Process industry pipeline material monitoring method and system

Abstract

The invention discloses a process industry pipeline material monitoring method and system, and relates to process industry material monitoring, and the method comprises the steps: analyzing process industry pipeline information to obtain demand data; determining an initial sensor site according to the pipeline layout data and the pipeline structure data; constructing a pipeline simulation model according to the pipeline layout data, the pipeline structure data and the pipeline material data; substituting the past pipeline operation data and the past pipeline fault data into a pipeline simulation model for simulation to generate a standard operation specification criterion and determine a target sensor site; and deploying a sensor according to the target sensor site to generate a sensor network, acquiring sensor network data information, substituting the sensor network data information into the pipeline simulation model to perform operation simulation, and performing real-time monitoring to obtain monitoring data. By means of the method, real-time monitoring of multiple pairs of pipeline materials can be achieved, workers can conveniently conduct process regulation and control to deal with emergencies in time, and the hysteresis quality of manual detection is overcome.

Description

Technical Field

[0001] The present invention relates to process industry material monitoring, and in particular to a process industry pipeline material monitoring method and system. Background Art

[0002] In process industries, this detection information is generally only available at the entrance and exit. A lot of detection information in the intermediate links is missing, which in turn causes time deviations in the feedback control process. Manual sampling and testing will also have this lag, which will waste a lot of manpower, material resources and product raw materials.

[0003] Deploying this sensor directly in the middle of the pipeline can monitor the physical properties of the material in real time.

[0004] In order to achieve the purpose of real-time monitoring, a process industry pipeline material monitoring method and system are urgently needed. Summary of the Invention

[0005] To solve the above problems, the present application proposes a process industry pipeline material monitoring method and system.

[0006] A process industry pipeline material monitoring method specifically includes:

[0007] S1. Obtain process industry pipeline information and past pipeline operation information, analyze the process industry pipeline information to obtain pipeline layout data, pipeline structure data, pipeline material data, past pipeline operation data, and past pipeline fault data;

[0008] S2. confirming the initial sensor location based on the pipeline layout data and pipeline structure data;

[0009] S3. Constructing a pipeline simulation model based on pipeline layout data, pipeline structure data, and pipeline material data;

[0010] S4, bringing past pipeline operation data and past pipeline failure data into the pipeline simulation model to simulate and generate standard operation specifications and determine target sensor locations;

[0011] S5. Deploy sensors at target sensor locations to generate a sensor network, obtain sensor network data information, and substitute the sensor network data information into the pipeline simulation model to perform operation simulation and real-time monitoring to obtain monitoring data;

[0012] S6. Issue early warnings based on standard operating specifications and real-time monitoring data.

[0013] Preferably, the specific content of confirming the initial sensor location according to the pipeline layout data and the pipeline structure data in S2 includes:

[0014] Determine the total inlet, total outlet, first straight pipe section, second straight pipe section, and curved pipe section of the pipeline according to the pipeline layout data;

[0015] Configure the first sensor site at the main inlet and the main outlet;

[0016] Disposing second sensor sites at both ends of the first straight pipe section;

[0017] The third sensor sites are respectively arranged on the inner and outer sides of the curved pipe section;

[0018] A fourth sensor site is configured on the central surfaces of the first straight pipe section, the second straight pipe section and the curved pipe section;

[0019] Determine a liquid conversion structure according to the pipeline structure data, and configure a fifth sensor site at a downstream pipeline port of the liquid conversion structure;

[0020] The first sensor site, the second sensor site, the third sensor site, the fourth sensor site and the fifth sensor site constitute initial sensor sites.

[0021] Preferably, the specific content of constructing the pipeline simulation model according to the pipeline layout data, pipeline structure data and pipeline material data in S3 is:

[0022] Determine the pipeline geographical location information and pipeline three-dimensional information based on pipeline layout data;

[0023] Construct a pipeline framework based on the pipeline geographical location information and pipeline direction information;

[0024] Determine the pipeline cross-sectional area and pipeline nodes according to the pipeline structure data, and modify the pipeline framework according to the pipeline cross-sectional area and pipeline nodes to obtain the initial pipeline model;

[0025] The pipeline material data is marked on the initial pipeline model to form a pipeline simulation model.

