August 9, 2022
Gholam Hossein Roshani

Gholam Hossein Roshani

Academic rank: Associate professor
Education: Ph.D in Nuclear Engineering
Faculty: Faculty of energy


Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products
Type Article
oil products monitoring; neural network; X-ray spectrum; MCNP code
Researchers Gholam Hossein Roshani، Peshawa Jammal Muhammad Ali، shiavn mohammed، Robert Hanus، Lokman Abdulkareem، Adnan Alhathal Alanezi، MohammadAmir Sattari، saba Amiri، Ehsan Nazemi، Ehsan Eftekhari zadeh، El Mostafa Kalmoun


Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.