CN115739864A - Water hammer analysis method and water hammer analysis system - Google Patents
Water hammer analysis method and water hammer analysis system Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 352
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- 230000004151 fermentation Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000001963 growth medium Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000011086 high cleaning Methods 0.000 description 1
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- 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 embodiment of the application provides a water hammer analysis method for CIP cleaning equipment and pipelines, which comprises the following steps: acquiring valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve; acquiring a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features; comparing the working state characteristic group of the current cleaning step with a corresponding characteristic threshold value to judge whether a water hammer occurrence condition is met; and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model. The working state characteristic group of the current cleaning step is obtained in real time and is compared with the corresponding characteristic threshold value, so that whether the water hammer really occurs or not is accurately judged, the suspicious valve causing the water hammer is secondarily determined, and the later targeted analysis is facilitated and the problem of the water hammer is solved.
Description
Technical Field
The invention relates to the field of water hammer analysis, in particular to a water hammer analysis method and a water hammer analysis system.
Background
The CIP cleaning is to clean the equipment or the pipeline in production, means that the whole production line performs full-automatic circular cleaning and disinfection in a closed loop on the premise of no manual intervention, is an ideal equipment and pipeline cleaning method, is widely applied in the food industry, the pharmaceutical industry and the like, and realizes automation. However, in the cleaning process of equipment, pipelines and the like, frequent operations such as cleaning liquid, pipeline switching and the like are involved, so that the flow velocity of liquid in the pipelines is suddenly changed, water hammer is caused, and the water hammer phenomenon is caused. The frequent occurrence of water hammer can cause the deformation, the rupture, the internal leakage of the valve and the like of the pipeline, and further economic loss is generated.
The reason for causing the water hammer is complicated, and generally, the generation of the water hammer mainly involves the following factors: whether the process pipeline design is reasonable, whether the key valves are added with dampers, whether the starting and stopping sequence of the valves and the pump is reasonable, whether the valve delay value is set reasonably, whether the process pipeline switching is reasonable, whether the design of a cleaning program is reasonable and the like. Therefore, optimization to eliminate water hammer can only be performed on this basis if the water hammer is found in time and the cause of the water hammer is found. And the production enterprises are always troubled by how to find the water hammer in time and find the reason causing the water hammer.
When a water hammer occurs, a large water hammer noise is generated. Therefore, in the prior art, the occurrence of the water hammer is often determined by listening to the sound on site manually, and the manual judgment mode is relatively random, so that the water hammer cannot be found in time, or other noises can be mistaken for the water hammer noise, and therefore, the accurate judgment on the occurrence of the water hammer cannot be made.
The reason for causing the water hammer is complex, and the occurrence of the water hammer has great abruptness, so that personnel can hardly find the water hammer in time and accurately capture relevant information of the water hammer (such as the occurrence time of the water hammer, the current cleaning step and the like), and the manual analysis of the water hammer is a long finding process, and the method has low efficiency and strong randomness, is difficult to find the root cause of the water hammer, and can not analyze and improve the process flow and improve equipment on the basis to eliminate the water hammer.
Disclosure of Invention
In view of the above, the present invention provides a water hammer analysis method and a water hammer analysis system, which are used to at least partially solve the above technical problems.
In a first aspect, an embodiment of the present application provides a water hammer analysis method for CIP cleaning equipment or a pipe, including the following steps:
acquiring valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve;
acquiring a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features;
comparing the working state characteristic group of the current cleaning step with a corresponding characteristic threshold value to judge whether a water hammer occurrence condition is met;
and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model.
In a possible implementation manner, the set of operating state characteristics of the current cleaning step at least includes: real-time noise of the on-site pipeline and real-time pipeline pressure supplied by the CIP station;
wherein, the step of comparing the working state characteristic group of the current cleaning step with the corresponding characteristic threshold value to judge whether the water hammer occurrence condition is met comprises the following steps:
comparing the real-time noise of the field pipeline with a noise threshold value of the current cleaning step;
comparing the real-time pipeline pressure supplied by the CIP station with a pressure threshold value of a current cleaning step;
and when the field pipeline real-time noise is greater than or equal to the noise threshold value of the current cleaning step and the real-time pipeline pressure supplied by the CIP station is greater than or equal to the pressure threshold value of the current cleaning step, judging that the water hammer occurs.
In a possible implementation manner, the step of determining a suspicious valve causing the water hammer based on the step of cleaning the water hammer and the correlation model further includes:
determining a related cleaning step based on the water hammer generation cleaning step and a cleaning program of the CIP cleaning station, wherein the related cleaning step is the first two steps of the water hammer generation cleaning step;
and determining a suspicious valve based on the incidence relation model, wherein the suspicious valve comprises a relevant valve of the cleaning step in which the water hammer occurs and a relevant valve of the relevant cleaning step.
