Artificial Neural Network-Based Viscosity Study of Polymeric Solutions

  • سال انتشار: 1401
  • محل انتشار: اولین همایش بین المللی هوش مصنوعی، علم داده و تحول دیجیتال در صنعت نفت و گاز
  • کد COI اختصاصی: OILANDGAS01_038
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 264
دانلود فایل این مقاله

نویسندگان

M Tavakolmoghadam

Assistant Professor, Deputy of Technology and International Affairs, Research Institute of Petroleum Industry, (RIPI) Tehran, Iran

T Kikhavani

Assistant Professor, Department of Chemical Engineering, Ilam University, Ilam ۶۹۳۱۵-۵۱۶, Iran

چکیده

In this study a supervised artificial neural network approach has been adopted to study the viscosity of polyvinylidene fluoride (PVDF)/Dimethylacetamide (DMAc) solutions. For this purpose, a total number of ۱۰۶۴ data for solution viscosity were measured over a broad range of conditions. After defining ۶ factors, including temperature, shear rate, solvent, and three different types of additives as adjusted parameters, Radial Basis Functions (RBF) were used to model the solution viscosity. The capability of the RBF model for describing the rheological behavior of the polymeric solution was examined under several operating conditions and favorable results were observed. The model developed by the RBF approach showed total Average Absolute Relative Error (AARE) and R۲ values of ۱.۲۹% and ۹۹.۸۶%, respectively. The results showed that the inclusion of ۶ input factors led to an acceptable estimation of PVDF/DMAc solutions viscosity. A sensitivity analysis accomplished based on the RBF model revealed that the fractions of organic and inorganic additives are the most effective factors on viscosity at low shear rates (γ ̇< ۱ s-۱) which are respectively followed by the solvent fraction, water fraction, temperature , and shear rate. While the shear rate showed the highest level of impact at γ ̇ > ۱ s-۱.

کلیدواژه ها

Polymeric Solution, Viscosity, Artificial Neural Network, Radial Basis Functions, Polyvinylidene fluoride

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.