the fluctuation is large, and the reproduction scale is not easy to expand. And a wired monitoring system with an industrial personal computer and a PLC is adopted by some factories, so that the problems of high wired access maintenance cost, poor system expandability, poor mobile performance and the like exist.
Disclosure of Invention
The invention mainly aims to provide a beer fermentation tank monitoring system, which finds out abnormal connection between equipment data and real-time operation data through twice fermentation simulation and establishes connection between the equipment data and the real-time operation data so as to be convenient for directly carrying out early warning after the follow-up abnormality, thereby greatly improving the efficiency and accuracy of monitoring and early warning.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a beer fermentation tank monitoring system, the system comprising: the data acquisition device is used for acquiring equipment data of the fermentation tank and real-time component data in beer fermentation; the virtual fermentation simulation device is used for carrying out twice beer fermentation simulation based on the collected equipment data and component data, and specifically comprises the following steps: first fermentation simulation: driving a first virtual beer fermentation tank based on equipment data, and collecting component data in the simulation process as a first simulation result; second fermentation simulation: substituting the real-time component data into a preset reverse push model, calculating to obtain ideal equipment data corresponding to the real-time component data, calculating to obtain equipment sub-data with highest abnormal weight in the equipment data based on the equipment abnormal weight model according to the difference between the ideal equipment data and the equipment data, modifying the corresponding equipment sub-data in the ideal equipment data into the equipment sub-data in the equipment data, driving a second virtual beer fermentation tank based on the modified ideal equipment data, and collecting the component data in the simulation process as a second simulation result; the fermentation connection establishment unit is used for establishing connection between the real-time component data and the equipment data, and specifically comprises the following steps: based on the difference between the first simulation result and the real-time component data, calculating to obtain component data with highest abnormal weight in the real-time component data by using a component abnormal weight model, taking the component data as a first component connection result, simultaneously, acquiring equipment sub-data with highest abnormal weight in the calculated equipment data during the second fermentation simulation, taking the equipment sub-data as an equipment online result, and based on the difference between the second simulation result and the real-time component data, calculating to obtain component data with highest abnormal weight in the real-time component data by using a component abnormal weight model, taking the component data as a second component connection result, judging whether the difference between the first component connection result and the second component connection result is within a set first threshold range, if so, connecting the equipment connection result and the first connection result to form an equipment component chain, otherwise, judging that the connection fails, and completing the connection between the equipment sub-data and the component data in the equipment data after the connection is carried out for a plurality of times; and the monitoring device is used for judging whether the difference between the first simulation result and the real-time component data exceeds a set second threshold range, and if so, sending out an abnormal warning based on the connection between the equipment sub-data and the component data in the equipment data.
Further, the device data at least includes: can pressure, rotational speed, temperature differential and static pressure differential.
Further, the real-time component data at least includes: the fermentation broth density was poor, glucose concentration, xylose concentration and ethanol concentration.
Further, the inverse model is expressed using the following formula:
wherein n is the number of component data in the component data, S is the calculated inverse coefficient, and x i A value of component data in the component data; and multiplying the value of each piece of equipment sub-data in the equipment data by the inverse coefficient after the inverse coefficient is calculated to obtain ideal equipment data corresponding to the real-time component data.
Further, the device anomaly weight model is expressed using the following formula: wherein T is an equipment abnormality weight coefficient of one piece of equipment sub-data in the calculated equipment data; e (E) i I is a positive integer, and the value range is 1 to the number of the equipment sub-data in the equipment data; e is a matrix formed by all the equipment sub-data of the equipment data; det is matrix determinant operation; the absolute value is calculated;
E istandard and the standard value corresponding to each piece of equipment sub-data is a set value.
Further, the component anomaly weight model is expressed using the following formula: wherein P is the equipment anomaly weight coefficient of one component data in the calculated real-time component data; g i I is a positive integer, and the value range is 1 to the number of component data in the real-time component data; g is a matrix formed by all component data of the real-time component data; det is matrix determinant operation; and I is the absolute value operation.
Further, the method for judging whether the difference between the first component connection result and the second component connection result is within the set first threshold value range includes: performing difference operation on the first component connection result and the second component connection result to obtain a difference result, and correcting the difference result by using the following formula:
obtaining a correction result; and judging whether the correction result is within a first threshold range.
