Water hammer analysis method and water hammer analysis system

Abstract

The embodiment of the present application provides a water hammer analysis method for CIP cleaning equipment and pipelines, including the following steps: obtaining valve information related to the cleaning step, establishing an association relationship model between the cleaning step and the related valve; obtaining a working state feature group of the current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features; comparing the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met; when it is determined that the water hammer occurrence condition is met, determining the suspicious valve causing the water hammer based on the cleaning step where the water hammer occurs and the association relationship model. By obtaining the working state feature group of the current cleaning step in real time and comparing it with the corresponding feature threshold, it is possible to accurately determine whether the water hammer actually occurs, and then secondarily determine the suspicious valve causing the water hammer, which is convenient for later targeted analysis and solving the water hammer problem.

Description

Technical Field

[0001] The invention relates to the field of water hammer analysis, and in particular to a water hammer analysis method and a water hammer analysis system. Background Art

[0002] CIP cleaning is the cleaning of equipment or pipelines in production. It means that the entire production line is fully automatically cyclically cleaned and disinfected in a closed loop without manual intervention. It is an ideal method for cleaning equipment and pipelines. It has been widely used in the food industry, pharmaceutical industry, etc., and has been automated. However, in the cleaning process of equipment, pipelines, etc., frequent operations such as cleaning fluid and pipeline switching are involved, which suddenly change the flow rate of the liquid in the pipeline, causing water hammer and water hammer. The frequent occurrence of water hammer will cause pipeline deformation, rupture, valve internal leakage, etc., and then cause economic losses.

[0003] The causes of water hammer are relatively complex. Generally speaking, the generation of water hammer mainly involves the following factors: whether the process pipeline design is reasonable, whether the key valve has a damper, whether the valve and pump start and stop sequence is reasonable, whether the valve delay value setting is reasonable, whether the process pipeline switching is reasonable, and whether the cleaning program design is reasonable. Therefore, only by timely discovering water hammer and finding the cause of water hammer can we optimize it on this basis to eliminate water hammer. How to timely discover water hammer and find the cause of water hammer has always troubled production companies.

[0004] When water hammer occurs, huge water hammer noise is generated. Therefore, in the prior art, it is often necessary to manually listen to the sound on site to determine the occurrence of water hammer. However, this manual judgment method is relatively random and cannot detect water hammer in time, or other noises may be mistaken for water hammer noise, thus failing to make an accurate judgment on whether water hammer occurs or not.

[0005] Since the causes of water hammer are relatively complex and the occurrence of water hammer is very sudden, it is difficult for personnel to detect water hammer in time and accurately grasp relevant information of water hammer (such as the time of occurrence of water hammer, current cleaning steps, etc.), and manual analysis of water hammer is a long discovery process with low efficiency, strong randomness, and difficulty in discovering the root cause of water hammer. Therefore, it is even more impossible to analyze and improve the process flow and improve the equipment to eliminate water hammer on this basis. Summary of the invention

[0006] In view of this, 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.

[0007] In a first aspect, an embodiment of the present application provides a water hammer analysis method for CIP cleaning equipment or pipelines, comprising the following steps:

[0008] Obtain valve information related to the cleaning steps and establish a correlation model between the cleaning steps and related valves;

[0009] Acquire a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features;

[0010] Comparing the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met;

[0011] When it is determined that the water hammer occurrence condition is met, a suspected valve causing the water hammer is determined based on the cleaning steps for the occurrence of the water hammer and the association model.

[0012] In a possible implementation, the working status feature group 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;

[0013] The step of comparing the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met includes:

[0014] Comparing the real-time noise of the on-site pipeline with the noise threshold of the current cleaning step;

[0015] comparing the real-time pipeline pressure supplied by the CIP station with a pressure threshold of the current cleaning step;

[0016] When the real-time noise of the on-site pipeline is greater than or equal to the noise threshold 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 of the current cleaning step, it is determined that water hammer occurs.

[0017] In a possible implementation, the step of determining a suspected valve causing water hammer based on the cleaning step of the water hammer and the association model further includes:

[0018] Determine associated cleaning steps based on the cleaning steps where water hammer occurs and the cleaning procedures of the CIP cleaning station, wherein the associated cleaning steps are the first two steps of the cleaning steps where water hammer occurs;

[0019] Based on the association relationship model, suspicious valves are determined, wherein the suspicious valves include valves related to the cleaning step where water hammer occurs and valves related to the associated cleaning steps.

[0020] In a possible implementation, the step of determining the associated cleaning step based on the cleaning step where the water hammer occurs and the cleaning procedure of the CIP cleaning station further includes:

[0021] Based on the cleaning procedure of the CIP cleaning station, a cleaning sequence chain model is established, wherein the sequence chain model includes a plurality of cleaning steps arranged in a cleaning sequence, wherein each of the sequence chains has a unique ID, and each of the cleaning steps has a unique step ID;

[0022] Based on the cleaning steps where water hammer occurs and the cleaning sequence chain model, associated cleaning steps are determined, wherein the associated cleaning steps are the first two steps of the cleaning steps where water hammer occurs.

