On rheological properties of environmentally friendly inorganic systems and their modeling by artificial neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F23%3A10252425" target="_blank" >RIV/61989100:27350/23:10252425 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27360/23:10252425

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2238785422019032" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2238785422019032</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jmrt.2022.12.014" target="_blank" >10.1016/j.jmrt.2022.12.014</a>

Result language

angličtina

Original language name

On rheological properties of environmentally friendly inorganic systems and their modeling by artificial neural networks

Original language description

This work aims to investigate the rheological properties, namely dependence of dynamic viscosity on temperature, chemical composition, and shear rate, of environmentally friendly inorganic systems using a high-temperature rotational viscometer up to 1550 oC. The liquidus and start and end softening temperatures of these systems were also studied. The environmentally hazardous calcium fluoride in the amount of up to 6 wt% was substituted by other components (B2O3, TiO2, and Na2O) to preserve the original utility properties of the investigated systems (low liquidus temperatures and viscosities). The effect of alternative additives ranging from 2 to 6 wt% on the required properties was more beneficial than that of fluoride. The most significant reduction in liquidus temperature of up to 185 oC was achieved by adding 6 wt% B2O3 while maintaining a low viscosity value. The addition of CaF2 (up to 6 wt%) had the least effect, lowering the liquidus temperature by only 22 oC as compared to the original system. In the case of TiO2 addition, the dependence of viscosity on chemical composition was non-linear and complex to predict with existing models. Therefore, it was modeled using artificial neural networks. The predicted viscosity values for a given temperature and chemical composition were in good agreement with the experimentally obtained values, as the maximum relative error between the measured and calculated viscosity values was less than 5%. The characterization of the internal structure of the investigated systems was performed by Energy Dispersive X-Ray (EDX), X-Ray Diffraction (XRD) analyses and Scanning Electron Microscopy (SEM). (C) 2022 The Author(s).

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10403 - Physical chemistry

Project

<a href="/en/project/EF17_049%2F0008399" target="_blank" >EF17_049/0008399: Development of inter-sector cooperation of RMSTC with the application sphere in the field of advanced research and innovations of classical metal materials and technologies using modelling methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Materials Research and Technology

ISSN

2238-7854

e-ISSN

—

Volume of the periodical

22

Issue of the periodical within the volume

JAN-FEB

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

1410-1422

UT code for WoS article

000976622800001

EID of the result in the Scopus database

2-s2.0-85147669425