[0026] Preferably, S4 brings past pipeline operation data and past pipeline fault data into the pipeline simulation model to simulate and generate standard operation specifications and determine the specific contents of the target sensor locations, including:

[0027] Past pipeline operation data and past pipeline failure data are brought into the pipeline simulation model to simulate and obtain several fault locations and mark them on the pipeline simulation model;

[0028] Extracting features from past pipeline fault data to obtain several fault features, wherein the fault features include corresponding fault locations;

[0029] Build a deep learning model and use it to train past pipeline operation data, past pipeline failure data, and several failure characteristics to form an operation-failure correlation relationship;

[0030] Based on the operation-fault correlation, data is eliminated from past pipeline fault data to obtain standard operation data;

[0031] Mapping standard operation data and pipeline material data to generate standard operation specifications;

[0032] Mapping several fault locations and pipeline material data to form a sensor optimization strategy;

[0033] The initial sensor site and the fault site constitute the target sensor site.

[0034] Preferably, the specific contents of obtaining sensor network data information and substituting the sensor network data information into the pipeline simulation model to perform operation simulation and real-time monitoring to obtain monitoring data in S5 include:

[0035] Substitute the sensor network data information into the pipeline simulation model to run the simulation and obtain the correlation between different fault locations;

[0036] Classify the sensor network into several category groups according to sensor categories;

[0037] Take the pipeline inlet as the origin, the direction of the horizontal plane parallel to the straight pipe section as the X coordinate axis, the direction of the horizontal plane perpendicular to the straight pipe section as the Y coordinate axis, the direction perpendicular to the horizontal plane as the Z coordinate axis, and the 45-degree intersection of the three axes as the 0 axis;

[0038] The target sensor locations in the direction parallel to the straight pipe section, the direction perpendicular to the straight pipe section, the direction perpendicular to the horizontal plane and the curved pipe section are projected onto the X-axis, Y-axis, Z-axis and 0-axis respectively to obtain projection points. The projection points are adjusted to be equidistant and the characteristic network is established by combining the correlation relationship.

[0039] The network is dynamically adjusted according to time to form a dynamic characteristic network.

[0040] Preferably, the specific contents of the early warning in S6 according to the standard operating specifications and the real-time monitoring data include:

[0041] Form a standard dynamic network space in the dynamic characteristic network according to standard operation specification rules;

[0042] Real-time monitoring of the dynamic network and statistics of the fluctuation range of each node in the current dynamic network;

[0043] Predict adjacent nodes based on the current node fluctuation range and its correlation;

[0044] Count the number of current abnormal nodes and predicted abnormal nodes and their fluctuation degree to evaluate the dynamic characteristic network and obtain the warning value;

[0045] According to the severity of the warning value, several warning levels are obtained. Warnings are issued based on the warning levels and the abnormal sensor locations are traced back.

[0046] Preferably, the expression of the warning value is:

[0047]

[0048] Among them, A is the warning value, n is the number of abnormal nodes at present, i is the i-th abnormal node, α i is the importance of the current abnormal node i, l i is the fluctuation degree of the current abnormal node i, t is the t-th predicted abnormal node, β t is the importance of the t-th predicted abnormal node, l i is the fluctuation degree of the t-th predicted abnormal node.

[0049] A process industry pipeline material monitoring system, comprising:

[0050] Data acquisition unit: obtains process industry pipeline information and past pipeline operation information, analyzes the process industry pipeline information to obtain pipeline layout data, pipeline structure data, pipeline material data, pipeline operation data and pipeline fault data;

[0051] Initial construction unit: confirms the initial sensor location based on the pipeline layout data and pipeline structure data, and builds the pipeline simulation model based on the pipeline layout data, pipeline structure data and pipeline material data;

[0052] Simulation construction unit: brings pipeline operation data and pipeline fault data into the pipeline simulation model to simulate and generate standard operation specifications and determine target sensor locations;

[0053] Monitoring and early warning unit: Deploy sensor networks according to target sensor locations, obtain sensor network data information, and substitute the sensor network data information into the pipeline simulation model for operation simulation and real-time monitoring to obtain monitoring data, and issue early warnings based on standard operation specifications and real-time monitoring data.

