CN114281047A - Process industrial production process monitoring and management method and system - Google Patents
Process industrial production process monitoring and management method and system Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 20
- 238000009776 industrial production Methods 0.000 title claims abstract description 12
- 238000007726 management method Methods 0.000 title claims description 8
- 238000004519 manufacturing process Methods 0.000 claims abstract description 178
- 230000007246 mechanism Effects 0.000 claims abstract description 47
- 238000013468 resource allocation Methods 0.000 claims abstract description 12
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Abstract
The invention discloses a monitoring and managing method and a monitoring and managing system for a process industrial production process, belonging to the field of industrial mechanism models; the method comprises the following specific steps: s1 accessing the edge computing device to collect parameters in the production and manufacturing process; s2, establishing a mechanism model of the production and manufacturing process by utilizing indoor and chemical reactions in the process industry; s3, taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model; s4, visualizing the production and manufacturing process by using digital twins, and simulating the production process by modifying; s5 simulating physical equipment failure through digital twins; the invention is beneficial to making a scientific production plan through production and manufacturing rehearsal, realizes resource optimization configuration, and reduces energy consumption and environmental pollution; the production process is effectively monitored, a safe and efficient production flow is ensured, and the production efficiency is improved; by the aid of equipment fault early warning, before the equipment really fails, abnormal conditions of the equipment are forecasted in time, and corresponding measures are taken.
Description
Technical Field
The invention discloses a monitoring and management method and a monitoring and management system for a process industrial production process, and relates to the technical field of industrial mechanism models.
Background
The process industry refers to the industry that produces through physical or chemical changes, and plays an important role in the national development strategy. The production is mostly carried out in high-temperature and high-pressure, flammable and explosive and toxic and harmful environments, has the characteristics of high pollution, high energy consumption, high risk and the like, and products in the process industry generally pass through a plurality of production processes, and one link of the processes goes wrong, so that the whole production process is influenced, and the great loss is caused. Therefore, monitoring and management of the production process have urgent needs and strategic significance for optimizing resource allocation, reducing energy consumption and pollution of process industry, improving productivity, reducing disaster recovery drilling cost and the like. With the rapid development of industrial big data, industrial mechanism models, digital twins and Internet of things technologies, the industrial digital reconstruction of the assisted process is possible through an information technology means.
The invention collects parameters in the manufacturing process by accessing the edge computing device. The method is characterized by going deep into the physical and chemical reaction principles of the process industry, establishing a mechanism model of the production and manufacturing process, taking resource allocation such as raw material consumption, energy consumption and the like in the production process as input parameters of the model, and deducing outputs such as carbon emission, waste material emission, energy consumption, production efficiency and the like by establishing various types of mechanism models. The production and manufacturing process is visualized through a digital twin, the production process is simulated through modifying parameters, scientific resource allocation is appointed on the premise of ensuring production and manufacturing safety and output, the production efficiency is improved, and energy consumption and pollution are reduced; the physical equipment fault simulation is realized through a digital twin technology, the emergency rehearsal is realized, and huge manpower and material resource costs wasted in disaster recovery drilling can be reduced.
The process industry is an industry for producing through physical and chemical changes, is a value-added process of raw materials, influences lives and production of people and the country, and occupies a strategic position in the industrial field. Due to the special environment of high temperature, high pressure, high combustion, high pollution, high energy consumption and the like, the method has very high requirements on scientific and reasonable production, disaster recovery, emergency and the like. In the past, the production process is guided by human experience, and the scientific production plan formulation is lacked because the production process depends on subjective factors of people.
Through the construction of a digital twin and industrial mechanism model, the production and manufacturing process can be monitored, production process preview is realized by adjusting parameters, managers can conveniently adjust resource proportion, scientific production plan is made, the production efficiency is ensured, the energy consumption, pollution and disaster recovery drilling cost is reduced, and the scientific production and manufacturing of the process industry is realized.
Therefore, the invention provides a method and a system for monitoring and managing a process industrial production process, so as to solve the problems.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for monitoring and managing a process industrial production process, wherein the adopted technical scheme is as follows: a monitoring and managing method for a process industrial production process comprises the following specific steps:
s1 accessing the edge computing device to collect parameters in the production and manufacturing process;
s2, establishing a mechanism model of the production and manufacturing process by utilizing indoor and chemical reactions in the process industry;
s3, taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model;
s4, visualizing the production and manufacturing process by using digital twins, and simulating the production process by modifying;
s5 simulates physical device failure by digital twinning.
The S1 is connected to the edge computing equipment to collect parameters in the production and manufacturing process, namely, the IOT technology is used for collecting production equipment information, and quasi-real-time production information is displayed under the digital twin platform.
And S5 combines physical cognition and a mathematical twin platform through a mechanism model algorithm, analyzes residual errors and performs fault early warning.
The S5 combines physical cognition with a mathematical twin platform through a mechanism model algorithm, and the specific steps of analyzing residual errors and carrying out fault early warning are as follows:
s501, an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error;
s502, analyzing the residual error to determine whether the process has a fault or not, and identifying the fault type.
A monitoring and managing system for the production process of the process industry specifically comprises a parameter collecting module, a production model establishing module, a resource model establishing module, a production simulation module and a fault simulation module:
a parameter collection module: accessing edge computing equipment to collect parameters in the production and manufacturing process;
a production model establishing module: establishing a mechanism model of a production and manufacturing process by utilizing indoor and chemical reactions in the process industry;
a resource model building module: taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model;
a production simulation module: visualizing the production and manufacturing process by utilizing digital twins, and simulating the production process by modifying parameters;
a fault simulation module: physical device failure is simulated by digital twinning.
The parameter collection module is connected to the edge computing equipment to collect parameters in the production and manufacturing process, namely, the IOT technology is used for collecting production equipment information, and quasi-real-time production information is displayed under the digital twin platform.
The fault simulation module combines physical cognition with a mathematical twin platform through a mechanism model algorithm, analyzes residual errors and carries out fault early warning.
The fault simulation module specifically comprises a residual error generation module and a residual error evaluation module:
a residual generation module: an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error;
a residual evaluation module: and analyzing the residual error to determine whether the process has a fault or not, and identifying the fault type.
The invention has the beneficial effects that: the invention is beneficial to making a scientific production plan through production and manufacturing rehearsal, realizes resource optimization configuration, and reduces energy consumption and environmental pollution; the production process is effectively monitored, a safe and efficient production flow is ensured, and the production efficiency is improved; by the aid of equipment fault early warning, before the equipment really fails, abnormal conditions of the equipment are forecasted in time, and corresponding measures are taken, so that loss caused by equipment faults is reduced to the greatest extent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention; FIG. 2 is a flow industrial production process monitoring and management framework based on an industrial mechanism model and a digital twinning technology according to an embodiment of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
First, a brief explanation of some of the professional data to which the present invention relates is given:
an industrial mechanism model: the method is an accurate mathematical model established by an internal mechanism in the production and manufacturing process; obtaining a mathematical model in the production and manufacturing process according to mass balance, energy balance, element balance and a chemical law, and generating different results by adjusting various parameters so as to simulate and predict the production and manufacturing process;
digital twinning technique: the digital twin can achieve simulation rehearsal, rehearsal decision, disaster recovery drilling and the like in the production process by constructing a virtual mirror image of a real scene and combining an industrial mechanism model; the production efficiency is ensured, and the energy consumption and pollution are reduced;
IOT (Internet of things): collecting parameters in the production and manufacturing process through various sensors, and monitoring the production process in a quasi-real-time manner by combining an industrial mechanism model and a digital twin;
the first embodiment is as follows:
a monitoring and managing method for a process industrial production process comprises the following specific steps:
s1 accessing the edge computing device to collect parameters in the production and manufacturing process;
s2, establishing a mechanism model of the production and manufacturing process by utilizing indoor and chemical reactions in the process industry;
s3, taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model;
s4, visualizing the production and manufacturing process by using digital twins, and simulating the production process by modifying;
s5 simulating physical equipment failure through digital twins;
the invention collects the parameters in the production and manufacturing process by accessing the edge computing equipment; going deep into the physical and chemical reaction principles of the process industry, establishing a mechanism model of the production and manufacturing process, taking resource allocation such as raw material consumption, energy consumption and the like in the production process as input parameters of the model, and deducing outputs such as carbon emission, waste material emission, energy consumption, production efficiency and the like by establishing various types of mechanism models; the production and manufacturing process is visualized through a digital twin, the production process is simulated through modifying parameters, scientific resource allocation is appointed on the premise of ensuring production and manufacturing safety and output, the production efficiency is improved, and energy consumption and pollution are reduced; the physical equipment fault is simulated by a digital twinning technology, so that emergency rehearsal is realized, and huge manpower and material resource costs wasted in disaster recovery drilling can be reduced;
