Sintering is a pre-process of blast furnace ironmaking,and the quality of sintered ore directly affects the quality and quantity of hot metal in the ironmaking process. Intelligent prediction and control of key parameters plays an important role in improving the quality of sintering ore in the sintering process. Firstly,the flowchart is introduced and its process characteristics is analyzed. Then, the predictive modeling research status for quality indicators and state parameters in the sintering process are reviewed. On this basis,the control methods about burn through point and ignition temperature in detail is illustrated. Finally,conclusions and prospects for the predictive and control modeling of key parameters in the sintering process are made.

Aiming at the outstanding problems of insufficient digitization of iron making process,low degree of intelligence,lack of a unified intelligent platform,and far from adapting to the development needs of intensification,digitization and intelligence,Angang Steel Co.,Ltd. established a big data centre for intensive control of blast furnace with blast furnace group as the core and covering other processes. The big data centre breaks the information silos of each regional information system,releases the effectiveness of data. The blast furnace group and subsidiary process form a centralized control and management centre for data sharing and efficient collaboration,realizing the blast furnace process upgrading from an intelligent unit to an intelligent platform. At the same time,the intelligent application model of blast furnace is built,which realizes the visualized intelligent monitoring of the safe production and operation of blast furnace,and guides the production operation of blast furnace,as well as improves the digitization and intelligence level of the production,technology and management of the blast furnace of Angang Steel Co., Ltd.

In order to further improve the intelligent manufacturing level of production planning and scheduling at Laiwu Iron and Steel Group Yinshan Steelmaking Plant,a systematic study was conducted on the complex scheduling plan of 4 furnaces,4 machines,and non matching furnace machines. By analyzing the operation cycle and production operation mode of the steelmaking refining continuous casting process,four bottom level matching principles were explored and summarized,including the dynamic total balance principle,furnace machine matching principle,non interrupted pouring principle,and space proximity principle. Based on the four principles of matching logic,a furnace machine matching scheduling model was established,which achieved automatic scheduling of steelmaking plans for multiple furnaces to multiple machines in the non matching state of the furnace machine. This ended the production scheduling mode that was completely centered on dispatchers and driven by manual experience,greatly improving scheduling efficiency and planning accuracy. In addition,the dynamic scheduling model of the furnace machine based on the matching logic of the four principles has strong flexibility and universality,and can be applied to more steel enterprises.

The use of metallurgical robots to replace manual labor to engage in some dangerous and repetitive labor has gradually become one of the important elements of the construction of unmanned production lines in major factories. In some application scenarios,the robot忆s end-effector needs to be in close contact with the workpiece. When an abnormal situation occurs,the robot may trigger a collision alarm,and require the operator to manually recover the robot,greatly affecting the production efficiency and posing a large safety risk. For those applications that require contact work,based on the Rapid language of ABB series of robots,a collision adaptive control program and anti-collision monitoring along with distance compensation program were developed. Then without manual intervention, the robot can automatically carry out the next action,to ensure the normal operation of the production process. Both solutions have been put into use in the steel plant,the effect is good,and improve the level of intelligence in the steel plant.

The blast furnace ironmaking process is of great significance in the iron and steel industry. However,due to the complexity of the blast furnace ironmaking process and the existence of high temperature,high pressure,and complex physical and chemical reactions,it is still a major challenge to establish an effective process monitoring model. Aiming at the requirement of multi-step real-time prediction of hot metal key quality indicators in the process of blast furnace ironmaking,this paper refers to the theory of continuous learning (CL) that simulates the human brain's hippocampus and neocortex,and constructs a CL-based time series data pre diction modeling framework based on multihead self-attention mechanism,one-dimensional residual neural network (ResNet),long short term memory network (LSTM) and multi-layer perceptron (MLP) in order to realize the multi-step realtime prediction task of hot metal key quality indicators of the blast furnace ironmaking process. The experimental results show that the proposed CL-based method is superior to the traditional deep learning models,achieves higher prediction accuracy,and with the increase of prediction time step,the proposed CL-based model shows strong robustness.

KR desulfurization is a typical approach for hot metal pretreatment. With the increasing demand of low sulfur steel products,achieving stable control of end sulfur content in molten iron and subsequently reducing the overall cost of desulfurization processes are of paramount importance. A critical aspect of this process is the ability to predict whether the desulfurization endpoint complies with the required standards. Therefore,a modeling method combining a sample class balance processing method based on cost-sensitive strategy and Bayesian-optimized extreme gradient boosting(XG-Boost) algorithm with a binary classification analysis method for solving end sulfur prediction problem in KR desulfurization process is proposed. Firstly,the end sulfur content of the desulfurization data is processed into conforming or non-conforming two categories based on the binary classification analysis method. The category sample weights are adjusted based on the cost-sensitive strategy to alleviate the imbalance issue for constructing the feature dataset. Then,using actual production data from a steel company,the model is trained via cross-validation with cost-sensitive strategy and Bayesian-optimized XGBoost with the optimal parameters are selected based on Macro-F1 metric to form the final desulfurization conformity prediction model for the KR process,achieving the data prediction for desulfurization conformity and non-conformity targets. Experimental results comparing with support vector machine(SVM) and back propagation neural network(BPNN) prediction models show that the proposed method can effectively deal with the imbalance issue in desulfurization data,showing good practical effects in desulfurization conformity prediction.

