The production of ferroalloys is a resource-intensive and energy-consuming process.To mitigate its adverse environmental effects,steel companies should implement a range of measures aiming at enhancing the utilization...The production of ferroalloys is a resource-intensive and energy-consuming process.To mitigate its adverse environmental effects,steel companies should implement a range of measures aiming at enhancing the utilization rate of ferroalloys.Therefore,a comprehensive ferroalloy model was proposed,incorporating a prediction model for alloying element yield based on case-based reasoning and support vector machine(CBR-SVM),along with a ferroalloy batching model employing an integral linear programming algorithm.In simulation calculations,the prediction model exhibited exceptional predictive performance,with a hit rate of 96.05%within 5%.The linear programming ingredient model proved effective in reducing costs by 20.7%,which was achieved through accurate adjustments to the types and quantities of ferroalloys.The proposed method and system were successfully implemented in the actual production environment of a specific steel plant,operating seamlessly for six months.This implementation has notably increased the product quality of the enterprise,with the control rate of high-quality products increasing from 46%to 79%,effectively diminishing the consumption and expenses associated with ferroalloys.The reduced usage of ferroalloys simultaneously reduces energy consumption and mitigates the adverse environmental impact of the steel industry.展开更多
基金supported by the National Natural Science Foundation of China(No.52174297).
文摘The production of ferroalloys is a resource-intensive and energy-consuming process.To mitigate its adverse environmental effects,steel companies should implement a range of measures aiming at enhancing the utilization rate of ferroalloys.Therefore,a comprehensive ferroalloy model was proposed,incorporating a prediction model for alloying element yield based on case-based reasoning and support vector machine(CBR-SVM),along with a ferroalloy batching model employing an integral linear programming algorithm.In simulation calculations,the prediction model exhibited exceptional predictive performance,with a hit rate of 96.05%within 5%.The linear programming ingredient model proved effective in reducing costs by 20.7%,which was achieved through accurate adjustments to the types and quantities of ferroalloys.The proposed method and system were successfully implemented in the actual production environment of a specific steel plant,operating seamlessly for six months.This implementation has notably increased the product quality of the enterprise,with the control rate of high-quality products increasing from 46%to 79%,effectively diminishing the consumption and expenses associated with ferroalloys.The reduced usage of ferroalloys simultaneously reduces energy consumption and mitigates the adverse environmental impact of the steel industry.
