首页|Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process
Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process
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In the coal-to-ethylene glycol (CTEG) process,precisely estimating quality variables is crucial for process monitoring,optimization,and control. A significant challenge in this regard is relying on offline labo-ratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays. The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models. To address this issue,our study introduces the transferable adversarial slow feature extraction network (TASF-Net),an innovative approach designed specifically for few-shot quality pre-diction in the CTEG process. TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process. Addi-tionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step. A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data. Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly. Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain. The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset. Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.
Chemical processNeural networksSlowness principleTransfer learningPrediction
Cheng Yang、Chao Jiang、Guo Yu、Jun Li、Cuimei Bo
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School of Electrical Engineering and Control Science,Nanjing Tech University,Nanjing 211816,China
Department of Intelligent Manufacturing,Nanjing Tech University,Nanjing 210009,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of China
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