CN113962405A - Intelligent submerged arc furnace remote operation and maintenance system - Google Patents
Intelligent submerged arc furnace remote operation and maintenance system Download PDFInfo
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Abstract
The invention provides an intelligent submerged arc furnace remote operation and maintenance system, which comprises: the submerged arc furnace production data perception layer comprises a terminal node, an execution node, a data server and an information transmission module which are deployed on a submerged arc furnace working site; the management system application layer comprises a data acquisition module, a data processing module, a data transmission module and an edge server, wherein the edge server is loaded with local submerged arc furnace intelligent application and assists in checking, managing and operating the working process of the submerged arc furnace; the cloud service layer comprises a cloud database and a cloud server, wherein the cloud database and the cloud server are deployed at the cloud end, the cloud database is used for storing data generated in the production activities of the submerged arc furnace, and the cloud server is loaded with cloud submerged arc furnace intelligent application and used for analyzing and calculating the data. The method can achieve the aim of intelligent operation and maintenance of the submerged arc furnace, and the solution can be suitable for new construction, modification and upgrading projects of the submerged arc furnace.
Description
Technical Field
The invention relates to the technical field of intelligent remote operation and maintenance of submerged arc furnace smelting equipment, in particular to an intelligent submerged arc furnace remote operation and maintenance system.
Background
At present, a new round of scientific and technological revolution represented by big data, cloud computing and industrial internet is rolling on the whole world, a new manufacturing industry system with information intercommunication, resource sharing, capability coordination and open cooperation is being constructed, and the innovation and development space of the manufacturing industry is greatly expanded. The submerged arc furnace smelting industry belongs to typical traditional discrete industrial scene application, the production process belongs to a typical nonlinear time-varying system, and a special system is urgently needed to be developed to upgrade the submerged arc furnace smelting process from automation to intelligence.
Disclosure of Invention
In view of the defects of the prior art, the invention provides an intelligent submerged arc furnace remote operation and maintenance system. The method carries out targeted system software and hardware design around data acquisition, life cycle management, a digital twin system, fault prediction, energy efficiency assessment, data management and safety management, finally achieves the goal of intelligent operation and maintenance of the submerged arc furnace, and the solution can be suitable for new construction, modification and upgrading projects of the submerged arc furnace.
The technical means adopted by the invention are as follows:
the utility model provides a hot stove remote operation and maintenance system in intelligence ore deposit, includes:
the submerged arc furnace production data perception layer comprises a terminal node, an execution node, a data server and an information transmission module, wherein the terminal node is deployed on a submerged arc furnace working site and comprises information acquisition equipment and a PLC (programmable logic controller), the information acquisition equipment is connected with the PLC and used for transmitting acquired data information to a management system application layer through the information transmission module, and the data server receives a control instruction transmitted by the management system application layer and controls the corresponding execution node;
the management system application layer comprises a data acquisition module, a data processing module, a data transmission module and an edge server, wherein the edge server is loaded with local submerged arc furnace intelligent application and assists in checking, managing and operating the working process of the submerged arc furnace, and the data acquisition module, the data processing module and a controller deployed at a control end carry out data interaction with the cloud service layer through the data transmission module;
the cloud service layer comprises a cloud database and a cloud server, wherein the cloud database and the cloud server are deployed at the cloud end, the cloud database is used for storing data generated in the production activities of the submerged arc furnace, and the cloud server is loaded with cloud submerged arc furnace intelligent application and used for analyzing and calculating the data.
Further, the system further comprises a cloud development layer, and the cloud development layer is used for updating and modifying the cloud submerged arc furnace intelligent application.
Further, the intelligent application of the submerged arc furnace comprises the following steps: electric furnace data acquisition, electric furnace life cycle management, electric furnace digital twinning, electric furnace fault prediction, electric furnace energy efficiency evaluation, data management and safety management.
Further, the data generated in the submerged arc furnace production activities includes: the temperature of the furnace bottom, the temperature of the furnace wall, the temperature of the furnace cover, the temperature of cooling water of the furnace cover, the insulation detection voltage of the furnace shell, the temperature of cooling water of an electrode shield, the temperature of cooling water of an electrode bottom ring, the current of an electrode, the current of an interelectrode and the detection data of a leakage point of electrode circulating water.
Furthermore, electric furnace fault prediction is carried out through the local submerged arc furnace intelligent application and the cloud submerged arc furnace intelligent application, and the obtained data generated in the submerged arc furnace production activities are analyzed and calculated through a classification label method respectively, so that a submerged arc furnace fault prediction result is obtained.
Further, the electric furnace energy efficiency is evaluated through the local submerged arc furnace intelligent application and the cloud submerged arc furnace intelligent application, data generated in the submerged arc furnace production process are comprehensively processed through a machine learning method respectively, specific characteristics of the data are extracted, a prediction model is established through a K neighbor algorithm, and the submerged arc furnace energy efficiency is comprehensively evaluated through introduction of Manhattan distance analysis and measurement.
Further, the hot stove intelligent application of local ore deposit and the hot stove intelligent application of high in the clouds carry out data management and include: the method comprises the following steps of system operation parameter monitoring, system abnormal parameter alarming, production and process data storage, historical trend recording and inquiring, report analysis and printing, login verification and account management.
Further, the safety management of the intelligent application of the local submerged arc furnace and the intelligent application of the cloud submerged arc furnace comprises the steps of building a full data flow supervision system, recording and early warning abnormal events and important operation parameters of all links, and defining different strategies according to safety levels.
Compared with the prior art, the invention has the following advantages:
1. the method takes the remote operation and maintenance of the submerged arc furnace project as a starting point, deeply analyzes the parameter requirements of data acquisition and data return, and develops and realizes reliable data acquisition and data cloud.
2. According to the invention, by establishing a high-efficiency data cloud channel, the bottleneck is broken for solving the product operation data, and data support is provided for product parameter optimization. Through effective data analysis, the cause of the equipment problem can be remotely and quickly positioned, the service cost is reduced, and the service quality is improved.
3. By means of the computing resources and computing power of the cloud platform, the optimal parameter combination is mined by comparing key data of the submerged arc furnace under different industries, different raw materials, different equipment parameters, different users and different operating parameters, operation guidance is provided for the users, optimization suggestions are provided for design, and suggestions are provided for marketing directions.
4. The intelligent control model is deployed on the cloud platform, so that the method is beneficial to keeping the result secret, upgrading the product, acquiring data and applying high-grade cloud resources (such as an algorithm library, elastic computing service and the like).
5. The invention can support and optimize the data based on the client APP, and scientifically and normatively deposit the production operation data to form high-availability data resources, so that production decision makers continuously accumulate production operation experience, and purposefully optimize the production process of products, and the production management level is continuously improved by means of the convenience and the sharing of the mobile internet.
Drawings
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 diagram of the intelligent submerged arc furnace remote operation and maintenance system architecture.
FIG. 2 is a flow chart of the operation of the intelligent submerged arc furnace remote operation and maintenance system.
FIG. 3 is a topological diagram of the intelligent submerged arc furnace remote operation and maintenance system.
FIG. 4 is a drawing of the digital twin planning of the submerged arc furnace in the intelligent submerged arc furnace remote operation and maintenance system.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, the present invention provides an intelligent submerged arc furnace remote operation and maintenance system, which includes: the submerged arc furnace production data management system comprises a submerged arc furnace production data sensing layer, a management system application layer and a cloud service layer. The submerged arc furnace production data perception layer comprises a terminal node, an execution node, a data server and an information transmission module, wherein the terminal node is arranged on a submerged arc furnace working site and comprises an information acquisition device and a PLC (programmable logic controller), the information acquisition device is connected with the PLC and sends acquired data information to a management system application layer through the information transmission module, and the data server receives a control instruction sent by the management system application layer and controls the corresponding execution node. The management system application layer comprises a data acquisition module, a data processing module, a data transmission module and an edge server, the edge server is loaded with local submerged arc furnace intelligent application, the submerged arc furnace working process is checked, managed and operated in an auxiliary mode, and the data acquisition module, the data processing module and a controller deployed at a control end carry out data interaction with the cloud service layer through the data transmission module. The cloud service layer comprises a cloud database and a cloud server, the cloud database and the cloud server are deployed at the cloud end, the cloud database is used for storing data generated in production activities of the submerged arc furnace, and the cloud server is loaded with cloud submerged arc furnace intelligent applications and used for analyzing and calculating the data. Further, the intelligent application of the submerged arc furnace comprises the following steps: electric furnace data acquisition, electric furnace life cycle management, electric furnace digital twinning, electric furnace fault prediction, electric furnace energy efficiency evaluation, data management and safety management.
The scheme and effect of the present invention will be further explained by specific application examples.
The embodiment of the invention discloses an intelligent submerged arc furnace remote operation and maintenance system which comprises a local end and a cloud end. The local application adopts a C/S structure, and the cloud application adopts a B/S structure. There are 4 levels in total: the system comprises a local intelligent layer, an IaaS + PaaS layer, a cloud development layer and a cloud application layer, wherein the system architecture is shown in figure 1, and the system operation flow chart is shown in figure 2.
The system comprises 7 subsystems in total: the system comprises an electric furnace data acquisition system, an electric furnace life cycle management system, an electric furnace digital twin system, an electric furnace fault prediction system, an electric furnace energy efficiency evaluation system, a data management system and a safety management system. The electric furnace data acquisition system is a bottom core of the whole system and is deployed in an edge server, on one hand, real-time data are acquired from a control system and are provided for other subsystems in an OPC data bus mode; on the other hand, the system is matched with a data management system to recycle output data of other subsystems and provide the output data to applications such as data presentation in a MySQL data table mode. The electric furnace digital twin system is an output center of the whole system, is deployed in an edge server, integrates 3 independent applications of an electric furnace life cycle management system, an electric furnace fault prediction system and an electric furnace energy efficiency evaluation system in a server-side and client-side mode, and adopts an interface to bear output results of electric furnace digital twin, electric furnace life cycle management and electric furnace fault prediction. The data management system is a scheduling core of the whole system, is deployed in a data application server, adopts a B/S mode, is matched with an electric furnace data acquisition system, reads and writes an OPC data bus in real time, and realizes input data requirements and output data circulation of all subsystems. The safety management system is a safety center of the whole system, is deployed in a data safety server, and monitors and protects the safety of input and output data of each subsystem in a mode of combining software and hardware.