[0023] In a possible implementation, the water hammer analysis method further includes:

[0024] Collecting the working status of the suspicious valve, and determining the target valve causing the water hammer based on the change of the working status of the suspicious valve;

[0025] The water hammer information is stored in a preset database, wherein the water hammer information includes the time when the water hammer occurs, the noise parameters when the water hammer occurs, the pipeline pressure parameters when the water hammer occurs, and the target valve causing the water hammer.

[0026] In a possible implementation, the step of collecting 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:

[0027] Collecting the time when the working state of the suspicious valve changes;

[0028] Calculating the difference between the water hammer occurrence time and the working state change time;

[0029] The difference is compared with a preset time difference, and a suspicious valve whose difference is less than or equal to the preset time difference is determined as a target valve, wherein the preset time difference is 10 seconds.

[0030] In a possible implementation, the water hammer analysis method further includes:

[0031] Obtaining a noise time curve and a pressure time curve of the CIP cleaning station;

[0032] Based on the water hammer occurrence time, the noise when the water hammer occurs in the noise time curve is obtained, and the consistency of the noise when the water hammer occurs in the noise time curve and the noise parameters when the water hammer occurs is verified;

[0033] Based on the water hammer occurrence time, the pipeline pressure when the water hammer occurs in the pressure-time curve is obtained, and 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 is verified;

[0034] Water hammer information in which the noise parameters when the water hammer occurs or the pipeline pressure parameters when the water hammer occurs are inconsistent with verification is removed from the preset database.

[0035] In a second aspect, an embodiment of the present application provides a water hammer analysis system for CIP cleaning equipment or pipelines, comprising:

[0036] The modeling module is configured to: obtain valve information related to the cleaning step and establish a correlation model between the cleaning step and the related valves;

[0037] The data acquisition module is configured to: acquire a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features;

[0038] A judgment module is configured to: compare the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met;

[0039] The first analysis module is configured to: when it is determined that the water hammer occurrence condition is met, determine the suspected valve causing the water hammer based on the cleaning steps of the water hammer occurrence and the association model.

[0040] In a possible implementation, the water hammer analysis system further includes a second analysis module and a storage module, wherein:

[0041] The second analysis module is configured to: collect the working status of the suspicious valve, and determine the target valve causing the water hammer based on the change of the working status of the suspicious valve;

[0042] The storage module is configured to store water hammer information in a preset database, wherein the water hammer information includes the time when the water hammer occurs, the noise parameters when the water hammer occurs, the pipeline pressure parameters when the water hammer occurs, and the target valve causing the water hammer.

[0043] In a possible implementation, the water hammer analysis system further includes a display module, and the display module is used to display a user interface on the terminal, wherein the user interface includes:

[0044] Real-time unit, used to display the real-time noise of the on-site pipeline and the current noise threshold, the real-time pipeline pressure supplied by the CIP station and the current pressure threshold;

[0045] Water hammer unit, used to display water hammer information, including:

[0046] Time subunit, used to display the time when water hammer occurs;

[0047] Noise subunit, used to display the noise parameters when water hammer occurs;

[0048] Pressure subunit, used to display the pipeline pressure parameters when water hammer occurs;

[0049] The valve subunit is used to display the target valve causing water hammer.

[0050] The water hammer analysis method and water hammer analysis system provided in the embodiment of the present application obtain the working state feature group of the current cleaning step in real time, and compare the working state feature group with the corresponding feature threshold, so as to accurately determine whether water hammer occurs. Based on the cleaning steps and the correlation model where water hammer occurs, the suspicious valve causing water hammer is determined, which facilitates the maintenance personnel to analyze and solve the water hammer problem in a targeted manner in the later stage. At the same time, the operator tracks and records the relevant water hammer information, and performs digital quantitative analysis and processing on the water hammer information. By analyzing the suspicious valve causing water hammer and determining the cause of water hammer, targeted improvements can be made to the process, equipment, and process, such as: adding valve air path dampers to slow down the speed of valve switching, determining which valve dampers need to be added to slow down the impact of valve or path switching, process optimization and program optimization, and other multiple means. Thereby, the impact of water hammer on the equipment is greatly reduced, the equipment is protected to the greatest extent possible, and the service life of the equipment is delayed. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a water hammer analysis method provided in an embodiment of the present application;

[0052] Figure 2 It is an exemplary cleaning sequence chain model and an association relationship model between cleaning steps and related valves provided in an embodiment of the present application;

[0053] Figure 3 is a flow chart of an exemplary water hammer analysis method provided in an embodiment of the present application;

[0054] List of reference numerals:

[0055] 201: Live pipeline real-time noise

[0056] 202: Noise threshold for the current cleaning step

[0057] 203: The real-time noise of the on-site pipeline is greater than or equal to the noise threshold of the current cleaning step

[0058] 301: Real-time pipeline pressure supplied by CIP station

[0059] 302: Pressure threshold of the current cleaning step

[0060] 303: The real-time pipeline pressure supplied by the CIP station is greater than or equal to the pressure threshold of the current cleaning step

[0061] 501: Water hammer occurs

[0062] 502: Determine the associated cleaning step;

[0063] 503: Identify the suspicious valve;

[0064] 504: Suspicious valve status change time

[0065] 505: Time of water hammer occurrence

[0066] 506: Time difference

[0067] 507: Is the time difference less than or equal to the preset time difference?