In one possible implementation, the step of determining an associated cleaning step based on the water hammer occurring cleaning step and a cleaning program of the CIP cleaning station further includes:
establishing a cleaning sequence chain model based on a cleaning program of the CIP cleaning station, wherein the sequence chain model comprises a plurality of cleaning steps arranged in a cleaning sequence, wherein each sequence chain has a unique ID, and each cleaning step has a unique step ID;
determining a correlation washing step based on the washing step of the water hammer and the washing sequence chain model, wherein the correlation washing step is the first two steps of the washing step of the water hammer.
In one possible implementation manner, the water hammer analysis method further includes:
collecting the working state of the suspicious valve, and determining a target valve causing water hammer based on the change of the working state of the suspicious valve;
storing water hammer information in a preset database, wherein the water hammer information comprises water hammer occurrence time, noise parameters when the water hammer occurs, pipeline pressure parameters when the water hammer occurs and a target valve causing the water hammer.
In one possible implementation manner, the step of acquiring the working state of the suspicious valve and determining the target valve causing the water hammer based on the change of the working state of the suspicious valve further includes:
collecting the change time of the working state of the suspicious valve;
calculating the difference value between the water hammer occurrence time and the working state change time;
and comparing the difference value with a preset time difference, and determining the suspicious valve of which the difference value is less than or equal to the preset time difference as a target valve, wherein the preset time difference is 10s.
In one possible implementation manner, the water hammer analysis method further includes:
acquiring a noise time curve and a pressure time curve of the CIP cleaning station;
acquiring the noise generated when the water hammer occurs in a noise time curve based on the water hammer occurrence time, and verifying the consistency of the noise generated when the water hammer occurs in the noise time curve and the noise parameter generated when the water hammer occurs;
acquiring pipeline pressure when the water hammer occurs in a pressure time curve based on the water hammer occurrence time, and verifying the consistency of the pipeline pressure when the water hammer occurs in the pressure time curve and the pipeline pressure parameter when the water hammer occurs;
and removing the water hammer information with inconsistent verification of the noise parameter when the water hammer occurs or the pipeline pressure parameter when the water hammer occurs from the preset database.
In a second aspect, embodiments of the present application provide a water hammer analysis system for CIP cleaning equipment or pipes, including:
a modeling module configured to: acquiring valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve;
a data acquisition module configured to: acquiring a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features;
a determination module configured to: comparing the working state characteristic group of the current cleaning step with a corresponding characteristic threshold value to judge whether a water hammer occurrence condition is met;
a first analysis module configured to: and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model.
In one possible implementation, the water hammer analysis system further includes a second analysis module and a storage module, wherein,
the second analysis module is configured to: collecting the working state of the suspicious valve, and determining a target valve causing water hammer based on the change of the working state of the suspicious valve;
the storage module is configured to: storing water hammer information in a preset database, wherein the water hammer information comprises water hammer occurrence time, noise parameters when a water hammer occurs, pipeline pressure parameters when the water hammer occurs, and a target valve causing the water hammer.
In a possible implementation manner, the water hammer analysis system further includes a display module, where the display module is configured to display a user interface on a terminal, where the user interface includes:
the real-time unit is used for displaying the real-time noise and the current noise threshold value of the field pipeline, the real-time pipeline pressure supplied by the CIP station and the current pressure threshold value;
a water hammer unit for displaying water hammer information, comprising:
the time subunit is used for displaying the water hammer occurrence time;
the noise subunit is used for displaying noise parameters when the water hammer occurs;
the pressure subunit is used for displaying the pipeline pressure parameter when the water hammer occurs;
a valve subunit for displaying a target valve causing the water hammer.
According to the water hammer analysis method and the water hammer analysis system, the working state feature set of the current cleaning step is obtained in real time, and the working state feature set is compared with the corresponding feature threshold value, so that whether the water hammer happens or not is accurately judged. And based on the cleaning step and the incidence relation model of the water hammer, a suspicious valve causing the water hammer is determined, so that the later-stage targeted analysis and the water hammer problem solving of maintenance personnel are facilitated, meanwhile, the operation personnel track and record related water hammer information, and the digital quantitative analysis and processing are carried out on the water hammer information. The reason for causing the water hammer is determined by analyzing the suspicious valve causing the water hammer, so that the process, equipment, flow and the like can be improved in a targeted manner, such as: and increasing the speed of slowing the opening and closing of the valve by the valve gas path damper, determining which valve dampers need to be increased to slow down valve or path switching impact, optimizing the process, optimizing the program and the like. Thereby greatly reducing the impact of the water hammer on the equipment, protecting the equipment to the maximum extent and delaying the service cycle of the equipment.
Drawings
Fig. 1 is a flowchart of a water hammer analysis method provided in an embodiment of the present application;
FIG. 2 is an exemplary cleaning sequence chain model and an association model of cleaning steps and associated valves provided by embodiments of the present application;
FIG. 3 is a flow chart of an exemplary water hammer analysis method provided by an embodiment of the present application;
list of reference numerals:
201 real-time noise of on-site pipeline
202: noise threshold of current cleaning step
203 real-time noise of the on-site pipeline is greater than or equal to the noise threshold value of the current cleaning step
301 real-time line pressure of cip station supply
302: pressure threshold of current cleaning step
303: the real-time pipe pressure supplied by the CIP station is greater than or equal to the pressure threshold of the current cleaning step
501: water hammer generation
502: determining a correlation cleaning step;
503: determining a suspicious valve;
504: time of change of state of a suspect valve
505: time of occurrence of water hammer
506: time difference
507: whether the time difference is less than or equal to the preset time difference
508: target valve
600: cleaning sequence chain model
700: association relation model
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other technical solutions obtained by a person of ordinary skill in the art based on the embodiments in the present application belong to the scope of protection of the present application.
