基于多目标遗传算法的炼焦生产过程优化控制

Optimization control based on the multi-objective genetic algorithm for coking plant production process

针对某钢铁企业实际炼焦生产过程的优化控制问题,提出一种多目标分层优化控制策略。首先采用主成分分析、灰色关联分析及改进前馈神经网络方法,建立综合生产目标与局部优化目标的关联模型,将综合生产目标映射为局部优化目标。然后建立以焦炭产量最大、焦炉能耗最小为优化目标,焦炭质量与工艺要求为约束条件,局部优化目标为决策变量的多目标优化模型。通过多目标遗传算法求解多目标优化问题,获得局部优化目标值。最后将局部优化目标作为各子过程控制系统的设定值及优化调度系统的决策参数,来动态调整过程操作参数,实现企业期望的综合生产目标。实际运行结果表明,提出的优化控制策略取得了较好的应用效果。

A multi-objective and layered optimization control strategy was proposed to deal with the optimization control problem of coking plant production process. Firstly, the correlation models between the comprehensive production targets and the local optimization targets were established with the principal component analysis, the gray correlation analysis and the imp＇roved Back Propagation （BP） neural network methods. The comprehensive production targets were mapped into local optimization targets. Secondly, a multi-objective optimization model was constructed with the maximum of coke yield and minimum of energy consumption as the optimization objective, the coke quality and production boundary as constraints, and the local optimization targets as decision variables. Then the multi-objective genetic algorithm was applied to solve this multi-objective optimization problem to obtain local optimization targets. Finally, the local optimization targets were served as the set values in the process control of subsystems and decision variables in the optimal scheduling system in order to realize the expectant comprehensive production targets by making dynamic adjustment of operation parameters. The practical operation results showed that the optimization control strategy proposed had good application effects.