Design of Soft-Sensing Model for Alumina Concentration Based on Improved Grey Wolf Optimization Algorithm and Deep Belief Network

To address the challenge of the real-time monitoring of alumina concentrations during the production process, this paper employs a Deep Belief Network (DBN) within the framework of deep learning to predict alumina concentration. Additionally, the improved Grey Wolf Optimizer (IGWO) is utilized to op...

Full description

Saved in:
Bibliographic Details
Published in:Processes Vol. 13; no. 3; p. 606
Main Authors: Li, Jianheng, Chen, Zhiwen, Zhong, Xiaoting, Li, Xiangquan, Xia, Xiang, Liu, Bo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Accuracy
Algorithms
Alumina
Aluminum
Aluminum oxide
Back propagation
Belief networks
Comparative analysis
Convergence
Deep learning
Efficiency
Electrolysis
Errors
Fault diagnosis
Genetic algorithms
Liu Bo
Mathematical optimization
Measurement techniques
Neural networks
Optimization algorithms
Parameters
Particle swarm optimization
R&D
Real time
Research & development
Search algorithms
Sustainable development
Variables
China
ISSN:2227-9717, 2227-9717
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To address the challenge of the real-time monitoring of alumina concentrations during the production process, this paper employs a Deep Belief Network (DBN) within the framework of deep learning to predict alumina concentration. Additionally, the improved Grey Wolf Optimizer (IGWO) is utilized to optimize key parameters of the DBN model, including the number of hidden layer nodes, reverse iteration count, and learning rate. An IGWO-DBN hybrid model is then constructed and compared against DBN models optimized by other techniques, such as the Sparrow Search Algorithm (SSA) and Particle Swarm Optimization (PSO), to evaluate the predictive performance. The comparative analysis reveals that, in terms of predictive accuracy, the IGWO-DBN model outperforms both the SSA-DBN and PSO-DBN models. Specifically, it achieves lower root mean square errors (RMSE) and mean absolute errors (MAE), alongside a higher coefficient of determination (R2). Furthermore, the IGWO-DBN model exhibits a faster convergence rate and a lower final convergence value, indicating superior generalization ability and robustness. Furthermore, the IGWO-DBN model not only demonstrates significant advantages in prediction accuracy for alumina concentration but also substantially reduces model training time through its efficient parameter optimization mechanism. The successful implementation of this model provides robust support for the intelligent and refined management of the aluminum electrolysis industry, aiding enterprises in reducing costs, improving production efficiency, and advancing the green and sustainable development of the industry.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-9717
2227-9717
DOI:10.3390/pr13030606