CIP cleaning, namely cleaning of enterprise equipment or pipelines, means that the whole production line performs full-automatic circular cleaning and disinfection in a closed loop on the premise of no manual intervention, is an ideal equipment and pipeline cleaning method, is widely applied to the food industry, the pharmaceutical industry and the like, and realizes automation. However, in the cleaning process of equipment, pipelines and the like, frequent operations such as cleaning liquid, pipeline switching and the like are involved, so that the flow velocity of liquid in the pipelines is suddenly changed, water hammer is caused, and the water hammer phenomenon is caused. The frequent occurrence of water hammer can cause the pipeline to break, causing leakage and further causing economic loss.
The reason for causing the water hammer is complicated, and generally, the generation of the water hammer mainly involves the following factors: whether the process pipeline design is reasonable, whether the key valves are added with dampers, whether the starting and stopping sequence of the valves and the pump is reasonable, whether the setting of the valve delay value is reasonable, whether the process pipeline switching is reasonable, whether the design of a cleaning program is reasonable and the like. Therefore, optimization to eliminate water hammer can only be performed on the basis of finding the water hammer in time and finding the reason for the water hammer. And the production enterprises are always troubled by how to find the water hammer in time and find the reason causing the water hammer.
When a water hammer occurs, a large water hammer noise is generated. Therefore, in the prior art, the occurrence of the water hammer is often determined by listening to the sound on site manually, and the manual judgment mode is relatively random, so that the water hammer cannot be found in time, or other noises can be mistaken for the water hammer noise, and therefore, the accurate judgment on the occurrence of the water hammer cannot be made.
The reasons for causing the water hammer are complex, and the occurrence of the water hammer has great abruptness, so that personnel are difficult to find the water hammer in time and accurately grasp relevant information of the water hammer (such as the occurrence time of the water hammer, the current cleaning step and the like), and the manual analysis of the water hammer is a long finding process, so that the efficiency is low, the randomness is strong, the root cause of the water hammer is difficult to find, and the process flow cannot be analyzed and improved on the basis, and equipment cannot be improved to eliminate the water hammer.
Based on the above problems, embodiments of the present application provide a water hammer analysis method and a water hammer analysis system to at least partially solve the above technical problems.
Specific implementations of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
For ease of understanding, the structure and operation of the CIP cleaning system will first be described.
The CIP cleaning system mainly comprises a CIP workstation, a CIP supply pipe network, a cleaning unit, a backflow pipe network and the like.
The CIP workstation is the heart of the entire CIP cleaning system. But parameters such as automatically regulated cleaning time, cleaner concentration, washing temperature, washing velocity of flow all can be recorded in case, the follow-up CIP cleaning process of tracing back and restoring reality of being convenient for.
The CIP supply pipe network refers to pipe fittings, valves, heating plates, control components thereof and the like between the outlet of the CIP workstation and a cleaned unit. The main function of the CIP supply pipe network unit is to transport cleaning solution from the CIP workstation to the unit being cleaned.
The cleaned unit refers to a cleaning target of the CIP workstation, and the cleaning target of the same CIP workstation is often more than one. For example, for a liquid preparation system, a cleaned unit refers to a core component in the production of a pharmaceutical liquid preparation process, and mainly comprises a liquid preparation tank and a liquid medicine conveying pipe network. The liquid preparation tank comprises a movable tank, a fermentation tank, a reaction tank, a culture medium preparation tank, a buffer solution preparation tank, a concentrated preparation tank, a diluted preparation tank, various sterile storage tanks and the like, and also comprises tank accessories required by process production, cleaning and disinfection, such as a lamp, a sight glass, a sampling valve, a stirrer, a sprayer, a liquid level sensor, a temperature sensor, a pressure sensor, a rupture disk, a respirator and the like; the liquid medicine conveying pipe network comprises pipelines, pipes, valves, titanium rod filters, microporous membrane cylinder filters, liquid medicine conveying pumps and the like for conveying and filtering liquid medicines.
The CIP return pipe network refers to return pumps, pipelines, pipes, valves, conversion plates, control components of the conversion plates and the like from a cleaned unit to a CIP workstation. The main function of the CIP return pipe network unit is to convey cleaned return liquid from the cleaned unit to the CIP workstation.
Fig. 1 is a flowchart of a water hammer analysis method provided in an embodiment of the present application. As shown in fig. 1, the method includes:
s102: acquiring valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve;
for example, taking the cleaning step of flushing the filling line with clean water as an example, in the cleaning step, clean water is required to be conveyed to the filling line through the pipeline of the supply pipe network and the valves V1001, V1005, V1003, V1009 and V1022, and the valves V1001, V1005, V1003, V1009 and V1022 are relevant valves for the cleaning step of flushing the filling line with clean water.
