CN116109089A - Digital monitoring management method for low-carbon transformation of steel mill - Google Patents
Digital monitoring management method for low-carbon transformation of steel mill Download PDFInfo
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Abstract
The invention discloses a digital monitoring management method for low-carbon transformation of a steel mill, which comprises a data state detection layer unit, a carbon data analysis layer unit, a carbon index early warning layer unit and a carbon emission accounting layer unit; the data state detection layer unit comprises a multi-data source integration module, a data tracing module, a carbon emission source day monitoring module and a carbon consumption source dynamic monitoring module; the carbon data analysis layer unit comprises a direct emission prediction model module, an indirect emission prediction model module, a technical emission reduction prediction model module and a process control model module; the early warning layer unit of the carbon index comprises a four-level index early warning module, a management and adjustment dynamic analysis module, a carbon consumption source structure analysis module and a carbon consumption key procedure management and control module; the carbon emission accounting layer unit comprises a process law accounting module, a carbon index matching module, a carbon index horse racing module, a carbon index pushing analysis module and a carbon consumption index design module.
Description
Technical Field
The invention relates to the field of metallurgical low-carbon numbers, in particular to a digital monitoring management method for low-carbon transformation of a steel mill.
Background
China is the largest large country for steel production in the world, the coarse steel yield is the first worldwide for more than 20 years, and the clear contrast is that the industry is high in quality, the ultra-low emission is improved, and a long path is taken.
In 2020, the steel yield of China reaches up to 10.65 hundred million tons, and the steel yield accounts for over 57% of the global proportion. However, carbon emissions account for more than 60% of the total carbon emissions of the global steel industry, and account for 15% of the total carbon emissions of the whole country. The carbon reaches a peak before the year 2030 of the promise of China, the carbon neutralization is realized before the year 2060, the iron and steel industry is taken as a major carbon emission household and is a key industry for reducing carbon, and the carbon consumption source is difficult to monitor and control. Under the pressure of reducing carbon emission and saving energy and reducing carbon in the steel industry, a carbon reduction path is continuously explored, and the problem of high carbon emission is solved by monitoring and controlling a carbon consumption source in the production process.
Disclosure of Invention
The invention aims to solve the problems that a carbon consumption source of a steel mill is difficult to monitor and control and carbon emission is high and low, and provides a digital monitoring management method for low-carbon transformation of the steel mill.
The technical scheme of the invention is as follows:
the method sequentially analyzes and outputs the acquired data through a data state detection layer unit, a carbon data analysis layer unit, a carbon index early warning layer unit and a carbon emission accounting layer unit;
the data state detection layer unit comprises a multi-data source integration module, a data tracing module, a carbon emission source day monitoring module and a carbon consumption source dynamic monitoring module;
the carbon data analysis layer unit comprises a direct emission prediction model module, an indirect emission prediction model module, a technical emission reduction prediction model module and a process control model module;
the early warning layer unit of the carbon index comprises a four-level index early warning module, a management and adjustment dynamic analysis module, a carbon consumption source structure analysis module and a carbon consumption key procedure management and control module;
the carbon emission accounting layer unit comprises a process law accounting module, a carbon index matching module, a carbon index horse racing module, a carbon index pushing analysis module and a carbon consumption index design module; wherein:
the data state detection layer unit is used for integrating basic carbon data sources of a steel mill, forming daily monitoring of carbon emission sources of important carbon consumption working procedures, tracing and dynamically monitoring carbon data, and reducing carbon emission by 0.2tCO2/t steel;
the carbon data analysis layer unit is used for receiving the data of the state detection of the data, solidifying a prediction method model of direct carbon emission, indirect carbon emission and technical emission reduction by utilizing the carbon emission service experience of the iron and steel enterprises, forming a carbon reduction structure analysis, and providing the exceeding trend of the process parameters of the carbon data and the carbon index for the process application of carbon reduction;
the carbon index early warning layer unit is used for classifying the carbon indexes according to the analysis result of the prediction method model of the analysis layer of the carbon data to form a four-level index system early warning of carbon, and dynamically adjusting the carbon indexes according to the structure of a carbon consumption source to enable the carbon indexes to be adjusted and controlled in the production process;
the carbon emission accounting layer unit is used for completing the accounting of carbon emission of a steel mill process law, racing and benchmarking the process carbon emission index through the collection of the quantity related to the emission source, completing the automatic counting and automatic accounting of the carbon emission based on the analysis of the carbon index after the accounting of the process law carbon emission, and adjusting a later carbon emission plan according to the result so as to guide the later production;
