Stacked autoencoder for operation prediction of coke dry quenching process |
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Affiliation: | 1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;2. Control System Center, Manchester University, Manchester M60 1QD, UK;3. Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Hong Kong 999077, China |
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Abstract: | Coke dry quenching (CDQ) is widely adopted for waste heat recovery in iron and steel plants. In this work, an economic benefit index was introduced to evaluate the performance of the CDQ system and stacked autoencoder (SAE) based deep neural networks are adopted for CDQ operation prediction. Based on the prediction results, a guidance is provided for online adjustment of the supplementary air flow rate, hence the efficiency and safety of the CDQ system can be improved. The case study on a real plant shows that the proposed method increases the economic efficiency of the CDQ process by 4.39%. |
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Keywords: | Coke dry quenching Stacked autoencoder Deep learning Economic benefit index Process modeling |
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