Improving the operation efficiency of cranes in the steelmaking area can reduce energy consumption for transportation while effectively linking the preceding and succeeding processes, which is of certain value for green production,cost reduction,and efficiency increase. In this regard, this article proposed a crane scheduling optimization method driven by simulation modeling and machine learning. Firstly,multi-agent is used to establish a production simulation model for the steelmaking area,which is driven by historical production plans and crane scheduling workflows. Subsequently,the simulation model is run multiple times to obtain a large number of high-quality crane operation samples through built-in sample evaluation formulas. Finally,a random forest model is employed to learn from the samples and obtain a machine learning model for matching cranes with transportation tasks. Experimental analysis shows that applying the machine learning model to crane scheduling decisions can increase the proportion of effective transportation time,thereby reducing energy consumption losses caused by mismatched transportation tasks,path avoidance,etc. This advantage is particularly significant under heavy production loads. Furthermore,the crane scheduling machine learning model is decoupled from the steelmaking plan,exhibiting high flexibility in practical app lications.

The camber defect in the hot rolling rough rolling area affects the shape quality and rolling stability of the strip steel. The research on online detection and control technology of camber has always been a hot topic of industry. This study develops an online detection and control technology for camber based on image recognition. By installing high-definition cameras in front and behind the stand to obtain strip steel images,image processing technology is used to measure and recognize the contour shape of the strip steel. Combined with actual measurement data,equipment data,model setting data,etc. during the rolling process,a camber adjustment model is constructed,online automatic feedforward control function for camber is implemented. This technology has been put into online application on a hot rolling production line in China,with an accuracy rate of over 95% for adjusting the camber direction. It can effectively reduce production costs and labor intensity,improve the automation level of the production line,and has high application value and development prospects.

Addressing the challenges of the heavy workload involved in scrap steel classification and the lack of uniform grading standards,this paper employs machine learning to identify and compare scrap steel images for determining the scrap steel grade. A scrap steel classification dataset is constructed,and the Siamese neural network is utilized to train the dataset,selecting the optimal weights to enable the model to accurately distinguish different types of scrap steel. The similarity between the scrap steel benchmark and the image to be tested is compared using the Siamese network calculation method. Based on the similarity results,the scrap steel grade is determined. When the similarity approaches 1,the scrap steel shapes are considered similar. When the similarity approaches 0,the shapes are deemed different,allowing for the determination of the distribution of scrap steel types in the image. Experimental results demonstrate that the method of scrap steel classification using similarity comparison and the Siamese neural network exhibits excellent accuracy and reliability. Compared with traditional manual classification methods,this approach not only significantly improves classification efficiency but also achieves standardization and consistency in scrap steel grading.

Cu-Fe alloys has excellent properties, and the increase in Fe content can also reduce costs. However,high-Fe-content Cu-Fe alloys are difficult to deform at room temperature,and the use of warm rolling can improve their machinability. Unlike cold rolling,the alloy will have a width expansion during warm rolling,which can affect the control of product dimensional accuracy during processing. Therefore,this study aims to investigate the effects of tension,reduction rate,and temperature on the width spread of Cu-Fe alloys sheets of a certain size under different warm rolling processes through experimental research,and develop a width spread mathematical model suitable for the warm rolling process of Cu-Fe alloys thin strips. The results show that tension has a small effect on width spread during warm rolling,and the increase in reduction rate and temperature can lead to an increase in width spread,but the effect of temperature is greater than that of reduction rate. Based on the Bakhchinov width spread formula,an optimization was performed to obtain a width spread model for the warm rolling of Cu-Fe alloys sheet samples. Through experimental verification on a hydraulic tension warm rolling mill,the model error is very small,with an accuracy range of ±10% . Therefore,the width spread model can be used to predict the width spread of Cu-Fe alloys sheets during the warm rolling process.

The cooling rate of the head and tail in the inner and outer rings is higher than that in the middle of the coil,and the performance of the head and tail cannot meet the technical requirements and the cutting rate of the product is higher. In order to improve the yield of high strength steel products,U-shaped cooling control strategy is used. At the early stage of U-cooling research and development,the mathematical model did not meet the requirements of U-cooling setting for multi-varieties and multi-processes in high strength steel production,and the coiling temperature control was not accurate. According to the existing problems in production,the coiling temperature control (CTC) model is upgraded,the setting function of single steel is added,and the temperature compensation function of transition section is developed,which solves the problems such as unqualified performance of high strength steel head and tail,and realizes high efficiency and low cost production of hot rolled high strength duplex steel.

Surface temperature of steel billets is an important parameter in steel production. The results are required accurate and can be traced to international temperature scales. However,the results are always inaccurate and untraceable. Firstly,an accurate method for surface temperature measurement based on changing radiation was proposed. The intrinsic radiation and multiple reflected radiation are obtained by converting the position of the tube. Using the dual-wavelength method to simultaneously measure surface temperature and emissivity,and the influence of uncertain emissivity is eliminated. A pyrometer is developed. For the issue of results that cannot be traced back to the international temperature scale,the contradictory conditions in calibration and measurement is analyzed. A method for constructing an ideal plane is proposed. The blackbody and thermal equilibrium conditions on the measured plane is met,so that the results can be traced to laboratory standards. Building a traceability platform and conduct experimental verification. Experimental results show that the average error of the pyrometer is 2. 36 ℃ at 600~1 000 ℃,and the standard uncertainty is 0. 75 ℃.