465 results about "Iron production" patented technology

The invention relates to the technical field of steel production plan optimization based on multi-dimensional constraint dynamic coupling, in particular to a molten iron operation plan arrangement method based on dynamic constraint modeling hybrid optimization, which comprises the following steps: acquiring order data, equipment state data and constraint rule data to generate structured input parameters comprising order priority labels; constructing a multi-objective optimization model based on the molten iron distribution constraint and the maintainable time length of the equipment, extracting the maintenance conflict relationship between the equipment groups, constructing a maintenance rule knowledge graph, and generating a dynamic constraint condition set; generating an initial molten iron distribution and maintenance plan through global search by adopting a genetic algorithm, and outputting a molten iron distribution path and an equipment maintenance time sequence through local optimization by combining a linear programming algorithm; according to the method, dynamic collaborative optimization of molten iron distribution and equipment maintenance is achieved, and the resource utilization rate, the order delivery rate and the production system stability are improved.

The invention discloses a rolling process stability control method based on multi-modal fusion fault diagnosis, and belongs to the technical field of intelligent control of steel production. Comprising the steps of collecting rolling process fault data, establishing an original fault data set, and dividing the original fault data set into a training set and a test set; simulating and generating fault data by adopting a mechanism model corresponding to each fault type, and performing data enhancement on the training set to obtain a mechanism enhancement data set; generating fault data by adopting a time sequence generative adversarial network, and performing data enhancement on the mechanism enhancement data set to obtain a fusion enhancement data set; constructing a fusion data enhanced multi-modal fusion fault diagnosis model based on the fusion enhanced fault data set; and the established fault diagnosis model is used for fault diagnosis in the rolling process, and a rolling stability control strategy is formulated based on a diagnosis result. According to the control method, the time domain feature and the frequency domain feature of the fault are fused to achieve high-precision fault diagnosis, then the corresponding control strategy is implemented based on the diagnosis result, and then rolling stability online control is achieved.

The invention provides a steel production risk dynamic early warning method and device, equipment and a medium, and the method comprises the steps: obtaining original data in a steel production process, carrying out the fluctuation analysis and stability analysis of the original data, carrying out the pairwise comparison of all dimension indexes based on the analyzed data, generating a weight matrix, calculating a comprehensive weight coefficient, and carrying out the dynamic early warning of the steel production risk. Inputting the comprehensive weight coefficient into a preset hybrid model, calculating a comprehensive safety risk value of the to-be-detected area in combination with a predefined membership function, triggering an early warning signal according to a comparison result of the risk value and a preset threshold value, executing an emergency response strategy, and after the response strategy is executed, performing early warning on the to-be-detected area. Model parameters are adjusted based on the verification data set, and full-life-cycle management is achieved through time sequence version comparison; according to the method, through dynamic weight calculation, non-deterministic risk factor characterization and a closed-loop optimization mechanism, risk identification perspectiveness and emergency response efficiency are improved.

The invention provides a steel plate surface defect detection method and system based on multi-source data fusion, and relates to the technical field of steel production automatic detection.The method comprises the steps that water stain pretreatment is conducted on the surface of a steel plate, the residual humidity is reduced to be below a preset low humidity threshold value, and a dry surface is obtained; based on the dry surface, collecting multi-source data through a 3D laser line scanning camera and a 2D industrial line scanning camera which are synchronously triggered, obtaining line scanning laser point cloud data, a line scanning reflectivity gray level image, a line scanning depth gray level image and an area array reflectivity color image, and establishing a space corresponding relation among the multi-source data; and forming a multi-source data set based on the spatial correspondence among the multi-source data. According to the method, a high-precision and high-robustness steel plate surface defect detection system covering the whole detection process is constructed by eliminating water stain interference, unifying a multi-source data reference, optimizing data quality, accurately identifying defects and realizing automatic feedback and data closed loop.

