热力发电

基于IHRO优化PGRC-LSTM的燃煤火电厂碳排放软测量模型研究

PDF下载

热力发电 | ,():

收起

热力发电 |

基于IHRO优化PGRC-LSTM的燃煤火电厂碳排放软测量模型研究

全屏

作者信息

通讯作者:

作者简介:

Research on Carbon Emission Soft-Sensing Model for Coal‑Fired Thermal Power Plants Based on IHRO‑Optimized PGRC‑LSTM

Affiliations

Corresponding Author:

doi: 10.19666/j.rlfd.202607055

文章导航

摘要

收起

【目的】针对燃煤火电厂小时级碳排放软测量中数据驱动模型物理一致性不足、残差偏差及关键参数难以协同选择的问题，构建兼顾精度与机理约束的软测量方法。

【方法】基于某燃煤机组2025年8760个小时样本，完成时间校核、异常筛查和标准化，利用烟气氧含量、流速、温度、湿度及压力构造氧量稀释、标准干烟气流量和燃烧强度代理特征。以长短期记忆网络（long short-term memory，LSTM）提取当前及历史运行信息，将流量、氧量和平滑正则嵌入训练，经岭回归校正校准集残差，并采用改进心率优化算法（improved heart rate optimizer，IHRO）在统一90次评价预算和20次搜索下联合选择预训练物理权重模型及残差校正强度。

【结果】12月744个小时测试中，物理引导残差校正长短期记忆网络（physics-guided residual correction-long short-term memory，PGRC-LSTM）平均MAPE为5.5528%，较LSTM降低1.4305个百分点。IHRO-PGRC-LSTM的MAE、RMSE、MAPE和R2分别为34176.5604 kg、46830.4620 kg、4.4631%和0.9511，四项平均指标均优于表5所列模型，MAPE较BO和HRO分别降低0.8342和0.3197个百分点。

【结论】所提方法可在现有烟气输入同步可得条件下提高碳排放软测量的平均精度，并为弱机理信息与数据驱动模型融合提供参考，其跨机组与跨季节适用性仍需进一步验证。

Abstract

收起

[Objective] Carbon emissions from coal-fired power plants vary with combustion organization, flue-gas conditions, and operating-load disturbances. Conventional inventory methods cannot represent these hourly variations in a timely manner, whereas purely data-driven soft sensors may produce estimates that conflict with available process knowledge. This study aims to improve the accuracy and physical consistency of hourly carbon-emission soft sensing and to coordinate the selection of physical regularization and residual-correction parameters under a limited search budget.

[Methods] The dataset contained 8,760 hourly records from a large coal-fired unit in eastern coastal China, covering 1 January to 31 December 2025. The records underwent temporal-consistency checks, screening of missing and physically invalid values, standardization using training-period statistics, and sliding-window construction. Data from January to October were used for training, November data were used for calibration, and 744 observations from December were reserved for testing. Flue-gas oxygen content, velocity, temperature, humidity, and pressure were used to construct proxy features for oxygen dilution, standard dry flue-gas flow, and combustion intensity. A long short-term memory (LSTM) network extracted current and historical operating information. Flow-direction, oxygen-direction, and temporal-smoothness regularization were incorporated into network training. Ridge regression then learned a mapping of the calibration residuals from 15 physics-related features, forming a physics-guided residual correction LSTM (PGRC-LSTM). An improved heart rate optimizer (IHRO) coupled heart-rate amplitude with fitness feedback, reused known fitness values, and performed a final boundary check in the favorable search interval. IHRO jointly selected one of three pretrained physical-weight models and the ridge regularization coefficient. All compared optimizers used the same candidate set, forward temporal-validation objective, search range, and budget of 90 evaluations. Twenty search seeds were used. Performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2).

[Results] Under the fixed configurations used for basic-model screening, LSTM achieved the lowest mean MAPE of 6.9833% among the 11 compared models. Incorporating physical regularization reduced the mean MAPE to 6.7923%, and residual correction further reduced it to 5.5528%. The PGRC-LSTM therefore lowered MAPE by 1.4305 and 1.2395 percentage points relative to LSTM and the physics-guided regularized LSTM, respectively. In the conditional direction-consistency analysis, its average combined violation rate was 22.33%, compared with 24.91% for LSTM; this statistic was interpreted together with prediction error rather than as independent proof of engineering improvement. After optimization, IHRO-PGRC-LSTM obtained an MAE of 34,176.5604 kg, an RMSE of 46,830.4620 kg, a MAPE of 4.4631%, and an R2 of 0.9511 on the December test set. All four mean metrics were the best among the models listed in Table 5. The mean MAPE was 1.0897, 0.8342, and 0.3197 percentage points lower than those of unoptimized PGRC-LSTM, Bayesian optimization, and the baseline heart rate optimizer, respectively. Its MAPE standard deviation was higher than that of Bayesian optimization, indicating that the observed advantage primarily concerned mean accuracy rather than the lowest search variability.

[Conclusion] The proposed method combines weak physical priors, residual correction, and constrained parameter search to improve hourly carbon-emission soft sensing when the required flue-gas inputs are synchronously available. Because online coal quality, total fuel and air flow, and flue-gas carbon dioxide concentration were unavailable, the imposed relationships are directional constraints rather than a strict carbon balance. The findings are limited to the historical unit data and search space examined. Independent-period, cross-season, and cross-unit validation is required before broader deployment.

引用本文

. 基于IHRO优化PGRC-LSTM的燃煤火电厂碳排放软测量模型研究. 热力发电, : - . DOI: 10.19666/j.rlfd.202607055

. Research on Carbon Emission Soft-Sensing Model for Coal‑Fired Thermal Power Plants Based on IHRO‑Optimized PGRC‑LSTM[J]. Thermal Power Generation, : - . DOI: 10.19666/j.rlfd.202607055

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

年第卷第期

PDF下载

101

引用本文

BibTeX

文章信息

doi: 10.19666/j.rlfd.202607055

首发时间：2026-10-16

补充材料

相关文章

文章信息

作者

出版历史

基金

作者信息

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/rlfd/CN/10.19666/j.rlfd.202607055

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT