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Computational intelligence approach for NO-x emissions minimization in a coal-fired utility boiler

机译：降低燃煤电站锅炉NOx排放量的计算智能方法

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The current work presented a computational intelligence approach used for minimizing NO_x emissions in a 300 MW dual-furnaces coal-fired utility boiler. The fundamental idea behind this work included NO_x emissions characteristics modeling and NO_x emissions optimization. First, an objective function aiming at estimating NO_x emissions characteristics from nineteen operating parameters of the studied boiler was represented by a support vector regression (SVR) model. Second, four levels of primary air velocities (PA) and six levels of secondary air velocities (SA) were regulated by using particle swarm optimization (PSO) so as to achieve low NO_x emissions combustion. To reduce the time demanding, a more flexible stopping condition was used to improve the computational efficiency without the loss of the quality of the optimization results. The results showed that the proposed approach provided an effective way to reduce NO_x emissions from 399.7 ppm to 269.3 ppm, which was much better than a genetic algorithm (GA) based method and was slightly better than an ant colony optimization (ACO) based approach reported in the earlier work. The main advantage of PSO was that the computational cost, typical of less than 25 s under a PC system, is much less than those required for ACO. This meant the proposed approach would be more applicable to online and real-time applications for NO_x emissions minimization in actual power plant boilers.
机译：当前的工作提出了一种用于最小化300 MW双炉燃煤电站锅炉NO_x排放的计算智能方法。这项工作的基本思想包括NO_x排放特性建模和NO_x排放优化。首先，通过支持向量回归（SVR）模型来表示一个目标函数，该函数旨在根据所研究锅炉的19个运行参数估算NO_x排放特征。其次，通过使用粒子群算法（PSO）调节四个一级空气速度（PA）和六个二级空气速度（SA），以实现低NO_x排放燃烧。为了减少时间需求，使用了更灵活的停止条件来提高计算效率，而不会损失优化结果的质量。结果表明，所提出的方法提供了一种将NO_x排放量从399.7 ppm减少到269.3 ppm的有效方法，这比基于遗传算法（GA）的方法要好得多，并且比基于蚁群优化（ACO）的方法要好一些。在早期的工作中。 PSO的主要优点是计算成本（在PC系统下通常不到25秒）比ACO所需的计算成本低得多。这意味着所建议的方法将更适用于在线和实时应用，以使实际电厂锅炉中的NO_x排放量最小化。

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