the invention is beneficial to making a scientific production plan through production and manufacturing rehearsal, realizes resource optimization configuration, and reduces energy consumption and environmental pollution; the production process is effectively monitored, a safe and efficient production flow is ensured, and the production efficiency is improved; by the aid of equipment fault early warning, before the equipment really fails, abnormal conditions of the equipment are forecasted in time, and corresponding measures are taken, so that loss caused by equipment faults is reduced to the greatest extent; by simulating disaster recovery drilling and guiding the operation flow of the disaster recovery drilling, the production recovery time and the like can be estimated, so that the cost of the disaster recovery drilling is reduced;
furthermore, the S1 is accessed to an edge computing device to collect parameters in the production and manufacturing process, namely, the IOT technology is used for collecting production device information, and quasi-real-time production information is displayed under a digital twin platform;
further, the S5 combines physical cognition and a mathematical twin platform through a mechanism model algorithm, analyzes residual errors and carries out fault early warning;
still further, the step S5 of combining physical cognition with a mathematical twin platform by a mechanism model algorithm, analyzing residual errors, and performing fault pre-warning includes the following steps:
s501, an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error;
s502, analyzing the residual error to determine whether the process has a fault or not, and identifying the type of the fault;
various information such as heat, force, chemical elements, positions and the like is collected through various devices such as sensors, scanners and the like of the Internet of things, and the production process is monitored in a quasi-real-time manner by combining digital twin and mechanism model technologies;
if the blast furnace collects the air quantity, the air temperature, the air pressure, the number of the air ports and the area of the air ports, deducing the kinetic energy of blast air, thereby calculating the penetration depth of the blast air to the center of the furnace along the axis of the air ports, further calculating the shape and the size of a convolute area and a combustion zone of the air outlet, and displaying the shape and the size in a visual mode; different blast furnaces have different optimal blast kinetic energies, the actual production can be guided by observing the shape and the size of a visual combustion zone, so that the blast furnaces can maintain the optimal combustion condition, the production efficiency is kept, and the discharge rate and the discharge time of blast furnace waste gas are reduced; the stability of the high pressure at the top of the blast furnace ensures the improvement of the blast volume, the increase of the blast volume can improve the production efficiency of the blast furnace, the model of the high pressure at the top of the blast furnace, the blast volume and the production image efficiency is established and displayed on a digital twin dynamic platform, and the running state of the blast furnace can be displayed more digitally by displaying the derivative attribute calculated by the model;
the method comprises the steps of (1) simulating and calculating an output by adjusting resource parameters in an industrial mechanism model so as to achieve the purposes of evaluating pollution, energy consumption and productivity, and visually displaying the conditions of production efficiency, energy consumption, pollution and the like by combining a digital twin simulation production process so as to realize production rehearsal before production; a scientific production plan is made according to the preview result, so that the energy consumption and pollution are reduced in the actual production, and the production rate is improved;
if a digital twin model of the blast furnace is adopted, model parameters are adjusted, the air quantity, the air temperature, the number of air ports and the area of the air ports are controlled, and parameters such as blast kinetic energy, combustion efficiency, production efficiency, waste gas emission and the like of the blast furnace are calculated; production preview is carried out through a digital twin platform before production, production can be guided, and a scientific production plan is appointed;
according to the operation rule of the process industrial equipment or the possibility precursor obtained by observation, the equipment fault early warning and state monitoring forecast the abnormal condition of the equipment in time before the equipment really breaks down, and corresponding measures are taken, so that the loss caused by the equipment fault is reduced to the maximum extent;
combining physical cognition with a digital twin platform through a mechanism model algorithm, and performing fault early warning through analyzing residual errors; the method mainly comprises two stages: first, a residual generation stage: an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error; secondly, a residual error evaluation stage: analyzing the residual error to determine whether the process has a fault or not, and further identifying the fault type; the construction of a residual sequence is realized by closely combining a control theory and adopting three methods of parameter estimation, state estimation and equivalent space;
if monitoring and early warning of the corrosion state of the furnace bottom, installing a temperature detection device below each layer of cooler refractory material at the furnace bottom, detecting the change of temperature along with time, converting the temperature into the residual thickness of the refractory material through an equipment operation mechanism model, and visually displaying the residual thickness of the refractory material and the 1150 ℃ isothermal line of the furnace bottom through a digital twin platform; calculating the heat flow intensity and the heat load of the cooling wall by collecting the information of the flow rate of a water pipe of the cooling wall, the temperature difference of the water, the area of the cooling wall and the like, and storing the information into a time sequence database; monitoring and early warning of the erosion state of the bottom of the blast furnace are achieved by combining the equipment fault early warning model and the digital twin platform;
the process industrial production process generally passes through a plurality of links, equipment failure occurs in different links, and the repairing mode and time are different; through the digital twin simulation disaster recovery drilling, the processing flow of the disaster recovery can be visually observed, and the system can quickly respond to the occurrence of a fault and guide the work of the disaster recovery.
