A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

Abstract

The content of free calcium oxide (f-CaO) in cement clinker is a key indicator for testing the quality of cement clinker. To address the problem that the content of f-CaO cannot be detected online, a multi-model fusion soft measurement method based on K-means++ clustering, empirical modal decomposition combined with multi-kernel relevance vector machines (EMD-MKRVM) is proposed to predict f-CaO content under different operating conditions. First, time-series analysis and matching of input variables with f-CaO content were performed, based on which a combination of empirical modal decomposition (EMD) and sample entropy (SE) denoising method was used to filter out high- frequency noise from the original data and extract effective signal information for reconstruction. Second, the K-means++ algorithm was used to cluster the processed training sample data, and multi-kernel relevance vector machine (MKRVM) sub-models were established by training the sample data of each sub-class and then the affiliation between the test samples and each sub-class was calculated as the weights of the sub-model output values, and the final model prediction output was obtained by multi-model fusion. Finally, the real data from cement plants were used for validation. The results show that compared with the single MKRVM model, multi–relevance vector machine (RVM) model, multi–support vector machine (SVM) model, and multi-MKRVM model using only EMD denoising method, the mean absolute error (MAE) of the multi-MKRVM model proposed in this paper was reduced by 42%, 7%, 14%, and 35%; root mean square error (RMSE) is reduced by 28%, 10%, 12%, and 21%; squares due to error (SSE) is reduced by 51%, 24%, 27%, and 41%; Theils inequality coefficient (TIC) is reduced by 27%, 17%, 21%, and 19%; [Formula: see text] is improved by 64%, 18%, 39%, and 91%; and Index of agreement (IA) is improved by 28%, 9%, 22%, and 13%. The multi-MKRVM model proposed in this paper has higher accuracy, better generalization ability and stability, and provides an effective method for f-CaO content prediction under complex multiple working conditions.

RETRACTED ARTICLE: E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm

“…When the initial CC is relatively discrete, the clustering effect is better. K-means++ is one of the improved algorithms of KMA, with the idea that the initial CC should be as far away as possible [24]. The first step is to randomly select an initial CC in the sample dataset, and calculate the minimum distance and selection probability from the data point to the CC.…”

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

“…Artificial intelligence (AI) technologies and machine learning (ML) algorithms have been increasingly utilized to solve hard problems that inherit nondeterminism and randomness in engineering practice of a variety of fields including complex system control [19,20], object detection [21], communication networks [22], oil pipeline monitoring [23] and leak detection [24], industrial control system protection [25], waste material recycling [26], etc. In recent years, more and more AI/ML practitioners collaborate with experts in different industrial domains to apply data-driven methods to help solve domain-specific problems and achieve the "Smart Manufacturing" and "Industry 4.0" goals of integrating computing machine intelligence into their respective manufacture production processes [1][2][3][4][5][6][7][10][11][12][13][14][15]. Numerous surveys about the applications of these AI/ML methods to different manufacturing industries have been published.…”

“…The availability of such unprecedented amounts of data together with the recent advances of artificial intelligence (AI) technologies such as ensemble learning, artificial neural networks (ANNs), etc., stimulate the incorporation of machine learning (ML)-based approaches into industrial manufacturing. For example, many such efforts in the cement industry are already underway to test and refine machine learning approaches to improve the control of their production devices including raw mills [1,2], rotary kiln [3,4], ball mills [5][6][7], conveyors [8,9], blenders [10], as well as other related manufacturing activities such as cement clinker quality control [11], concrete porosity prediction [12], energy consumption estimation [13], electricity cost optimization [14], hydrating behavior prediction [15], fault detection and diagnosis [4], etc.…”

Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

“…The structure system of innovative learning model takes the elements of learner, learning content, learning resources, learning data, learning activities, learning evaluation [23] as the first-level elements, and individual learners, group learners, knowledge goals, method goals, emotional goals, digital materials, learning tools, learning systems, practice data, homework data, test data, learning problems, learning process, learning testing, diagnostic evaluation, formative evaluation, summative evaluation, and final evaluation as the first-level elements. Evaluation, formative evaluation, summative evaluation and other 17 specific elements for the second level elements [24] fully embody the whole process of the intelligent learning model to build a scientific, objective and comprehensive innovative learning model structure system. The specific schematic diagram is shown in Figure 3.…”