[0068] 508: Target valve

[0069] 600: Cleaning Sequence Chain Model

[0070] 700: Association model DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other technical solutions obtained by ordinary technicians in the field belong to the scope of protection of the present application.

[0072] CIP cleaning refers to the cleaning of enterprise equipment or pipelines. It means that the entire production line is fully automatically circulated and disinfected in a closed loop without manual intervention. It is an ideal method for cleaning equipment and pipelines. It has been widely used in the food industry, pharmaceutical industry, etc., and has been automated. However, in the cleaning process of equipment, pipelines, etc., frequent operations such as cleaning fluid and pipeline switching are involved, which suddenly changes the flow rate of the liquid in the pipeline, causing water hammer and water hammer. The frequent occurrence of water hammer will cause pipeline rupture, leakage, and subsequent economic losses.

[0073] The causes of water hammer are relatively complex. Generally speaking, the generation of water hammer mainly involves the following factors: whether the process pipeline design is reasonable, whether the key valve has a damper, whether the valve and pump start and stop sequence is reasonable, whether the valve delay value setting is reasonable, whether the process pipeline switching is reasonable, and whether the cleaning program design is reasonable. Therefore, only by timely discovering water hammer and finding the cause of water hammer can we optimize it on this basis to eliminate water hammer. How to timely discover water hammer and find the cause of water hammer has always troubled production companies.

[0074] When water hammer occurs, huge water hammer noise is generated. Therefore, in the prior art, it is often necessary to manually listen to the sound on site to determine the occurrence of water hammer. However, this manual judgment method is relatively random and cannot detect water hammer in time, or other noises may be mistaken for water hammer noise, thus failing to make an accurate judgment on whether water hammer occurs or not.

[0075] Since the causes of water hammer are relatively complex and the occurrence of water hammer is very sudden, it is difficult for personnel to detect water hammer in time and accurately grasp relevant information of water hammer (such as the time of occurrence of water hammer, current cleaning steps, etc.), and manual analysis of water hammer is a long discovery process with low efficiency, strong randomness, and difficulty in discovering the root cause of water hammer. Therefore, it is even more impossible to analyze and improve the process flow and improve the equipment to eliminate water hammer on this basis.

[0076] Based on the above problems, the 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.

[0077] The specific implementation of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0078] To facilitate understanding, the structure and working principle of the CIP cleaning system are first explained.

[0079] The CIP cleaning system is mainly composed of CIP workstation, CIP supply pipe network, cleaning unit, return pipe network, etc.

[0080] The CIP workstation is the core of the entire CIP cleaning system. It can automatically adjust parameters such as cleaning time, cleaning agent concentration, cleaning temperature, cleaning flow rate, etc. All operations can be recorded to facilitate subsequent tracking and restoration of the actual CIP cleaning process.

[0081] The CIP supply network refers to the pipe fittings, valves, heating plates and control components from the outlet of the CIP workstation to the unit being cleaned. The main function of the CIP supply network unit is to transport the cleaning solution from the CIP workstation to the unit being cleaned.

[0082] The cleaned unit refers to the cleaning target of the CIP workstation, and the same CIP workstation often has more than one cleaning target. For example, for the liquid preparation system, the cleaned unit refers to the core component in the pharmaceutical liquid preparation process production, which mainly includes two parts: the liquid preparation tank and the liquid medicine delivery network. The liquid preparation tank includes mobile tanks, fermentation tanks, reaction tanks, culture medium preparation tanks, buffer preparation tanks, concentrated preparation tanks, diluted preparation tanks and various sterile storage tanks, etc., and also includes tank accessories required for process production, cleaning and disinfection, such as lights, sight glasses, sampling valves, agitators, sprayers, liquid level sensors, temperature sensors, pressure sensors, bursting discs and respirators, etc.; the liquid medicine delivery network includes pipes, pipe fittings, valves, titanium rod filters, microporous membrane cartridge filters and liquid medicine delivery pumps for liquid medicine delivery and filtration.

[0083] The CIP return pipe network refers to the return pump, pipes, pipe fittings, valves, conversion panels and control components from the cleaned unit to the CIP workstation. The main function of the CIP return pipe network unit is to transport the cleaned return liquid from the cleaned unit to the CIP workstation.

[0084] Figure 1 : is a flow chart of a water hammer analysis method provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0085] S102: Acquire valve information related to the cleaning step, and establish a correlation model between the cleaning step and the related valves;

[0086] For example, taking the cleaning step of flushing the filling pipeline with clean water as an example, in this cleaning step, clean water needs to be transported to the filling pipeline through the pipes of the supply network and valves V1001, V1005, V1003, V1009, and V1022. Valve V1001, V1005, V1003, V1009, and V1022 are the relevant valves for the cleaning step of flushing the filling pipeline with clean water.

[0087] S103: Acquire a working state feature group of the current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features.

[0088] When water hammer occurs, some working state characteristics of the cleaning system will change, such as noise, pressure changes in the pipe, pipe vibration, etc. If only one of the working state characteristics is used as the basis for judging water hammer, it is difficult to make an accurate judgment and it is easy to make a misjudgment. Therefore, multiple working state characteristics can be combined as a basis for judgment, thereby improving the accuracy of judgment.

[0089] Specifically, in a possible implementation of the present application, the working state feature group of the current cleaning step includes at least: the real-time noise of the on-site pipeline and the real-time pipeline pressure supplied by the CIP station. When water hammer occurs, the flow rate of the liquid in the pipeline suddenly changes, causing water hammer, and the water pressure will also change. Therefore, the water pressure can be used as one of the judgment bases for water hammer. In addition, during the production and installation process of the CIP cleaning system, a number of pipeline pressure acquisition devices have been configured in the cleaning pipeline to obtain the water pressure in the pipeline in real time. Therefore, the user can realize the real-time acquisition of pipeline pressure without installing additional equipment, which reduces the user's cost.

[0090] When water hammer occurs, huge noise will be generated in the pipeline. Therefore, sound sensors can be installed on the pipeline, and water hammer noise can also be used as a criterion for judging water hammer. Specifically, noise detection points for collecting water hammer noise can be arranged in the factory. The following factors can be considered in the arrangement of noise monitoring points: dispersed distribution based on equipment partitioning and grouping, priority distribution based on pipelines with high cleaning frequency, priority distribution based on pipelines or tank areas that are prone to water hammer, and dynamic movement to new detection points after locating and eliminating noise detection points after water hammer.

[0091] S104: Compare the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met.

[0092] Specifically, in an achievable implementation, the real-time noise of the on-site pipeline is compared with the noise threshold of the current cleaning step, and the real-time pipeline pressure supplied by the CIP station is compared with the pressure threshold of the current cleaning step. When the real-time noise of the on-site pipeline is greater than or equal to the noise threshold 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 of the current cleaning step, it is determined that water hammer occurs.

[0093] In this embodiment, noise and pipeline pressure are used as the judgment criteria for water hammer. Only when the real-time noise of the on-site pipeline is greater than or equal to the noise threshold 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 of the current cleaning step, can the occurrence of water hammer be determined. Compared with the judgment method based on a single feature, this embodiment greatly improves the accuracy of judgment.

[0094] S105: When it is determined that the water hammer occurrence condition is met, the suspected valve causing the water hammer is determined based on the cleaning steps for the water hammer occurrence and the association model.

[0095] In a CIP cleaning system, there are thousands or even tens of thousands of valves. For the entire factory, there are several cleaning systems and the number of valves is even greater. Therefore, to determine the cause of water hammer, it is necessary to determine the suspected valve causing water hammer from all valves. The generation of water hammer is often related to the action state of the valve through which the water flows, the action time of the valve, and the order of the action of the valve. In this embodiment, according to the cleaning step where water hammer occurs, the valve related to the cleaning step can be searched in the association model, and these related valves are regarded as suspected valves causing water hammer, so as to quickly lock the valve causing water hammer.

[0096] Specifically, in an achievable implementation, step S105 further includes:

[0097] S1051: Determine associated cleaning steps based on the cleaning step where water hammer occurs and the cleaning procedure of the CIP cleaning station, wherein the associated cleaning steps are the first two steps of the cleaning step where water hammer occurs;

[0098] S1052: Determine suspicious valves based on the association relationship model, wherein the suspicious valves include valves related to the cleaning step where water hammer occurs and valves related to the associated cleaning steps.

[0099] The generation of water hammer is often related to the action state of the valve through which the water flows, the action time of the valve, and the order of the valve action. The action of the valve generally occurs when the cleaning steps are switched. Therefore, taking the first two steps of the cleaning step where water hammer occurs as the associated cleaning steps is conducive to more accurate locking of the suspicious valve.

[0100] Specifically, in an achievable implementation, step S1051 further includes:

[0101] S10511: Based on the cleaning procedure of the CIP cleaning station, a cleaning sequence chain model is established, wherein the sequence chain model includes a plurality of cleaning steps arranged in a cleaning order, wherein each of the sequence chains has a unique ID, and each of the cleaning steps has a unique step ID.

[0102]

[0103] Table 1

[0104] For ease of understanding, step S10511 will be described below in conjunction with Table 1. Table 1 is a simple model of a cleaning sequence chain for a certain filling equipment. As shown in Table 1, P501 is the cleaning sequence chain ID. The cleaning sequence chain includes five cleaning steps arranged in cleaning order: pre-washing, alkali washing, intermediate water washing, acid washing, and final water washing, wherein each of the cleaning steps has a unique step ID (M301, M302, M303, M304, M305).

