Data-based adaptive online prediction model for plant-wide production indices

• Published in: Knowledge and information systems Vol. 41; no. 2; pp. 401 - 421
• Main Authors: Liu, Changxin, Ding, Jinliang, Toprac, Anthony J., Chai, Tianyou
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2014; Springer Nature B.V
• Subjects: Accuracy; CAM; Computer aided manufacturing; Computer Science; Data Mining and Knowledge Discovery; Data processing; Database Management; Datasets; Dynamics; Genetic algorithms; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Knowledge management; Laboratories; Mathematical models; Neural networks; Online; Online data bases; Product quality; Production planning; Regular Paper; Samples; Statistical analysis; Statistical methods; Studies; Training; United States > US; Production indices; Support vector regression; Online prediction; Adaptive parameters
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-014-0757-8

A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes.