{'fraction_non_alphanumeric': 0.015901060070671377, 'fraction_numerical': 0.0035335689045936395, 'mean_word_length': 5.294444444444444, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multivariate process monitoring of an experimental blast furnace

Process monitoring by use of multivariate projection methods has received increasing attention as it can reduce the monitoring problem for richly instrumented industrial processes with many correlated variables. This article discusses the monitoring and control of a continuously operating experimental blast furnace (EBF). A case study outlines the need for monitoring and control of the EBF and the use of principal components (PCs) to monitor the thermal state of the process. The case study addresses design, testing and online application of PC models for process monitoring. The results show how the monitoring problem can be reduced to following just a few PCs instead of many original variables. The case study highlights the problem of multivariate monitoring of a process with frequently shifting operating modes and process drifts and stresses the choice of a good reference data set of ‘normal’ process behavior. Possible solutions for adaptations of the multivariate models to process changes are also discussed. Copyright © 2009 John Wiley & Sons, Ltd.