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Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning
Author(s) -
Chen Heng,
Peng Weike,
Nie Chunming,
Xu Gang,
Lei Jing
Publication year - 2021
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202100045
Subject(s) - condenser (optics) , power (physics) , artificial neural network , rotational speed , power station , engineering , simulation , computer science , artificial intelligence , electrical engineering , mechanical engineering , physics , light source , quantum mechanics , optics
An artificial intelligence approach using machine learning (ML) is applied to predict and optimize the operational performance of the air‐cooled condenser (ACC) in a large‐scale power plant. The in situ data of one whole year are collected from a typical coal‐fired power plant equipped with an ACC. The ML models for predicting the gross power output and the ACC power consumption are established using the artificial neural network (ANN) and random forest (RF) algorithms, and appropriate parameters are determined for the ML models. As the RF models with lower errors are demonstrated to be superior to the ANN models, the RF models are selected. Then the RF models are simplified by identifying the primary input parameters and exploiting fewer input parameters. Based on the derived RF models, the operational performance of the ACC is examined when the total rotational frequency of air fans varies, and the maximum net power output is obtained with the optimal total rotational frequency of fans. Due to the optimization, the average net power output of the plant can be promoted by 0.9774 MW, and the mean of the total rotational frequency of fans increases by 11.40 Hz. Therefore, the proposed approach is beneficial and advantageous.