the state detection layer unit of the data performs the following operations: the data from the steel mill level to the operation area level are converged uniformly, data fusion and sharing are realized, data-driven intelligent application is supported, the collection of carbon emission basic data of each base of the steel mill is realized, a steel mill carbon emission database is constructed, the total carbon emission amount and the carbon emission intensity of each production base are extended from a steel mill low-carbon intelligent management and control platform;
l4 and L3 data are cloud-entered: management data is obtained from L4 and L3 in an ETL batch extraction mode, and generally, month and day are used as frequencies; on-line monitoring data, generally using day, class, hour and minute as frequency, writing the data into corresponding RDS/MPP/STS according to categories;
cloud edge data integration:
(1) edge data node: collecting L1 and L2 data, and carrying out data alignment at the edge end;
(2) and (3) message sending: the edge end data preparation is completed, and a fetch signal is sent to a cloud message center;
(3) message acquisition: the cloud message center receives the message and actively extracts corresponding files or data from the edge according to the message signal in the TSS configuration;
(4) and (3) data writing: after receiving the file or the data, the cloud end writes the data into the corresponding RDS/MPP/STS according to the classification;
l2 data directly access the cloud:
configuring a timing extraction task operation in a cloud TSS task;
configuring corresponding extraction operation in the ETL tool to finish the extraction of the L2 related data;
the analysis layer unit of the carbon data performs the following operations:
(1) data analysis and pretreatment: according to the emission of actual carbon-containing elements of coking, sintering, blast furnace, converter and steel rolling, collecting the corresponding gas chemical components and the consumption of solid fuel, determining emission factors, analyzing and processing abnormal value information, and providing an input interface for technicians to specify the allowable fluctuation range of each carbon emission technical index;
(2) modeling data: establishing a mathematical model, abstracting a carbon emission source into 4 linear or quadratic programming problems, wherein the optimization targets of the problems are mainly that the carbon emission is the lowest, and the limiting condition is that the final solid fuel technical index falls in a designated fluctuation interval;
(3) the algorithm module: establishing an algorithm framework based on a simplex method, and finding out an optimal solution with the lowest carbon emission according to actual data acquisition conditions and calculation force evaluation results and by combining methods such as simulated emission factors, homotopy algorithm, genetic algorithm, grid search and the like in a specific implementation process;
(4) visual display: the method comprises two modes, wherein the first mode is a carbon emission index plan meeting the actual production requirements, and the plan provides 3 links of optimal carbon emission intensity; the second is a response optimizer, which provides a manual adjustment interface beyond the optimal solution for technicians, and meets the production requirements of various process carbon emission intensities;
the early warning layer unit of the carbon index performs the following operations: constructing a low-carbon four-level index system, classifying carbon indexes according to four levels of steel mill level, factory level, sub-factory level and operation area level/team, pre-tightening early warning and classified pushing are carried out on index parameters which are beyond the upper limit and the lower limit of control, the steel mill carbon consumption is advanced to online early warning in the past after being engaged, early warning and prediction are carried out in advance on core carbon indexes, the process is controlled, the steel mill carbon consumption is controlled in full life cycle of a main line, the plan value is compared with the control, the carbon consumption is intelligently controlled, and the whole flow early warning is controlled according to the flow direction of molten steel;
the accounting layer unit of carbon emissions performs the following operations:
(1) identifying an emission source: according to MES and EMS+system data sources, collecting, analyzing and determining an emission source for generating carbon emission, wherein raw materials, fuel consumption and product output in the production process are required to be subdivided into emission units for statistics and collection;
(2) selecting and acquiring emission factor data;
(3) respectively calculating direct carbon emission and indirect carbon emission, including fuel combustion emission, industrial production process emission, electricity used by net purchase and heat generated emission;
(4) calculating the total emission amount of greenhouse gases; and (3) completing the calculation of carbon emission of the steel mill process law, racing horses and aligning targets of the process carbon emission indexes, and completing automatic number taking and automatic calculation of the carbon emission based on the analysis of the carbon indexes after the calculation of the process law carbon emission, so as to guide post production alignment targets.
The invention has the beneficial effects that:
1. as a whole-flow low-carbon intelligent factory based on a unified industrial Internet platform, the low-carbon technology is widely applied to new technologies such as artificial intelligence, big data, internet of things, mobile interconnection, 5G and the like, a super-large-scale steel low-carbon intelligent control application system is built on one platform, and the whole-flow low-carbon tube control business of an enterprise is supported, so that the technology is advanced, the architecture is advanced, and the system is in an advanced position in China or even in the world.