S103: and acquiring a working state feature group of the current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features.
When a water hammer occurs, some operating characteristics of the cleaning system may change, such as noise, pressure within the pipe may change, the pipe may vibrate, and so on. If only one of the operating state characteristics is used as the basis for judging the water hammer, the judgment cannot be made accurately, and misjudgment is easy to occur. Therefore, a plurality of working state characteristics can be combined together to be used as a judgment basis, so that the judgment accuracy is improved.
Specifically, in a possible implementation manner of the present application, the working state feature group of the current cleaning step at least includes: live pipe live noise and live pipe pressure supplied by the CIP station. When the water hammer occurs, the flow speed of the liquid in the pipeline changes suddenly to cause water hammer, and the water pressure also changes, so that the water pressure can be used as one of judgment bases of the water hammer. In addition, in the production and installation process of the CIP cleaning system, a plurality of pipeline pressure acquisition devices are configured in the cleaning pipeline and used for acquiring the water pressure in the pipeline in real time, so that a user can acquire the pipeline pressure in real time without installing additional devices, and the cost of the user is reduced.
When the water hammer happens, huge noise can be generated in the pipeline, so that a sound sensor can be arranged on the pipeline, and the water hammer noise is also used as a judgment standard of the water hammer. Specifically, a noise detection point for collecting the water hammer noise may be disposed in a factory, wherein the arrangement of the noise detection point may take the following factors into consideration: the method comprises the steps of performing distributed point distribution based on equipment partition grouping, performing preferential point distribution based on a pipeline with high cleaning frequency, performing preferential point distribution based on a pipeline or a tank area which is easy to generate water hammer in the prior art, positioning a noise detection point, dynamically moving to a new detection point after the water hammer is eliminated, and the like.
S104: and comparing the working state characteristic group of the current cleaning step with the corresponding characteristic threshold value to judge whether the water hammer occurrence condition is met.
Specifically, in one implementable embodiment, the live pipe real-time noise is compared to a noise threshold for the current cleaning step, and the real-time pipe pressure supplied by the CIP station is compared to a pressure threshold for the current cleaning step. And when the field pipeline real-time noise is larger than or equal to the noise threshold value of the current cleaning step and the real-time pipeline pressure supplied by the CIP station is larger than or equal to the pressure threshold value of the current cleaning step, judging that the water hammer occurs.
In this embodiment, noise and pipe pressure are used as the criteria for determining the water hammer, and the occurrence of water hammer can only be determined if the real-time noise of the pipe in the field is greater than or equal to the noise threshold of the current cleaning step and the real-time pipe pressure supplied by the CIP station is greater than or equal to the pressure threshold of the current cleaning step are both satisfied. Compared with a single-feature judgment mode, the method and the device greatly improve the judgment accuracy.
S105: and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model.
In a CIP cleaning system, there are thousands or even tens of thousands of valves. There are several cleaning systems for the entire plant, and the number of valves is greater. Therefore, to determine the cause of the water hammer, it is necessary to determine the suspected valve causing the water hammer from all the valves. The generation of water hammer is often related to the action state of the valve through which water flows, the action time of the valve and the sequence of the actions of the valve. In this embodiment, according to the cleaning step in which the water hammer occurs, the valves related to the cleaning step can be found in the association relation model, and the related valves are taken as suspicious valves causing the water hammer, so that the valves causing the water hammer are quickly locked.
Specifically, in an implementable embodiment, step S105 further includes:
s1051, determining a related cleaning step based on the cleaning step of the water hammer and the cleaning program of the CIP cleaning station, wherein the related cleaning step is the first two steps of the cleaning step of the water hammer;
and S1052, determining a suspicious valve based on the incidence relation model, wherein the suspicious valve comprises a relevant valve of the cleaning step of the water hammer and a relevant valve of the relevant cleaning step.
The generation of water hammer is often related to the action state of the valve through which water flows, the action time of the valve and the sequence of the actions of the valve, and the actions of the valve generally occur when the cleaning step is switched. Therefore, the first two steps of the water hammer cleaning step are used as the related cleaning steps, and the suspicious valve can be locked more accurately.
Specifically, in an implementable embodiment, step S1051 further comprises:
s10511, establishing a cleaning sequence chain model based on a cleaning program of the CIP cleaning station, wherein the sequence chain model comprises a plurality of cleaning steps arranged in a cleaning sequence, wherein each sequence chain has a unique ID, and each cleaning step has a unique step ID.
TABLE 1
For ease of understanding, step S10511 will be described below with reference to table 1. Table 1 shows a simplified model of a cleaning sequence chain for a filling plant. As shown in Table 1, where P501 is the wash sequence chain ID. The wash sequence chain includes five wash steps arranged in a wash sequence: pre-wash, alkaline wash, intermediate water wash, acid wash, final water wash, wherein each of said wash steps has a unique step ID (M301, M302, M303, M304, M305).
