In automation control for metallurgical unmanned overhead cranes,a critical challenge lies in balancing operational speed and anti-swing measures to enhance crane efficiency while ensuring safe operation. A metallurgical unmanned crane anti-swing full-range speed control method was pro- posed based on controller parameter switching derived from human expertise under multi-run states in this article. The method first plans the full-range speed control for the overhead crane linked by a large and small vehicle under anti-swing conditions. Then anti-swing control is optimized by switching PID parameters based on human crane operation experience under different running states,forming an integrated feedforward-feedback hybrid control for the unmanned overhead crane. Simulation experi- ments demonstrate that,compared to the commonly used manual operation of large and small vehicle linkage control,this method reduces overall operation time by 24. 9% . Field experiments show thatthe maximum swing angle is controlled within the range of 0. 45°-0. 85 °,and positioning accuracy is maintained within 10 mm. The relevant indicators are in a leading position in similar domestic scenar- ios. This method has been successfully applied and validated in the steel industry in China.

Crane scheduling in steelmaking and continuous casting section is a typical multi-machine and multi-task constraint problem,which is of great significance to the connection and forward flow of each process and the control of the production rhythm of the whole steelmaking plant. In order to im- prove the efficiency of crane operation and ensure the stability of production,a crane scheduling mod- el based on spatio-temporal heuristic algorithm was established by analyzing the process flow and the constraints of crane job scheduling in steelmaking and continuous casting section. Partition rules,task allocation rules,collision avoidance rules and state update rules are designed to characterize the oper- ation process of the crane. The model is solved by a heuristic method,which can avoid the high com- putational complexity of traditional theoretical methods in large-scale problem solving. The results show that the model can provide a reasonable scheduling scheme and effectively avoid the spatio-tem- poral conflict during the operation of the crane. Compared with the actual production scheduling method,the performance of the crane under the heuristic algorithm model has been improved,and the operation efficiency of the crane has been improved.

Continuous annealing is a pivotal process in the production of high value-added cold-rolled strip steel. Efficiently allocating strip steel across different continuous annealing lines and determining the optimal processing sequence are essential not only for enhancing production efficiency and prod- uct quality but also for ensuring the timely delivery of final products. To address these challenges,a mixed integer programming model was established. This model accounts for the distribution and se- quencing of strip steel on parallel continuous annealing production lines,incorporating process regula- tions and production organization requirements to achieve on-time order fulfillment. Furthermore,an improved tabu search algorithm was developed to expediently solve industrial-scale problems. This al- gorithm introduces multiple neighborhoods and dynamically alternates among them to balance explo- ration and exploitation in search processes. In the small-scale instance tests,the proposed algorithm’ssolutions had an average deviation of 5. 46% from the optimal solutions. In the large-scale instance tests,the proposed algorithm’s solutions showed an average improvement of 24. 95% over the heuris- tic solutions. Based on four weeks of actual data,the test results indicate that intelligent scheduling, compared to manual scheduling,improved on-time contract delivery rates by 11. 2% and reduced the number of transitional material usages by 9. 8 times. 

In order to achieve more accurate calculation of alloy addition amount,a comprehensive al- gorithm based on genetic algorithm ( GA) optimization for BP neural network was adopted. During the training process of the BP neural network,the refining starting composition of the molten steel was used as input parameters for the BP neural network model. Then,the fitness function of the GA was used to optimize and adjust the weight and threshold of BP neural network,predicting the refining endpoint steel composition of the LF furnace. By comparing the prediction results of BP neural net- work algorithm and GA-BP neural network algorithm, it was found that the GA-BP algorithm has smaller mean absolute error (MAE) and mean square error (MSE) ,and the prediction results aremore accurate and basically consistent with the actual steel composition,indicating that this model can be used in production. Based on the GA-BP neural network model,the alloy addition amount was determined according to the composition of the molten steel at the beginning of LF refining and con- trol composition requirements. After deploying a predictive model in the 140 ton ladle LF refining sys- tem of a steel plant and tracking 188 furnace data,the difference between the actual amount of alloy added and the predicted amount was within ±30 kg,then the prediction accuracy of high manganese alloy was 91. 3% ,high chromium alloy was 90. 4% ,ferrosilicon alloy was 90. 2% ,and the predic- tion accuracy of carburant was 91% in furnaces,from which can guide the determination of alloy ad- dition amount in the actual refining process. 