2. The whole-flow low-carbon number intelligent steel mill supports the whole-flow low-carbon number intelligent ultra-large scale data application, the traditional information architecture needs to be comprehensively renovated, and the control difficulty is extremely high and no precedent can be circulated because of tens of thousands of steel mill data indexes. Through hard exploration and innovation, the method well solves the phenomenon that carbon data are scattered and cannot be in centralized connection, mainly solves the problems of data island and the like including the authenticity, the integrity and the consistency of data, ensures that the value of the carbon data is not affected when the carbon data is used for accounting, can efficiently acquire and process mass data, has a four-level index system of cross-procedure and cross-system, and ensures that low-carbon data of enterprises exert due value.
3. The carbon emission system and the energy system of the whole plant are monitored and managed on line, emission reduction and energy conservation are realized, and the power assisting base is identified through ultralow emission. The construction of the whole process low-carbon intelligent steel plant promotes the green, low-carbon and energy-saving of the steel industry, improves the energy utilization efficiency of the steel industry, reduces the resource consumption, reduces the CO2 emission and greatly improves the product quality.
Drawings
Fig. 1 is a schematic block diagram of the structure of each unit module of the present invention.
Description of the embodiments
The invention provides a method for digital monitoring management of low-carbon transformation of a steel mill, which solves the problem of low-carbon number intelligent transformation of the steel mill, realizes the construction of the system by taking low-carbon and number intelligent as paths and constructing an information platform shared by managers at all levels through digital monitoring management, integrates automation and informatization into a field KPI of carbon emission management, and feeds back and controls in real time; and the field problem is converted from passive exposure to active early warning. A unified steel mill big data platform is constructed by a carbon consumption source four-level index system, data from a company level to an operation area level are converged uniformly, data fusion sharing is realized, and a digital monitoring application driven by data is supported to be landed; through construction of four-level index systems from company level to operation area level, each level of management and control index system has clear venation, is drilled layer by layer, quickly discovers problems and solves the problems; based on an index system, a series of digital monitoring management applications are constructed, steel mill management is advanced into the fact (online monitoring and abnormal pushing) and is advanced (early warning and prediction) after the steel mill management is performed, the problems of ' invisible, insufficient ' and other pain points ' in the management are solved, and a main production line low-carbon index management and control mode is changed from ' after ' to ' in the fact ' and ' in the advance '.
The method sequentially analyzes and outputs the acquired data through a data state detection layer unit, a carbon data analysis layer unit, a carbon index early warning layer unit and a carbon emission accounting layer unit;
the data state detection layer unit comprises a multi-data source integration module, a data tracing module, a carbon emission source day monitoring module and a carbon consumption source dynamic monitoring module;
the carbon data analysis layer unit comprises a direct emission prediction model module, an indirect emission prediction model module, a technical emission reduction prediction model module and a process control model module;
the early warning layer unit of the carbon index comprises a four-level index early warning module, a management and adjustment dynamic analysis module, a carbon consumption source structure analysis module and a carbon consumption key procedure management and control module;
the carbon emission accounting layer unit comprises a process law accounting module, a carbon index matching module, a carbon index horse racing module, a carbon index pushing analysis module and a carbon consumption index design module; wherein:
the data state detection layer unit is used for integrating basic carbon data sources of a steel mill, forming daily monitoring of carbon emission sources of important carbon consumption working procedures, tracing and dynamically monitoring carbon data, and reducing carbon emission by 0.2tCO2/t steel;
the carbon data analysis layer unit is used for receiving the data of the state detection of the data, solidifying a prediction method model of direct carbon emission, indirect carbon emission and technical emission reduction by utilizing the carbon emission service experience of the iron and steel enterprises, forming a carbon reduction structure analysis, and providing the exceeding trend of the process parameters of the carbon data and the carbon index for the process application of carbon reduction;
the carbon index early warning layer unit is used for classifying the carbon indexes according to the analysis result of the prediction method model of the analysis layer of the carbon data to form a four-level index system early warning of carbon, and dynamically adjusting the carbon indexes according to the structure of a carbon consumption source to enable the carbon indexes to be adjusted and controlled in the production process;
the carbon emission accounting layer unit is used for completing the accounting of carbon emission of a steel mill process law, racing and benchmarking the process carbon emission index through the collection of the quantity related to the emission source, completing the automatic counting and automatic accounting of the carbon emission based on the analysis of the carbon index after the accounting of the process law carbon emission, and adjusting a later carbon emission plan according to the result so as to guide the later production;
the state detection layer unit of the data performs the following operations: the data from the steel mill level to the operation area level are converged uniformly, data fusion and sharing are realized, data-driven intelligent application is supported, the collection of carbon emission basic data of each base of the steel mill is realized, a steel mill carbon emission database is constructed, the total carbon emission amount and the carbon emission intensity of each production base are extended from a steel mill low-carbon intelligent management and control platform;
l4 and L3 data are cloud-entered: management data is obtained from L4 and L3 in an ETL batch extraction mode, and generally, month and day are used as frequencies; on-line monitoring data, generally using day, class, hour and minute as frequency, writing the data into corresponding RDS/MPP/STS according to categories;
cloud edge data integration:
(1) edge data node: collecting L1 and L2 data, and carrying out data alignment at the edge end;
(2) and (3) message sending: the edge end data preparation is completed, and a fetch signal is sent to a cloud message center;
(3) message acquisition: the cloud message center receives the message and actively extracts corresponding files or data from the edge according to the message signal in the TSS configuration;
(4) and (3) data writing: after receiving the file or the data, the cloud end writes the data into the corresponding RDS/MPP/STS according to the classification;
l2 data directly access the cloud:
configuring a timing extraction task operation in a cloud TSS task;
configuring corresponding extraction operation in the ETL tool to finish the extraction of the L2 related data;
wherein: l1 is a basic automation system, L2 is a process computer system, L3 is a production execution system, and L4 is a low-carbon digital monitoring management system. The L1 system is a Siemens TDC+S7-400 series automatic control system and is communicated with the L2 system through a text form.
