to predict volume percentages and flow regimes with high accuracy. In recent years, the use of feature extraction techniques such as time domain [16; 17], frequency domain [18], and time-frequency domain [19] has been widely considered by researchers in this field. In all the above mentioned studies, the researchers presented different characteristics for distinguishing the type of flow regimes and determining the volume percentages. [PERSON] et al. used GMDH neural networks to recognize the type of flow regimes [20]. They simulated a structure including a Cs-137 radioisotope, a Pyrex pipe, and a NaI detector using the MCNPX transport code. They extracted the time domain characteristics of the recorded signal and considered them as neural network inputs. Estimation of volume percentages with a RMSE of less than 1.11 was the result of this investigation.  
Recently, the use of X-ray tubes has been very popular with researchers because of their many benefits. The use of X-ray tubes as a source has the following advantages compared to other sources such as radioisotopes: X-ray tubes can adjust the emitted photon energy, whereas the photon emission energy is constant in radioisotopes. It should also be noted that the activity of radioisotopes decreases over time, but X-ray tubes do not follow this rule; X-ray tubes can be turned on and off. They are also easier to transport than radioisotopes. In their recent study, [PERSON] et al. proposed a system for monitoring fluids passing through transfer tubes [21]. Although they were able to predict the volumetric ratio of petroleum products with a MAE of less than 2.72, the use of feature extraction techniques can increase the accuracy of such systems. Much research has been conducted to measure the volume fractions of two-phase [22; 23] and three-phase [24; 25] flows using X-ray tubes. Researchers have claimed that the feature extraction techniques can help increase the accuracy of determining the type of flow regimes. In their study, [PERSON] et al. [26] simulated a laboratory structure with an MCNP transport code in which they implemented three homogeneous, stratified, and annular regimes in different volume percentages. A cesium-137 source and a NaI detector were used in this simulation. Using the wavelet feature extraction technique combined with a neural network, they succeeded to fully recognize flow regimes and estimate volume percentages with an acceptable accuracy. In another study, [PERSON] and colleagues implemented four flow regimes in a horizontal pipe named slug, bubble, and transitional plug-bubble flows to determine the type of flow regimes using a gamma ray absorption technique and wavelet feature extraction [27]. Further studies [28; 29] have explored the applications of wavelet feature extraction in the field of radiation absorption signals.  
Inspired by previous research, in this study, an attempt has been made to propose a high-precision monitoring system that can determine the volume ratio of different oil products. This article is organized as follows: first, the structure of the simulation will be explained in detail; the next section presents the discrete wavelet transform (DWT) method for extracting the received signal characteristics; in the third section, multilayer perceptron (MLP) neural networks will be discussed, and the results and accuracy of the designed networks will be shown; the last section concludes the study.