Abstract
Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Cholesky factorization, the online sequential multiple kernel extreme learning machine algorithm (COS-MKELM) is proposed. The LDLT form Cholesky factorization of the matrix is introduced to avoid the large operation amount of inverse matrix calculation. In addition, the stored initial information is utilized to realize online model identification. Then, three regression datasets are used to test the performance of the COS-MKELM algorithm. Finally, an online prediction model for fCaO content is built based on COS-MKELM. Experimental results demonstrate that the fCaO content model improves the performance in terms of learning efficiency, regression accuracy, and generalization ability. In addition, the online prediction model can be corrected in real-time when the production conditions of cement clinker change.
Highlights
As seen from the root mean square error (RMSE) values for testing data listed in Table 1 and the output results shown in Figures 1–3, the predicted testing values based on COS-multiple kernel extreme learning machine (MKELM) are closer to the actual values than the other comparison algorithms
Three regression datasets were used to test the performance of the COS-MKELM algorithm
Experiment results show that the proposed COS-MKELM algorithm, which is based on the fusion of the three kernel functions method and the online sequential learning method, shows fast learning efficiency
Summary
The multivariate time series analysis and convolutional neural network method [20] and the kernel extreme learning machine algorithm [21] were proposed for online cement clinker quality monitoring. Some of the above scholars established the prediction models of cement clinker fCaO content based on the online neural network. It is necessary to deeply study the new online sequential learning neural network algorithm applicable to the cement clinker fCaO content prediction model. In order to avoid the large calculation of the inverse matrix and reduce the computational complexity of online sequential learning process, the LDLT form Cholesky factorization based online sequential multiple kernel extreme learning machine (COS-MKELM) algorithm is proposed. The cement clinker fCaO content online prediction model is built by the COS-MKELM algorithm.
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