Affiliations 

  • 1 Department of Biomedical Engineering, Al-Mustaqbal University Collage, Babylon, Iraq. hasanshker1@gmail.com
  • 2 Department of Biomedical Engineering, Al-Mustaqbal University Collage, Babylon, Iraq. amir.saud92@gmail.com
  • 3 Faculty of Information Sciences and Engineering, Management & Science University, Shah Alam 40100, Selangor, Malaysia. safaaengineer@gmail.com
Materials (Basel), 2019 May 29;12(11).
PMID: 31146451 DOI: 10.3390/ma12111752

Abstract

Porous γ-alumina is widely used as a catalyst carrier due to its chemical properties. These properties are strongly correlated with the physical properties of the material, such as porosity, density, shrinkage, and surface area. This study presents a technique that is less time consuming than other techniques to predict the values of the above-mentioned physical properties of porous γ-alumina via an artificial neural network (ANN) numerical model. The experimental data that was implemented was determined based on 30 samples that varied in terms of sintering temperature, yeast concentration, and socking time. Of the 30 experimental samples, 25 samples were used for training purposes, while the other five samples were used for the execution of the experimental procedure. The results showed that the prediction and experimental data were in good agreement, and it was concluded that the proposed model is proficient at providing high accuracy estimation data derived from any complex analytical equation.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

Similar publications