The invention relates to the field of business management, in particular to a production and manufacturing resource scheduling-oriented mixed integer programming optimization system, which comprises six modules: a data acquisition module acquires industrial big data streams; the data mapping module processes the data flow and constructs a real-time data model; the dynamic modeling module is used for automatically generating a mixed integer programming model according to a built-in steel production process model library based on a real-time data model; the model solving module generates a resource scheduling scheme through an optimization engine; the instruction analysis module analyzes a specific management and operation instruction; and the scheduling recalculation module starts a new round of mixed integer programming modeling and solving according to the increment information. According to the system, information such as resources is summarized through a mixed integer programming method, a specific production instruction is generated through a solver, a new round of modeling and solving are started when an accident occurs, rapid and accurate response to resource scheduling is achieved, and the response efficiency of resource scheduling and the reliability of the system are improved.

The invention provides an electric power system source-load coordinated optimization scheduling method considering the flexibility of a steel production process, and relates to the technical field of electric power system scheduling, comprising the following steps: carrying out operation characteristic analysis on the production load of a steel enterprise, and respectively establishing an electric arc furnace model, an air separation system model and a steel rolling line model; constructing an adjustable capacity model under the coupling of the power supply system and the production process; based on the operation cost of the iron and steel enterprise power system, constructing an iron and steel enterprise flexibility day-ahead scheduling model; the electric arc furnace model, the air separation system model, the steel rolling line model, the adjustable capacity model and the iron and steel enterprise flexibility day-ahead scheduling model are integrated into a mixed integer linear programming scheduling model, the mixed integer linear programming scheduling model is solved through a solving tool, and an optimal scheduling scheme of system source-load coordination is obtained. The flexibility of a power system can be remarkably improved, and the problem of supply and demand imbalance caused by wind power output fluctuation is effectively relieved.

The invention provides an intelligent ore blending system and device before iron making, a storage medium and a program product, and relates to the technical field of metallurgical engineering. The pre-ironmaking intelligent ore blending system comprises a raw material knowledge graph module, a raw material distribution module and a raw material distribution module, wherein the raw material knowledge graph module is used for constructing ore attributes and a raw material knowledge graph and realizing multi-dimensional coding of the raw material attributes; the metallurgy prediction module is used for realizing joint optimization of the model through fusion of an ore blending multi-objective optimization model and a large-model multi-modal model; the robust optimization module is used for fusing a transfer learning algorithm and constructing an anti-interference ore blending optimization decision model; and the closed-loop control module is used for integrating an online detection instrument and a dynamic parameter correction model and realizing hour-level ratio closed-loop adjustment. The deep neural network, the multi-objective optimization algorithm and the metallurgical process mechanism model are integrated, collaborative optimization of raw material cost saving and stable operation of the blast furnace is achieved, and the method is suitable for ore proportion decision making at the front end of sintered ore production.

The invention relates to a steel production-oriented multi-base multi-production-line multi-process global plan optimization method and system. The method comprises the following steps of: receiving and standardizing order demand data; executing hierarchical processing and distribution of multi-base orders, and outputting a base-level order set; deducting and marking the net production demand quantity of the base-level orders; executing multi-production-line order allocation in a single base, and generating a production line level order set; performing cross-process collaborative batch grouping on the production line level order set based on steel making-continuous casting-rolling-post-processing process constraints to generate a cross-process batch plan; constructing a global evaluation model, performing comprehensive evaluation on an intermediate result, and generating a parameter correction instruction based on an evaluation result for adjusting parameters, constraints or distribution strategies of subsequent modules; and finally, generating a production plan scheme with consistent cross-base, cross-production-line and cross-process, and triggering rolling optimization update when disturbance occurs. According to the invention, unified coordination and global optimization of multi-module plan results are realized.

The invention belongs to the technical field of ironmaking and steelmaking, and particularly discloses a direct reduction iron production process and system of variable feed gas. The production process comprises the following steps: under a basic working condition, a hydrogen-rich working condition or an all-hydrogen working condition, introducing high-temperature reducing gas into a shaft furnace to produce the direct reduced iron; the production system comprises a shaft furnace, a self-heating modification device, an upgrading device, a heating furnace and a preheating furnace, and is provided with three hydrogen supplementing points and two hydrogenation points. By changing the structure of the raw material gas, supplementing hydrogen at three positions or supplementing or adding hydrogen at at least one of two hydrogenation points, increasing the H2 content of the reducing gas at the inlet of the shaft furnace and the like, the carbon emission, the process energy consumption and the pellet reduction difficulty are reduced.

