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间接冷却塔冬季运行状态自推演预测与工况变化分析

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间接冷却塔冬季运行状态自推演预测与工况变化分析

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Closed-loop recursive prediction and winter operating condition analysis of indirect cooling towers

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doi: 10.19666/j.rlfd.202607011

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【目的】针对燃煤电厂间接冷却塔热惯性强、环境扰动随机，传统数值模拟与物理建模无法精准推演未来运行状态，导致冬季防冻只能采用保守运行策略，系统安全性与经济性难以兼顾的问题，本文提出分模块机器学习模型优选策略。

【方法】针对扇区、总回水温度、凝汽器的各模块差异化特性，分别比较了四种算法，并选取长短时记忆神经网络、人工神经网络与随机森林三种机器学习算法作为各模块最优预测算法，解决单一模型无法适配全系统非线性、时序性预测的难题，并提出基于循环水温闭环迭代的机器学习自推演预测框架。该框架突破传统静态输入预测模式，以水循环流动路径构建节点水温动态递推闭环，实现运行状态自主连续演化预测。

【结果】基于某2×1 000 MW 机组间冷塔冬季实际运行数据验证，框架可递推预测未来1 h的运行状态，可以确保95%的时间内关键运行参数扇区回水温度、总回水温度、机组背压、总进水温度的预测精度分别在±0.65 ℃、±0.6 ℃、±0.57 kPa、±0.3 ℃。基于所构建的自推演框架，可对关键工况开展虚拟调控与情景推演，定量预测不同运行策略和外部工况变化对系统未来运行状态的影响。

【结论】本方法无需复杂物理解析式与仿真建模，可有效解决间冷塔因运行滞后性大、运行参数耦合强导致的预测难题，为电厂间冷塔冬季安全经济运行与智能调控提供全新技术方案。

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[Objective] Indirect cooling towers in coal-fired power plants are characterized by strong thermal inertia and stochastic environmental disturbances. These features make it difficult for conventional numerical simulation and physics-based modeling approaches to accurately predict future operating states. As a result, winter anti-freezing operation often relies on conservative control strategies, making it difficult to simultaneously ensure system safety and operating economy. To address this problem, this study proposes a modular machine-learning model selection strategy for future-state prediction of indirect cooling towers.

[Methods] Considering the differentiated characteristics of the sector, total return-water temperature, and condenser modules, four machine-learning algorithms were comparatively evaluated for the corresponding prediction tasks. According to the prediction performance of different modules, long short-term memory (LSTM), artificial neural network (ANN), and random forest (RF) were selected as the optimal prediction algorithms for the respective modules. This modular configuration avoids the limitation of using a single model to represent the nonlinear and time-dependent characteristics of the entire indirect cooling system. On this basis, a machine-learning-based self-evolution prediction framework with closed-loop iteration of circulating-water temperature was developed. Different from the conventional static-input prediction mode, the proposed framework follows the actual circulating-water flow path and establishes a dynamic recursive closed loop among key water-temperature nodes. The predicted water temperature at the current step is used in the subsequent prediction step, allowing the future operating state of the system to evolve continuously along the prediction horizon. In this way, the proposed framework can continuously propagate the predicted operating state through the sector, total return-water, and condenser modules and thereby realize recursive future-state prediction.

[Results] The proposed framework was validated using actual winter operating data from an indirect cooling tower of a 2×1 000 MW coal-fired power unit. The results demonstrate that the framework can recursively predict the operating state of the system for the following 1 h. During 95% of the prediction period, the prediction errors of the key operating parameters, including sector return-water temperature, total return-water temperature, unit back pressure, and total inlet-water temperature, were maintained within ±0.65 °C, ±0.60 °C, ±0.57 kPa, and ±0.30 °C, respectively. Based on the developed self-evolution prediction framework, virtual regulation and scenario-based prediction can also be performed for key operating conditions. By changing candidate operating strategies or external operating conditions within the prediction framework, the future response of the indirect cooling system can be quantitatively evaluated before actual regulation is implemented. Therefore, the framework is not limited to reproducing the future trajectory under the current operating condition, but can also be used to analyze how different operating strategies and external-condition variations may affect the subsequent system state.

[Conclusion] This method eliminates the need for complex physical analytical expressions and simulation modeling, effectively solving prediction challenges caused by significant operational lag and strong coupling of operational parameters in indirect cooling towers. It provides a novel technical solution for safe, economical operation and intelligent control of indirect cooling towers in power plants during winter.

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. 间接冷却塔冬季运行状态自推演预测与工况变化分析. 热力发电, : - . DOI: 10.19666/j.rlfd.202607011

. Closed-loop recursive prediction and winter operating condition analysis of indirect cooling towers[J]. Thermal Power Generation, : - . DOI: 10.19666/j.rlfd.202607011

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doi: 10.19666/j.rlfd.202607011

首发时间：2026-10-23

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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

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