The analysis layer unit of the carbon data performs the following operations:
(1) data analysis and pretreatment: according to the emission of actual carbon-containing elements of coking, sintering, blast furnace, converter and steel rolling, collecting the corresponding gas chemical components and the consumption of solid fuel, determining emission factors, analyzing and processing abnormal value information, and providing an input interface for technicians to specify the allowable fluctuation range of each carbon emission technical index;
(2) modeling data: establishing a mathematical model, abstracting a carbon emission source into 4 linear or quadratic programming problems, wherein the optimization targets of the problems are mainly that the carbon emission is the lowest, and the limiting condition is that the final solid fuel technical index falls in a designated fluctuation interval;
(3) the algorithm module: establishing an algorithm framework based on a simplex method, and finding out an optimal solution with the lowest carbon emission according to actual data acquisition conditions and calculation force evaluation results and by combining methods such as simulated emission factors, homotopy algorithm, genetic algorithm, grid search and the like in a specific implementation process;
(4) visual display: the method comprises two modes, wherein the first mode is a carbon emission index plan meeting the actual production requirements, and the plan provides 3 links of optimal carbon emission intensity; the second is a response optimizer, which provides a manual adjustment interface beyond the optimal solution for technicians, and meets the production requirements of various process carbon emission intensities;
the early warning layer unit of the carbon index performs the following operations: constructing a low-carbon four-level index system, classifying carbon indexes according to four levels of steel mill level, factory level, sub-factory level and operation area level/team, pre-tightening early warning and classified pushing are carried out on index parameters which are beyond the upper limit and the lower limit of control, the steel mill carbon consumption is advanced to online early warning in the past after being engaged, early warning and prediction are carried out in advance on core carbon indexes, the process is controlled, the steel mill carbon consumption is controlled in full life cycle of a main line, the plan value is compared with the control, the carbon consumption is intelligently controlled, and the whole flow early warning is controlled according to the flow direction of molten steel;
the accounting layer unit of carbon emissions performs the following operations:
(1) identifying an emission source: according to MES and EMS+system data sources, collecting, analyzing and determining an emission source for generating carbon emission, wherein raw materials, fuel consumption and product output in the production process are required to be subdivided into emission units for statistics and collection;
(2) selecting and acquiring emission factor data;
(3) respectively calculating direct carbon emission and indirect carbon emission, including fuel combustion emission, industrial production process emission, electricity used by net purchase and heat generated emission;
(4) calculating the total emission amount of greenhouse gases; and (3) completing the calculation of carbon emission of the steel mill process law, racing horses and aligning targets of the process carbon emission indexes, and completing automatic number taking and automatic calculation of the carbon emission based on the analysis of the carbon indexes after the calculation of the process law carbon emission, so as to guide post production alignment targets.
The data state detection layer unit completes the following operations:
and (3) custom analysis: and providing customized query based on authorized data, data screening, conventional function statistical analysis, chart presentation, query result set sharing, query scheme saving sharing and the like for service users.
Custom theme: according to the business logic between the data, a self-organizing function based on the authorized data is provided for the business user to form a new data theme.
Custom report: providing a template storage function of an autonomous query analysis scheme for service users so as to quickly query and download data next time;
and (3) data downloading: and a convenient and fast detailed data downloading function is provided for a user on the basis of the self-defined theme and the autonomous analysis function.
