基于LSTM與EMD結(jié)合的電廠循環(huán)冷卻水系統(tǒng)運(yùn)行狀態(tài)預(yù)測(cè)研究

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關(guān)鍵詞：循環(huán)冷卻水系統(tǒng)；運(yùn)行狀態(tài)預(yù)測(cè)；遺傳算法中圖分類號(hào)：TM621 文獻(xiàn)標(biāo)志碼：ADOI：10.19968/j.cnki.hnkj.1003-5168.2025.14.002文章編號(hào)：1003-5168（2025）14-0013-04

Research on Operation Status Prediction of the Circulating Cooling WaterSystem in PowerPlantsBased onthe Combinationof LSTM and EMD

LIU Feiyu （Intelligent Control Industry College，Henan Chemical Technician College，Kaifeng 4750Oo，China）

Abstract： [Purposes] To address the insufficient prediction accuracy of the operation status in power plant circulating cooling water system，a prediction method combining Genetic Algorithm-optimized Bidirectional Long Short-Term Memory neural networks （GA-BiLSTM） and Empirical Mode Decomposition （EMD）is proposed.[Methods]EMD is employed to decompose the original data into multiple Intrinsic Mode Function （IMF）components，thereby reducing data complexity.With the help of the Genetic Algorithm（GA），the hyperparameters of the Bidirectional Long Short-Term Memory neural network （BiLSTM） are optimized to improve the performance of the model. The decomposed IMF components are then individuallyfed into theoptimized GA-BiLSTM model for prediction，with final resultsobtained through reconstruction.[Findings] Experimental results demonstrate that all prediction error metrics of this model remain at low levels，with a 55% improvement in prediction accuracy compared to conventional models.[Conclusions] The prediction method based on the combination of LSTMand EMD can provide strong assurance for stable operation of the circulating cooling water system in power plants.

Keywords： circulating cooling water system; operation status prediction; Genetic Algorithm

0 引言

電廠循環(huán)冷卻水系統(tǒng)在工業(yè)生產(chǎn)中占據(jù)關(guān)鍵地位，其穩(wěn)定運(yùn)行對(duì)保障設(shè)備正常運(yùn)轉(zhuǎn)、提高生產(chǎn)效率和降低能耗具有重要意義。（剩余4538字）