Abstract
Alumina concentration is an important parameter in the production process of aluminum electrolysis. Due to the complex production environment in the industrial field and the complex physical and chemical reactions in the aluminum reduction cell, nowadays it is still unable to carry out online measurement and real-time monitoring. For solving this problem, a soft-sensing model of alumina concentration based on a deep belief network (DBN) is proposed. However, the soft-sensing model may have some limitations for different cells and different periodic working conditions such as local anode effect, pole changing, and bus lifting in the same cell. The empirical mode decomposition (EMD) and particle swarm optimization (PSO) with the DBN are combined, and an EMD–PSO–DBN method that can denoize and optimize the model structure is proposed. The simulation results show that the improved soft-sensing model improves the accuracy and universality of prediction.
Funder
Science and Technology Project of Jiangxi Provincial Department of Education, China
Industrial Project of Jingdezhen Science and Technology Bureau, China
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference32 articles.
1. A prediction method based on wavelet transform and multiple models fusion for chaotic time series;Tian;Chaos Solitons Fractals,2017
2. Soft-sensor modeling of silicon content in hot metal based on sparse robust LS-SVR and multi-objective optimization;Guo;Chin. J. Eng.,2016
3. BCOISOA-BP network in grinding particle size soft sensor applications;Zhou;Chin. J. Eng.,2017
4. Gao, J., Yang, X., Huang, J., and Peng, K. (2022). A data-driven fault detection and fault-tolerant control scheme for large-scale systems and its application on multi-area interconnected power systems. IET Control Theory Appl.
5. Double-Layer Distributed Monitoring Based on Sequential Correlation Information for Large-Scale Industrial Processes in Dynamic and Static States;Huang;IEEE Trans. Ind. Inform.,2020
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