S10512, determining a correlation cleaning step based on the cleaning step of the water hammer and the cleaning sequence chain model, wherein the correlation cleaning step is the first two steps of the cleaning step of the water hammer.
For example, in step M304, a water hammer occurs, and as can be seen from table 1, the associated cleaning steps are the first two steps of step M304, i.e., step M302 and step M303.
In the application, the working state characteristic group of the current cleaning step is obtained in real time, and the working state characteristic group is compared with the corresponding characteristic threshold value, so that whether the water hammer happens or not is accurately judged. And based on the cleaning step and the incidence relation model of the water hammer, the suspicious valve causing the water hammer is determined, so that the later targeted analysis and the water hammer problem solving of the maintenance personnel are facilitated, and meanwhile, the tracking and recording of the related water hammer information by the operation personnel are facilitated, and the digital quantitative analysis and processing of the water hammer information are carried out. The reason for causing the water hammer is determined by analyzing the suspicious valve causing the water hammer, so that the process, equipment, flow and the like can be improved in a targeted manner, such as: and increasing the speed of slowing the opening and closing of the valve by the valve gas path damper, and determining which valve dampers need to be increased to slow down switching impact, process optimization, program optimization and other multiple means. Therefore, the impact of the water hammer on the equipment is greatly reduced, the equipment is protected to the maximum extent, and the service cycle of the equipment is delayed.
Based on the embodiment shown in fig. 1, in an embodiment of the present application, the method further includes:
s106, collecting the working state of the suspicious valve, and determining a target valve causing water hammer based on the change of the working state of the suspicious valve;
in S105, the suspicious valves including the valves related to the water hammer occurring cleaning step and the valves related to the first two steps of the water hammer occurring cleaning step are locked from all the valves of the CIP cleaning system, and although the suspicious valves causing the water hammer are already locked in S105, the number of the suspicious valves is still large, and if the suspicious valves are analyzed one by one, the efficiency is low, and much manpower and material resources are wasted. As described above, the generation of the water hammer is usually related to the action of the valve, so that the working state of the suspected valve in a period before the water hammer occurs can be further collected, and the valve with the changed working state is taken as the target valve for causing the water hammer.
And S107, storing the water hammer information in a preset database, wherein the water hammer information comprises the water hammer occurrence time, the noise parameter when the water hammer occurs, the pipeline pressure parameter when the water hammer occurs and a target valve causing the water hammer.
The water hammer information is stored in a preset database, and the water hammer information stored in the database can be analyzed, for example, points needing to be optimized are determined according to the operation of the steps and the water hammer generation times, the steps needing to be optimized are determined according to the water hammer sequencing record, the optimized points are refined based on the steps and the corresponding curve information, the curve record is carried out on the key information, the transverse analysis of the related data trend is facilitated, and the like.
Specifically, in an implementable embodiment, step S106 further includes:
s1061, collecting the working state change time t1 of the suspicious valve;
s1062, calculating the difference value between the water hammer occurrence time t2 and the working state change time t1;
and S1063, comparing the difference value with a preset time difference t, and determining that the suspicious valve of which the difference value is less than or equal to the preset time difference is a target valve, wherein the preset time difference is 10S, namely if t2-t1 is less than or equal to 10S, the suspicious valve is the target valve causing water hammer.
The occurrence of water hammer is often related to the action of the valve, and in particular, water hammer generally occurs a period of time after the operating state of the valve changes. Therefore, the working state of the suspicious valve in a period of time before the water hammer occurs can be further collected, and the valve with the working state changed in the predetermined time is taken as the target valve for causing the water hammer.
Based on the embodiment shown in fig. 1, in an embodiment of the present application, the water hammer analysis method further includes:
s1081, acquiring a noise time curve and a pressure time curve of the CIP cleaning station;
s1082, acquiring noise generated when the water hammer occurs in a noise time curve based on the water hammer occurrence time, and verifying the consistency of the noise generated when the water hammer occurs in the noise time curve and noise parameters generated when the water hammer occurs;
s1083, acquiring the pipeline pressure when the water hammer occurs in a pressure-time curve based on the water hammer occurrence time, and verifying the consistency of the pipeline pressure when the water hammer occurs in the pressure-time curve and the pipeline pressure parameter when the water hammer occurs;
s1084, removing the water hammer information with inconsistent verification of the noise parameter when the water hammer occurs or the pipeline pressure parameter when the water hammer occurs from the preset database.
The CIP cleaning station can generate a noise time curve and a pressure time curve in real time. In order to ensure the authenticity of the water hammer information stored in the preset database, the water hammer information in the preset database can be verified based on the noise time curve and the pressure time curve, and the verification is not successfulAnd removing the water hammer information. Specifically, for example, the water hammer information of the water hammer 055 described in the preset database includes: time of water hammer occurrence T 055 Noise parameter N at the occurrence of water hammer 055 Pipeline pressure parameter P when water hammer occurs 055 And so on. Obtaining T in a noise time plot 055 Noise N at the moment, judgment N 055 If equal to N (with some possible error), T is obtained in the pressure-time curve 055 Noise P at the moment, judgment P 055 If it is equal to P (with some error), if N is not equal to P 055 Not corresponding to N or P 055 If the water hammer is inconsistent with the P, the verification is not passed, and the water hammer 055 is removed from the preset database.
In order to facilitate a clearer understanding of the technical solution of the present application, the following describes the specific process of fig. 1 in detail by using specific examples in conjunction with fig. 2 and fig. 3.
In block 600, a cleaning sequence chain model is built.
Based on a cleaning program of the CIP cleaning station, a cleaning sequence chain model is established for different cleaning objects, wherein the cleaning data chain model comprises a plurality of cleaning steps arranged according to a cleaning sequence, each cleaning sequence chain model has a unique sequence chain ID, and each cleaning step has a displaced step ID. In which, in fig. 2, some wash sequence chain models are exemplarily shown, such as wash sequence chain C051, wash sequence chain C052, and wash sequence chain C699. Taking the cleaning sequence chain C051 as an example, the cleaning sequence chain C051 includes cleaning steps M301, M302, M303, M304 and M305 arranged in a cleaning sequence.
In block 700, a model of the relationship between the cleaning steps and the associated valves is established.
In a CIP cleaning system, each valve has a unique valve ID. And (3) analyzing the flow path of the liquid in each cleaning step, determining the valves through which the liquid needs to flow in the cleaning step, using the valves as the relevant valves of the cleaning step, and establishing an association relation model between each cleaning step and the relevant valves. As shown in fig. 2, the relevant valves of the cleaning step M301 are: v1001, V1003, V1004, V1008, V2001, V3005; the relevant valves for the cleaning step M302 are: v1121, V1353, V1004, V1008, V1111, V3535; the relevant valves for the cleaning step M303 are: v5301, V1062, V1994, V1018, V3501, V0005; the relevant valves for the cleaning step M304 are: v7541, V1862, V3094, V3689, V3600, V0010, V2235, V5963; the relevant valves for the cleaning step M305 are: v0001, V1352, V3874, V4569, V3699, V0010, V3435, V5632.
Acquiring field pipeline real-time noise through a sound sensor arranged on the pipeline in a block 201, and comparing the field pipeline real-time noise with a noise threshold value of the current cleaning step in a block 202;
acquiring real-time tubing pressure supplied by the CIP station through a tubing pressure acquisition device in block 301 and comparing the real-time tubing pressure supplied by the CIP station with a pressure threshold for the current cleaning step in block 302;
acquiring real-time pipeline pressure supplied by the CIP station through a pressure sensor in the pipeline, and comparing the real-time pipeline pressure supplied by the CIP station with a pressure threshold value of the current cleaning step;
in block 203, the magnitude relationship between the live pipe real-time noise and the noise threshold for the current cleaning step is determined, and in block 303, the magnitude relationship between the real-time pipe pressure supplied by the CIP station and the pressure threshold for the current cleaning step is determined.
When the live pipe real-time noise is greater than or equal to the noise threshold for the current cleaning step and the real-time pipe pressure supplied by the CIP station is greater than or equal to the pressure threshold for the current cleaning step, in block 501, a water hammer is determined to have occurred.
Based on the wash step and wash sequence chain model for which the water hammer occurred, an associated wash step is determined in block 502;
based on the cleaning step at the time of the water hammer occurrence and the cleaning sequence chain model, the first two steps of the cleaning step at the time of the water hammer occurrence are determined as associated cleaning steps, for example, the cleaning step at the time of the water hammer occurrence is M305, and the associated cleaning steps are M303 and M304.
Based on the water hammer occurring wash step, the correlation wash step, and the correlation model, a suspect valve is determined in block 503. As can be seen from fig. 2, the relevant valves of the cleaning steps M305, M303 and M304 are suspect valves, which include: cleaning relevant valves of step M303: v5301, V1062, V1994, V1018, V3501, V0005; purge step M304 associated valves: v7541, V1862, V3094, V3689, V3600, V0010, V2235, V5963; purge step M305, associated valves: v0001, V1352, V3874, V4569, V3699, V0010, V3435, V5632.
In block 504, a status change time of the suspect valve is obtained.
Based on the time of change of state of the suspect valve in block 504 and the time of occurrence of the water hammer in block 505, a time difference is calculated in block 506.
In block 507, the relationship between the time difference and the preset time difference is determined, wherein the suspected valve having the time difference smaller than or equal to the preset time difference is the target valve in block 508.
The CIP cleaning system will record the operating status of each valve in the background. In this embodiment, the working state change time of the suspected valve may be collected based on the working state of the valve recorded by the CIP cleaning system, and the difference between the water hammer occurrence time and the working state change time may be calculated, where the suspected valve with the difference being less than or equal to 10s is the target valve. For ease of understanding, the steps for determining the target valve are described in detail below in conjunction with table 2.