The invention discloses a curve model filtering-based high-speed wire rotational flow pool liquid level correction control method and system, and relates to the technical field of steel production, and the method comprises the following steps: collecting a liquid level signal, and constructing a fixed curve model according to a process stage; a staged deviation zone is set according to the model, deviation is compared and calculated in real time, and abnormity judgment is carried out; performing previous normal value substitution, model prediction value substitution or triggering process alarm on the abnormal section according to the duration to obtain a substitution sequence; and performing composite filtering on the alternative sequence, and outputting a corrected liquid level for closed-loop control and visualization. Through four-stage fixed curve modeling and staged deviation band judgment, graded substitution is triggered by a pull-back amount, a phase drift amount and a logic consistency rate, and compound filtering closed-loop control is performed on a substitution sequence, so that accurate anomaly detection, low false alarm, continuous curve and stable control are realized.

The invention belongs to the technical field of steel production, and relates to a method for producing a low-cost high-strength galvanized sheet based on thin-strip cast rolling. After molten steel is subjected to thin-strip continuous casting to obtain a casting strip, the casting strip is rolled into a thin strip in a single pass mode, the thin strip is coiled after being cooled through aerial fog, then hot-dip galvanizing is conducted on a continuous hot-dip galvanizing production line after acid pickling is conducted, the annealing temperature of a galvanizing section is 640-720 DEG C, and then the thin strip is placed at the room temperature for more than 35 days after leveling is conducted. According to the method, supersaturated solid solution of C and N atoms in the thin strip casting and rolling sub-rapid solidification process is fully utilized, the high solid solution state is maintained by combining hot rolling and galvanizing section short-time and low-temperature design, finally, strength jump is driven by means of natural aging after production, and the high-strength galvanized plate with good stamping performance is obtained under a simple C-Si-Mn component system. The problems that the high-strength galvanized plate forming capacity of a traditional high-carbon route is insufficient, and the alloy cost of a low-carbon microalloy route is high are solved, and meanwhile carbon emission in the product production process is greatly reduced.

The invention discloses a 9Ni steel process optimization method based on artificial intelligence, and relates to the technical field of steel production, and the 9Ni steel process optimization method comprises the following steps: collecting multi-source process parameters and optimization target parameters; performing feature processing on the collected data to generate target feature data; constructing a 9Ni steel performance multi-target prediction model based on the target feature data, and training the model until convergence; and constructing a multi-objective optimization function by utilizing the converged 9Ni steel performance multi-objective prediction model, and solving the multi-objective optimization function by adopting a stochastic optimization algorithm under an operation constraint condition and outputting an optimal process parameter combination. According to the method, deep learning modeling, cluster cleaning, feature engineering construction, deep learning and a random optimization strategy are comprehensively applied, a data-driven intelligent process optimization system for the 9Ni steel manufacturing process is formed, the achievement rate of target performance indexes can be remarkably increased, the parameter trial and error period is shortened, and the implementation efficiency of the 9Ni steel manufacturing process is improved. And intelligent adjustment and cost optimization of a process path can be realized.

The invention discloses a steel production line dynamic scheduling system based on data integration analysis, which belongs to the technical field of steel production line plan scheduling, and is characterized in that a dynamic knowledge graph driven by integrated multiple models is constructed, and scattered and heterogeneous data acquired in real time is converged into a data stream; an initial scheduling scheme is produced based on original order data and production line resource data in the'data stream ', simulation deduction is performed based on digital twin driving, an optimized planned production list is obtained, production is executed with the planned production list as a reference, real-time production data and change order data in the real-time'data stream' are received, and the real-time production data and the change order data in the real-time'data stream 'are obtained. Process deviation and order change in the actual production process are dynamically responded, correlation root cause analysis and scheduling decision making are carried out on multiple disturbance factors based on model and map cooperation, dynamic scheduling based on priority and resource constraint is achieved, it is ensured that the actual production condition is consistent with a production plan after scheduling optimization, and production flexibility and adaptability are improved.

