Accurate prediction of viscosity of mixed oils

Khoshmardan, Maryam Abdollahi; Mehrizadeh, Masoud; Zand, Nima; Najafi‐Marghmaleki, Adel

doi:10.1080/10916466.2021.1902351

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…With the advent of the Industrial revolution 4.0 [41], the combination of all the different simulation techniques with data-science based prediction models (ML, DL, ANN), has become the need of the hour. Different such models have been developed for a number of applications, such as the prediction of viscosity using data-driven modelling for quaternary slag systems [42], prediction of viscosity of oils using hybrid models [43], and prediction of viscosity of nanofluids using Deep Learning (DL) [44]. However, there is no considerable study on the use of machine learning that extensively allows making predictions on the properties of high alumina slag as a function of suitable compositions and temperatures.…”

Section: Introductionmentioning

confidence: 99%

Optimization of high alumina slag practice in blast furnace ironmaking: an industrial approach. Part 2: Data-driven aspects

Pal,

Sahoo,

Biswajeet

et al. 2023

Ironmaking & Steelmaking

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The prediction of transport and thermodynamic properties of the blast furnace slag is an experimentally tedious job to accomplish as seen in part-1 of this work. Literature studies have shown that the use of machine learning in the determination of the properties as a function of composition is an effective technique for glassy slags. However, the application of machine learning and data science techniques in the prediction of high alumina slag properties has not been studied extensively so far. In this paper, the use of Support Vector Machine (SVM) and ExtraTrees Regressor have been done to predict the viscosity, liquidus temperature, and other thermodynamic properties of blast furnace type slag in the high alumina regime, i.e. Al 2 O 3 varying from 18 to 22 wt-%. The minimization of detrimental effects of high alumina slag has been studied by varying the MgO content and CaO/SiO 2 ratio in the range of 8-12 wt-% and 0.8-1.2, respectively. The accuracy of models has been tuned to be fairly high and the results of the prediction have been discussed with possible solutions to operate under the high alumina regime of blast furnace slag.

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