Further, the method for judging whether the difference between the first simulation result and the real-time component data exceeds the set second threshold range by the monitoring device comprises the following steps: performing a difference operation on each data in the first simulation result and each component data corresponding to the real-time component data, performing a normalization operation to obtain a normalization difference result, and correcting the normalization difference result by using the following formula: x=normalized difference result exp (1+n), obtaining normalized correction result; and judging whether the normalized correction result is within a second threshold range.
Further, the process of sending out the abnormal warning by the monitoring device comprises the following steps: and under the condition that the difference between the first simulation result and the real-time component data exceeds a set second threshold range, calculating to obtain component data with highest abnormal weight in the real-time component data based on a component abnormal weight model, and then directly determining equipment sub-data corresponding to the component data with highest abnormal weight based on connection between equipment sub-data in the equipment data and the component data, directly finding equipment corresponding to the equipment sub-data, and giving out an abnormal warning to the equipment.
Further, the data acquisition device includes: data entry means and sensor means; the data input device is used for directly inputting equipment data; the sensor device is used for sensing real-time component data in the fermentation tank.
The beer fermentation tank monitoring system has the following beneficial effects:
1. the efficiency is high: according to the invention, through establishing the connection between the real-time component data and the equipment data in the beer fermentation process, the abnormality can be quickly found and positioned, so that the monitoring efficiency is improved.
2. The accuracy is high: when the invention establishes the connection between the real-time component data and the equipment data, the two simulations are used for establishing and cross-verifying, thereby improving the accuracy and avoiding the false alarm condition in the monitoring process.
Drawings
FIG. 1 is a schematic diagram of a system for monitoring a beer fermentation tank according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of experimental results of the concentration of monitoring components of a monitoring system for a beer fermentation tank according to the embodiment of the present invention;
fig. 3 is a schematic diagram of experimental results of changes in concentration and morphology of monitoring components of a monitoring system for a beer fermentation tank according to the change of the rotation speed of a device.
Detailed Description
The method of the present invention will be described in further detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, a beer fermentation monitoring system, the system comprising: the data acquisition device is used for acquiring equipment data of the fermentation tank and real-time component data in beer fermentation; the virtual fermentation simulation device is used for carrying out twice beer fermentation simulation based on the collected equipment data and component data, and specifically comprises the following steps: first fermentation simulation: driving a first virtual beer fermentation tank based on equipment data, and collecting component data in the simulation process as a first simulation result; second fermentation simulation: substituting the real-time component data into a preset reverse push model, calculating to obtain ideal equipment data corresponding to the real-time component data, calculating to obtain equipment sub-data with highest abnormal weight in the equipment data based on the equipment abnormal weight model according to the difference between the ideal equipment data and the equipment data, modifying the corresponding equipment sub-data in the ideal equipment data into the equipment sub-data in the equipment data, driving a second virtual beer fermentation tank based on the modified ideal equipment data, and collecting the component data in the simulation process as a second simulation result; the fermentation connection establishment unit is used for establishing connection between the real-time component data and the equipment data, and specifically comprises the following steps: based on the difference between the first simulation result and the real-time component data, calculating to obtain component data with highest abnormal weight in the real-time component data by using a component abnormal weight model, taking the component data as a first component connection result, simultaneously, acquiring equipment sub-data with highest abnormal weight in the calculated equipment data during the second fermentation simulation, taking the equipment sub-data as an equipment online result, and based on the difference between the second simulation result and the real-time component data, calculating to obtain component data with highest abnormal weight in the real-time component data by using a component abnormal weight model, taking the component data as a second component connection result, judging whether the difference between the first component connection result and the second component connection result is within a set first threshold range, if so, connecting the equipment connection result and the first connection result to form an equipment component chain, otherwise, judging that the connection fails, and completing the connection between the equipment sub-data and the component data in the equipment data after the connection is carried out for a plurality of times; and the monitoring device is used for judging whether the difference between the first simulation result and the real-time component data exceeds a set second threshold range, and if so, sending out an abnormal warning based on the connection between the equipment sub-data and the component data in the equipment data.