The invention relates to the technical field of steel production management, and particularly discloses an intelligent safety management system for steel production, which comprises a multi-source data acquisition module, a data fusion processing module, a digital twin modeling module, a production risk identification module, a dynamic risk assessment module and an intelligent early warning decision module. The method comprises the following steps: acquiring multi-source data of steel production, carrying out fusion processing on the multi-source data, constructing a digital twinborn model of a steel production line, updating the digital twinborn model of steel production based on security situation awareness data, generating a corresponding production risk coefficient and an environment risk coefficient, and further establishing a steel production dynamic risk assessment model. Performing dynamic risk assessment on steel production; by introducing multi-source heterogeneous data fusion, digital twin model dynamic evolution and quantitative risk assessment technologies, the real-time performance, accuracy and decision-making efficiency of steel production safety management are improved, and an innovative solution is provided for safety management and control of a complex industrial system.

The invention relates to an intelligent rolling mechanism model data management system based on containerization micro-service collaboration, belongs to the technical field of steel production automation and intelligentization, and solves the problems that in the prior art, a model is difficult to expand, a unified standard is lacked, and data management is dispersed. Comprising the steps that an API gateway service sends a model calling request to an authority management micro-service and a service registration and discovery micro-service; the model management micro-service packages the rolling mechanism model into a dynamic link library file, and generates a standardized interface description document and a multi-language calling template based on the dynamic link library file; generating a model container instance based on the dynamic link library file, the standardized interface description document and the multi-language calling template; and the authority management micro-service is used for verifying user authority and executing rolling process calculation based on the multi-language calling template and the standardized interface description document through the positioned target model container instance.

The invention provides an intelligent marking and analyzing method for steel production quality based on multi-process space-time mapping. The method comprises the following steps: acquiring a time sequence curve of characteristic parameters, material tracking data and material quality data of a target material in each process in production; according to the material tracking data, establishing a mapping model of the processing position, the process processing time and the cross-process processing position; standardized marking is conducted on the machining position and the machining time interval based on the mapping model; extracting corresponding time sequence curve features based on the annotation interval; analyzing the difference between the target material and the reference material based on feature comparison; and generating a diagnosis result according to the analysis result. The technical problems that in the prior art, the process curve feature extraction mode is single, cross-process data are difficult to associate, and the quality problem tracing efficiency is low are solved.

A process cooling water (PCW) system and method for an iron or steelmaking application, the PCW system and method including: sampling a plurality of characteristics of PCW using a plurality of sensors; calculating one or more of Langelier Saturation Index (LSI) and Ryznar Stability Index (RSI) based on the plurality of characteristics using a LSI / RSI measurement unit coupled to the plurality of sensors; and injecting one or more anti-scalant chemicals into the PCW responsive to the calculated one or more of the LSI and the RSI in real time using an anti-scalant injection system to thereby control scale formation in the PCW.

The invention relates to a steel structure performance control method and system based on a large language model, and belongs to the field of steel manufacturing. In order to solve the problems that traditional steel rolling water cooling parameter control depends on an empirical formula, prediction precision is low, adjusting efficiency is poor and working condition adaptability is insufficient, initial parameters (chemical components, temperature, technological parameters and the like) and mechanical property data of steel are collected, and a historical knowledge base is established; preprocessing the to-be-predicted data, calculating the similarity between the to-be-predicted data and historical cases, screening reference cases, and constructing Prompt input containing the cases and the current working condition; a large language model (GPT / GLM) is utilized to generate high-precision water tank parameters, and the model is optimized through a continuous learning mechanism. The method has the technical effects that the prediction error is smaller than or equal to 5%, the parameter adjustment efficiency is improved by 70%, the rejection rate is reduced by 3%-5%, complex working conditions such as equipment aging and component fluctuation are dynamically adapted, and an intelligent and high-robustness cooling control scheme is provided for steel production.

The method for controlling the suction of process gases emitted within a vessel (22) of an electric arc furnace (20) during its operation comprises an adaptation step of adapting the flowrate (Y) of sucked in process gases based on the value of at least one parameter, said at least one parameter comprising a flowrate (X2) of carbonaceous matter added within the vessel (22) during operation of the electric arc furnace (20).

