On the optimization of a rolling-force model for a hot strip finishing line

Wang, Jingcheng; Chen, Chunzhao

doi:10.1016/j.isatra.2007.02.008

Cited by 7 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, research on the OOP of hot rolling in the iron and steel industry mainly concentrates on the optimization of the computing models, but few papers focus on its operation optimization problem. Wang and Chen 3 proposed a rolling-force calculation model and used data mining techniques to adjust the value of key coefficients. Yamada et al 4 developed a mill setup calculation system and a method to adapt the algorithms designed for the load distribution of seven-stand mill to the four-stand rolling mill.…”

Section: Introductionmentioning

confidence: 99%

Operation Optimization in the Hot-Rolling Production Process

Chen

Wang

Tang

2014

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

The operation optimization problem (OOP) is an important issue in production process control and optimization in process industries, because the desired solution of OOP is the optimal setting for control variables, and this setting affects the product quality to a great extent. In this paper, the OOP in the hot-rolling production process of iron and steel industry is investigated. The OOP lies between the production scheduling layer and the process control layer in the integrated automation system in iron and steel industry, and its main task is to set the optimal values for control variables (i.e., rolling force, exit thickness, etc.) based on the production process constraints. These values are then set as the targets for the process control layer. Different from previous operation optimization of hot rolling process considered in the literature, the mathematical model constructed in this paper considers more practical process constraints such as the ramping constraints, to get high-quality products. To efficiently solve this problem, a hybrid self-adaptive genetic algorithm (HSaGA) is presented. The main feature of the HSaGA is that it utilizes four different crossover operators and can self-adaptively select the operators that are most appropriate for the current problem. Such a strategy can help to improve the convergence speed and robustness of the genetic algorithm. Besides this strategy, a solution selection strategy is also proposed to select parent solutions to perform the crossover operation. This strategy divides the entire population into two partsone with good quality in the objective function value and the other one with good quality in diversityand then the parent solutions are selected from them, to improve the search diversity during evolution and, thus, avoid being trapped in a local optimum. Experiments are carried out based on benchmark test problems and practical OOP of the hot-rolling production process. The computational results show that the proposed HSaGA is superior to many state-of-the-art evolutionary algorithms in the literature for benchmark problems and can obtain better results than the empirical method for the OOP of the hot-rolling process.

show abstract

Section: Introductionmentioning

confidence: 99%