Performance analysis of hot metal temperature prediction in a blast furnace and expert suggestion system proposal using neural, statistical and fuzzy models

Basit Öğe Kaydı
dc.authoridOrak, Ilhami Muharrem/0000-0002-7219-4209
dc.authoridtunckaya, yasin/0000-0002-6690-2694
dc.contributor.authorBozkurt, Erdogan
dc.contributor.authorOrak, Ilhami M.
dc.contributor.authorTunckaya, Yasin
dc.date.accessioned2024-09-29T16:01:09Z
dc.date.available2024-09-29T16:01:09Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBlast Furnace (BF) production methodology is one of the most complex process of iron & steel plants as it is dependent on multi-variable process inputs and disturbances to be modelled properly. Due to expensive investment costs, it is critical to operate a BF by reducing operational expenses, increasing the performance of raw material and fuel consumptions to optimize overall furnace efficiency and stability, also to maximize the lifetime. The chemical compositions and temperature of hot metal are important indicators while evaluating the operation, therefore, if the future values of hot metal temperature can be predicted in advance instead of subsequent measuring, then the BF staff can take earlier counteractions on several operational parameters such as coke to ore ratio, distribution matrix, oxygen enrichment rate, blast moisture rate, permeability, flame temperature, cold blast temperature, cold blast flow and pulverized coal injection rate, etc. to control the furnace optimally. In this study, Artificial Neural Networks (ANN) model is proposed combined with NARX (Nonlinear autoregressive exogenous model) time series approach to track and predict furnace hot metal temperature by selecting the most suitable process inputs and past values of hot metal temperatures using the real data which is collected from the BF operated in Turkey during 2 months of operation. Various data mining techniques are applied due to requirements of charge cycling and operating speed of the furnace which secures novelty and effectiveness of this study comparing previous articles. Furthermore, a statistical tool, Autoregressive Integrated Moving Average (ARIMA) model, is also executed for comparison. ANN prediction results of 0.92, 8.59 and 0.41 are found very satisfactory comparing ARIMA (1,1,1) model outputs of 0.73, 97.4 and 9.32 for R-2 (Coefficient of determination), RMSE (Root mean squared error) and MAPE (Mean absolute percentage error) respectively. Consequently, an expert suggestion system is proposed using fuzzy if-then rules with 5x5 probability matrix design using the last predicted HMT value and the average of the last 5 HMT values to decide furnace's warming or cooling movements state in mid-term and maintain the operational actions interactively in advance.en_US
dc.identifier.doi10.1051/metal/2021043
dc.identifier.issn2271-3646
dc.identifier.issn2271-3654
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85106933191en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1051/metal/2021043
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5538
dc.identifier.volume118en_US
dc.identifier.wosWOS:000657609400006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEdp Sciences S Aen_US
dc.relation.ispartofMetallurgical Research & Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural networksen_US
dc.subjectblast furnaceen_US
dc.subjectexpert suggestionen_US
dc.subjecthot metal temperatureen_US
dc.subjectpredictionen_US
dc.titlePerformance analysis of hot metal temperature prediction in a blast furnace and expert suggestion system proposal using neural, statistical and fuzzy modelsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu