1. Key Laboratory of Process Industry Automation of Ministry of Education and Liaoning Province, Northeastern University, Shenyang 110004, Liaoning, China;2. BaoSteel Corporation, Shanghai 201900, China
Modeling and Optimization for Piercing Energy Consumption
1. Key Laboratory of Process Industry Automation of Ministry of Education and Liaoning Province, Northeastern University, Shenyang 110004, Liaoning, China;2. BaoSteel Corporation, Shanghai 201900, China
ժҪ Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square method. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the optimization method based on the energy consumption prediction model can obtain the optimum process parameters effectively and also provide reliable evidences for practical production.
Abstract��Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square method. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the optimization method based on the energy consumption prediction model can obtain the optimum process parameters effectively and also provide reliable evidences for practical production.
XIAO Dong;PAN Xiao��li;YUAN Yong;MAO Zhi��zhong;WANG Fu��li. Modeling and Optimization for Piercing Energy Consumption[J]. �й������ڿ���, 2009, 16(2): 40-0.
XIAO Dong;PAN Xiao��li;YUAN Yong;MAO Zhi��zhong;WANG Fu��li. Modeling and Optimization for Piercing Energy Consumption. Chinese Journal of Iron and Steel, 2009, 16(2): 40-0.
2009, Vol. 16 