The invention relates to the technical field of industrial big data analysis, and discloses a molten iron model construction method, device and equipment and a medium, and the method comprises the steps: obtaining historical production data of molten iron, including raw material parameters, process parameters and molten iron quality parameters; on the basis of the obtained molten iron quality parameters, according to the molten iron quality requirement and the lowest coke ratio principle, optimal process parameter combinations corresponding to different raw material parameters are screened out through Pareto optimization and / or industrial standard filtering; and constructing a regression model by adopting an ensemble learning algorithm, taking the obtained raw material parameters as input data, taking the screened optimal process parameter combination as output data, training the regression model, inputting to-be-produced raw material parameters into the trained regression model, and obtaining the optimal process parameter combination. According to the method provided by the invention, the process parameters considering the molten iron quality, the coke ratio and the equipment safety can be obtained according to the parameters of the raw materials to be produced, and the process optimization of molten iron production is realized.

The invention discloses a continuous casting process anomaly monitoring method based on multi-process variable fusion and deep learning, and relates to the field of metallurgical industry automation control and intelligent manufacturing. Accurate identification and classification of abnormal intervals and intelligent judgment and comprehensive analysis of abnormal levels are realized, and linkage of result visualization and real-time alarm is carried out. Compared with the prior art, the method has the advantages of being high in interval recognition capacity, high in interpretability, high in real-time performance, flexible in deployment, high in adaptability and the like, and is suitable for real-time abnormal interval detection, positioning and comprehensive analysis in the continuous casting process in steel production.

The application discloses a production quality data processing method and device and electronic equipment. The production quality data processing method comprises the following steps: generating quality data for a first production process by a first system node; wherein the first system node is a node corresponding to a first system in a first block chain; the first system is an information system for the first production process in a steel production system; the first production process is a process of one of the following production links: ironmaking, steelmaking, rolling and quality inspection; the quality data is encapsulated into a quality data block in a first format; and the quality data block is broadcasted in the first block chain, so that the quality data block is stored in a distributed database of the first block chain after consensus verification. According to the embodiment of the application, the reliability of the quality data of the steel production process can be improved.

The invention relates to a steel grade performance prediction system and method based on machine learning, the system comprises a user interaction module, a data processing module, a model prediction module and a result output module, steel grade chemical component parameters and process parameters input by a user are received, and a pre-processed neural network model and a K-means clustering algorithm are combined to predict the performance of a steel grade. And rapid prediction of key performances such as yield strength, tensile strength and elongation of the steel grade is realized. The method solves the problems that traditional steel grade performance detection depends on experiments, consumes time and labor and is limited in precision, multi-dimensional parameters are fused through a machine learning algorithm, prediction efficiency and accuracy are improved, and the method can be widely applied to process optimization and performance regulation and control of steel production.

The invention discloses a multi-dimensional evaluation method of a blast furnace process extreme energy efficiency technology, and relates to the field of steel production energy efficiency evaluation. Aiming at six extreme energy efficiency technologies of a blast furnace process, a'technical unit energy-saving cost (CCEa)-net present value (NPV)-internal return rate (IRR) 'ternary index system is constructed, an energy-saving supply curve and a cost-benefit analysis method are combined, and comprehensive evaluation is realized through four steps: calculating the energy-saving and emission-reducing potential when the blast furnace uses anthracite, and introducing exclusive emission factor correction; calculating the unit energy-saving cost considering the multi-pollutant synergistic benefit, and combining the exclusive reachable rate of the technology; the full-life-cycle cost effectiveness is analyzed from the perspective of enterprises, and technology maturity correction is introduced; and carrying out dynamic weighted sorting based on a TOPSIS method, and outputting technical priorities. The method breaks through the limitation of single-dimensional evaluation, realizes the collaborative evaluation of economic and environmental benefits, and provides a decision basis for the low-carbon transformation of the blast furnace.

The present application relates to the technical field of steel production, and particularly relates to a method for stable operation of a ring heating furnace, which comprises the following steps: a continuous heating mode: setting the heating area of the ring heating furnace as follows: the first area is a preheating area, the second area to the sixth area are heating areas, and the seventh area to the eighth area are soaking areas; heating the ingot to a first set temperature in the preheating area, continuously heating the ingot to a second set temperature according to a set temperature gradient from the second area to the sixth area in the heating area; cooling the ingot to a third set temperature according to a first set temperature gradient in the soaking area, the third set temperature being greater than the first set temperature; a shutdown standby mode: determining standby time; if the standby time is less than a first set time length, the temperature of each area of the ring heating furnace remains unchanged. The present application can effectively heat the ingot, save the use of fuel, and ensure the efficiency of heating.

