煤-氨混燃锅炉建模及近邻传播-最小支持向量机多模态验证优化

田乐乐; 王印松

doi:10.19805/j.cnki.jcspe.2026.250474

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煤-氨混燃锅炉建模及近邻传播-最小支持向量机多模态验证优化

动力设备与系统 | 更新时间：2026-09-15

- 煤-氨混燃锅炉建模及近邻传播-最小支持向量机多模态验证优化
- Modeling of Coal-Ammonia Co-firing Boiler and Multi-modal Validation Optimization Using Affinity Propagation-Least Squares Support Vector Machine
- 动力工程学报 2026年46卷第9期页码：1-14
- 作者机构：
- 作者简介：
  
  田乐乐(1997—),男,河北保定人,硕士,研究方向为锅炉煤-氨混燃建模与控制优化,E-mail:843855327@qq.com。
- 基金信息：
  
  国家能源集团科技支撑项目（GJNY-2023-9）
- DOI：10.19805/j.cnki.jcspe.2026.250474
  中图分类号： TK229.91
- 纸质出版：2026
- 稿件说明：
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田乐乐, 王印松. 煤-氨混燃锅炉建模及近邻传播-最小支持向量机多模态验证优化[J]. 动力工程学报, 2026, 46(9): 1-14.
田乐乐, 王印松. 煤-氨混燃锅炉建模及近邻传播-最小支持向量机多模态验证优化[J]. 动力工程学报, 2026, 46(9): 1-14. DOI： 10.19805/j.cnki.jcspe.2026.250474.

DOI： 10.19805/j.cnki.jcspe.2026.250474

摘要

针对传统单相煤燃烧锅炉模型的局限性，构建了完整的煤-氨混燃锅炉全流程模型，提出了一种改进的近邻传播-最小二乘支持向量机（affinity propagation-least squares support vector machine，AP-LS-SVM）动态多模态方法，并利用该方法进行了模型验证与精度优化。该模型由燃料预处理、燃烧室动力学和关键参数输出三大模块组成，可实现从燃料特性到锅炉输出的动态模拟。基于某电厂600 MW混燃锅炉实际数据，采用互信息（mutual information，MI）算法进行特征选择与数据重构，确定输入输出参数间的最优延迟时间，并利用所提AP-LS-SVM方法进行了验证。结果表明：模型拟合优度高，均大于0.950；相较于传统方法，各关键参数的预测误差显著降低，优化率达22.5%~58.1%；该方法有效提升了模型在掺氨比例0%~30%宽范围多变工况下的预测精度与适应性。

Abstract

To overcome limitations of traditional single-phase coal combustion boiler models

a comprehensive full-process model for a coal-ammonia co-firing boiler was established. An improved affinity propagation-least squares support vector machine (AP-LS-SVM) dynamic multi-modal approach was proposed and applied for model validation and accuracy enhancement. The model consisted of three main modules: fuel pretreatment

combustion chamber dynamics

and key parameter output

which enabled dynamic simulation from fuel properties to boiler outputs. Based on actual operational data from a 600 MW co-firing boiler

the mutual information (MI) algorithm was employed for feature selection and data reconstruction to identify the optimal time delays between input and output parameters

and the proposed AP-LS-SVM method was then used for validation. Results show that the model achieves high goodness-of-fit values

all exceeding 0.950. Compared with conventional methods

prediction errors for all key parameters are significantly reduced

with optimization rates ranging from 22.5% to 58.1%. The method effectively improves the prediction accuracy and adaptability of the model under wide and varying operating conditions with ammonia co-firing ratios from 0% to 30%.

关键词

Keywords

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