基于数据子空间PLS建模技术的热轧轧制力优化设定

针对机理模型难以刻画的热轧精轧生产过程, 采用基于数据子空间的偏最小二乘方法建立热轧轧制力数据模型, 并构建轧制力优化模型, 利用改进的粒子群优化算法对优化模型计算求解. 结果表明, 使用数据驱动方法建立的轧制力数据模型能够揭示精轧过程轧制力的机理规律, 可以替代机理模型在实际系统中的应用. 通过对整体优化模型的求解, 可以提高热轧精轧产品的质量, 降低能源消耗, 表明基于数据驱动的建模和优化方法在实际生产中具有较大的应用价值.

Optimization of rolling force of hot rolling by using data subspace PLS modeling technique

For the problem that the physics model is difficult to describe complex finishing mill process for hot rolling production, the rolling force data model of hot rolling is build by using the data subspace-based partial least squares method. Then it is used as part of the rolling force optimization model, and the modified particle swarm optimization intelligent optimization algorithm solves the problem. By the computational analysis, the rolling force data model established by the data-driven approach is able to reveal the physics laws of the rolling force, which can be used in the actual system instead of the physics model. By solving the overall optimization model, the quality of the hot rolling finishing products is improved and energy consumption is reduced. The data-driven based modeling and optimization method has great value in the actual production process.