Data dictionary management: data dictionary management function providing all data tables (including custom topics)
Data sharing authorization: the data tables and data items are authorized by the user or by the role group.
Data access history: providing tracking, recording, statistics data access log information functions for administrator users, including table names, fields, sql statements, query time, access users, etc.
The main functional modules of the system are as follows: carbon emission standard preparation, carbon emission standard maintenance, carbon emission standard inquiry and the like.
And (3) preparing a carbon emission standard: in practical use, the carbon emission standard is generated through a certain conversion method, and a continuous best value of a plurality of months and a single month best value are provided as references for revising the carbon emission standard.
Carbon emission standard maintenance: on the basis of the reference carbon emission standard calculated by the system according to the empirical algorithm, each business function department and each factory part implement maintenance according to the actual generation condition.
Carbon emission standard inquiry: and the compiled carbon emission standard is managed through version and provided for service personnel to use, so that inquiry and reference are facilitated.
The carbon data analysis layer unit completes the following operations:
(1) data analysis and pretreatment: collecting corresponding gas chemical components and consumption of solid fuel according to the actual carbon element emission of steel mill coking, sintering, blast furnace, converter and steel rolling, determining emission factors, analyzing and processing abnormal value information, and providing an input interface for technicians to specify the allowable fluctuation range of each carbon emission technical index;
(2) modeling data: a mathematical model is built that abstracts the carbon emission source into 4 linear (or quadratic) planning problems. The optimization objective of these problems is mainly that the carbon emission is the lowest, and the limitation condition is that the final solid fuel technical index falls in a designated fluctuation range. Through inspection, all the limiting conditions are linear, so that the solvability of the problem is ensured.
(3) The algorithm module: an algorithm framework based on a simplex method is established. In the specific implementation process, according to the actual data acquisition condition and the calculation power evaluation result, the optimal solution scheme with the lowest carbon emission is found out by combining methods such as simulated emission factors, homotopy algorithm, genetic algorithm (limited calculation power), grid search (sufficient calculation power) and the like.
(4) Visual display: two styles are included. The first is a carbon emission index plan meeting the actual production requirements, and the plan provides 3 links of optimal carbon emission intensity; and the second is a response optimizer, which provides a manual adjustment interface beyond the optimal solution for technicians, and meets the production requirements of various process carbon emission intensities.
The carbon index early warning layer unit completes the following operations: the production indexes related to iron making, steel making and steel rolling in a steel plant are divided into 1 to 4 levels according to index levels:
(1) the level index is the steel mill level, is positioned to cooperate with the steel mill leading decision, and adjusts matters with larger influence at the same time, and carries out all-weather overall process control;
(2) the level index is a plant level, is positioned to surround the steel mill production operation target task and combine the site execution condition and the problem, and coordinates, combines actions and issues management and scheduling instructions from the view point of the plant level;
(3) the level index is a branch level, and the responsibility of the branch level is positioned to be gathered to realize the upper production and operation requirements, so that the dynamic management and control of the branch level process is realized;
(4) the level index is an operation area level/team level, which is a field execution layer of the production business of the steel mill, covers all production units, and ensures effective monitoring and instant early warning of the operation of the field operation post.
The indexes of each level are buckled in a loop, traced back layer by layer, and early warning is set according to different levels, so that the production and operation requirements of a steel mill are met, managers of each level are helped to quickly find out the production problem of the regional level and make adjustment in time, and the dynamic management and control of the process are realized.
The carbon emission accounting layer unit performs the following operations: the conventional carbon index and carbon data analysis system has a plurality of fixed reports, and according to the actual use condition, the statistical class of the steel mill is used for a long time, but the data requirements related to most of business reports often change. Therefore, after the report forms are developed in the implementation process of the system, the use frequency is low, and the system is not accessed any more slowly. Therefore, in the steel mill operation decision support system, a visual data management and analysis platform integrating data analysis, data downloading, data authorization and data tracking is provided for steel mill business personnel, a visual data analysis and report query function is provided for the business personnel in a data authorization mode, and a professional user can conveniently define a report by himself. More work is done by system design developers to build basic topics and professional topics meeting the business needs of the steelworks, where users analyze the data themselves. Therefore, development and implementation of a large number of fixed reports are reduced, and the data value of the steel mill information system is greatly improved.
Examples
Carbon emission budget
The carbon emission budget subsystem is a core system for supporting carbon emission management, and provides data services for a plurality of subsystems such as carbon emission analysis, carbon emission performance and the like, so that automation of carbon emission budget treatment is realized. The subsystem takes a carbon emission center as a minimum carbon emission collection unit, takes a product as a carbon emission budget object (consistent with granularity of carbon emission accounting), adopts a step-by-step synthesis method to specify the carbon emission budget of the product, and does not consider inventory factors in budget establishment.
