Equilibrium optimization for high efficiency and low pollution combustion of power-generation boilers using game differential evolution algorithm

doi:10.11949/j.issn.0438-1157.20161480

Abstract

Abstract:

Improving thermal efficiency and reducing pollutant emissions such as NO_x is a critical problem to be solved for conservation and emission reduction in power plant energy. A combustion optimization model was established by quantum genetic algorithm (QGA) optimized least squares support vector machine (LSSVM-QGA). The model predicted boiler thermal efficiency and NO_x emissions at average relative error of 0.054% and 1.229%, respectively, which demonstrated high prediction accuracy, generalization ability and applicability. Based on the model, a method of differential evolution algorithm (DE) of self-adaptive scaling/crossover factors and sharing function followed by evolutionary Nash equilibrium was proposed for multi-objective optimization of boiler's combustion. Results show that optimization method based on NASH equilibrium can get optimal set of solutions to operational variables, which improve operating conditions and keep power generation boiler at a stable equilibrium state of combustion.

Key words: utility boiler, optimization, least square support vector machine, model, prediction, differential evolution algorithm, NASH equilibrium

摘要：

提高电站锅炉热效率，降低NO_x等污染物的排放量是电站节能减排必须解决的问题。经过采用经量子遗传算法）QGA）优化参数后的最小二乘支持向量机（LSSVM-QGA）建立燃烧优化模型，预测的锅炉热效率和NO_x排放量的平均相对误差分别达到了0.054%和1.229%，其预测精度及泛化能力均较优，有更强的适用性能。在此模型基础上，提出一种采用自适应缩放因子与交叉因子和共享函数机制的差分进化算法（DE），通过其演化博弈论中的NASH均衡，实现锅炉燃烧的多目标优化，结果表明，基于NASH均衡的优化方法可以得到操作变量的最优解集，能够更好地改善运行工况，最终可以实现削峰填谷，使电站锅炉保持一个稳定均衡的燃烧状态。

关键词: 电站锅炉, 优化, 最小二乘支持向量机, 模型, 预测, 差分进化算法, NASH均衡

CLC Number:

ZHAO Minhua, HU Yi, LI Jin, WANG Yusheng, WU Rui, SONG Le. Equilibrium optimization for high efficiency and low pollution combustion of power-generation boilers using game differential evolution algorithm[J]. CIESC Journal, 2017, 68(6): 2455-2464.

赵敏华, 胡毅, 李金, 王羽笙, 吴蕊, 宋乐. 使用博弈差分算法的电站锅炉高效低污染燃烧均衡优化[J]. 化工学报, 2017, 68(6): 2455-2464.

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