Referring to fig. 3, the hardware deployment and topology thereof of the present invention are described in detail as follows:
the edge server is arranged in a local only layer and is configured as follows: a CPU: intel to strong E-2226G (6 cores, 12MB cache, 3.4GHz, 4.7GHz Turbo), memory: 32GB, hard disk: 512GB solid-state +4TB mechanical hard disk, display card: RTX 2080. As a preferred embodiment of the present invention, the edge server is configured with 2 (1 for each electric furnace, a liquid crystal display, a keyboard, a mouse, an HDMII line, and a USB extension line), and is mainly responsible for customizing and deploying the data acquisition system, the life cycle management system, the digital twin system, the failure prediction system, the electric furnace energy efficiency evaluation system, and the electric furnace power circle system. And installing a MySQL database to realize local data storage. And outputting the data display interface integrated by the system to a field splicing screen through the industrial television system splicing controller by utilizing the HDMI interface.
The data application server is arranged at a data perception layer and is configured as follows: a CPU: i7-9700K, memory: 32GB, hard disk: 512GB solid-state +4TB mechanical hard disk, display card: RTX 2060. The system is responsible for deploying a scheduling management system, managing and integrating software and hardware systems of the edge servers (205, 206) at the same time, and completing data distribution, storage and management in the local area network by taking a data processing task as a core.
The data security server (210) configures: a CPU: i7-9700K, memory: 16GB, hard disk: 512GB solid-state +2TB mechanical hard disk, display card: RTX 2060. The data security server is responsible for local data cloud-up and network security management, so that the security isolation between a local automation control network and an external internet is realized, the data needing cloud-up is pushed to the cloud server by setting security software, hardware and a data encryption algorithm, the data security server has a function of continuous transmission on a broken network, data acquisition items needing remote addition or modification, software installation programs needing version upgrading, program patches needing addition and the like are acquired from the cloud server and issued to the edge server, the requirements of subsequent field manual operation are met, and the data security and the network security are guaranteed in an all-round way manner.
The cloud server is arranged on a cloud service layer and is configured as follows: a CPU: 8 cores, a memory: 16GB, system disk: 60GB, data disk: 100GB, bandwidth: 20Mbps, operating system: windows Server 2019. The method is responsible for providing a cloud application deployment environment, and comprises the following steps: electric furnace energy efficiency evaluation, electric furnace life cycle management, electric furnace fault prediction, electric furnace digital twinning and the like. The system is responsible for receiving and processing data of local cloud, and is matched with the data security server to realize remote data modification and field software program upgrading maintenance. Is responsible for providing remote technical support, including: remote device management, remote fault diagnosis, remote data maintenance, data presentation, page publishing, terminal access, message pushing and the like.
The cloud database is arranged on a cloud service layer and is configured as follows: and (3) engine version: MySQL 5.7, example type: main and standby, performance specification: 4 cores/16 GB, memory space: 300GB, maximum connection number: 2500. and the cloud data is received, and a data source and operation result storage are provided for the cloud application.
In addition, there is an associated cloud component configured to perform data migration, machine learning, data visualization, and message push, among others.
In order to realize corresponding functions, the system in this embodiment is configured to have 7 subsystems, which are an electric furnace data acquisition system, an electric furnace life cycle management system, an electric furnace digital twin system, an electric furnace fault prediction system, an electric furnace energy efficiency evaluation system, a data management system, and a safety management system. The following are each specifically described.
(01) Electric furnace data acquisition system
(ii) hardware configuration
The method adopts a high-performance embedded or rack-mounted edge server (high CPU, high memory and high display card), installs a genuine Windows 10 enterprise edition, and locally meets the system resource requirement of running a Unity 3D release application program edition result (the number of three-dimensional model surfaces is about 1000 thousands). Locally meet the deployment and application of Python 3.5 and higher versions. The HDMI interface is configured, and the output resolution is not lower than 1920 × 1080. The whole machine has compact overall dimension and stable operation, and supports the installation of a machine frame and the installation in a machine cabinet. The method needs to be comprehensively supported according to different PLC controllers of the field control system: communication protocols such as Modbus TCP, TCP/IP, OPC UA, third-party custom protocol and the like. An Ethernet (RJ45) communication interface is adopted to control the network security switch as a boundary. The data acquisition equipment does not need to be configured in the control system, and the control system does not need to be separately configured with communication software or protocol conversion software.
