Experimental Study and Numerical Modeling of Rheological and Flow Behavior of Xanthan Gum Solutions Using Artificial Neural Network-Reference-Cited by-同舟云学术

Experimental Study and Numerical Modeling of Rheological and Flow Behavior of Xanthan Gum Solutions Using Artificial Neural Network

Published:2014-08-25 Issue:12 Volume:35 Page:1793-1800
ISSN:0193-2691
Container-title:Journal of Dispersion Science and Technology
language:en
Short-container-title:Journal of Dispersion Science and Technology

Author:

Mirarab Razi Meisam,Kelessidis Vassilios C.,Maglione Roberto,Ghiass Majid,Ghayyem Mohammad Ali

Publisher

Informa UK Limited

Subject

Polymers and Plastics,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

http://www.tandfonline.com/doi/pdf/10.1080/10256018808623883

Reference26 articles.

1. Optimization study of xanthan gum production using response surface methodology

2. Gallino , G. Guarneri , A. Poli , G. , and Xiao , L. ( 1996 ) SPE 36426-MS Annual Technical Conference and Exhibition, Denver, Colorado.

3. Drilling fluids: State of the art

4. Ash , S.G. Clarke-Sturman , A.J. Calvert , R. , and Nisbet , T.M. ( 1983 ) SPE 12085-MS Annual Technical Conference and Exhibition, San Francisco, California.

5. Beck , F.E. Powell , J.W. , and Zamora , M. ( 1993 ) SPE 25767-MS Drilling Conference, Amsterdam, Netherlands.

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Application Potential of Artificial Intelligence and Numerical Simulation in the Research and Formulation Design of Drilling Fluid Gel Performance;Gels;2024-06-17

2. Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis;Journal of the Mechanical Behavior of Materials;2024-01-01

3. Rheological behavior of polyvinylidene fluoride/dimethylacetamide solutions: Experimental investigation and machine learning modeling;Physics of Fluids;2023-09-01

4. Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives;Materials;2023-03-23

5. Application of artificial intelligence and machine learning to food rheology;Advances in Food Rheology and Its Applications;2023