Based on the 1 580 mm hot rolling production line of a certain factory,in response to the problem that traditional thickness models cannot accurately reflect actual thickness,an improved stochastic configuration network ( SCN) based strip thickness prediction model was proposed. Firstly, from the perspective of rolling mechanism, the reasons for thickness fluctuations in hotrolled strip products were analyzed. Secondly, based on the original SCN, a stochastic configuration network based on hunting prey optimization algorithm ( HPO-SCN) and a stochastic configuration network based on hunting prey optimization algorithm and orthogonal triangular decomposition ( HPO-QR-SCN) were proposed. Then,using onsite measurement devices,parameters of three different thick ness specifications of strip steel products were collected to form a database of strip thickness. Parame ters related to export thickness were selected as input values for the model,and the Pauta rule was used to preprocess the original rolling data. SCN,HPO-SCN, and HPO-QR-SCN prediction models were established,and their prediction results were compared. The experimental results show that the proposed HPO-QRS-CN thickness prediction model has the shortest prediction time and the highest accuracy,with a model determination coefficient of 0. 963 8. At the same time,based on the model with the best predictive performance,the influence of rolling force and roll gap on the exit thickness of the strip steel was tested,and the results were in line with actual physical laws. The asymptotic be havior of the model was tested,with a root mean square error ERMS (RMSE) of 0. 059 8,indicating good approximation performance. 

As a key index to evaluate the shape quality of medium and heavy plate,the plate crown plays an important role in determining the market competitiveness of steel companies. With the devel- opment of computer technology,intelligent control has become the most focused subject in steel in- dustry. However,artificial intelligence models of predicting plate crown still lacks interpretation and are difficult to effectively guide the practice. Based on problems above,a medium and heavy plate crown prediction model based on data mining and multi-model fusion was proposed. Firstly,rolling data was projected onto spaces which have less dimensions,which solved the multi-source heteroge- neity problem of data. Secondly, samples were expanded by using the improved synthetic minority oversampling technique ( SMOTE) method based on Bootstrap,which solved the problem of low mod- el learning performance and accuracy caused by imbalance data. Then,an integrated model was pro- posed,which used predicted value of Ridge regression model as the main value and prediction of BPneural network model as the deviation. The experimental results show that mean square error (MSE) of this model is 0. 004 mm 2 ,and absolute error within 100 of model is more than 95% . On one hand, the range of prediction error is further reduced and the prediction accuracy of the medium and heavy plate crown is improved through multi-model fusion. On the other hand,the changes of feature varia- bles affect the plate crown is quantitatively obtained by reversely deducing plate crown prediction for- mula,which not only made data reflect and effectively guide the actual production,but also improved interpretability of plate crown prediction model as well. This paper provided a strong reference for control of plate crown and useful new ideas for future development and research of related fields.

To improve the accuracy of parameter prediction for the leveling process of high-strength plate,enhance production efficiency and product quality,the particle swarm optimization ( PSO) al- gorithm was introduced to optimize the weights and biases of the BP neural network,and the PSO-BP model was constructed. Through the training and testing of 550 sets of actual production data,the re- sults show that the PSO-BP model significantly outperforms the traditional method in terms of predic- tion accuracy. In the optimization process,combining the Levenberg-Marquardt (LM) algorithm with the PSO algorithm,the optimal network weights and thresholds are systematically searched for by iter- atively updating the positions and velocities of the particles. This method effectively overcomes the problems of BP neural network that is easy to fall into local optimal solutions and slow convergence speed. The experimental results show that the root mean square error (RMSE) and error rate of the PSO-BP model on the test set are improved by 0. 075 and 5. 5% ,respectively,which indicates thatthe model possesses excellent adaptability and reliability,and also shows that the results of this re- search are of great practical significance for parameter optimization in industrial production.

With the rapid development of the engineering machinery manufacturing industry,the heat treatment process of steel plates has higher requirements for plate shape. The quenching process is a key factor in determining the shape quality of heat-treated plates. Since steel plates experience both phase change stress and thermal stress during the quenching process,defects are prone to appear on quenched plates,affecting the quality of the final product shape. Therefore,there is an urgent need for a method that can identify the shape of steel plates to provide meaningful guidance for controlling the quenching shape of steel plates. To address the shape recognition problem,a quenching process shape recognition neural network based on an attention mechanism and lightweight design was proposed. Additionally,the plate shape image is processed using K-means to enhance defect characterization. Tests were conducted on the slab shape dataset,and the experimental results demonstrate that this lightweight network module has higher recognition accuracy mean and a single inference speed im- provement of 20-50 ms compared to other networks. 

In the automatic gauge control ( AGC) system for hot rolling,a combination of pressure AGC and monitoring AGC is often used for control. In the case of a large preset deviation,monitoring AGC is prone to causing excessive adjustment amplitude of the final stand roll gap due to its fast ad- justment and hysteresis characteristics,resulting in wave phenomenon. Based on actual rolling process conditions,a monitoring AGC control method based on multiple factors such as bending roll control and loop control was proposed. Based on the relative reduction rate of each stand,upper and lower limits of bending roll force adjustment,adaptive range of loop angle,variable monitoring proportion adjustment range,and monitoring AGC hysteresis deviation,a monitoring AGC control method for pre- cision rolling units was constructed. Practical applications have shown that the monitoring AGC con- trol strategy has achieved the expected results,increasing the speed and stability of strip head rolling, and effectively improving the overall thickness hit rate of the strip.

To avoid the problem of weakened active compensation effect for roll eccentricity caused by improper dead zone setting in the thickness control system,a dynamic dead zone method for rolling force under active compensation of roll eccentricity was designed. For the thickness control system of strip with thickness deviation dead zone,through in-depth theoretical analysis and mathematical deri- vation,the system analyzes the influence of the thickness deviation dead zone on the thickness accu- racy under the condition of active roller eccentricity compensation. A dynamic dead zone of rolling force that can follow the change of roll eccentricity state was designed,and the working principles un- der three working conditions were given. Combined with theoretical derivation,it was verified that it could further reduce the influence of roll eccentricity on plate thickness deviation when shared with active compensation for roll eccentricity. Simulation experiments were conducted using an eccentricitymodel identified based on production data,and the results show that in a thickness control system with active compensation for roll eccentricity,the designed rolling force dynamic dead zone achieves better plate thickness accuracy compared to the two cases of not using the dead zone and using the plate thickness difference dynamic dead zone. 

In response to the practical problem of materials being unable to flow out of the conical material silos under their own weight,resulting in arching,skinning and blockage,which leads to im- balanced feeding ratio and affects normal production, this article aims to ensure the continuity of process production and studies a monitoring and preventing blockage system for nonferrous metal smelting material silos based on optimized fuzzy control. From the perspective of optimizing the silo structure,a rotating scraper type anti blocking device is added inside the silo,while considering the motion characteristics of material particles and supplemented by simulation for optimization verifica- tion. The monitoring system for material flow conditions was studied,a method of using laser scanners was proposed to continuously measure the material flow to form a grid analysis plane object,and judge the degree of material blockage in the silo in real time,so as to carry out anti blockage treatment in the early stage of blockage. Optimized fuzzy control is introduced,and preventing blockage strategies is developed for silo monitoring to improve the real-time and fast performance of the system. 

A method of unifying the coordinate system of multi-line laser cameras and the coordinate system of rotating axis based on the light cylindrical shaped target was proposed,which aims to realize the high precision dimensional measurement of the shaped workpiece. By combining the data collect- ed by three linear laser cameras and the dimensional prior information of cylindrical shaped targets,a series of nonlinear equations with the attitude of linear laser cameras as variables are established. By using RANSAC and nonlinear least squares algorithm to solve the equations,the attitude of the line laser camera is obtained and verified by practical measurement. The results show that the calibration accuracy of this method can reach 0. 043 mm,and the accuracy of measuring the train wheel with a diameter of 760 mm can reach 0. 27 mm,and the percentage accuracy can reach 0. 036% . This cali-bration method provides a new idea in the field of rotation measurement of shaped workpiece.