Example two:
a monitoring and managing system for the production process of the process industry specifically comprises a parameter collecting module, a production model establishing module, a resource model establishing module, a production simulation module and a fault simulation module:
a parameter collection module: accessing edge computing equipment to collect parameters in the production and manufacturing process;
a production model establishing module: establishing a mechanism model of a production and manufacturing process by utilizing indoor and chemical reactions in the process industry;
a resource model building module: taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model;
a production simulation module: visualizing the production and manufacturing process by utilizing digital twins, and simulating the production process by modifying parameters;
a fault simulation module: simulating physical equipment failure by digital twinning;
furthermore, the parameter collection module is connected to the edge computing equipment to collect parameters in the production and manufacturing process, namely, the IOT technology is used for collecting production equipment information, and quasi-real-time production information is displayed on the digital twin platform;
furthermore, the fault simulation module combines physical cognition with a mathematical twin platform through a mechanism model algorithm, analyzes residual errors and performs fault early warning;
still further, the fault simulation module specifically includes a residual error generation module and a residual error evaluation module:
a residual generation module: an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error;
a residual evaluation module: and analyzing the residual error to determine whether the process has a fault or not, and identifying the fault type.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A process industrial production process monitoring and management method is characterized by comprising the following specific steps:
s1 accessing the edge computing device to collect parameters in the production and manufacturing process;
s2, establishing a mechanism model of the production and manufacturing process by utilizing indoor and chemical reactions in the process industry;
s3, taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model;
s4, visualizing the production and manufacturing process by using digital twins, and simulating the production process by modifying;
s5 simulates physical device failure by digital twinning.
2. The method as claimed in claim 1, wherein the S1 access edge computing device collects parameters in the manufacturing process, that is, collects information of the manufacturing device by using IOT technology of internet of things, and displays the near real-time production information under the digital twin platform.
3. The method as claimed in claim 2, wherein the S5 analyzes residual error for fault pre-warning by combining physical cognition and a mathematical twin platform through a mechanism model algorithm.
4. The method as claimed in claim 3, wherein the step of S5 combining physical cognition and mathematical twin platform by mechanism model algorithm, analyzing residual error and performing fault pre-warning comprises the following steps:
s501, an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error;
s502, analyzing the residual error to determine whether the process has a fault or not, and identifying the fault type.
5. A process industrial production process monitoring and management system is characterized by specifically comprising a parameter collection module, a production model establishment module, a resource model establishment module, a production simulation module and a fault simulation module:
a parameter collection module: accessing edge computing equipment to collect parameters in the production and manufacturing process;
a production model establishing module: establishing a mechanism model of a production and manufacturing process by utilizing indoor and chemical reactions in the process industry;
a resource model building module: taking resource allocation in production as a parameter of the model, and establishing a corresponding mechanism model;
a production simulation module: visualizing the production and manufacturing process by utilizing digital twins, and simulating the production process by modifying parameters;
a fault simulation module: physical device failure is simulated by digital twinning.
6. The system as claimed in claim 5, wherein the parameter collecting module is connected to the edge computing device to collect parameters in the manufacturing process, i.e. the IOT technology is used to collect information of the production equipment, and the quasi-real-time production information is displayed on the digital twin platform.
7. The system of claim 6, wherein the fault simulation module combines physics cognition with a mathematical twin platform through a mechanism model algorithm, analyzes residual errors and performs fault pre-warning.
8. The system of claim 7, wherein the fault simulation module specifically comprises a residual error generation module and a residual error evaluation module:
a residual generation module: an accurate mathematical model is established through an equipment operation mechanism to estimate output, and the output is compared with an actual measured value to obtain a residual error;
a residual evaluation module: and analyzing the residual error to determine whether the process has a fault or not, and identifying the fault type.
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