Standard carbon emission regimes and schedule management are not separated in the execution of the carbon emission budget. The implementation of a standard carbon emission system can lead the establishment of the carbon emission budget to be simpler and more scientific, greatly improve the budget precision, greatly strengthen the management strength and depth and form a better evaluation and control system. The planned value management is one of important means, so that the scientificity and the compliance of the carbon emission standard can be effectively improved, and the carbon emission budget compiled by the carbon emission standard is more reasonable and feasible.
The main functional modules of the system are as follows: basic rule maintenance, basic data preparation, carbon emission budgeting inquiry and the like.
Basic rule maintenance: logical look-up tables and offline manual data are maintained.
Basic data preparation: according to the product yield, the feeding structure and the standard yield of each process, the feeding consumption is calculated, the productivity balance and the energy balance are carried out, the corresponding budget factor amount is generated, the comprehensive main raw material standard, the subject price standard and the additional carbon emission standard are generated, the recovery and the leading balance are carried out, and the preparation is provided for budget.
Carbon emission budgeting: according to the basic data, carbon emission budget planning is carried out on each account cover, then the account covers are summarized and communicated to form the carbon emission budget of the all-steel plant, and meanwhile, each step of checking list is provided.
Carbon emission budget query: query and download of carbon emission budget report is provided.
Analysis of carbon emissions
Carbon emission analysis requires accurate evaluation of the execution results of enterprise carbon emission plans, finding out various factors affecting the carbon emission level and the cause thereof, seeking ways and methods for further reducing carbon emission, and accurately selecting carbon emission levels suitable for new situations.
The subsystem enables the steel mill management layer to know the analysis content which comprehensively and integrally reflects the comprehensive carbon emission condition of the steel mill production, and to master the carbon emission standard comparison condition of the steel mill and the advanced enterprises in the groups. Meanwhile, the subsystem can support financial staff to deeply study the comprehensive carbon emission condition of the production of the all-steel plant in detail and analyze the five-factor influence of the carbon emission in detail.
Meanwhile, on the basis of following the comprehensive carbon emission analysis calculation method, an analysis view angle is directed to each specific production and auxiliary unit, the analysis granularity is finer than that of the comprehensive carbon emission analysis, and the personalized analysis is carried out by combining the conditions of the specific units.
The implementation of the carbon emission analysis can achieve the purposes of unique, accurate and efficient carbon emission data. Realizing hierarchical drilling from steel mill carbon emission to unit carbon emission, product carbon emission and subject carbon emission, flexibly inquiring and analyzing factors; the method is convenient for users to efficiently and accurately analyze the carbon emission change reasons and further promotes cost reduction.
The carbon emission analysis report function is optimized and perfected, the carbon emission thematic analysis report in a fixed format comparison analysis and a self-defined mode can be realized, and the carbon emission thematic analysis report is displayed in a quick, efficient, visual and multi-mode manner.
The system functions include: key index tracking, comprehensive carbon emission analysis, factory carbon emission analysis, carbon emission standard alignment analysis and basic data maintenance.
Key index tracking: the key carbon emission indexes of the steel mill are closely tracked, and the change trend is analyzed, wherein the indexes comprise Mao Jiaobi, the wool-ore ratio, the coal injection ratio, the electricity consumption, the steel material consumption, the lime unit consumption, the gas recovery, the yield (iron making, steelmaking, hot rolling, cold rolling, thick plates and the like), the roller consumption, the paint consumption, the processing cost and the like.
Comprehensive carbon emission analysis: and carrying out item-by-item detail analysis on the comprehensive carbon emission of the steel mill according to a five-factor analysis method.
Factory carbon emission analysis: and carrying out item-by-item detail analysis on the carbon emission of each plant part according to a five-factor analysis method, wherein the function of the analysis is the same as that of the steel plant-level comprehensive carbon emission analysis.
Carbon emission benchmarking analysis: and taking the subject comparison table of the subject enterprise as an intermediate bridge, and carrying out the carbon emission subject of the enterprise inside and outside the group.
Overview: and the multi-dimensional visual display function of carbon emission analysis is realized.
Basic data maintenance: logical look-up tables and offline manual data are maintained.