Capability of system
Each edge server has the capability of simultaneously acquiring not less than 1000 PLC variables locally, and the data security server and the cloud server have the capability of simultaneously receiving, storing and dumping not less than 2000 variables, so that cloud components and services of Huacheng clouds and Alice clouds can be comprehensively compatible.
③ quantity of data collected
Each electric furnace PLC comprises 300 Boolean type data (bool), 300 signed shaping data (int) and 200 single-precision floating point type data (float), the sampling interval is 1s, and the maximum time for single batch uploading is less than 10 s.
Fourthly, collecting time stamp
The data items of the same sampling period in each edge server take no more than 100ms for single batch sampling. And the low-delay batch packaging and uploading in the data uploading interval are allowed, and the real-time data is ensured to be updated in time.
Collecting interval
The data acquisition period is 1 s-3600 s; different data items support setting different sampling intervals, and the common sampling intervals are as follows: 1s, 10s, 60s and 3600s, supports the PLC to control the acquisition starting time, improves the accuracy of single-batch data acquisition, and has the function of non-fixed-period data acquisition.
Sixth transmission requirement
The data has a data verification function in the transmission process, has a perfect response overtime coping mechanism and a multithreading cooperation mechanism, has no packet loss in the whole process, and optimizes the table structure setting and the data flow mechanism of the database according to the size, the time limit and the characteristics of the data.
Seventhly, remote management
Possess perfect remote management function (this part of function is integrated and is realized through data security server), include: the method comprises the steps of state monitoring, log running, parameter changing, acquisition start-stop, data item changing (variable adding, modifying or deleting), rule changing, firmware upgrading and the like, and the remote configuration issuing mode supports excel editing, batch import and export and Chinese character support. The management end can remotely control the collection starting and stopping time, adjust the collection mode as required and flexibly control the data volume.
Cleaning of data
Noise and inconsistent data need to be removed at the edge side, the function needs to meet the requirement of remote operation at the same time, and the remote issuing and configuration modification of the data cleaning rule are realized by utilizing a stable connecting channel. The data cleansing functions provided include: the processing functions of duplicate removal, transformation, filtration and the like are realized, and the function of modifying the data reporting rule is realized.
Ninthly data processing
The data processing scheme based on database management is provided and used for different data requirements of functional modules such as data presentation and data return, the functions are integrated in an edge server and a cloud server, and the whole-process data is controllable, visible and traceable.
Echo of the data in the r
And the cloud control software instruction utilizes the reserved API and the built safety data link to be transmitted back to the MySQL database of each edge server through the data safety server, the rewriting instruction issuing cycle is 15-3600 s, and the instruction length of each batch is 10-30 int-type data. A stable remote maintenance channel can be constructed for remotely viewing and modifying acquisition device configurations and other related settings.
The problems of data dispersion and data isolated island in the submerged arc furnace industry are serious at present, a project lacks unified network planning, and a central control room only concentrates electric furnace control related data so as to meet the requirements that production operation is taken as a main part and a large amount of valuable data are lost in each production link. The invention adopts a high-performance acquisition mode, the time for single batch sampling is not more than 100ms, and the consistency of data quality is improved; the original local electric furnace intelligent application is adopted, and a terminal cloud cooperation solution is designed, so that the cloud analysis is not completely relied on, and the adaptability of system application is enhanced; and the data cleaning strategy which accords with the characteristics of the electric furnace data is adopted, so that the bandwidth occupation of a cloud channel on the data is reduced.
(02) Electric furnace life cycle management system
The electric furnace life cycle management solution is deployed in an edge server and a cloud server, and technical requirements of local application and cloud application are met. For 2 electric stove within range key equipment such as furnace body and electrode, establish multidimensional life cycle model, developments show equipment maintenance information, supplementary manufacturing enterprise's formulation maintenance plan is avoided the fault shutdown, improves the reliability and the stability of equipment, keeps the operating efficiency of equipment.
The system needs to be organically combined with a fault prediction system, and the usability and the practicability of a life cycle model after equipment replacement and maintenance and after equipment maintenance are implemented are fully considered, so that the system is convenient to modify and flexibly adjust according to needs.
Real-time analysis and calculation of device health indices, including but not limited to:
furnace body health index
Input variables are: the furnace body health index calculation method comprises the following steps of fully combining measured values of 5-dimensional detection instruments including furnace bottom temperature (7 thermocouples), furnace wall temperature (22 thermocouples), furnace cover temperature (3 thermocouples), furnace cover cooling water temperature (13 thermal resistors), furnace shell insulation detection voltage (7 voltage detection devices), and a fault prediction system to complete real-time calculation and data presentation of furnace body health indexes.
② electrode-health index
Input variables are: the temperature of cooling water of a first electrode shield (6 thermal resistors), the temperature of cooling water of a first electrode bottom ring (6 thermal resistors), a first electrode current (current detection device), an interelectrode current (current detection device) and a first electrode circulating water leakage point detection (PLC program output) are combined with a fault prediction system fully to complete the real-time calculation, data analysis and data presentation of a health index of the electrode, wherein the measurement values of the detection instruments of 5 dimensions are obtained.
