基于案例推理的篦冷机熟料冷却过程智能优化控制

CBR-based Intelligently Optimized Index Setting for Clinker Cooling Process with Grate Cooler

针对水泥生产过程中篦冷机熟料冷却过程关键工艺指标的游离氧化钙和料层厚度难以建立精确数学模型,且采用常规的控制方法难以进行有效控制的难题,将案例推理和常规控制相结合,提出了基于案例推理的篦冷机熟料冷却过程工艺指标优化控制方法.以稳定熟料中游离氧化钙质量分数、料层厚度的区间控制为目标,由智能优化设定模型自动更新各基础控制回路的设定值,从而避免了人工设定的主观性和随意性.该方法已经成功应用于某水泥厂篦冷机熟料冷却过程,取得了显著的应用效果.

In the cement production process it is hard to develop an accurate mathematical model for both f-CaO and stack bed thickness which characterize the key technical indices in the cooling process with a grate cooler.However,it is also hard to control efficiently the process by conventional methods.Combining the case-based reasoning（CBR）with conventional control method in clinker cooling process,a new control method is proposed to optimize intelligently the index setting by CBR,thus stabilizing the interval control of the mass fraction of f-CaO in clinker and stack bed thickness. The intelligently optimized index model can update automatically all basic loop setpoints so as to avoid the subjectivity and randomness due to the arbitrary manual setting. The approach proposed has been successfully applied to the clinker cooling process with grate cooler in a cement plant, and the application results showed its effectiveness.