Nuclear engineering

Physics

Electrode

Organic chemistry

Process engineering

X-ray tube

Collimator

Mathematics

Common emitter

Anode

Optics

Statistics

Engineering

Radiation

Petroleum product

Artificial intelligence

Multilayer perceptron

Chemistry

Materials science

Monte Carlo method

Volume (thermodynamics)

Tube (container)

Computer science

Petroleum

Quantum mechanics

Optoelectronics

Artificial neural network

Mechanical engineering

Detector

Processes

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.

Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products