(iii) electrode health index
Input variables are: the temperature of the cooling water of the second electrode shield (6 thermal resistors), the temperature of the cooling water of the second electrode bottom ring (6 thermal resistors), the second electrode current (current detection device), the interelectrode current (current detection device) and the second electrode circulating water leakage point detection (PLC program output) are combined with a fault prediction system to complete the real-time calculation, data analysis and data presentation of the health index of the second electrode, wherein the measurement values of the detection instruments of 5 dimensions are fully combined with the fault prediction system.
(iv) electrode health index
Input variables are: the temperature of cooling water of three electrode protection screens (6 thermal resistors), the temperature of cooling water of three electrode bottom rings (6 thermal resistors), three currents of electrodes (current detection devices), interelectrode currents (current detection devices) and three electrode circulating water leakage point detection (PLC program output) are measured by 5-dimensional detection instruments, the measured values are fully combined with a fault prediction system, and real-time calculation, data analysis and data presentation of three electrode health indexes are completed.
In the non-life cycle model and system application of the current submerged arc furnace industry, a user carries out conventional maintenance and maintenance on equipment according to manual experience and a management plan, and great uncertainty exists in the aspects of human occupation, spare part management and the like. The method adopts the original ore-smelting furnace equipment health degree analysis algorithm, utilizes the mechanism parameters and the operation parameters of key equipment to carry out real-time health degree analysis and residual life estimation on ore-smelting furnace core equipment (a furnace body and electrodes), and avoids the occurrence of unplanned furnace shutdown to the maximum extent.
(03) Electric furnace digital twin system
The electric furnace digital twin solution is deployed in an edge server and a cloud server, and meets the technical requirements of local application, cloud application and laboratory simulation operation. The electric furnace data acquisition system is matched, and the process data is adopted to drive the three-dimensional imaging display of 2 electric furnace main bodies and the batching system equipment, and the working posture, the running state, the parameter information and the like of the equipment are displayed dynamically and in a full view angle. Inputting conditions: 2 drawings of the electric furnace main body, the short net, the electrode and the furnace top charging system (different three-dimensional models are adopted for local and cloud ends) are matched with necessary equipment process explanations. And 8 55-inch liquid crystal splicing screens are adopted on site and used for displaying an output picture after 4 systems of a data twin system, a life cycle management system, a fault prediction system and an electric furnace energy efficiency evaluation system of the No. 1 electric furnace and the No. 2 electric furnace are integrated.
Through reasonable distribution, flexible arrangement and overall consideration, high fidelity, high integration and high availability are realized according to the importance degree and the change frequency of the data. Referring to fig. 4, the digital twin interface plan of a single electric furnace includes the following details:
the mouse suspension display method comprises the steps of overview, a storage bin, a short net, a furnace body and 4 preset visual angles sharing windows, after a mouse is suspended on a specific part, related equipment data blurring background suspension display is achieved, and view operations such as mouse zooming, translation and 3D rotation are supported.
Secondly, furnace top feeding and electrode action, data display and operation requirements are the same as those of the electric furnace main body, and the rest are 2D graphs or diagrams which are displayed in a real-time refreshing mode. Each frame supports full screen zoom operations.
And thirdly, a fault prediction information window adopts multicolor dynamic rolling texts to support manual confirmation and pull-down strip viewing of history records, and the fault information and the left electric furnace 3D overview are displayed in a linkage manner.
And fourthly, the equipment health index window adopts a multicolor annular diagram, supports manual modification and setting, and realizes linkage display with the 3D overview of the electric furnace on the left side after reaching a corresponding risk threshold value.
Estimating variables: 200 parts of an electric furnace body, 100 parts of furnace top charging, 60 parts of electrode action, 20 parts of power circle, 40 parts of fault prediction, 20 parts of life cycle, 30 parts of energy efficiency evaluation and 6 parts of real-time curve.
And sixthly, a digital twin WEB version is developed by adopting HTML5, compatible with a mainstream browser, considering the specification and the operation humanization of a mobile end window, reasonably splitting each window, and simplifying the data display and operation requirements compared with a field version.
And the running response delay of the local application is less than 0.5s, and the running response delay of the cloud application is less than 2.0 s.
The current submerged arc furnace industry has no digital twinning application, the main reason is that parameters of submerged arc furnace core equipment components are lacked, but the digital twinning technology is applied to a plurality of fields such as steel, petrifaction and the like, and has a foundation for transplanting application in a submerged arc furnace modification project or a newly-built project. The electric furnace data twinning solution based on the original end cloud synchronous deployment is adopted, the working conditions of the equipment are presented in multiple angles, the life cycle management of the electric furnace, the electric furnace fault prediction and the energy efficiency evaluation of the electric furnace are integrated, and the practicability of the system application is improved.
