558 results about "Iron production" patented technology

The invention discloses an energy acquisition and monitoring system based on big data, particularly relates to the field of energy monitoring, is used for solving the problem of energy consumption abnormity identification and management optimization in steel production, and is characterized in that energy parameters are acquired in a production process, and an energy signature reference model of multi-dimensional energy consumption characteristics is constructed based on historical data and equipment operation characteristics; an accurate reference is provided for dynamic comparison and anomaly detection; potential anomalies are accurately identified by utilizing comprehensive analysis of energy consumption deviation distribution and load characteristics, and key influence factors and root causes are deeply mined through a multivariable machine learning algorithm; in combination with a predictive scheduling engine and an online optimization algorithm, process parameters, equipment running states and task schedules are dynamically adjusted, abnormities are effectively eliminated, and energy consumption balance is recovered; therefore, the real-time monitoring and intelligent anomaly detection of the process energy consumption are realized, the refinement level of energy management is improved, the energy utilization efficiency and production stability of iron and steel enterprises are improved, and the potential production risk is reduced at the same time.

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 belongs to the technical field of steel production equipment fault diagnosis, and particularly relates to a steel production equipment fault diagnosis system based on a large language model. The system comprises a data processing module, a large language model training module and a retrieval enhancement generation module. Through domain data processing and model training, an intelligent diagnosis platform specially used for steel production equipment is constructed. The data processing module is responsible for performing feature extraction on the sampled data and constructing a vector database to ensure that the model can perform efficient semantic matching; the training module performs field adaptive adjustment on the large language model to improve the reasoning ability of the large language model in a specific field; the retrieval enhancement generation module realizes fault analysis and technical guidance with strong pertinence and high accuracy by integrating a vector database and a generative answer mode; the system runs locally or on a privatized server, all data processing, model calculation and generation processes are ensured to be completed in a controlled environment, and the privacy of user data is fully protected.

The invention relates to the technical field of steel production and processing, and discloses an electrical steel strip annealing temperature real-time monitoring and regulation system. The system comprises a temperature data acquisition module which is responsible for acquiring temperature and cooling rate data in an annealing process and acquiring expected parameters; the temperature deviation analysis module is used for calculating a deviation coefficient through a dynamic weighting algorithm to construct a matrix; the dynamic regulation and control module is used for generating regulation and control parameters based on a fuzzy logic control algorithm; the abnormity grading module is used for grading annealing abnormity; and the real-time feedback module transmits the regulation and control parameters and the abnormal identifier to the control end. The system also has the functions of adjusting a rule base according to the characteristics of the steel strip, backtracking historical data, optimizing regulation and control, adaptively updating an abnormal threshold value and the like. Through cooperative work of all the modules, real-time monitoring and accurate regulation and control of the annealing temperature are achieved, the annealing quality and production efficiency of the electrical steel strip are improved, and the cost is reduced.

The invention provides an intelligent comprehensive system and method for steel production process parameter optimization, and the system comprises a data configuration module which is used for collecting quality-related data in a steel production process; the influence factor analysis module is used for processing data related to quality by using a feature engineering technology and identifying key variables; the quality index prediction module is used for constructing a quality prediction model by using a machine learning algorithm and key variables; the process parameter optimization module is used for constructing a process parameter optimization model based on the prediction result and the quality index control target, and analyzing and recommending an optimal process parameter control range by utilizing an intelligent algorithm; and the model monitoring optimization module is used for evaluating and optimizing the operation effects of the quality prediction model and the process parameter optimization model in real time. According to the method, the whole-process intelligent management from data configuration and key factor identification to quality prediction, process parameter optimization and model dynamic monitoring is realized, a closed-loop optimization mechanism is formed, and the production efficiency and the product quality are effectively 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 relates to the technical field of steel production, and discloses an electrical steel strip rolling process intelligent scheduling and energy consumption prediction system, which comprises a data acquisition module, a dynamic scheduling module, an energy consumption prediction module, an anomaly detection module and an optimization feedback module. The data acquisition module acquires data by using a distributed sensor network; the dynamic scheduling module optimizes scheduling based on an improved genetic algorithm; the energy consumption prediction module adopts a deep space-time convolutional neural network to predict energy consumption; the anomaly detection module detects anomaly based on an isolated forest algorithm; and the optimization feedback module adjusts a scheduling strategy through a reinforcement learning framework. The system realizes intelligent scheduling of the rolling process and accurate prediction of energy consumption, can effectively improve production efficiency, reduce energy consumption, timely discover abnormity and optimize a scheduling scheme, and has remarkable economic benefits and production management advantages.

