Multi-model prediction and optimization for NO<sub>x</sub> emission based on fuzzy clustering

YIN Zhe; YANG Chunlai; YUAN Xiaolei; WU Bin; LYU You

doi:10.19666/j.rlfd.202109188

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Multi-model prediction and optimization for NO_x emission based on fuzzy clustering

Thermal Power Generation Vol. 51, Issue 4, Pages: 105-111(2022)

更新时间：2025-09-28

- Multi-model prediction and optimization for NO_x emission based on fuzzy clustering
- 作者机构：
  
  1. 国网河北能源技术服务有限公司
  2. 华北电力大学控制与计算机工程学院
  3. 华北电力大学电站能量传递转化与系统教育部重点实验室
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202109188
  CLC： TP18;X773
- Published：2022
- 稿件说明：
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YIN Zhe, YANG Chunlai, YUAN Xiaolei, et al. Multi-model prediction and optimization for NO_x emission based on fuzzy clustering[J]. Thermal Power Generation, 2022, 51(4): 105-111.
DOI：

YIN Zhe, YANG Chunlai, YUAN Xiaolei, et al. Multi-model prediction and optimization for NO_x emission based on fuzzy clustering[J]. Thermal Power Generation, 2022, 51(4): 105-111. DOI： 10.19666/j.rlfd.202109188.

摘要

火电机组广泛参与调峰导致运行工况频繁变动，使得锅炉出口NO_x排放的控制难度增大。基于对燃烧状态的影响将锅炉运行参数分为可调参数和不可调参数，并利用不可调参数分布将运行数据样本进行模糊均值聚类划分，以此实现锅炉出口NO_x排放特性的多模型预测。同时，利用粒子群算法对各工况所属子模型的可调参数进行寻优来实现NO_x排放的优化。对某1 000 MW燃煤机组数据仿真表明，提出的NO_x排放多模型预测方法比单一模型具有更好的精度，且模型训练时间更短，经过粒子群算法参数优化后NO_x排放质量浓度降低了9.98%。

Abstract

The extensive participation of thermal power units in peak regulation leads to frequent fluctuations in operating conditions

which increases the difficulty of NO_x emission control at boiler outlet. According to its influence on the combustion

the boiler operation parameters are divided into manipulatable parameters and nonmanipulatable parameters. The data samples are classified according to the non-manipulatable parameter distribution and fuzzy c-means clustering. On this basis

a multi-model method is proposed to predict NO_x emission at boiler outlet. Meanwhile

particle swarm optimization algorithm is used to optimize the manipulatable parameters of the sub-models under each working condition

thus to achieve optimization of the NO_x emission at the boiler outlet. The simulation on operation data of a 1 000 MW coal-fired unit shows that

the proposed multi-model for NO_x emission prediction has higher prediction accuracy and consumes less training time. After optimization by particle swarm algorithm

the NO_x emission at the boiler outlet reduces by 9.98%.

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Multi-model prediction and optimization for NOx emission based on fuzzy clustering

DOI：10.19666/j.rlfd.202109188

摘要

Abstract

关键词

Keywords

references

Multi-model prediction and optimization for NO_x emission based on fuzzy clustering