(04) Electric furnace fault prediction system
The fault prediction solution is deployed in the edge server and the cloud server, and technical requirements of localized application and cloud application are met. According to the key fault attributes of the electric furnace production equipment, the possible abnormal or fault trend of the equipment is judged in advance through modeling, algorithm and manual intervention. By means of the improvement of the prediction precision, the service life of the equipment is prolonged, the maintenance cost of the equipment is reduced, and the production stability is improved.
The input variables include: the temperature of the furnace bottom (7 thermocouples), the temperature of the furnace wall (22 thermocouples), the temperature of the furnace cover (3 thermocouples), the temperature of cooling water of the furnace cover (13 thermal resistors), the insulation detection voltage of the furnace shell (7 voltage detection devices), the temperature of cooling water of a protective screen (6 thermal resistors of each electrode), the temperature of cooling water of a bottom ring (6 thermal resistors of each electrode), the current of the electrode (current detection device), the current of an interelectrode (current detection device), the detection of leakage points of circulating water (PLC program output), production practices are combined, process explanation of an electrical parameter standard curve is added, and a judgment basis or a data model is concluded. A single fault prediction model may contain multiple variables (which may be of different classes, different attributes, different dimensions). The failure prediction result is divided into the following parts according to risk grades: and (4) adopting different early warning output forms according to the risk level for slight faults, general faults and serious faults. And finally, data processing, data integration, data analysis, algorithm application, trend analysis and data presentation are realized.
The fault prediction of the no core equipment of hot stove trade in ore deposit at present uses, mainly relies on the ability level of maintenance team, generally waits until the trouble takes place the back, and the blowing out carries out relevant maintenance operation again, causes great influence to electric stove production stability. The method adopts an original fault prediction technology based on data driving, utilizes multidimensional detection variables, carries out early warning on faults and extraction of influence factors on core equipment (a furnace body and electrodes) of the submerged arc furnace, and improves the smelting production stability of the submerged arc furnace.
(05) Electric furnace energy efficiency evaluation system
The smelting process of the calcium carbide furnace is a complex high-temperature physical and chemical reaction process, relates to dozens of reaction variables, belongs to a typical nonlinear time-varying system with a complex structure and an unknown mechanism, and is mainly determined by manual experience and subjective analysis in production control, so that the production is not stable enough, and the fluctuation range of power consumption indexes is large. The system mainly solves the problems that a calcium carbide furnace control system does not have real-time evaluation standards and human factor influence, extracts excellent production operation data, takes machine learning as a core, creates intelligent optimization algorithm application suitable for the calcium carbide furnace, comprehensively integrates process variables, comprehensively processes detection data, extracts specific characteristics of the data, combines cloud training and edge deployment, applies a K neighbor algorithm to create a prediction model, refers to Manhattan distance analysis and measurement, performs mixed prediction on multiple models, combines a calcium carbide production process theory, promotes model evaluation and optimization, diagnoses fault causes in time, pushes production guidance suggestions in real time, assists production control personnel in decision analysis, effectively reduces product power consumption, and improves the intelligent level of the calcium carbide furnace.
The current production control of the submerged arc furnace industry mainly depends on manual experience judgment and subjective analysis, the dependence degree on the furnace length and the personal technical level of an operator is high, and the problems that the production working condition is not stable enough, the power consumption index fluctuation range is large and the like exist. According to the invention, an original energy efficiency evaluation technology based on machine learning is adopted, excellent production operation data is extracted, a prediction model is created by applying a K-nearest neighbor algorithm, Manhattan distance analysis and measurement and calculation are introduced, mixed prediction is carried out on multiple models, decision analysis of production control personnel is assisted, the electric furnace is promoted to work at a high-quality process parameter level for a long time, and the power consumption of products is effectively reduced.
(06) Data management system
And managing and integrating software and hardware systems of the edge server, and completing data distribution, storage and management in the local area network by taking a data processing task as a core.
The MySQL database is deployed in the local edge server and the data application server and used for archiving the cleaned data and providing a data source calling interface for third-party local application programs such as life cycle management and digital twin. A set of MySQL databases (cloud servers or cloud databases) are deployed at the cloud end and used for cloud end data filing, and meanwhile, data source calling interfaces are provided for cloud end third-party application programs and cloud components. According to the characteristics of field data and the application requirements of a cloud end, the database types and the data table structure are reasonably planned and configured from the perspective of optimizing a storage structure and accelerating query response, data conversion and custom periodic task scheduling of a relational database are supported, external data calling interface reservation is supported, and historical data and real-time data analysis are supported, wherein the data history archiving and storage time is not less than 6 months, and the single historical data query time span is not less than 1 month. The method comprises the steps of local and cloud data storage system butt joint, automatic synchronization, history back-up and data scheduling, and a set of high-availability data storage solution is formed.