In particular, beer fermenters generally include a number of equipment components: carbon dioxide input/output port, solenoid valve, fermentation cylinder, agitator, material add the mouth, observe and control the mouth, heat preservation, cooling water export, stirring rod, outlet, beer delivery outlet, cooling water entry and sampling port.
These equipment components are provided with different data, such as the rotation speed of the stirrer, the temperature of the heat-insulating layer and the like. While different equipment data will result in differences in real-time composition data during fermentation.
In practice, fermentation abnormalities generally occur for several reasons: improper operation, abnormal operation of the device or abnormal data collection.
And the difference in these anomalies results in a high level of complexity in the monitoring. Errors are also easily produced. Therefore, the invention performs virtual simulation on the process, and the virtual simulation process can be understood as obtaining a virtual beer tank through computer modeling and then driving the beer tank by using equipment data, so that repeated simulation can be realized, and the cost is low.
On the other hand, when virtual simulation is carried out, the invention carries out cross verification twice, and the process of cross verification can greatly improve the accuracy.
Example 2
On the basis of the above embodiment, the device data includes at least: can pressure, rotational speed, temperature differential and static pressure differential.
Specifically, referring to FIG. 2, during beer fermentation, the concentration of real-time components changes with fermentation time. Which normally follows a certain law. Therefore, in establishing the connection relationship between the device and the component, multiple data acquisitions are required, instead of one data pass to establish all connections.
Example 3
On the basis of the above embodiment, the real-time component data includes at least: the fermentation broth density was poor, glucose concentration, xylose concentration and ethanol concentration.
Referring to fig. 3, in practice, the rotational speed of beer fermentation also affects various real-time composition data changes. The concentrations and forms of the various components are not different at different rotational speeds, and the composition percentages are also different. Thus, during the connection establishment process, double verification is required due to multi-factor interference. And in the verification process, the main factor needs to be determined based on the calculation of the weight, rather than simply performing the calculation.
Example 4
On the basis of the above embodiment, the reverse push model is expressed using the following formula:wherein n is the number of component data in the component data, S is the calculated inverse coefficient, and x i A value of component data in the component data; and multiplying the value of each piece of equipment sub-data in the equipment data by the inverse coefficient after the inverse coefficient is calculated to obtain ideal equipment data corresponding to the real-time component data.
Specifically, the inverse model is a mathematical model established by finding ideal equipment data.
Example 5
On the basis of the above embodiment, the device anomaly weight model is expressed using the following formula:wherein T is an equipment abnormality weight coefficient of one piece of equipment sub-data in the calculated equipment data; e (E) i I is a positive integer, and the value range is 1 to the number of the equipment sub-data in the equipment data; e is a matrix formed by all the equipment sub-data of the equipment data; det is matrix determinant operation; the absolute value is calculated; e (E) istandard And the standard value corresponding to each piece of equipment sub-data is a set value.
Specifically, the weight model is used because the influence caused by different devices is different, so that the weight is required to be calculated to obtain the weight coefficient, so that the corresponding device sub-data with the highest weight coefficient is found, and the device corresponding to the device sub-data is found.
Example 6
On the basis of the above embodiment, the component anomaly weight model is expressed using the following formula:wherein P is the equipment anomaly weight system of one component data in the calculated real-time component dataA number; g i I is a positive integer, and the value range is 1 to the number of component data in the real-time component data; g is a matrix formed by all component data of the real-time component data; det is matrix determinant operation; and I is the absolute value operation.
Similarly, the same is true for real-time component data, and the most important component data needs to be determined by the thought of the weight coefficient.
Example 7
On the basis of the above embodiment, the method for determining whether the difference between the first component connection result and the second component connection result is within the set first threshold value range includes: performing difference operation on the first component connection result and the second component connection result to obtain a difference result, and correcting the difference result by using the following formula:obtaining a correction result; and judging whether the correction result is within a first threshold range.
Specifically, beer fermentation process is normal life activity of beer yeast under certain condition with fermentable matter in wort, and the metabolic product is beer. The fermentation conditions, product requirements and flavors are different due to different yeast types, and the fermentation modes are also different. Beer can be classified into upper fermented beer and lower fermented beer according to the type of yeast fermentation. Beer fermentation technology can be generally classified into conventional fermentation technology and modern fermentation technology. Modern fermentation mainly adopts a cylindrical open-air conical fermentation tank for fermentation, continuous fermentation, high-concentration dilution fermentation and other modes.