TABLE 2
The left-most column of table 2 is the suspect valve that caused the water hammer as determined by the above procedure. Further, the working state change information and the working state change time of the suspicious valve are obtained through the working state of each valve recorded by the background of the CIP cleaning system. Taking the valve V1352 as an example, the valve V1352 is in an open state before operation and in a closed state after operation, and the operation time (i.e., the operating state change) is 20220601 (year, month, day) -08 (hour, minute, second). And subtracting the action time of the valve from the water hammer occurrence time to obtain a time difference, and taking the suspicious valve of which the time difference is less than 10S as a target valve. As can be seen from table 2, the target valves that caused the water hammer are: v1062, V3600 and V1352.
Example 2
Embodiment 2 is a water hammer analysis system provided in an embodiment of the present application. The water hammer analysis system is used for executing the water hammer analysis method provided by the method embodiment. The water hammer analysis system 50 includes: the device comprises a modeling module, a data acquisition module, a judgment module and a first analysis module.
Wherein the modeling module is configured to: obtaining the valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve
The data acquisition module is configured to: acquiring a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features
The determination module is configured to: comparing the working state characteristic group of the current cleaning step with a corresponding characteristic threshold value to judge whether a water hammer occurrence condition is met;
the first analysis module is configured to: and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model.
Further, the data acquisition module is configured to: live pipe real-time noise and live pipe pressure supplied by the CIP station. The determination module is configured to: comparing the real-time noise of the field pipeline with a noise threshold value of the current cleaning step; comparing the real-time pipeline pressure supplied by the CIP station with a pressure threshold value of a current cleaning step; and when the field pipeline real-time noise is larger than or equal to the noise threshold value of the current cleaning step and the real-time pipeline pressure supplied by the CIP station is larger than or equal to the pressure threshold value of the current cleaning step, judging that the water hammer occurs.
Further, the first analysis module is configured to: determining a correlation cleaning step based on the water hammer occurring cleaning step and a cleaning program of the CIP cleaning station, wherein the correlation cleaning step is the first two steps of the water hammer occurring cleaning step; and determining a suspicious valve based on the incidence relation model, wherein the suspicious valve comprises a relevant valve of the cleaning step in which the water hammer occurs and a relevant valve of the relevant cleaning step.
In an implementable implementation, based on embodiment 2, the water hammer analysis system further comprises a verification module configured to: acquiring a noise time curve and a pressure time curve of the CIP cleaning station; acquiring the noise generated when the water hammer occurs in a noise time curve based on the water hammer occurrence time, and verifying the consistency of the noise generated when the water hammer occurs in the noise time curve and the noise parameter generated when the water hammer occurs; acquiring pipeline pressure when the water hammer occurs in a pressure-time curve based on the water hammer occurrence time, and verifying the consistency of the pipeline pressure when the water hammer occurs in the pressure-time curve and the pipeline pressure parameter when the water hammer occurs; and removing the water hammer information with inconsistent verification of the noise parameter when the water hammer occurs or the pipeline pressure parameter when the water hammer occurs from the preset database.
Based on example 2, in one implementable implementation, the water hammer analysis system further comprises a second analysis module and a storage module, wherein the second analysis module is configured to: collecting the working state of the suspicious valve, and determining a target valve causing water hammer based on the change of the working state of the suspicious valve; the storage module is configured to: storing water hammer information in a preset database, wherein the water hammer information comprises water hammer occurrence time, noise parameters when a water hammer occurs, pipeline pressure parameters when the water hammer occurs, and a target valve causing the water hammer.
In an implementable implementation, based on embodiment 2, the water hammer analysis system further comprises a display module for displaying a user interface at the terminal, wherein the user interface comprises a real-time unit and a water hammer unit, wherein the real-time unit is for displaying live pipe real-time noise and current noise threshold, a real-time pipe pressure supplied by the CIP station, and a current pressure threshold; the water hammer unit is used for displaying water hammer information.
Specifically, the water hammer unit comprises a time subunit for displaying the occurrence time of the water hammer, a noise subunit for displaying noise parameters when the water hammer occurs, a pipeline pressure parameter pressure subunit for displaying the occurrence time of the water hammer, and a valve subunit for displaying a target valve causing the water hammer.
The water hammer analysis system provided in this embodiment is used to implement the corresponding water hammer analysis method in the foregoing method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again. In addition, the functional implementation of each module of the water hammer analysis system of this embodiment may refer to the description of the corresponding part in the foregoing method embodiment, and is not described herein again.
While the invention has been shown and described in detail in the drawings and in the preferred embodiments, the invention is not limited to the embodiments disclosed, and those skilled in the art will appreciate that various combinations of code auditing means in the various embodiments described above may be employed to obtain further embodiments of the invention, which are also within the scope of the invention.
Claims (10)
1. A water hammer analysis method for CIP cleaning equipment or pipelines is characterized by comprising the following steps:
acquiring valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve;
acquiring a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features;
comparing the working state characteristic group of the current cleaning step with a corresponding characteristic threshold value to judge whether a water hammer occurrence condition is met;
and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model.
