Alumina Concentration Detection Based on the Kernel Extreme Learning Machine
The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters,
... d high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM. Liang proposed a predictive model of an aluminum reduction cell based on LS-SVM . Li et al.  proposed a new fuzzy expert control method based on smart identification, multi-control mode, and decision making mechanisms to achieve alumina concentration prediction and real time control. The GM (1, 1) model is introduced into the aluminum concentration estimate by Zhang et al.  . However, the computational burden of the above nonlinear predictive models is still large when the dimension of the input variable increases. The learning speed and accuracy of these networks are, in general, far slower and cannot meet the requirements of real time detection. The extreme learning machine (ELM) is a novel single hidden layer feed forward neural network proposed by Huang. In ELM, the input weights and the bias of hidden nodes are generated randomly without human tuning and the output weights are determined based on the method of least squares. Unlike the traditional feed forward neural network learning algorithm, ELM has fast training speed and gets rid of the opportunity to converge to local minima  . The salient features of ELM are that its hidden layer parameters do not require manual intervention and can be assigned randomly before training, and the output weight is determined analytically via the least squares estimation method, making it easy to implement with better generalization performance and faster learning speed    . Nowadays, because of its good generalization, the ELM algorithm has been applied in many aspects like image segmentation , fault diagnosis , human action recognition, and human computer interface  , and so on. The initial weights of ELM were set randomly, so it made algorithm unstable. Huang and others proposed a KELM algorithm that takes the ideas of the kernel function to the extreme learning machine . Zhou et al.  proposed Illumination correction of dyeing products based on Grey-Edge and kernel extreme learning machine. Zhang et al.  proposed a method for electricity price forecasting based on a kernel extreme learning machine. Compared with the ELM model, the KELM model has better stability and generalization abilities. In this paper, we proposed a KELM based alumina concentration forecast model for the online detection. As the alumina concentration forecast field has little work on ELM or KELM based prediction models, our work is the first to tackle this problem with KELM. The experimental results showed that the proposed method has a better performance compared to the current approaches used in this area. The remaining parts of this paper are arranged as follows: Section 2 gives some preliminaries, including a brief introduction of ELM and KELM. The proposed KELM and alumina concentration prediction model is detailed in Section 3, including the model set up and the problem analysis of the proposed approach. The model for experimental implementation and evaluation is presented in Section 4. The discussions and conclusions are given in Section 5.