And manual data entry sources are provided for other subsystems, cloud data calling requirements are considered, and the cloud data calling requirements belong to a local data bus or a database for unified management. From the perspective of electric furnace production operation and maintenance, functional modules for monitoring system operation parameters, alarming system abnormal parameters, storing production and process data, recording and inquiring historical trends, analyzing and printing reports, verifying login, managing accounts and the like are designed. And reserving functional module interfaces for data decision, optimal scheduling, performance management and the like so as to be conveniently butted with an upper MES system of the smelting plant.
The current submerged arc furnace industry mainly relies on upper management software of a control system to carry out data management and provide historical data query and filing. The problems of short data storage time, slow data query response, complex data export process and the like generally exist. The invention adopts a full-flow data management means based on event triggering, has the functions of automatic archiving, automatic migration, automatic backup, automatic cleaning and the like, has the data storage time of more than 6 months, adopts the aggregation query technology, can control the data query response at the second level, and supports the one-key export of historical data into an Excel file.
(07) Security management system
And (3) planning and setting perfect data security guarantee measures in each link of a local part and a cloud part, and adopting bidirectional data transmission encryption, dynamic password authentication, multi-level authority setting and firewall setting. The permission levels include: role assignments and configurations are made by administrators, maintenance personnel, visitors, inquirers. The cloud planning construction full data flow supervision system records and pre-warns abnormal events and important operation parameters of all links, defines different strategies according to security levels, and sets necessary emergency treatment measures to avoid fault expansion and upgrading. The system administrator has the highest security authority and can manage all user authorities of other members such as login, modification, viewing, creation and the like.
The prior submerged arc furnace industry mainly adopts a safety switch or sets a network safety module in a local area network, basically does not relate to the safety isolation and intercommunication of an internal network and an external network, and lacks the application of a network safety management technology. The invention adopts the double network card data security server, and is additionally provided with situation perception software, thereby reducing the risk of the control network being attacked; the method that the edge side actively searches for the cloud data center is adopted, so that the risk that the edge side network equipment nodes are exposed to the public network is reduced; and a perfect log logging system is adopted, so that the capability of tracing the source after manual operation record is improved.
The operation flow and the action effect of each subsystem in the embodiment are as follows:
step 1: and starting the edge server, confirming that the electric furnace field equipment and the electric furnace PLC are in normal operation states, modifying the configuration file of the 01 electric furnace data acquisition system, starting the electric furnace data acquisition system, and opening corresponding functional modules and interfaces as required. So far, the functions of data acquisition, data bus, local data filing and the like of the system are realized.
Step 2: starting a '02 electric furnace life cycle management system', '04 electric furnace fault prediction system' and '05 electric furnace energy efficiency evaluation system' in an edge server, wherein the 3 systems are mutually independent, share a data bus of a '01 electric furnace data acquisition system', acquire input data sources according to different periods, independently operate and output data respectively, and write back the data to a local database of the edge server as input data of other subsystems. So far, the functions of life cycle analysis, fault prediction, energy efficiency assessment and the like are realized.
And 3, step 3: starting a '03 electric furnace digital twin system' in an edge server, setting options such as display and hiding, triggering and the like, and then entering an automatic polling mode to dynamically, real-timely, multi-angle and non-shelteredly display the running parameters and running states of an electric furnace main body and key equipment. Meanwhile, the output achievements of a 02 electric furnace life cycle management system, a 04 electric furnace fault prediction system and a 05 electric furnace energy efficiency evaluation system are integrated, and visual data visualization is provided for users. So far, the functions of digital twinning, data watching board and the like are realized.
And 4, step 4: starting a data application server, confirming the state of a data bus interface, starting a 06 data management system, uniformly managing and scheduling and distributing all data of a 01 electric furnace data acquisition system, a 02 electric furnace life cycle management system, a 04 electric furnace fault prediction system and a 05 electric furnace energy efficiency evaluation system, filing process data to a local database, and analyzing and processing the data based on a big data analysis and data mining technical means. So far, the functions of data archiving, data query, data analysis, data mining, data reporting, data presentation and the like are realized.
And 5, step 5: and starting the data security server, confirming the states of the security switch, the cloud server, the cloud database and the cloud component, matching with the '06 data management system', comprehensively managing and controlling the network-out data and the network-in data of the edge server and the data application server, and performing secondary processing and distribution on the desensitized data. Therefore, functions such as data encryption, password authentication, cloud data transmission, cloud data circulation, network management, authority management, user management and the like are achieved.