Example 8
On the basis of the above embodiment, the method for determining whether the difference between the first simulation result and the real-time component data exceeds the set second threshold range by the monitoring device includes: performing a difference operation on each data in the first simulation result and each component data corresponding to the real-time component data, performing a normalization operation to obtain a normalization difference result, and correcting the normalization difference result by using the following formula: x=normalized difference result exp (1+n), obtaining normalized correction result; and judging whether the normalized correction result is within a second threshold range.
Specifically, normalization is a dimensionless processing means, which changes the absolute value of the physical system value into a certain relative value relation. Simplifying the calculation and reducing the magnitude. For example, after each frequency value in the filter is normalized by the cutoff frequency, the frequency is the relative value of the cutoff frequency, and no dimension exists. After the impedance is normalized by the internal resistance of the power supply, each impedance has a relative impedance value, and the dimension of ohm is not available. After all operations are finished, all the operations of inverse normalization are restored. Frequently used in signal processing kits is the nyquist frequency, which is defined as one half of the sampling frequency, and is used for normalization in order selection of the filter and the cut-off frequency in the design. For example, for a system with a sampling frequency of 500hz, a normalized frequency of 400hz would be 400/500=0.8, with a normalized frequency range between 0, 1. If the normalized frequency is converted to an angular frequency, the normalized frequency is multiplied by 2 pi, and if the normalized frequency is converted to hz, the normalized frequency is multiplied by half the sampling frequency.
Example 9
On the basis of the above embodiment, the process of sending out an anomaly warning by the monitoring device includes: and under the condition that the difference between the first simulation result and the real-time component data exceeds a set second threshold range, calculating to obtain component data with highest abnormal weight in the real-time component data based on a component abnormal weight model, and then directly determining equipment sub-data corresponding to the component data with highest abnormal weight based on connection between equipment sub-data in the equipment data and the component data, directly finding equipment corresponding to the equipment sub-data, and giving out an abnormal warning to the equipment.
Specifically, the main equipments for beer fermentation are a fermenter and a seed tank, each of which is accompanied by a raw material (Jin Hansen) preparation, a cooking, a sterilizing and cooling equipment, a ventilation adjusting and sterilizing equipment, a stirrer, etc. A fermentation tank: and the production task of the product is born. Seed pot: the purpose is to ensure the amount of cells necessary for the fermentation tank culture.
Example 10
On the basis of the above embodiment, the data acquisition device includes: data entry means and sensor means; the data input device is used for directly inputting equipment data; the sensor device is used for sensing real-time component data in the fermentation tank.
While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are by way of example only, and that various omissions, substitutions, and changes in the form and details of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is limited only by the following claims.
Claims (7)
1. A beer fermentation tank monitoring system, the system comprising: the data acquisition device is used for acquiring equipment data of the fermentation tank and real-time component data in beer fermentation; the virtual fermentation simulation device is used for carrying out twice beer fermentation simulation based on the collected equipment data and component data, and specifically comprises the following steps: first fermentation simulation: driving a first virtual beer fermentation tank based on equipment data, and collecting component data in the simulation process as a first simulation result; second fermentation simulation: substituting the real-time component data into a preset reverse push model, calculating to obtain ideal equipment data corresponding to the real-time component data, calculating to obtain equipment sub-data with highest abnormal weight in the equipment data based on the equipment abnormal weight model according to the difference between the ideal equipment data and the equipment data, modifying the corresponding equipment sub-data in the ideal equipment data into the equipment sub-data in the equipment data, driving a second virtual beer fermentation tank based on the modified ideal equipment data, and collecting the component data in the simulation process as a second simulation result; the fermentation connection establishment unit is used for establishing connection between the real-time component data and the equipment data, and specifically comprises the following steps: based on the difference between the first simulation result and the real-time component data, calculating to obtain component data with highest abnormal weight in the real-time component data by using a component abnormal weight model, taking the component data as a first component connection result, simultaneously, acquiring equipment sub-data with highest abnormal weight in the calculated equipment data during the second fermentation simulation, taking the equipment sub-data as an equipment online result, and based