Carbon emission performance
The key point of the carbon emission performance evaluation is that the system measures can be practically executed, which is ensured by the assessment evaluation and the incentive punishment. Setting up standards, metering performance, assessment and incentives are the main procedures and measures to enhance the control effect. The carbon emission center should establish a standard regular performance measurement and evaluation system, and a detailed performance measurement and evaluation material is required each month, and the carbon emission analysis is based on the performance measurement report of the carbon emission center. By revealing and analyzing the differences between standard and actual carbon emissions, the direction of effort for cost-effective synergy can be found. And performance evaluation is carried out on the controllable carbon emission part of the carbon emission center, so that the specific situation of each carbon emission center in standard formulation can be reflected. The carbon emission performance subsystem is mainly used for performance measurement through three layers of a steel plant level, a plant level and a carbon emission center.
And combining the management requirement of the performance elastic evaluation model based on the detail product dimension with the reduction of the carbon emission intensity as a guide, and optimizing and perfecting the performance system of the existing steel plant: the actual carbon emission is calculated to the actual carbon emission composition of the granularity of the detail specification product with input information according to the standard of the solid fossil fuel and the actual carbon emission quantity of the carbon emission; the budget is calculated to the actual carbon emission composition of the granularity of the detail specification product with input information according to the standard of solid fossil fuel and the standard time of carbon emission; generating two sets of elastic performance results: 1) Locking the elastic performance results of the emission factors based on the 1 st and 2 nd results; 2) And on the basis of the first set of results, combining the carbon emission coefficient and the difficulty coefficient of the detail product to generate a set of elastic performance results.
Company level: the carbon emission performance of the all-steel plant is evaluated, and the evaluation data can be displayed according to the plant part or according to summarized carbon emission projects, and the evaluation data can be drilled into specific carbon emission centers and carbon emission source subject granularity layer by layer.
Factory, carbon emission center stage: and carrying out carbon emission performance evaluation on each plant part carbon emission through two drilling paths of a plant part carbon emission center and a plant part carbon emission project, and finally obtaining the granularity of a specific carbon emission center and a specific carbon emission subject.
Basic data maintenance: logical look-up tables and offline manual data are maintained.
Claims (1)
1. The digital monitoring and managing method for low-carbon transformation of steel mill is characterized by that the method sequentially utilizes the state detection layer unit, carbon data analysis layer unit, early warning layer unit of carbon index and accounting layer unit of carbon emission to analyze and output the collected data;
the data state detection layer unit comprises a multi-data source integration module, a data tracing module, a carbon emission source day monitoring module and a carbon consumption source dynamic monitoring module;
the carbon data analysis layer unit comprises a direct emission prediction model module, an indirect emission prediction model module, a technical emission reduction prediction model module and a process control model module;
the early warning layer unit of the carbon index comprises a four-level index early warning module, a management and adjustment dynamic analysis module, a carbon consumption source structure analysis module and a carbon consumption key procedure management and control module;
the carbon emission accounting layer unit comprises a process law accounting module, a carbon index matching module, a carbon index horse racing module, a carbon index pushing analysis module and a carbon consumption index design module; wherein:
the state detection layer unit of the data is used for integrating basic carbon data sources of a steel mill, forming daily monitoring of carbon emission sources of important carbon consumption working procedures, tracing and dynamically monitoring the carbon data, and reducing carbon emission by 0.2tCO2/t steel;
the carbon data analysis layer unit is used for receiving the data of the state detection of the data, solidifying a prediction method model of direct carbon emission, indirect carbon emission and technical emission reduction by utilizing the carbon emission service experience of the iron and steel enterprises, forming a carbon reduction structure analysis, and providing the exceeding trend of the process parameters of the carbon data and the carbon index for the process application of carbon reduction;
the carbon index early warning layer unit is used for classifying the carbon indexes according to the analysis result of the prediction method model of the analysis layer of the carbon data to form a four-level index system early warning of carbon, and dynamically adjusting the carbon indexes according to the structure of a carbon consumption source to enable the carbon indexes to be adjusted and controlled in the production process;
the carbon emission accounting layer unit is used for completing the accounting of carbon emission of a steel mill process law, racing and benchmarking the process carbon emission index through the collection of the quantity related to the emission source, completing the automatic counting and automatic accounting of the carbon emission based on the analysis of the carbon index after the accounting of the process law carbon emission, and adjusting a later carbon emission plan according to the result so as to guide the later production;
