Research Update: Predicting Heat Transfer Coefficients with an Artificial Neural Network Webinar
June 30, 2020 | 9:00 AM CDT (UTC -05:00)
Artificial neural networks (ANNs) allow the solution of hard problems such as
- determining important parameters affecting crude oil fouling
- analyzing operating data to troubleshoot heat exchangers
- developing correlations through multivariate nonlinear regression of complex data
To evaluate a neural network model for heat transfer coefficients, we used a multilayer, feed-forward ANN with shellside vacuum condensation data from the Low Pressure Condensation Unit (LPCU) and the Multipurpose Condensation Unit (MCU). In the webinar, we describe this type of neural network and discuss the findings of this study. We also highlight challenges and identify possible future uses of neural networks at HTRI.
Registration Deadline: June 28, 2020
Facilitated by Parimah Kazemi
Numerical Analyst, earned her BS in Mathematics and Chemistry, as well as her PhD in Mathematics from the University of North Texas, Denton, Texas (TX (USA). Following her graduation she worked as a Postdoctoral Researcher at Ulm University, Ulm, Germany and at Paris 6 University, Paris, France, where she obtained funding from the French National Center for Scientific Research and the German Aerospace Agency to develop state-of-the-art algorithms for simulation and visualization. Kazemi subsequently spent two years as a Visiting Assistant Professor/Lecturer at the University of Wisconsin – Madison, Madison, Wisconsin (WI); Beloit College, Beloit, WI; and Ripon College, Ripon, WI. Kazemi also worked in the private sector as a Senior Analyst for Daniel H. Wagner Associates, Hampton, Virginia, on projects for the US Navy. She has authored scientific publications in refereed journals on scientific computation, physics, and analysis, as well as given presentations both nationally and internationally. AT HTRI, her focus is on implementing robust, computationally efficient numerical methods in our software.