Thermal Performance Optimization of Air-Cooled Condensers

The thermal performance of air-cooled condensers (ACCs) underpins the efficiency and reliability of power plants and large-scale cooling systems that eschew water-based heat rejection. Optimisation efforts have centred on mitigating adverse ambient effects such as cross-winds, recirculation of hot plumes and freezing in cold climates. Computational fluid dynamics and field measurements reveal that asymmetric flow through fan arrays can elevate turbine backpressure, reduce heat rejection and raise energy consumption. To address these challenges, researchers have developed fan-speed partitioning schemes, aerodynamic guides and dynamic control strategies. Recent advances in data-driven modelling and machine-learning surrogates enable real-time performance prediction, while multi-model predictive controllers allow divisional adjustment of fan groups according to wind direction and magnitude. Collectively, these innovations improve cooling uniformity, lower parasitic fan power and ensure stable operation in diverse environments, contributing to reduced water usage, enhanced grid flexibility and decarbonisation objectives in thermal power generation.

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Thermal Performance Optimization of Air-Cooled Condensers publication trend

The graph below shows the total number of articles in thermal performance optimization of air-cooled condensers across all publications each year (not limited to Nature Index journals).

Technical terms

Air-cooled condenser (ACC): A heat-exchanger system that uses ambient air to condense steam in power and industrial plants without water consumption.

Backpressure: The pressure at the condenser outlet that opposes steam expansion in a turbine and affects its efficiency.

Plume recirculation: The return of warmed exhaust air into the condenser inlet region, reducing cooling performance.

Surrogate model: A data-driven approximation that predicts system performance rapidly, bypassing computationally intensive simulations.

Partition control: A strategy that divides fan arrays into groups and adjusts speeds locally to counteract non-uniform inflow conditions.

Predictive control: A control methodology that uses dynamic models to forecast future behaviour and optimise operational settings in advance.

References

1. Cluster Partition Operation Study of Air-Cooled Fan Groups in a Natural Wind Disturbance. Energies (2023).
2. Application of machine learning to develop a real-time air-cooled condenser monitoring platform using thermofluid simulation data. Energy and AI (2021).
3. Cooling Performance Optimization of Direct Dry Cooling System Based on Partition Adjustment of Axial Flow Fans. Energies (2020).
4. Cooling Performance Enhancement of Air-Cooled Condensers by Guiding Air Flow. Energies (2019).
5. Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms. Energies (2024).
6. Multi-Model-Based Predictive Control for Divisional Regulation in the Direct Air-Cooling Condenser. Energies (2022).
7. Numerical Study on the Influence Mechanism of Crosswind on Frozen Phenomena in a Direct Air-Cooled System. Energies (2020).
8. Effect of Environmental Wind on Performance of a 600MW Direct Air-cooled Unit Based on FLUENT. E3S Web of Conferences (2019).