The invention relates to the technical field of steel production, and particularly discloses a bar line temperature control method which comprises the following steps: heating a steel billet: feeding the steel billet into a heating furnace for heating to reach a set rolling starting temperature; steel billet head and tail temperature difference control: installing a closed-loop control water tank system on a rolling line, and arranging a temperature detection device; precisely controlling the temperature of a rolled piece; developing a rolling line temperature control model; controlling red rust on the surface of the steel; designing a performance prediction model; temperature monitoring and adjustment: in the whole production process, establishing a rolling line PLC management and control platform; the rolled piece subjected to temperature control is conveyed to a cooling bed, the steel feeding temperature of the cooling bed is controlled to be 875 DEG C, and the up-down fluctuation is smaller than 5 DEG C; energy consumption in the production process is effectively reduced, closed-loop automatic adjustment of rolled piece temperature control is achieved, unmanned intervention can be achieved, the production efficiency and the automation level are improved, stable product performance is achieved, the surface of a finished product is free of red rust, and the quality and appearance of steel 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 belongs to the technical field of steel production, and relates to a method for improving the accuracy of final rolling temperature prediction based on a deep learning algorithm. According to the method, multiple key data, such as finish rolling inlet temperature, rolling force, cooling water flow and rolling speed, influencing the finish rolling temperature of hot rolled strip steel are selected through correlation analysis, and a generalized regression neural network (COA-GRNN) optimized by a raccoon optimization algorithm is utilized to establish a finish rolling temperature prediction model. The model realizes remote monitoring of the finish rolling temperature, so that production personnel can predict the trend of the finish rolling temperature in advance and adjust the finish rolling temperature in time, thereby ensuring the quality stability of strip steel and the yield of production.

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 discloses a production method of a non-annealing cold heading steel hot-rolled wire rod, and relates to the technical field of steel production, the method comprises the following steps: selecting a steel billet meeting the production specification requirement of the cold heading steel wire rod, and carrying out coping finishing and preheating treatment on the steel billet to be processed; the steel billet is conveyed to a large-compression-ratio rolling unit, the hot rolling process is monitored in real time, hot rolling segmented regulation and control are conducted, multi-pass temperature gradient rolling is achieved, and a cold heading steel wire rod formed through hot rolling is obtained; and the hot-rolled cold heading steel wire rod is conveyed to a heat preservation tunnel furnace, a cold heading steel wire rod finished product is obtained through segmented controlled cooling and surface treatment, and production of the annealing-free cold heading steel hot-rolled wire rod is achieved. According to the method, the advanced rolling mill unit with the large compression ratio is adopted for producing the cold heading steel wire rods of different specifications, the heat preservation tunnel furnace is adopted, the cooling temperature and the cooling speed of the wire rods are controlled, production of the annealing-free cold heading steel hot-rolled wire rods is achieved, the production process is greatly simplified, the production period is shortened, the production cost is reduced, and the production efficiency is improved.

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 belongs to the field of steel production, and relates to a method for producing high-elongation ultrathin hot-rolled low-carbon steel based on double-roller cast rolling. According to the method, molten steel is subjected to double-roller continuous casting and sub-rapid solidification to form a cast strip, is subjected to one-pass hot rolling to form a thin strip, is subjected to aerial fog cooling to 550-750 DEG C and is coiled, the coiled thin strip is placed in a cover type annealing furnace to be heated and subjected to heat preservation for 1-2 days, and then the hot-rolled thin strip is obtained after straightening and flattening. According to the method, a low-carbon, low-manganese, low-silicon and microalloy-element-free component system is adopted, a hot-rolled thin strip with the high elongation rate is produced through a short-process double-roller cast rolling production line, uniform polygonal ferrite is obtained through online waste heat annealing process design, the strength uniformity and the elongation rate are obviously improved, and production energy consumption is obviously reduced. The finished product is thin in thickness, good in low-temperature toughness, obviously superior to a traditional cold-hot-rolled low-carbon steel product in isotropy, and suitable for producing thin-specification components with complex shapes through cold bending and deep stamping forming.

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 discloses a system and a method for producing sponge iron by coupling a gasification furnace and a gas-based shaft furnace. The system comprises a coal pretreatment device for pretreating received raw material coal; the gasification furnace receives the raw material coal treated by the coal pretreatment device through a conveying device, and synthesis gas is obtained through reaction; the mixer receives furnace top gas generated by the gas-based shaft furnace, mixes the furnace top gas with synthesis gas to obtain mixed gas, and outputs the mixed gas to the dust remover; the high-temperature desulfurization device is used for carrying out desulfurization treatment on the dedusted mixed gas by using a high-temperature desulfurization agent, and the high-temperature decarbonization device is used for carrying out decarbonization treatment on the desulfurized mixed gas and outputting the decarbonized mixed gas to the gas-based shaft furnace; iron ore and / or oxidized pellets are / is arranged in the gas-based shaft furnace, the decarbonized mixed gas is received, and sponge iron is generated through reaction. According to the method, the synthesis gas is cooled by using the furnace top gas, additional cooling equipment is not needed, the cost is reduced, the energy utilization rate is increased, and cleaner and more efficient sponge iron production is realized.

The invention discloses an optimal energy consumption prediction and energy efficiency intelligent diagnosis method in a steel production process, and belongs to the technical field of steel production processes, and the method comprises the following steps: building a steel production database based on the steel production process by taking a heat or a batch as a unit, and carrying out the preprocessing of data in the steel production database; establishing a steel generation process mechanism model; building a black box model based on the correlation analysis and the neural network model, and training the black box model; based on coupling of the steel production process mechanism model and the trained black box model, establishing an energy consumption prediction model for performing energy consumption prediction on the steel production process energy consumption factors; energy consumption of energy consumption factors in the steel generation process is determined based on an energy consumption prediction model through a machine learning method, then optimal energy consumption in the steel generation process is determined through an energy consumption calculation formula in the steel production process, and historical optimal energy consumption is determined according to historical data.

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 belongs to the technical field of metallurgy, and provides a method for reducing erosion of a stopper rod in pure iron continuous casting production, which comprises the following steps of: blowing argon to seal a long nozzle bowl opening in a steel casting process, adding a covering agent to execute casting and adding twice in the process, and continuously injecting argon into a tundish impact area and a stopper rod area in the production process. A spinel stopper rod is adopted, and the outer diameter of the slag line part of the stopper rod is increased; by protecting the casting process design and optimizing the size and material of the stopper rod, the problem that the stopper rod is broken in the pure iron production process is solved, and stable and efficient continuous casting production of pure iron is achieved.

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 present invention discloses a method for reducing the hydrogen-rich reduction swelling of pellet ore based on stepwise reduction in a hydrogen-based shaft furnace, belonging to the technical field of direct reduced iron production. The pellet ore is successively reduced in the first-stage hydrogen-based shaft furnace (temperature: 780 - 880 °C, volume fraction of H2 + CO > 80%, and volume ratio of H2 / (H2 + CO) is 0.6 - 0.8) and the second-stage hydrogen-based shaft furnace (temperature: 980 - 1040 °C, volume fraction of H2 + CO > 90%, and volume ratio of H2 / (H2 + CO) ≥ 0.8), and then cooled to obtain direct reduced iron. This method can reduce the maximum reduction swelling rate of the entire pellet ore during the reduction process to less than 10% without using pellet additives such as calcium and magnesium. While ensuring the reduction efficiency of the hydrogen-based shaft furnace, it can produce direct reduced iron with quality indicators meeting the requirements and higher iron grade, which is conducive to realizing the clean, efficient, and short-process utilization of iron ore resources.

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 discloses a method for inhibiting abnormal expansion of ultrahigh-grade pellets through gradient heating reduction of a hydrogen-based shaft furnace, and belongs to the technical field of direct reduction iron production. The method comprises the following steps: continuously adding ultrahigh-grade pellets from the top of a hydrogen-based shaft furnace, and sequentially passing through a first heating reduction zone (the heating speed is 10-20 DEG C / min, and the temperature reaches 900-930 DEG C), a second heating reduction zone (the heating speed is not less than 30 DEG C / min, and the temperature reaches 990-1020 DEG C), a constant-temperature reduction zone and a cooling zone of the hydrogen-based shaft furnace to obtain the direct reduction iron. The method is suitable for the hydrogen-based shaft furnace with various reducing atmospheres, the maximum reduction expansion rate of the ultrahigh-grade pellets in the reduction process of the hydrogen-based shaft furnace can be smaller than 15% on the premise that the original iron grade of the pellets is not reduced, high-purity direct reduction iron with qualified production indexes is produced, and reduction of follow-up electric furnace smelting energy consumption and the molten iron refining process are facilitated.

The invention belongs to the field of steel production, and relates to a method for producing 460MPa-grade hot-rolled thin strip steel based on double-roller cast rolling. Molten steel is subjected to double-roller continuous casting and sub-rapid solidification to form a cast strip, is subjected to one-pass hot rolling to form a thin strip, is subjected to aerial fog cooling to 530-610 DEG C and is coiled, the coiled thin strip is placed in a cover type annealing furnace to be heated and subjected to heat preservation for 1-2 days, and a hot-rolled thin strip finished product is obtained after finishing. According to the method, a medium-carbon low-manganese low-silicon microalloy-element-free component system is adopted, the high-strength hot-rolled thin strip with the low yield ratio is produced through a short-process double-roller cast rolling production line, and the production energy consumption and the alloy cost are remarkably reduced. A bainite, ferrite and pearlite composite structure with the large grain size is obtained by combining the precise control aerial fog cooling process with the online waste heat annealing process design, the yield ratio is low in performance, the low-temperature toughness is good, and the strength uniformity is good. And the isotropy of the finished product is obviously superior to that of a traditional hot-rolled low-alloy product, and the finished product is suitable for producing thin-specification components through cold bending and punch forming.

Disclosed herein, in some aspects, are systems and methods for producing a material comprising iron through self-reduction of iron ore using bio-oil and / or other reducing agents (e.g., bio-based reducing agents), such as biocrude, ethanol, or other bio-based liquids or biologically sourced liquids. The bio-oil and / or other reducing agents can be mixed with the iron ore to form a furnace mixture, which can be heated, such that the components of the bio-oil and / or other reducing agents in the furnace mixture reduce the iron ore to form an iron product (e.g., a material that includes metallic iron). In some cases, the pre-formed furnace mixture allows for the reducing agents to interact with the iron more readily, thereby providing for quicker reaction rates, and thereby quicker reduction of iron ore, as compared to direct reduction iron production.