2. The water hammer analysis method of claim 1, wherein the set of operating condition characteristics of the current cleaning step includes at least: real-time noise of the on-site pipeline and real-time pipeline pressure supplied by the CIP station;
wherein, the step of comparing the working state characteristic group of the current cleaning step with the corresponding characteristic threshold value to judge whether the water hammer occurrence condition is met comprises the following steps:
comparing the real-time noise of the field pipeline with a noise threshold value of the current cleaning step;
comparing the real-time pipeline pressure supplied by the CIP station with a pressure threshold value of a current cleaning step;
and when the field pipeline real-time noise is larger than or equal to the noise threshold value of the current cleaning step and the real-time pipeline pressure supplied by the CIP station is larger than or equal to the pressure threshold value of the current cleaning step, judging that the water hammer occurs.
3. The water hammer analysis method of claim 2, wherein the step of determining a suspect valve causing a water hammer based on the step of cleaning where a water hammer occurs and the correlation model, further comprises:
determining a correlation cleaning step based on the water hammer occurring cleaning step and a cleaning program of the CIP cleaning station, wherein the correlation cleaning step is the first two steps of the water hammer occurring cleaning step;
and determining a suspicious valve based on the incidence relation model, wherein the suspicious valve comprises a relevant valve of the cleaning step in which the water hammer occurs and a relevant valve of the relevant cleaning step.
4. The water hammer analysis method of claim 3, wherein the step of determining a correlation cleaning step based on the water hammer occurring cleaning step and a cleaning program of the CIP cleaning station, further comprises:
establishing a cleaning sequence chain model based on a cleaning program of the CIP cleaning station, wherein the sequence chain model comprises a plurality of cleaning steps arranged in a cleaning sequence, wherein each sequence chain has a unique ID, and each cleaning step has a unique step ID;
determining a correlation washing step based on the washing step of the water hammer and the washing sequence chain model, wherein the correlation washing step is the first two steps of the washing step of the water hammer.
5. The water hammer analysis method of claim 1, further comprising:
collecting the working state of the suspicious valve, and determining a target valve causing water hammer based on the change of the working state of the suspicious valve;
storing water hammer information in a preset database, wherein the water hammer information comprises water hammer occurrence time, noise parameters when a water hammer occurs, pipeline pressure parameters when the water hammer occurs, and a target valve causing the water hammer.
6. The water hammer analysis method of claim 1, wherein the step of acquiring the operating state of the suspect valve, and determining a target valve causing a water hammer based on a change in the operating state of the suspect valve, further comprises:
collecting the change time of the working state of the suspicious valve;
calculating the difference value between the water hammer occurrence time and the working state change time;
and comparing the difference value with a preset time difference, and determining a suspicious valve of which the difference value is less than or equal to the preset time difference as a target valve, wherein the preset time difference is 10s.
7. The water hammer analysis method of claim 5, further comprising:
acquiring a noise time curve and a pressure time curve of the CIP cleaning station;
based on the water hammer occurrence time, acquiring noise when the water hammer occurs in a noise time curve, and verifying consistency of the noise when the water hammer occurs in the noise time curve and noise parameters when the water hammer occurs;
acquiring pipeline pressure when the water hammer occurs in a pressure-time curve based on the water hammer occurrence time, and verifying the consistency of the pipeline pressure when the water hammer occurs in the pressure-time curve and the pipeline pressure parameter when the water hammer occurs;
and removing the water hammer information with inconsistent verification of the noise parameter when the water hammer occurs or the pipeline pressure parameter when the water hammer occurs from the preset database.
8. A water hammer analysis system for CIP cleaning equipment or piping, comprising:
a modeling module configured to: acquiring valve information related to the cleaning step, and establishing an incidence relation model of the cleaning step and the related valve;
a data acquisition module configured to: acquiring a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step comprises two or more working state features;
a determination module configured to: comparing the working state characteristic group of the current cleaning step with a corresponding characteristic threshold value to judge whether a water hammer occurrence condition is met;
a first analysis module configured to: and when the condition for generating the water hammer is judged to be met, determining a suspicious valve causing the water hammer based on the step of cleaning the generated water hammer and the incidence relation model.
9. The water hammer analysis system of claim 8, further comprising a second analysis module and a storage module, wherein,
the second analysis module is configured to: collecting the working state of the suspicious valve, and determining a target valve causing water hammer based on the change of the working state of the suspicious valve;
the storage module is configured to: storing water hammer information in a preset database, wherein the water hammer information comprises water hammer occurrence time, noise parameters when the water hammer occurs, pipeline pressure parameters when the water hammer occurs and a target valve causing the water hammer.
10. The water hammer analysis system of claim 9, further comprising a display module for displaying a user interface at a terminal, wherein the user interface comprises:
the real-time unit is used for displaying the real-time noise and the current noise threshold value of the field pipeline, the real-time pipeline pressure supplied by the CIP station and the current pressure threshold value;
a water hammer unit for displaying water hammer information, comprising:
the time subunit is used for displaying the water hammer occurrence time;
the noise subunit is used for displaying noise parameters when the water hammer occurs;
the pressure subunit is used for displaying the pipeline pressure parameter when the water hammer occurs;
a valve subunit for displaying a target valve causing the water hammer.
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