基于PCR-多案例融合的焦化烟气脱硝过程指标优化设定

PCR-multi-case fusion method for setting optimal process indices of coking flue gas denitration

国内首座炼焦烟气脱硫脱硝一体化装置运行过程机理复杂、受上游焦化工况影响导致入口烟气指标频繁波动、且过程未知干扰严重,难以采用传统建立精确的数学模型进行过程指标设定值的求解。为了解决这一问题,提出了一种基于案例推理技术的焦化烟气脱硝过程指标优化设定方法。同时,由于焦炉换向操作的存在使相关特征描述值剧变,传统案例重用方法中采用单一特征描述当前工况极有可能导致结果存在偏差,针对这一问题,提出一种基于主成分回归多案例融合的案例检索与重用方法。通过进行仿真计算及实际工业应用,表明所提方法可以根据不同工况特征获得合适的操作参数设定值,有效地将出口NOx浓度控制在工艺要求的区间内,并能极大地降低装置运行能耗。

Due to complex process mechanism,frequently changeable inlet flue gas indices induced by upstreamcoking conditions,and severe interference of process unknowns,it is difficult to determine process indices bytraditional exact mathematical models for the first domestic coking flue gas desulfurization and denitrationintegrated unit.A case-based reasoning method was proposed to optimize indices of the coking flue gas denitrationprocess.Meanwhile,abrupt change of some correlation description indices,which was caused by coke ovenreversion,may lead to deviation from results because single feature was used to describe current working conditionin traditional case reuse method.A case retrieval and reuse method was further proposed from principal componentregression multiple case fusion.The results of numerical simulation and application in the coking plant show thatthis method can appropriately obtain operating parameter settings at different characteristic conditions,effectivelycontrol NOx outlet concentration within process specification,and greatly reduce power consumption of the equipment.