Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

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.

Res-BiLSTMs model based on multi-task attention for real-time measurement of the free calcium oxide content

“…Furthermore, Wang et al developed a PSO-LSSVM-based soft sensor technique for local cement f-CaO by building a local modeling dataset [10], which was experimentally shown to have greater generalization capability than global modeling. Additionally, some researchers optimized the pertinent parameters using the perturbation chaos particle swarm optimization (PRCPSO) algorithm with random perturbations and applied these multikernel LSSVM model (Multiple Kernel Least Square Support Vector Machine (MKLSSVM) was used for the prediction of f-CaO) by linearly weighting three different types of kernel functions [11]. However, these enhanced models use only the process variables associated with the rotary kiln-related as auxiliary variables.…”