[0054] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the process industry pipeline material monitoring method is called.

[0055] A storage medium, characterized in that computer executable instructions are stored in the storage medium, and when the computer executable instructions are loaded and executed by a processor, the content of the process industry pipeline material monitoring method is implemented.

[0056] In summary, compared with traditional technologies, the present invention provides a process industry pipeline material monitoring method and system. The present invention deploys sensors to generate a sensor network, establishes a pipeline simulation model for operation simulation, and performs real-time monitoring and early warning, thereby reducing manual lag and improving pipeline safety performance.

[0057] The technical method of the present invention is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a step diagram of a process industry pipeline material monitoring method of the present invention;

[0059] Figure 2 This is a unit diagram of a process industry pipeline material monitoring system according to the present invention. DETAILED DESCRIPTION

[0060] The technical method of the present invention is further described below through the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and values described in these embodiments do not limit the scope of this application.

[0061] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0062] Technologies, systems, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0063] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0064] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0065] The present invention provides a process industry pipeline material monitoring method and system.

[0066] A process industry pipeline material monitoring method specifically includes:

[0067] S1. Obtain process industry pipeline information and past pipeline operation information, analyze the process industry pipeline information to obtain pipeline layout data, pipeline structure data, pipeline material data, past pipeline operation data, and past pipeline fault data;

[0068] S2. confirming the initial sensor location based on the pipeline layout data and pipeline structure data;

[0069] Furthermore, the specific contents of confirming the initial sensor location according to the pipeline layout data and pipeline structure data in S2 include:

[0070] Sensor sites may include sites where concentrations fluctuate, where concentrations are uncertain after fluctuations, where danger may occur, where damage may occur, and where sensors may be required depending on the detection method.

[0071] The same sensor site can be installed with one or more different sensors to achieve different purposes according to its properties, such as flow statistics, liquid level statistics, pressure statistics, etc.

[0072] Determine the total inlet, total outlet, first straight pipe section, second straight pipe section, and curved pipe section of the pipeline according to the pipeline layout data;

[0073] Configure the first sensor site at the main inlet and the main outlet;

[0074] A second sensor site is configured at both ends of the first straight pipe segment. This is a site configured for a similar method such as the time difference method, which transmits ultrasonic waves in both the upstream and downstream directions of the pipeline and calculates the flow rate by the time difference between the downstream and upstream flows.

[0075] The third sensor sites are respectively arranged on the inner and outer sides of the curved pipe section;

[0076] A fourth sensor site is configured on the central surfaces of the first straight pipe section, the second straight pipe section and the curved pipe section;

[0077] Determine a liquid conversion structure according to the pipeline structure data, and configure a fifth sensor site at a downstream pipeline port of the liquid conversion structure;

[0078] The first sensor site, the second sensor site, the third sensor site, the fourth sensor site and the fifth sensor site constitute initial sensor sites.

[0079] Different sensor sites may be set at none or multiple times.

[0080] S3. Constructing a pipeline simulation model based on pipeline layout data, pipeline structure data, and pipeline material data;

[0081] Furthermore, the specific contents of constructing the pipeline simulation model in S3 based on the pipeline layout data, pipeline structure data and pipeline material data are as follows:

[0082] Determine the pipeline geographical location information and pipeline three-dimensional information based on pipeline layout data;

[0083] Construct a pipeline framework based on the pipeline geographical location information and pipeline direction information;

[0084] Determine the pipeline cross-sectional area and pipeline nodes according to the pipeline structure data, and modify the pipeline framework according to the pipeline cross-sectional area and pipeline nodes to obtain the initial pipeline model;

[0085] The pipeline material data is marked on the initial pipeline model to form a pipeline simulation model.

