基于机理与数据的液氨气化系统动态建模与控制

刘渊; 崔立明; 初伟; 周末; 崔子健; 王印松

doi:10.19666/j.rlfd.202505091

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基于机理与数据的液氨气化系统动态建模与控制

热力发电 2025年54卷第8期页码：72-83

绿氢绿氨多能协同优化 | 更新时间：2026-08-11

- 基于机理与数据的液氨气化系统动态建模与控制
- Dynamic modeling and control of liquid ammonia gasification system based on mechanism and data
- 作者机构：
  
  1. 华北电力大学自动化系
  2. 神华集团有限责任公司
  3. 烟台龙源电力技术股份有限公司
- 作者简介：
- 基金信息：
  
  国家科技支撑计划项目(GJNY-2023-9)~~
- DOI：10.19666/j.rlfd.202505091
  中图分类号： TM621;TQ038
- 纸质出版：2025
- 稿件说明：
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[1]刘渊,崔立明,初伟,等.基于机理与数据的液氨气化系统动态建模与控制[J],2025,54(08):72-83.
[1]刘渊,崔立明,初伟,等.基于机理与数据的液氨气化系统动态建模与控制[J],2025,54(08):72-83. DOI： 10.19666/j.rlfd.202505091.

DOI：10.19666/j.rlfd.202505091.

摘要

氨能作为低碳燃料在工业燃烧领域具有重要应用前景，但液氨气化环节的动态特性复杂，传统机理模型难以满足高精度控制需求。针对掺氨燃烧系统中液氨气化过程建模精度不足导致的控制不稳定问题，提出一种机理与数据融合的动态建模方法。通过建立基于热力学定律的非线性机理模型，结合基于递推模糊C均值（RFCM）聚类和递推最小二乘（RLS）算法的数据驱动模型，构建具有自适应权重优化的混合动态模型；进而开发解耦控制策略，实现气化系统的精准调控。实验验证表明：所提出的模型显著提升了液氨气化过程的预测精度，基于该动态模型的解耦控制方案实现了稳定的氨气供给，验证了机理与数据融合的动态模型对控制系统的有效性和工程实用性。该方法为氨燃料燃烧系统的智能控制提供了有效解决方案，对清洁能源技术的工程应用具有推广价值。

Abstract

Ammonia

as a low-carbon fuel

has important application prospects in the field of industrial combustion. However

the dynamic characteristics of liquid ammonia gasification process are complex

and conventional mechanism models are difficult to meet high-precision control requirements. To address the problem of unstable control caused by insufficient modeling accuracy in the liquid ammonia gasification process in ammonia combustion systems

a dynamic modeling method that combines mechanism with data fusion is proposed. By establishing a nonlinear mechanism model based on thermodynamic laws

and combining with a data-driven model based on recursive fuzzy C-means(RFCM) clustering and recursive least square(RLS) algorithm

a hybrid dynamic model with adaptive weight optimization is constructed. On this basis

decoupling control strategies are developed to achieve precise control of the gasification systems. Experimental verification shows that

the proposed model significantly improves the prediction accuracy of the gasification process

and the decoupling control scheme based on this dynamic model achieves stable ammonia supply

verifying the effectiveness and engineering practicality of the dynamic model that integrates mechanism and data for the control system. This method provides an effective solution for intelligent control of ammonia fuel combustion systems and has promotional value for the engineering application of clean energy technology.

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