The invention discloses an accounting method for fuel gas energy consumption of a steel billet in a heating furnace, and relates to the field of hot rolling production. According to the accounting method for the fuel gas energy consumption of the steel billets in the heating furnace, the physical attributes of the steel billets, in-furnace and out-furnace temperature data and temperature distribution of all sections in the heating process are combined, and the energy consumption of each steel billet in different heating areas can be accurately calculated. Through detailed energy consumption analysis of each steel billet, the method not only provides powerful data support for improvement of the energy efficiency of the heating furnace, but also provides more accurate decision basis for energy optimization, production scheduling and cost control in steel production. The method has high practicability and wide application prospects, is not only suitable for the existing steel production process, but also provides important technical support for future energy management and intelligent scheduling.

The application discloses a high-toughness bainite non-quenched and tempered steel and a manufacturing method thereof, and belongs to the technical field of steel production. The high-toughness bainite non-quenched and tempered steel comprises the following components in percentage by mass: C: 0.10-0.30%, Si: 0.2-0.6%, Mn: 1.8-2.8%, P: 0.003-0.03%, S: 0.04-0.07%, Cr: 0.3-0.8%, V: 0.06-0.15%, Zr: 0.001-0.01%, Te: 0.001-0.01%, N: 0.01-0.02%, O: 0.0005-0.005%, and the balance of Fe and inevitable impurity elements. By adding Zr and Te and cooperating with a specific preparation process, the application remarkably improves the inclusion morphology and distribution and refines the structure, thereby greatly improving the toughness while maintaining high strength.

This invention relates to the field of steel production quality inspection technology, specifically to a phototube protection device, comprising a protective cover body, a mounting structure, and a light-receiving hole; wherein, the top of the protective cover body is provided with a closed structure; the mounting structure is disposed on the protective cover body for fixing the protective cover body to the bracket of the phototube; the light-receiving hole is opened on the front of the protective cover body, and the opening size of the light-receiving hole is smaller than the detection range of the phototube body to limit the light-receiving range of the phototube, thereby reducing the influence of external light, welding sparks, and other interference factors on the detection signal; the light signal received by the phototube of this invention is constrained to the target area directly in front of the light-receiving hole; thus, the probability of false triggering of the detection signal is reduced, and the detection accuracy of the phototube is improved.

A gas production apparatus includes: a separator configured to separate and capture a separated gas including carbon dioxide as a main component from an exhaust gas of exhaust gas equipment; reactors which are downstream of the separator, each of the reactors: (i) containing a reductant configured to contact the separated gas to produce carbon monoxide through a reduction reaction of carbon dioxide; (ii) being configured to separate at least some oxygen atoms split off from carbon dioxide in the reduction reaction; and (iii) having a reducing agent containing a metal oxide configured to reduce carbon dioxide as the reductant; a reducer configured to supply a reducing gas containing a reducing substance configured to reduce the reducing agent oxidized by contact with carbon dioxide; a pressure regulator configured to regulate a pressure of the separated gas; and a flow regulator configured to regulate a flow rate of the separated gas.

Provided is a reduced iron production method that can suppress clustering of iron ore pellets. A reduced iron production method according to the present invention is characterized by comprising: a pellet production step for mixing an iron ore powder, a binder, and an auxiliary material, granulating the mixture to obtain green pellets, and baking the green pellets to obtain iron ore pellets; and a reduction step for charging the iron ore pellets in a vertical shaft furnace, and directly reducing the same by using a reducing gas to obtain reduced iron pellets. The reduced iron production method is characterized in that: raw materials of the iron ore powder include an iron ore having a compositional makeup in which the total Fe amount is 63 mass% or less and the sum of SiO2 and Al2O3 is 4 mass% or more; and the reduced iron pellets satisfy the following formula. (SiO2+Al2O3+CaO+MgO+FeO) / M.Fe ≥ 0.15