[0086] S4, bringing past pipeline operation data and past pipeline failure data into the pipeline simulation model to simulate and generate standard operation specifications and determine target sensor locations;

[0087] Furthermore, S4 brings past pipeline operation data and past pipeline failure data into the pipeline simulation model to simulate and generate standard operation specifications and determine the specific contents of the target sensor locations, including:

[0088] Past pipeline operation data and past pipeline failure data are brought into the pipeline simulation model to simulate and obtain several fault locations and mark them on the pipeline simulation model;

[0089] Extracting features from past pipeline fault data to obtain several fault features, wherein the fault features include corresponding fault locations;

[0090] Build a deep learning model and use it to train past pipeline operation data, past pipeline failure data, and several failure characteristics to form an operation-failure correlation relationship;

[0091] Based on the operation-fault correlation, data is eliminated from past pipeline fault data to obtain standard operation data;

[0092] Mapping standard operation data and pipeline material data to generate standard operation specifications;

[0093] Mapping several fault locations and pipeline material data to form a sensor optimization strategy;

[0094] The initial sensor site and the fault site constitute the target sensor site.

[0095] The target sensor site can meet the dual effects of material detection and pipeline monitoring, facilitating material detection and thus improving pipeline safety.

[0096] S5. Deploy sensors at target sensor locations to generate a sensor network, obtain sensor network data information, and substitute the sensor network data information into the pipeline simulation model to perform operation simulation and real-time monitoring to obtain monitoring data;

[0097] Furthermore, in S5, the sensor network data information is obtained and substituted into the pipeline simulation model to run the simulation and monitor the monitoring data in real time. The specific contents include:

[0098] Substitute the sensor network data information into the pipeline simulation model to run the simulation and obtain the correlation between different fault locations;

[0099] Classify the sensor network into several category groups according to sensor categories;

[0100] Take the pipeline inlet as the origin, the direction of the horizontal plane parallel to the straight pipe section as the X coordinate axis, the direction of the horizontal plane perpendicular to the straight pipe section as the Y coordinate axis, the direction perpendicular to the horizontal plane as the Z coordinate axis, and the 45-degree intersection of the three axes as the 0 axis;

[0101] The target sensor locations in the direction parallel to the straight pipe section, the direction perpendicular to the straight pipe section, the direction perpendicular to the horizontal plane and the curved pipe section are projected onto the X-axis, Y-axis, Z-axis and 0-axis respectively to obtain projection points. The projection points are adjusted to be equidistant and the characteristic network is established by combining the correlation relationship.

[0102] A characteristic network can include several characteristics, each forming a network. The purpose of forming a network is to provide a macroscopic visual understanding of the overall control. The corresponding values of the characteristics, such as pressure, are displayed on a presentation plane formed by the X, Y, Z, and 0 axes. Furthermore, the distance between the numerical points on the presentation plane can be used to determine whether there are abnormal fluctuations or abnormal correlations.

[0103] The network is dynamically adjusted according to time to form a dynamic characteristic network.

[0104] S6. Issue early warnings based on standard operating specifications and real-time monitoring data.

[0105] Furthermore, the specific contents of early warning in S6 based on standard operating regulations and real-time monitoring data include:

[0106] Form a standard dynamic network space in the dynamic characteristic network according to standard operation specification rules;

[0107] Real-time monitoring of the dynamic network and statistics of the fluctuation range of each node in the current dynamic network;

[0108] Predict adjacent nodes based on the current node fluctuation range and its correlation;

[0109] Count the number of current abnormal nodes and predicted abnormal nodes and their fluctuation degree to evaluate the dynamic characteristic network and obtain the warning value;

[0110] According to the severity of the warning value, several warning levels are obtained. Warnings are issued based on the warning levels and the abnormal sensor locations are traced back.

[0111] Furthermore, the expression of the warning value is:

[0112]

[0113] Among them, A is the warning value, n is the number of abnormal nodes at present, i is the i-th abnormal node, α i is the importance of the current abnormal node i, l i is the fluctuation degree of the current abnormal node i, t is the t-th predicted abnormal node, β t is the importance of the t-th predicted abnormal node, l i is the fluctuation degree of the t-th predicted abnormal node.

[0114] like Figure 2 As shown, a process industry pipeline material monitoring system includes:

[0115] Data acquisition unit: obtains process industry pipeline information and past pipeline operation information, analyzes the process industry pipeline information to obtain pipeline layout data, pipeline structure data, pipeline material data, pipeline operation data and pipeline fault data;

[0116] Initial construction unit: confirms the initial sensor location based on the pipeline layout data and pipeline structure data, and builds the pipeline simulation model based on the pipeline layout data, pipeline structure data and pipeline material data;

[0117] Simulation construction unit: brings pipeline operation data and pipeline fault data into the pipeline simulation model to simulate and generate standard operation specifications and determine target sensor locations;

[0118] Monitoring and early warning unit: Deploy sensor networks according to target sensor locations, obtain sensor network data information, and substitute the sensor network data information into the pipeline simulation model for operation simulation and real-time monitoring to obtain monitoring data, and issue early warnings based on standard operation specifications and real-time monitoring data.

[0119] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the process industry pipeline material monitoring method is implemented.

[0120] A storage medium, characterized in that computer executable instructions are stored in the storage medium, and when the computer executable instructions are loaded and executed by a processor, the content of the process industry pipeline material monitoring method is implemented.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A process industry pipeline material monitoring method, characterized in that: The following steps are involved: S1. Obtain process industry pipeline information and past pipeline operation information, analyze the process industry pipeline information to obtain pipeline layout data, pipeline structure data, pipeline material data, past pipeline operation data, and past pipeline fault data; S2. confirming the initial sensor location based on the pipeline layout data and pipeline structure data; S3. Constructing a pipeline simulation model based on pipeline layout data, pipeline structure data, and pipeline material data; S4, bringing past pipeline operation data and past pipeline failure data into the pipeline simulation model to simulate and generate standard operation specifications and determine target sensor locations; S5. Deploy sensors at target sensor locations to generate a sensor network, obtain sensor network data information, and substitute the sensor network data information into the pipeline simulation model to perform operation simulation and real-time monitoring to obtain monitoring data; S6. Issue early warnings based on standard operating specifications and real-time monitoring data.

2. A process industry pipeline material monitoring method according to claim 1, characterized in that: The specific contents of S2 in confirming the initial sensor locations based on the pipeline layout data and pipeline structure data include: Determine the total inlet, total outlet, first straight pipe section, second straight pipe section, and curved pipe section of the pipeline according to the pipeline layout data; Configure the first sensor site at the main inlet and the main outlet; Disposing second sensor sites at both ends of the first straight pipe section; The third sensor sites are respectively arranged on the inner and outer sides of the curved pipe section; A fourth sensor site is configured on the central surfaces of the first straight pipe section, the second straight pipe section and the curved pipe section; Determine a liquid conversion structure according to the pipeline structure data, and configure a fifth sensor site at a downstream pipeline port of the liquid conversion structure; The first sensor site, the second sensor site, the third sensor site, the fourth sensor site and the fifth sensor site constitute initial sensor sites.

3. A process industry pipeline material monitoring method according to claim 2, characterized in that: The specific contents of constructing the pipeline simulation model in S3 based on pipeline layout data, pipeline structure data, and pipeline material data are as follows: Determine the pipeline geographical location information and pipeline three-dimensional information based on pipeline layout data; Construct a pipeline framework based on the pipeline geographical location information and pipeline direction information; Determine the pipeline cross-sectional area and pipeline nodes according to the pipeline structure data, and modify the pipeline framework according to the pipeline cross-sectional area and pipeline nodes to obtain the initial pipeline model; The pipeline material data is marked on the initial pipeline model to form a pipeline simulation model.

