In order to reduce the NOx emissions concentration of a circulation fluidized bed boiler, a modified teaching-learning-based optimization algorithm (MTLBO) is proposed, which introduces a new population group mechanism into the conventional teaching learning based optimization algorithm. The MTLBO still has two phases: Teaching phase and Learning phase. In teaching phase, all students are divided into two groups based on the mean marks of the class, the two groups present different solution updating strategies, separately. In learning phase, all students are divided into two groups again, where the first group includes the top half of the students and the second group contains the remaining students. The two groups also have different solution updating strategies. Performance of the proposed MTLBO algorithm is evaluated by 14 unconstrained numerical functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed. In addition, the tuned extreme learning machine by MTLBO is applied to establish the NOx emission model. Based on the established model, the MTLBO is used to optimize the operation conditions of a 330 MW circulation fluidized bed boiler for reducing the NOx emissions concentration. Experimental results reveal that the MTLBO is an effective tool for reducing the NOx emissions concentration.

为了降低循环流化床锅炉的NOx排放浓度，提出了一种改进的基于教学学习的优化算法（MTLBO），将一种新的种群群机制引入了传统的基于教学学习的优化算法。MTLBO仍然有两个阶段：教学阶段和学习阶段。在教学阶段，根据班级的平均成绩将所有学生分为两组，两组分别提出不同的解决方案更新策略。在学习阶段，所有学生再次分为两组，第一组包括上半部分的学生，第二组包括其余的学生。两组还具有不同的解决方案更新策略。所提出的MTLBO算法的性能通过14个无约束的数值函数进行评估。与TLBO和其他几种最先进的优化算法相比，结果表明MTLBO具有更好的解决方案质量和更快的收敛速度。此外，还使用了MTLBO调谐的极限学习机来建立NOx排放模型。基于已建立的模型，MTLBO用于优化330 MW循环流化床锅炉的运行条件，以降低NOx排放浓度。实验结果表明，MTLBO是降低NOx排放浓度的有效工具。此外，还使用了MTLBO调谐的极限学习机来建立NOx排放模型。基于已建立的模型，MTLBO用于优化330 MW循环流化床锅炉的运行条件，以降低NOx排放浓度。实验结果表明，MTLBO是降低NOx排放浓度的有效工具。此外，还使用了MTLBO调谐的极限学习机来建立NOx排放模型。基于已建立的模型，MTLBO用于优化330 MW循环流化床锅炉的运行条件，以降低NOx排放浓度。实验结果表明，MTLBO是降低NOx排放浓度的有效工具。