## 1 Introduction  
During recent years, much research has been conducted on two-phase inside microchannels to survey transport phenomena. There are many examples for the process involving two-phase flow in microchannels: microreactors, miniature heat exchangers, fuel injection in internal combustion equipment, miniature refrigeration systems, cooling of high-powered electronic systems, fusion reactors for cooling plasma-facing components, and fuel cells with evaporator components [1, 2, 3, 4]. Nevertheless, two-phase flows inside microchannels come with disadvantages, too. They impose a higher pressure drop through the fluid flow passing through narrow channels. Hence, both pressure drop and heat transfer considerations should simultaneously be taken into account during the design process of these miniaturized heat exchangers. Numerous experimental datasets have been collected and studied by scholars, to investigate inside two-phase flow microchannels [5; 6; 7; 8]. For instance, there are published experimental works that explore fluid characteristics inside microchannels with a two-phase flow of nitrogen, R12, R32, R134a, R236 ea, R245 fa, R404a, R410a, and R422 with different qualities and mass fluxes at different channel hydraulic diameters [9; 10; 11]. [PERSON] and [PERSON] [12] considered a boiling of the dielectric liquid fluorient FC-77 in parallel microchannels to investigate heat transfer coefficients and pressure. They studied the flow and boiling phenomena for a comprehensive range of channels in four distinct flow regimes, including slug, annular, bubbly, and alternating churn/annular/wispy-annular flow regimes. [PERSON] et al. [13] experimentally examined the characteristics of flow boiling of deionized water inside a microchannel consisting of 14 parallel channels with \(0.15\times 0.25\) mm rectangular shape. They selected the heat flux, mass flux, and inlet temperature as significant factors for the variation of pressure drop. Based on the experiment results, they concluded from the pressure drop trend that with the increasing quality of vapor, instability will be increased. Meanwhile, researchers have tried to validate the adiabatic pressure drop correlations of two-phase flow inside microchannels for different conditions by comparing them with the comprehensive databases at hand. Some researchers [14; 15; 16; 17; 18] have studied the heat transfer coefficient for flow boiling of oxygen-free streams inside copper microchannel heat sinks. The experimental results are in good agreement with the corresponding numerical predictions. [PERSON] et al. [19] proposed a set of new equations to describe a two-phase pressure drop, along with the homogeneous flow of different working fluids at different tube diameters and fluid flux values. Although many correlations are provided by researchers for predicting pressure drops, having a comprehensive correlation for an extensive range of factors for linear multivariate modeling is too difficult. A common way to obtain a comprehensive correlation is to use non-linear empirical modeling techniques, such as artificial neural network (ANN), which is developed based on the brain's biological neuron network function [20; 21; 22; 23]. The artificial neural network method has been increasingly used to solve engineering problems. Using an appropriate form of the algorithm to train and test the network configurations would be followed by the successful application of ANN. [PERSON] et al. [24] suggested that the functional relations between the pertinent dimensionless numbers in nucleate boiling can be correlated to convective heat transfer through a genetic algorithm. The flow pattern of two-phase water and air stream was presented by [PERSON] et al. [25]. They found predictor correlations by means of applying the ANN method on a circular Y-junction minichannel with a 2.1 mm diameter. According to the literature, there has been limited effort focused on evaluating the pressure drop by means of the ANN technique.