4. A process industry pipeline material monitoring method according to claim 3, characterized in that: S4 brings past pipeline operation data and past pipeline failure data into the pipeline simulation model to simulate and generate standard operation specifications and determine the specific contents of the target sensor locations, including: Past pipeline operation data and past pipeline failure data are brought into the pipeline simulation model to simulate and obtain several fault locations and mark them on the pipeline simulation model; Extracting features from past pipeline fault data to obtain several fault features, wherein the fault features include corresponding fault locations; Build a deep learning model and use it to train past pipeline operation data, past pipeline failure data, and several failure characteristics to form an operation-failure correlation relationship; Based on the operation-fault correlation, data is eliminated from past pipeline fault data to obtain standard operation data; Mapping standard operation data and pipeline material data to generate standard operation specifications; Mapping several fault locations and pipeline material data to form a sensor optimization strategy; The initial sensor site and the fault site constitute the target sensor site.

5. A process industry pipeline material monitoring method according to claim 4, characterized in that: In S5, the sensor network data information is obtained and substituted into the pipeline simulation model for running simulation and real-time monitoring. The specific contents of the monitoring data include: Substitute the sensor network data information into the pipeline simulation model to run the simulation and obtain the correlation between different fault locations; Classify the sensor network into several category groups according to sensor categories; Take the pipeline inlet as the origin, the direction of the horizontal plane parallel to the straight pipe section as the X coordinate axis, the direction of the horizontal plane perpendicular to the straight pipe section as the Y coordinate axis, the direction perpendicular to the horizontal plane as the Z coordinate axis, and the 45-degree intersection of the three axes as the 0 axis; The target sensor locations in the direction parallel to the straight pipe section, the direction perpendicular to the straight pipe section, the direction perpendicular to the horizontal plane and the curved pipe section are projected onto the X-axis, Y-axis, Z-axis and 0-axis respectively to obtain projection points. The projection points are adjusted to be equidistant and the characteristic network is established by combining the correlation relationship. The network is dynamically adjusted according to time to form a dynamic characteristic network.

6. A process industry pipeline material monitoring method according to claim 5, characterized in that: The specific contents of early warning in S6 based on standard operating regulations and real-time monitoring data include: Form a standard dynamic network space in the dynamic characteristic network according to standard operation specification rules; Real-time monitoring of the dynamic network and statistics of the fluctuation range of each node in the current dynamic network; Predict adjacent nodes based on the current node fluctuation range and its correlation; Count the number of current abnormal nodes and predicted abnormal nodes and their fluctuation degree to evaluate the dynamic characteristic network and obtain the warning value; According to the severity of the warning value, several warning levels are obtained. Warnings are issued based on the warning levels and the abnormal sensor locations are traced back.

7. A process industry pipeline material monitoring method according to claim 6, characterized in that: The expression of the warning value is: Among them, A is the warning value, n is the number of abnormal nodes at present, i is the i-th abnormal node, α i is the importance of the current abnormal node i, l i is the fluctuation degree of the i-th current abnormal node, t is the t-th predicted abnormal node, β t is the importance of the t-th predicted abnormal node, l i is the fluctuation degree of the t-th predicted abnormal node.

8. A process industry pipeline material monitoring system, characterized in that: include: Data acquisition unit: obtains process industry pipeline information and past pipeline operation information, analyzes the process industry pipeline information to obtain pipeline layout data, pipeline structure data, pipeline material data, pipeline operation data and pipeline fault data; Initial construction unit: confirms the initial sensor location based on the pipeline layout data and pipeline structure data, and builds the pipeline simulation model based on the pipeline layout data, pipeline structure data and pipeline material data; Simulation construction unit: brings pipeline operation data and pipeline fault data into the pipeline simulation model to simulate and generate standard operation specifications and determine target sensor locations; Monitoring and early warning unit: Deploy sensor networks according to target sensor locations, obtain sensor network data information, and substitute the sensor network data information into the pipeline simulation model for operation simulation and real-time monitoring to obtain monitoring data, and issue early warnings based on standard operation specifications and real-time monitoring data.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the method implements the content of the process industry pipeline material monitoring method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the content of the process industry pipeline material monitoring method according to any one of claims 1 to 7.

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