Enhancing Boiler Operational Economy via Data-Driven Collaborative Combustion Optimization
- 1Shaoxing Shangyu Hangxie Thermoelectricity Co., Ltd. , ,
Abstract
The growing need for flexible operation and reliable steam supply challenges coal-fired boilers to maintain an operating economy through fixed or single-variable combustion adjustments. This study develops a condition-aware, data-driven framework for coordinated combustion optimization. Historical stable-operation samples from the boiler were first classified according to the main steam flow rate and apparent fuel-related operating state. For each operating category, an MSE-constrained RF-NGBoost surrogate model was developed to predict the coal fan variable operating cost. The trained surrogate models then served as the offline response environment for a twin delayed deep deterministic policy gradient (TD3) agent, which searched for coordinated target set points of flue-gas oxygen content, bed temperature, and bed pressure drop under operating constraints. The RF-NGBoost models achieved R2 values of 0.967–0.982, RMSE values of 0.387–0.483 CNY/t, and mean absolute error values of 0.255–0.396 CNY/t across the six operating categories. For representative low-, medium-, and high-load conditions, the offline TD3 optimization identified condition-specific target set points associated with predicted coal-electricity operating cost reductions of 0.36–1.07% within the historical feasible domain. In an on-site comparison comprising two representative two-day operating periods, the TD3-derived target-set point strategy was associated with a 0.51% reduction in the actual coal-electricity operating cost compared with manual experience-based adjustment while maintaining main-steam supply and predefined operational constraints. The proposed framework links condition-specific cost modeling, offline coordinated optimization, and field verification to support economically oriented combustion adjustment in circulating fluidized bed boilers.
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BibTeX
@article{sui2026enhancing,
title = {{Enhancing Boiler Operational Economy via Data-Driven Collaborative Combustion Optimization}},
author = {Yilei Sui and Qiong Wang and Cheng Zhao and Weifeng Cai and Hongjie Pan and Xiaojun Lei and Jiayu Zhao and Liting Ding and Maoyong Xu and Songwei Shi},
journal = {ACS Omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c06404},
url = {https://pubs.acs.org/acsodf/article/11/41/63454/5448450/Enhancing-Boiler-Operational-Economy-via-Data},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Enhancing Boiler Operational Economy via Data-Driven Collaborative Combustion Optimization
AU - Yilei Sui
AU - Qiong Wang
AU - Cheng Zhao
AU - Weifeng Cai
AU - Hongjie Pan
AU - Xiaojun Lei
AU - Jiayu Zhao
AU - Liting Ding
AU - Maoyong Xu
AU - Songwei Shi
PY - 2026
DA - 2026/10/10
JO - ACS Omega
DO - 10.1021/acsomega.6c06404
UR - https://pubs.acs.org/acsodf/article/11/41/63454/5448450/Enhancing-Boiler-Operational-Economy-via-Data
AB - The growing need for flexible operation and reliable steam supply challenges coal-fired boilers to maintain an operating economy through fixed or single-variable combustion adjustments. This study develops a condition-aware, data-driven framework for coordinated combustion optimization. Historical stable-operation samples from the boiler were first classified according to the main steam flow rate and apparent fuel-related operating state. For each operating category, an MSE-constrained RF-NGBoost surrogate model was developed to predict the coal fan variable operating cost. The trained surrogate models then served as the offline response environment for a twin delayed deep deterministic policy gradient (TD3) agent, which searched for coordinated target set points of flue-gas oxygen content, bed temperature, and bed pressure drop under operating constraints. The RF-NGBoost models achieved R2 values of 0.967–0.982, RMSE values of 0.387–0.483 CNY/t, and mean absolute error values of 0.255–0.396 CNY/t across the six operating categories. For representative low-, medium-, and high-load conditions, the offline TD3 optimization identified condition-specific target set points associated with predicted coal-electricity operating cost reductions of 0.36–1.07% within the historical feasible domain. In an on-site comparison comprising two representative two-day operating periods, the TD3-derived target-set point strategy was associated with a 0.51% reduction in the actual coal-electricity operating cost compared with manual experience-based adjustment while maintaining main-steam supply and predefined operational constraints. The proposed framework links condition-specific cost modeling, offline coordinated optimization, and field verification to support economically oriented combustion adjustment in circulating fluidized bed boilers.
SN - 2470-1343
ER -