[0105] S10512: Based on the cleaning steps where water hammer occurs and the cleaning sequence chain model, determine the associated cleaning steps, wherein the associated cleaning steps are the first two steps of the cleaning steps where water hammer occurs.

[0106] For example, water hammer occurs in step M304. As can be seen from Table 1, its associated cleaning steps are the first two steps of step M304, namely, step M302 and step M303.

[0107] In this application, by obtaining the working state feature group of the current cleaning step in real time and comparing the working state feature group with the corresponding feature threshold, it is accurately determined whether water hammer occurs. Based on the cleaning steps and the correlation model where water hammer occurs, the suspicious valve causing water hammer is determined, which is convenient for maintenance personnel to analyze and solve the water hammer problem in a targeted manner in the later stage, and it is also convenient for operators to track and record relevant water hammer information, and perform digital quantitative analysis and processing on the water hammer information. By analyzing the suspicious valves causing water hammer and determining the cause of water hammer, targeted improvements can be made to the process, equipment, and process, such as: adding valve air circuit dampers to slow down the speed of valve switching, determining which valve dampers need to be added to slow down the switching impact, process optimization and program optimization, and other multiple means. Thereby greatly reducing the impact of water hammer on the equipment, protecting the equipment to the greatest extent possible, and delaying the service life of the equipment.

[0108] based on Figure 1 In one embodiment of the present application, the method further comprises:

[0109] S106: collecting the working status of the suspicious valve, and determining the target valve causing the water hammer based on the change of the working status of the suspicious valve;

[0110] In S105, suspicious valves are locked from all valves in the CIP cleaning system, wherein the suspicious valves include valves related to the cleaning step where water hammer occurs and valves related to the first two steps of the cleaning step where water hammer occurs. Although step S105 has locked the suspicious valves that cause water hammer, the number of suspicious valves is still large. If the suspicious valves are analyzed one by one, the efficiency is low and a lot of manpower and material resources are wasted. As mentioned above, the generation of water hammer is usually related to the action of the valve. Therefore, the working status of the suspicious valves in the period before the water hammer occurs can be further collected, and the valves with changed working status are used as the target valves that cause water hammer.

[0111] S107: Storing water hammer information in a preset database, wherein the water hammer information includes the time when the water hammer occurs, the noise parameters when the water hammer occurs, the pipeline pressure parameters when the water hammer occurs, and the target valve that causes the water hammer.

[0112] The water hammer information is stored in a preset database and can be analyzed. For example, the points that need to be optimized can be determined based on the step operation and the number of times water hammer occurs. The steps that need to be optimized can be determined by sorting the records based on the number of water hammer times. The optimization points can be refined based on the steps and the corresponding curve information. The key information can be recorded in a curve to facilitate horizontal analysis of related data trends.

[0113] Specifically, in an achievable implementation, step S106 further includes:

[0114] S1061: collecting the working state change time t1 of the suspicious valve;

[0115] S1062: Calculate the difference between the water hammer occurrence time t2 and the working state change time t1;

[0116] S1063: Compare the difference with the preset time difference t to determine that the suspicious valve whose difference is less than or equal to the preset time difference is the target valve, wherein the preset time difference is 10s, that is, if t2-t1≤10s, the suspicious valve is the target valve that causes water hammer.

[0117] The occurrence of water hammer is often related to the action of the valve. Specifically, water hammer usually occurs within a period of time after the working state of the valve changes. Therefore, the working state of the suspicious valve in the period before the water hammer occurs can be further collected, and the valve whose working state changes within the predetermined time is regarded as the target valve causing the water hammer.

[0118] based on Figure 1 In one embodiment of the present application, the water hammer analysis method further includes:

[0119] S1081: Obtaining a noise time curve and a pressure time curve of the CIP cleaning station;

[0120] S1082: Based on the water hammer occurrence time, obtain the noise when the water hammer occurs in the noise time curve, and verify the consistency of the noise when the water hammer occurs in the noise time curve and the noise parameter when the water hammer occurs;

[0121] S1083: Based on the water hammer occurrence time, obtain the pipeline pressure when the water hammer occurs in the pressure-time curve, and verify the consistency between the pipeline pressure when the water hammer occurs in the pressure-time curve and the pipeline pressure parameter when the water hammer occurs;

[0122] S1084: Remove water hammer information that is inconsistent with the verification of the noise parameters when the water hammer occurs or the pipeline pressure parameters when the water hammer occurs from the preset database.

[0123] 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 water hammer information that fails the verification can be removed. Specifically, for example, the water hammer information of water hammer 055 recorded in the preset database includes: water hammer occurrence time T 055 , Noise parameter N when water hammer occurs 055 , Pipeline pressure parameter P when water hammer occurs 055 etc. Obtain T in the noise time curve 055 Noise N at the moment, judgment N 055 Is it equal to N (may have a certain error), get T in the pressure-time curve 055 The noise P at the moment, judgment P 055 Is it equal to P (with a certain error), if N 055 Not consistent with N or P 055 If it is inconsistent with P, the verification fails and water hammer 055 is removed from the preset database.

[0124] In order to more clearly understand the technical solution of this application, Figure 2 and Figure 3 Through specific examples Figure 1 The specific process is described in detail.

[0125] In block 600, a cleaning sequence chain model is established.

[0126] Based on the cleaning procedure of the CIP cleaning station, a cleaning sequence chain model is established for different cleaning objects, wherein the cleaning data chain model includes several cleaning steps arranged in a cleaning order, each cleaning sequence chain model has a unique sequence chain ID, and each cleaning step has a displacement step ID. Figure 2 The cleaning sequence chain model of some parts is shown in the figure, such as cleaning sequence chain C051, cleaning sequence chain C052, and cleaning sequence chain C699. Taking cleaning sequence chain C051 as an example, cleaning sequence chain C051 includes cleaning steps M301, M302, M303, M304, and M305 arranged in cleaning order.

[0127] In block 700, a relationship model between cleaning steps and related valves is established.

[0128] In the CIP cleaning system, each valve has a unique valve ID. By analyzing the flow path of the liquid in each cleaning step, the valves that the liquid needs to flow through in the cleaning step are determined, and these valves are used as the relevant valves of the cleaning step to establish the association relationship model between each cleaning step and the relevant valves. Figure 2 As shown, the valves related to the cleaning step M301 are: V1001, V1003, V1004, V1008, V2001, V3005; the valves related to the cleaning step M302 are: V1121, V1353, V1004, V1008, V1111, V3535; the valves related to the cleaning step M303 are: V5301, V1062, V1994, V10 18, V3501, V0005; the relevant valves of cleaning step M304 are: V7541, V1862, V3094, V3689, V3600, V0010, V2235, V5963; the relevant valves of cleaning step M305 are: V0001, V1352, V3874, V4569, V3699, V0010, V3435, V5632.

[0129] In block 201, a real-time noise of the pipeline is obtained by a sound sensor arranged on the pipeline, and the real-time noise of the pipeline is compared with a noise threshold of the current cleaning step in block 202;

[0130] In block 301, the real-time pipeline pressure supplied by the CIP station is obtained by a pipeline pressure acquisition device, and the real-time pipeline pressure supplied by the CIP station is compared with the pressure threshold of the current cleaning step in block 302;

[0131] Obtain the real-time pipeline pressure supplied by the CIP station through a pressure sensor in the pipeline, and compare the real-time pipeline pressure supplied by the CIP station with the pressure threshold of the current cleaning step;

[0132] In box 203, the magnitude relationship between the real-time noise of the on-site pipeline and the noise threshold of the current cleaning step is determined. In box 303, the magnitude relationship between the real-time pipeline pressure supplied by the CIP station and the pressure threshold of the current cleaning step is determined.

[0133] When the real-time noise of the on-site pipeline is greater than or equal to the noise threshold 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 of the current cleaning step, in box 501, it is determined that water hammer occurs.

[0134] Based on the cleaning steps and the cleaning sequence chain model of water hammer occurrence, the associated cleaning steps are determined in block 502;

[0135] Based on the cleaning steps and cleaning sequence chain model when water hammer occurs, the first two steps of the cleaning steps when water hammer occurs are determined as associated cleaning steps. For example, if the cleaning step when water hammer occurs is M305, the associated cleaning steps are M303 and M304.

[0136] Based on the cleaning steps, associated cleaning steps and associated relationship model of water hammer occurrence, a suspicious valve is determined in block 503. Figure 2 It can be seen that the related valves of cleaning steps M305, M303 and M304 are suspicious valves, including: the related valves of cleaning step M303: V5301, V1062, V1994, V1018, V3501, V0005; the related valves of cleaning step M304: V7541, V1862, V3094, V3689, V3600, V0010, V2235, V5963; the related valves of cleaning step M305: V0001, V1352, V3874, V4569, V3699, V0010, V3435, V5632.

[0137] In block 504 , the state change time of the suspicious valve is obtained.

[0138] Based on the state change time of the suspected valve in box 504 and the occurrence time of the water hammer in box 505 , the time difference is calculated in box 506 .

[0139] In box 507 , the relationship between the time difference and the preset time difference is determined, wherein the suspicious valve with a time difference less than or equal to the preset time difference is the target valve in box 508 .

[0140] The CIP cleaning system will record the working status of each valve in the background. In this embodiment, based on the working status of the valve recorded by the CIP cleaning system, the working status change time of the suspicious valve can be collected, and the difference between the water hammer occurrence time and the working status change time can be calculated, wherein the suspicious valve with a difference less than or equal to 10s is the target valve. For ease of understanding, the steps for determining the target valve will be specifically described below in conjunction with Table 2.