on the difference between the second simulation result and the real-time component data, calculating to obtain component data with highest abnormal weight in the real-time component data by using a component abnormal weight model, taking the component data as a second component connection result, judging whether the difference between the first component connection result and the second component connection result is within a set first threshold range, if so, connecting the equipment connection result and the first connection result to form an equipment component chain, otherwise, judging that the connection fails, and completing the connection between the equipment sub-data and the component data in the equipment data after the connection is carried out for a plurality of times; the monitoring device is used for judging whether the difference between the first simulation result and the real-time component data exceeds a set second threshold range, and if so, an abnormal warning is sent out based on the connection between the equipment sub-data and the component data in the equipment data;
the inverse model is expressed using the following formula: wherein n is the number of component data in the component data, S is the calculated inverse coefficient, and x i A value of component data in the component data; multiplying the value of each piece of equipment sub-data in the equipment data by the inverse coefficient after the inverse coefficient is calculated to obtain ideal equipment data corresponding to the real-time component data;
the device anomaly weight model is expressed using the following formula: wherein T is an equipment abnormality weight coefficient of one piece of equipment sub-data in the calculated equipment data; e (E) i I is a positive integer, and the value range is 1 to the number of the equipment sub-data in the equipment data; e is a matrix formed by all the equipment sub-data of the equipment data; det is matrix determinant operation; the absolute value is calculated; e (E) istandard The standard value corresponding to each piece of equipment sub-data is a set value;
the component anomaly weight model is expressed using the following formula: wherein P is the equipment anomaly weight coefficient of one component data in the calculated real-time component data; g i I is a positive integer, and the value range is 1 to the number of component data in the real-time component data; g is a matrix formed by all component data of the real-time component data; det is matrix determinant operation; and I is the absolute value operation.
2. The system of claim 1, wherein the device data comprises at least: can pressure, rotational speed, temperature differential and static pressure differential.
3. The system of claim 1, wherein the real-time component data comprises at least: the fermentation broth density was poor, glucose concentration, xylose concentration and ethanol concentration.
4. The system of claim 1The system is characterized in that the method for judging whether the difference value between the first component connection result and the second component connection result is within the set first threshold value range comprises the following steps: performing difference operation on the first component connection result and the second component connection result to obtain a difference result, and correcting the difference result by using the following formula:obtaining a correction result; and judging whether the correction result is within a first threshold range.
5. The system of claim 4, wherein the monitoring means for determining whether the difference between the first simulation result and the real-time component data exceeds a set second threshold range comprises: performing a difference operation on each data in the first simulation result and each component data corresponding to the real-time component data, performing a normalization operation to obtain a normalization difference result, and correcting the normalization difference result by using the following formula: x=normalized difference result exp (1+n), obtaining normalized correction result; and judging whether the normalized correction result is within a second threshold range.
6. The system of claim 5, wherein the monitoring means for issuing an anomaly alert comprises: and under the condition that the difference between the first simulation result and the real-time component data exceeds a set second threshold range, calculating to obtain component data with highest abnormal weight in the real-time component data based on a component abnormal weight model, and then directly determining equipment sub-data corresponding to the component data with highest abnormal weight based on connection between equipment sub-data in the equipment data and the component data, directly finding equipment corresponding to the equipment sub-data, and giving out an abnormal warning to the equipment.
7. The system of claim 1, wherein the data acquisition device comprises: data entry means and sensor means; the data input device is used for directly inputting equipment data; the sensor device is used for sensing real-time component data in the fermentation tank.
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| CN104678864A (en) * | 2013-11-30 | 2015-06-03 | 西安众智惠泽光电科技有限公司 | Beer fermentation tank monitoring system |
| CN107491622A (en) * | 2017-09-14 | 2017-12-19 | 中国华电科工集团有限公司 | A kind of biomass ferment analogue system and the Equipments Choosing Method based on the system |
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| CN107491622A (en) * | 2017-09-14 | 2017-12-19 | 中国华电科工集团有限公司 | A kind of biomass ferment analogue system and the Equipments Choosing Method based on the system |
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