Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

Abstract

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.

Operation optimization of cold‐end system of direct air‐cooled units considering the effect of environmental wind

“…The BPNN was widely used in nonlinear system modeling. [28][29][30][31] In BPNN modeling, the regression value R was closer to 1, which means that the training results of the model were better. In this study, the exhaust steam flow, ambient temperature, and fan speed were taken as inputs, and the backpressure and net output of the unit were taken as outputs.…”

“…The BPNN was widely used in nonlinear system modeling 28–31 . In BPNN modeling, the regression value R was closer to 1, which means that the training results of the model were better.…”