Load Distribution Optimization System Based on MSCGA

Wang, Yan; Yan, Ying; Qiao, Shanping

doi:10.1109/his.2009.141

Cited by 3 publications

(3 citation statements)

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“…Constraints (6), (8), and (10) ensure that the rolling force, the rolling torque, and the rolling power of the first stand should be less than those of the second stand, because these values of the first stand should not be too large, so that the slab can be successfully bit in the first stand. Constraints (7), (9), and (11) require that the rolling force, the rolling torque, and the rolling power from the third stand to the last stand should be in a nonascending order, to guarantee the production stability. Constraint (12) is the temperature constraint requiring that the deviation between the exit temperature and the target temperature in the last stand should be lower than a given value a. Constraint (13) ensures that the sum of the whole reduction at all stands equals to the difference between the entrance thickness of the first stand and the exit thickness of the last stand.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Besides the optimization of the computing models, some papers also focus on the optimization of the load distribution (including rolling force, rolling torque, exit thickness, reduction, crown, and so on) for the OOP. Wang et al 7 presented an algorithm called MSCGA to solve the load distribution optimization problem of a hot strip mill to achieve a balance of rolling forces. Li et al 8 introduced a Newton descendent numeric algorithm to obtain a better distribution of strip thickness along the stands, and the computational results showed that the Newton descendent numeric algorithm is suitable for online application.…”

Section: Introductionmentioning

confidence: 99%

“…Many variables in the OOP interact with each other and their calculation models are very complex. In this paper, we also investigate the setting calculation of load distribution for the OOP of the hot-rolling process and, for this problem, develop a nonlinear model that is different from the models constructed in refs − . This model can be viewed as a high nonlinear single objective problem in continuous space, and it is very difficult to be solved by an exact method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Operation Optimization in the Hot-Rolling Production Process

Chen

Wang

Tang

2014

Ind. Eng. Chem. Res.

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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.

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