scholarly journals An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Sen Zhang ◽  
Xi Chen ◽  
Yixin Yin
Keyword(s):  
Extreme Learning Machine ◽  
Random Search ◽  
Aluminum Reduction Cell ◽  
Online Measurement ◽  
Alumina Concentration ◽  
Soft Sensing ◽  
Aluminum Reduction ◽  
Reduction Cell ◽  
Learning Machine ◽  
The Stability

The concentration of alumina in the electrolyte is of great significance during the production of aluminum; it may affect the stability of aluminum reduction cell and the current efficiency. However, the concentration of alumina is hard to be detected online because of the special circumstance in the aluminum reduction cell. At present, there is lack of fast and accurate soft sensing methods for alumina concentration and existing methods can not meet the needs for online measurement. In this paper, a novel soft sensing method based on a modified extreme learning machine (MELM) for online measurement of the alumina concentration is proposed. The modified ELM algorithm is based on the enhanced random search which is called incremental extreme learning machine in some references. It randomly chooses the input weights and analytically determines the output weights without manual intervention. The simulation results show that the approach can give more accurate estimations of alumina concentration with faster learning speed compared with other methods such as BP and SVM.

Estimation of spatial alumina concentration in an aluminum reduction cell using a multilevel state observer

AIChE Journal ◽  
2017 ◽  
Vol 63 (7) ◽  
pp. 2806-2818 ◽  
Author(s):  
Yuchen Yao ◽  
Cheuk-Yi Cheung ◽  
Jie Bao ◽  
Maria Skyllas-Kazacos ◽  
Barry J. Welch ◽  
...  
Keyword(s):  
State Observer ◽  
Aluminum Reduction Cell ◽  
Alumina Concentration ◽  
Aluminum Reduction ◽  
Reduction Cell

An improved cell situation identification approach with convolutional neural network and wavelet extreme learning machine

2020 ◽  
pp. 095965182093566
Author(s):  
Yongxiang Lei ◽  
Xiaofang Chen ◽  
Yongfang Xie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Reduction Process ◽  
Classification Model ◽  
Aluminum Production ◽  
Aluminum Reduction ◽  
Reduction Cell ◽  
Learning Machine

In the aluminum reduction process, the flame hole is an influential index of the whole production situation, which reflects the distribution of the physical field of the reduction cell, the current efficiency and the lifespan of the aluminum reduction cell. Therefore, flame hole situation detection and identification are critical and significant in the whole process of aluminum electrolysis. However, in the practical industrial production, flame hole identification result is coming from the experimental operation workers, with the loss of workers and different experimental levels, the real-time measurement of the flame hole index is still a challenge beyond solution. This article develops a flame hole image classification method based on wavelet extreme learning machine. First, a deep feature set is extracted from the original images with a convolutional neural network. Then, a classification model based on an extreme learning machine with wavelet activation function is developed. Finally, the proposed convolutional neural network–weighted extreme learning machine model is applied to superheat degree real-time detection in the industrial electrolysis cell. The proposed method is evaluated on aluminum production which outperforms existing other superheat degree methods in accuracy and robustness.

Advanced Model-Based Estimation and Control of Alumina Concentration in an Aluminum Reduction Cell

JOM ◽  
2022 ◽  
Author(s):  
Jing Shi ◽  
Yuchen Yao ◽  
Jie Bao ◽  
Maria Skyllas-Kazacos ◽  
Barry J. Welch ◽  
...  
Keyword(s):  
Aluminum Reduction Cell ◽  
Alumina Concentration ◽  
Aluminum Reduction ◽  
Reduction Cell ◽  
Model Based ◽  
Estimation And Control ◽  
And Control ◽  
Advanced Model

Research on the Penetration Depth in Aluminum Reduction Cell with New Type of Anode and Cathode Structures

JOM ◽  
2014 ◽  
Vol 66 (7) ◽  
pp. 1202-1209 ◽  
Author(s):  
Liu Yan ◽  
Li Yudong ◽  
Zhang Ting’an ◽  
Feng Naixiang
Keyword(s):  
Penetration Depth ◽  
Aluminum Reduction Cell ◽  
Aluminum Reduction ◽  
Reduction Cell ◽  
New Type

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

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