Alshawarghi, HamzahElkamel, AliMoshiri, BehzadHourfar, Farzad2020-01-052020-01-052019-09-02https://doi.org/10.1016/j.compchemeng.2019.06.005http://hdl.handle.net/10012/15397The final publication is available at Elsevier via https://doi.org/10.1016/j.compchemeng.2019.06.005. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper introduces the development of empirical predictive models and detection methods that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The predictive models investigated in this work are designed based on different techniques such as statistical fingerprinting, artificial neural network (ANN), and multiway projection to latent structures (MPLS). Robustness issues related to each method are discussed and performance comparisons have been done for the presented techniques. Furthermore, model fusion theory has been applied to improve the prediction accuracy of the developed models’ defined output- the value of off-gas water vapor- which is known as one the most vital variables to guarantee a safe and reliable operation. Finally based on the proposed predictive models, a water leak detection methodology is introduced and implemented on an industrial AC EAF and a comprehensive discussion has been done to evaluate the performance of the developed algorithm. To this aim, two fault detection methods have been applied. Fault detection method #1 has been designed using statistical fingerprinting technique, while the other one has been developed based on machine learning-based models and also fusion of the models’ outputs.enAttribution-NonCommercial-NoDerivatives 4.0 InternationalEAF water detection frameworkfingerprintingartificial neural networkmultiway projection to latent structuresmodel fusionfault detectionArticle