[0141]

[0142]

[0143] Table 2

[0144] The leftmost column of Table 2 shows the suspected valves that caused water hammer as determined in the above steps. Furthermore, the working status of each valve recorded in the background of the CIP cleaning system is used to obtain the working status change information and working status change time of the suspected valve. Taking valve V1352 as an example, valve V1352 is in an open state before actuation and in a closed state after actuation. The actuation time (i.e., working status change) is 20220601 (year-month-day)-08:35:49 (hour-minute-second). The time difference is obtained by subtracting the valve actuation time from the water hammer occurrence time, and the suspected valves with a time difference of less than 10S are taken as target valves. As can be seen from Table 2, the target valves that cause water hammer are: V1062, V3600, and V1352.

[0145] Example 2

[0146] Embodiment 2 is a water hammer analysis system provided in the embodiment of the present application. The water hammer analysis system is used to execute the water hammer analysis method provided in the above method embodiment. The water hammer analysis system 50 includes: a modeling module, a data acquisition module, a judgment module, and a first analysis module.

[0147] The modeling module is configured to: obtain valve information related to the cleaning steps, and establish a correlation model between the cleaning steps and the related valves.

[0148] The data acquisition module is configured to: acquire a working state feature group of the current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features.

[0149] The judgment module is configured to: compare the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met;

[0150] The first analysis module is configured to: when it is determined that the water hammer occurrence condition is met, determine the suspected valve causing the water hammer based on the cleaning steps for the occurrence of the water hammer and the association model.

[0151] Furthermore, the data acquisition module is configured to: compare the real-time noise of the on-site pipeline and the real-time pipeline pressure supplied by the CIP station. The judgment module is configured to: compare the real-time noise of the on-site pipeline with the noise threshold of the current cleaning step; compare the real-time pipeline pressure supplied by the CIP station with the pressure threshold of the current cleaning step; when the real-time noise of the on-site pipeline is greater than or equal to the noise threshold 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 of the current cleaning step, it is determined that water hammer occurs.

[0152] Further, the first analysis module is configured to: determine the associated cleaning steps based on the cleaning steps where water hammer occurs and the cleaning procedure of the CIP cleaning station, wherein the associated cleaning steps are the first two steps of the cleaning steps where water hammer occurs; determine the suspicious valves based on the association relationship model, wherein the suspicious valves include the related valves of the cleaning steps where water hammer occurs and the related valves of the associated cleaning steps.

[0153] Based on Example 2, in an achievable implementation, the water hammer analysis system also includes a verification module, which is configured to: obtain a noise time curve and a pressure time curve of the CIP cleaning station; based on the water hammer occurrence time, obtain the noise when the water hammer occurs in the noise time curve, and verify the consistency of the noise when the water hammer occurs in the noise time curve with the noise parameters when the water hammer occurs; based on the water hammer occurrence time, obtain the pipeline pressure when the water hammer occurs in the pressure time curve, and verify the consistency of the pipeline pressure when the water hammer occurs in the pressure time curve with the pipeline pressure parameters when the water hammer occurs; remove water hammer information that is inconsistent with the noise parameters when the water hammer occurs or the pipeline pressure parameters when the water hammer occurs from the preset database.

[0154] Based on Example 2, in an achievable implementation, the water hammer analysis system further includes a second analysis module and a storage module, wherein the second analysis module is configured to: collect the working status of the suspicious valve, and determine the target valve causing the water hammer based on the change in the working status of the suspicious valve; the storage module is configured to: store the water hammer information in a preset database, wherein the water hammer information includes the time when the water hammer occurs, the noise parameters when the water hammer occurs, the pipeline pressure parameters when the water hammer occurs, and the target valve causing the water hammer.

[0155] Based on Example 2, in an achievable implementation, the water hammer analysis system also includes a display module, which is used to display a user interface on the terminal, wherein the user interface includes a real-time unit and a water hammer unit, wherein the real-time unit is used to display the real-time noise of the on-site pipeline and the current noise threshold, the real-time pipeline pressure supplied by the CIP station and the current pressure threshold; the water hammer unit is used to display water hammer information.

[0156] Specifically, the water hammer unit includes a time subunit for displaying the time when the water hammer occurs, a noise subunit for displaying the noise parameters when the water hammer occurs, a pressure subunit for displaying the pipeline pressure parameters when the water hammer occurs, and a valve subunit for displaying the target valve that causes the water hammer.

[0157] The water hammer analysis system provided in this embodiment is used to implement the corresponding water hammer analysis methods in the aforementioned multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here. In addition, the functional implementation of each module of the water hammer analysis system of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be described in detail here.

[0158] The present invention has been shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A water hammer analysis method for a CIP cleaning station, characterized in that: The following steps are involved: Obtain valve information related to the cleaning steps and establish a correlation model between the cleaning steps and related valves; Acquire a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features; Comparing the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met; When it is determined that the water hammer occurrence condition is met, based on the cleaning steps of the water hammer occurrence and the association model, the suspected valve causing the water hammer is determined, which includes the following sub-steps: Determine the associated cleaning steps based on the cleaning steps where water hammer occurs and the cleaning procedures of the CIP cleaning station, wherein the associated cleaning steps are the first two steps of the cleaning steps where water hammer occurs; Based on the association relationship model, suspicious valves are determined, wherein the suspicious valves include valves related to the cleaning step where water hammer occurs and valves related to the associated cleaning steps. The working status of the suspicious valve is collected, and based on the change of the working status of the suspicious valve, the target valve causing the water hammer is determined.

2. The water hammer analysis method according to claim 1, characterized in that: The working state feature group of the current cleaning step includes at least: the real-time noise of the on-site pipeline and the real-time pipeline pressure supplied by the CIP cleaning station; The step of comparing the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met includes: Comparing the real-time noise of the on-site pipeline with the noise threshold of the current cleaning step; Comparing the real-time pipeline pressure supplied by the CIP cleaning station with the pressure threshold of the current cleaning step; When the real-time noise of the on-site pipeline is greater than or equal to the noise threshold of the current cleaning step and the real-time pipeline pressure supplied by the CIP cleaning station is greater than or equal to the pressure threshold of the current cleaning step, it is determined that water hammer occurs.

3. The water hammer analysis method according to claim 1, characterized in that: The step of determining the associated cleaning steps based on the cleaning steps where water hammer occurs and the cleaning procedures of the CIP cleaning station further comprises: Based on the cleaning procedure of the CIP cleaning station, a cleaning sequence chain model is established, wherein the sequence chain model includes a plurality of cleaning steps arranged in a cleaning sequence, wherein each of the sequence chains has a unique ID, and each of the cleaning steps has a unique step ID; Based on the cleaning steps where water hammer occurs and the cleaning sequence chain model, associated cleaning steps are determined, wherein the associated cleaning steps are the first two steps of the cleaning steps where water hammer occurs.

4. The water hammer analysis method according to claim 1, characterized in that: The water hammer analysis method further comprises: The water hammer information is stored in a preset database, wherein the water hammer information includes the time when the water hammer occurs, the noise parameters when the water hammer occurs, the pipeline pressure parameters when the water hammer occurs, and the target valve causing the water hammer.

5. The water hammer analysis method according to claim 4, characterized in that: The step of collecting the working status of the suspicious valve and determining the target valve causing the water hammer based on the change of the working status of the suspicious valve further includes: Collecting the time when the working state of the suspicious valve changes; Calculating the difference between the water hammer occurrence time and the working state change time; The difference is compared with a preset time difference, and a suspicious valve whose difference is less than or equal to the preset time difference is determined as a target valve, wherein the preset time difference is 10 seconds.

6. The water hammer analysis method according to claim 4, characterized in that: The water hammer analysis method further comprises: Obtaining a noise time curve and a pressure time curve of the CIP cleaning station; Based on the water hammer occurrence time, the noise when the water hammer occurs in the noise time curve is obtained, and the consistency of the noise when the water hammer occurs in the noise time curve and the noise parameters when the water hammer occurs is verified; Based on the water hammer occurrence time, the pipeline pressure when the water hammer occurs in the pressure-time curve is obtained, and 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 is verified; Water hammer information in which the noise parameters when the water hammer occurs or the pipeline pressure parameters when the water hammer occurs are inconsistent with verification is removed from the preset database.

7. A water hammer analysis system for a CIP cleaning station, characterized in that: include: The modeling module is configured to: obtain valve information related to the cleaning step and establish a correlation model between the cleaning step and the related valves; The data acquisition module is configured to: acquire a working state feature group of a current cleaning step, wherein the working state feature group of the current cleaning step includes two or more working state features; A judgment module is configured to: compare the working state feature group of the current cleaning step with the corresponding feature threshold to determine whether the water hammer occurrence condition is met; The first analysis module is configured to: when it is determined that the water hammer occurrence condition is met, determine the associated cleaning steps based on the cleaning steps where the water hammer occurs and the cleaning procedures of the CIP cleaning station, wherein the associated cleaning steps are the first two steps of the cleaning steps where the water hammer occurs; determine the suspicious valves based on the association relationship model, wherein the suspicious valves include the valves associated with the cleaning steps where the water hammer occurs and the valves associated with the associated cleaning steps; The second analysis module is configured to: collect the working status of the suspicious valve, and determine the target valve causing the water hammer based on the change of the working status of the suspicious valve.

8. The water hammer analysis system according to claim 7, characterized in that: The water hammer analysis system also includes a storage module, which is configured to: store water hammer information in a preset database, wherein the water hammer information includes the time when the water hammer occurs, the noise parameters when the water hammer occurs, the pipeline pressure parameters when the water hammer occurs, and the target valve that causes the water hammer.

9. The water hammer analysis system according to claim 8, characterized in that: The water hammer analysis system further includes a display module, and the display module is used to display a user interface on the terminal, wherein the user interface includes: Real-time unit, used to display the real-time noise of on-site pipelines and the current noise threshold, the real-time pipeline pressure supplied by the CIP cleaning station and the current pressure threshold; Water hammer unit, used to display water hammer information, including: Time subunit, used to display the time when water hammer occurs; Noise subunit, used to display the noise parameters when water hammer occurs; Pressure subunit, used to display the pipeline pressure parameters when water hammer occurs; The valve subunit is used to display the target valve causing water hammer.

Images