the state detection layer unit of the data performs the following operations: the data from the steel mill level to the operation area level are converged uniformly, data fusion and sharing are realized, data-driven intelligent application is supported, the collection of carbon emission basic data of each base of the steel mill is realized, a steel mill carbon emission database is constructed, the total carbon emission amount and the carbon emission intensity of each production base are extended from a steel mill low-carbon intelligent management and control platform;
l4 and L3 data are cloud-entered: management data is obtained from L4 and L3 in an ETL batch extraction mode, and generally, month and day are used as frequencies; on-line monitoring data, generally using day, class, hour and minute as frequency, writing the data into corresponding RDS/MPP/STS according to categories;
cloud edge data integration:
(1) edge data node: collecting L1 and L2 data, and carrying out data alignment at the edge end;
(2) and (3) message sending: the edge end data preparation is completed, and a fetch signal is sent to a cloud message center;
(3) message acquisition: the cloud message center receives the message and actively extracts corresponding files or data from the edge according to the message signal in the TSS configuration;
(4) and (3) data writing: after receiving the file or the data, the cloud end writes the data into the corresponding RDS/MPP/STS according to the classification;
l2 data directly access the cloud:
configuring a timing extraction task operation in a cloud TSS task;
configuring corresponding extraction operation in the ETL tool to finish the extraction of the L2 related data;
the analysis layer unit of the carbon data performs the following operations:
(1) data analysis and pretreatment: according to the emission of actual carbon-containing elements of coking, sintering, blast furnace, converter and steel rolling, collecting the corresponding gas chemical components and the consumption of solid fuel, determining emission factors, analyzing and processing abnormal value information, and providing an input interface for technicians to specify the allowable fluctuation range of each carbon emission technical index;
(2) modeling data: establishing a mathematical model, abstracting a carbon emission source into 4 linear or quadratic programming problems, wherein the optimization targets of the problems are mainly that the carbon emission is the lowest, and the limiting condition is that the final solid fuel technical index falls in a designated fluctuation interval;
(3) the algorithm module: establishing an algorithm framework based on a simplex method, and finding out an optimal solution with the lowest carbon emission according to actual data acquisition conditions and calculation force evaluation results and by combining methods such as simulated emission factors, homotopy algorithm, genetic algorithm, grid search and the like in a specific implementation process;
(4) visual display: the method comprises two modes, wherein the first mode is a carbon emission index plan meeting the actual production requirements, and the plan provides 3 links of optimal carbon emission intensity; the second is a response optimizer, which provides a manual adjustment interface beyond the optimal solution for technicians, and meets the production requirements of various process carbon emission intensities;
the early warning layer unit of the carbon index performs the following operations: constructing a low-carbon four-level index system, classifying carbon indexes according to four levels of steel mill level, factory level, sub-factory level and operation area level/team, pre-tightening early warning and classified pushing are carried out on index parameters which are beyond the upper limit and the lower limit of control, the steel mill carbon consumption is advanced to online early warning in the past after being engaged, early warning and prediction are carried out in advance on core carbon indexes, the process is controlled, the steel mill carbon consumption is controlled in full life cycle of a main line, the plan value is compared with the control, the carbon consumption is intelligently controlled, and the whole flow early warning is controlled according to the flow direction of molten steel;
the accounting layer unit of carbon emissions performs the following operations:
(1) identifying an emission source: according to MES and EMS+system data sources, collecting, analyzing and determining an emission source for generating carbon emission, wherein raw materials, fuel consumption and product output in the production process are required to be subdivided into emission units for statistics and collection;
(2) selecting and acquiring emission factor data;
(3) respectively calculating direct carbon emission and indirect carbon emission, including fuel combustion emission, industrial production process emission, electricity used by net purchase and heat generated emission;
(4) calculating the total emission amount of greenhouse gases; and (3) completing the calculation of carbon emission of the steel mill process law, racing horses and aligning targets of the process carbon emission indexes, and completing automatic number taking and automatic calculation of the carbon emission based on the analysis of the carbon indexes after the calculation of the process law carbon emission, so as to guide post production alignment targets.
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| CN116975689A (en) * | 2023-07-31 | 2023-10-31 | 杭州超腾能源技术股份有限公司 | Intelligent carbon emission identification and control method and system |
| CN117575635A (en) * | 2024-01-16 | 2024-02-20 | 四川绿豆芽信息技术有限公司 | Carbon index tracing method and system |
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| CN116975689A (en) * | 2023-07-31 | 2023-10-31 | 杭州超腾能源技术股份有限公司 | Intelligent carbon emission identification and control method and system |
| CN116975689B (en) * | 2023-07-31 | 2024-02-06 | 杭州超腾能源技术股份有限公司 | Intelligent carbon emission identification and control method and system |
| CN117575635A (en) * | 2024-01-16 | 2024-02-20 | 四川绿豆芽信息技术有限公司 | Carbon index tracing method and system |
| CN117575635B (en) * | 2024-01-16 | 2024-03-29 | 四川绿豆芽信息技术有限公司 | Carbon index tracing method and system |
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