In conclusion, the invention designs and realizes the internal and external data interfaces and data flow of each subsystem and each sub-module, meets the technical requirements of local application in an electric furnace workshop, cloud deployment and public network access, has stronger function expansion capability, creates a set of electric furnace intelligent control system integrating an automation technology, a network communication technology, a big data technology, an artificial intelligence technology and electric furnace production, operation and maintenance experience, improves the production efficiency of the electric furnace and creates the economic benefit of enterprises.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The utility model provides a hot stove long-range operation and maintenance system in intelligence ore deposit which characterized in that includes:
the submerged arc furnace production data perception layer comprises a terminal node, an execution node, a data server and an information transmission module, wherein the terminal node is deployed on a submerged arc furnace working site and comprises information acquisition equipment and a PLC (programmable logic controller), the information acquisition equipment is connected with the PLC and used for transmitting acquired data information to a management system application layer through the information transmission module, and the data server receives a control instruction transmitted by the management system application layer and controls the corresponding execution node;
the management system application layer comprises a data acquisition module, a data processing module, a data transmission module and an edge server, wherein the edge server is loaded with local submerged arc furnace intelligent application and assists in checking, managing and operating the working process of the submerged arc furnace, and the data acquisition module, the data processing module and a controller deployed at a control end carry out data interaction with the cloud service layer through the data transmission module;
the cloud service layer comprises a cloud database and a cloud server, wherein the cloud database and the cloud server are deployed at the cloud end, the cloud database is used for storing data generated in the production activities of the submerged arc furnace, and the cloud server is loaded with cloud submerged arc furnace intelligent application and used for analyzing and calculating the data.
2. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, further comprising a cloud development layer, wherein the cloud development layer is used for updating and modifying cloud submerged arc furnace intelligent applications.
3. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, wherein the submerged arc furnace intelligent application comprises: electric furnace data acquisition, electric furnace life cycle management, electric furnace digital twinning, electric furnace fault prediction, electric furnace energy efficiency evaluation, data management and safety management.
4. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, wherein the data generated in the submerged arc furnace production activities comprises: the temperature of the furnace bottom, the temperature of the furnace wall, the temperature of the furnace cover, the temperature of cooling water of the furnace cover, the insulation detection voltage of the furnace shell, the temperature of cooling water of an electrode shield, the temperature of cooling water of an electrode bottom ring, the current of an electrode, the current of an interelectrode and the detection data of a leakage point of electrode circulating water.
5. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, wherein electric furnace fault prediction is performed by a local submerged arc furnace intelligent application and a cloud submerged arc furnace intelligent application, and data generated in the submerged arc furnace production activities are analyzed and calculated by using a classification label method respectively, so that a submerged arc furnace fault prediction result is obtained.
6. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, wherein the local submerged arc furnace intelligent application and the cloud submerged arc furnace intelligent application perform furnace energy efficiency assessment, data generated in submerged arc furnace production activities are comprehensively processed by using a machine learning method respectively, specific characteristics of the data are extracted, a prediction model is created by using a K-nearest neighbor algorithm, and the submerged arc furnace energy efficiency is comprehensively assessed by using Manhattan distance analysis and measurement and calculation.
7. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, wherein the data management of the local submerged arc furnace intelligent application and the cloud submerged arc furnace intelligent application comprises: the method comprises the following steps of system operation parameter monitoring, system abnormal parameter alarming, production and process data storage, historical trend recording and inquiring, report analysis and printing, login verification and account management.
8. The intelligent submerged arc furnace remote operation and maintenance system according to claim 1, wherein the safety management of the local submerged arc furnace intelligent application and the cloud submerged arc furnace intelligent application comprises the construction of a full data flow supervision system, the recording and early warning of abnormal events and important operating parameters of each link, and the planning of different strategies according to safety levels.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114286061A (en) * | 2022-02-14 | 2022-04-05 | 上海交通大学 | Sintering site real-time video acquisition and analysis system based on cloud edge collaboration |
| CN114518732A (en) * | 2022-02-10 | 2022-05-20 | 宁夏瑞资联实业有限公司 | Furnace control system of DCS submerged arc furnace |
| CN115102828A (en) * | 2022-08-26 | 2022-09-23 | 歌尔股份有限公司 | Fault analysis method and device |
| CN115685858A (en) * | 2023-01-05 | 2023-02-03 | 苏州慧工云信息科技有限公司 | JIT-based electronic billboard data controller and control method |
| CN116379793A (en) * | 2023-06-02 | 2023-07-04 | 青岛智控菲特软件科技有限公司 | Submerged arc furnace short-net regulation and control data processing method |
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- 2021-09-02 CN CN202111027962.2A patent/CN113962405A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114518732A (en) * | 2022-02-10 | 2022-05-20 | 宁夏瑞资联实业有限公司 | Furnace control system of DCS submerged arc furnace |
| CN114286061A (en) * | 2022-02-14 | 2022-04-05 | 上海交通大学 | Sintering site real-time video acquisition and analysis system based on cloud edge collaboration |
| CN115102828A (en) * | 2022-08-26 | 2022-09-23 | 歌尔股份有限公司 | Fault analysis method and device |
| CN115685858A (en) * | 2023-01-05 | 2023-02-03 | 苏州慧工云信息科技有限公司 | JIT-based electronic billboard data controller and control method |
| CN116379793A (en) * | 2023-06-02 | 2023-07-04 | 青岛智控菲特软件科技有限公司 | Submerged arc furnace short-net regulation and control data processing method |
| CN116379793B (en) * | 2023-06-02 | 2023-08-15 | 青岛智控菲特软件科技有限公司 | Submerged arc furnace short-net regulation and control data processing method |
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