Determining the amount of void fraction of multiphase flows in pipelines of the oil, chemical and petrochemical industries is one of the most important challenges. Performance of capacitance based two phase flow meters highly depends on the fluid properties. Fluctuation of the liquid phase properties such as density, due to temperature and pressure changes, would cause massive errors in determination of the void fraction. A common approach to fix this problem is periodic recalibration of the system, which is a tedious task. The aim of this study is proposing a method based on artificial intelligence (AI), which offers the advantage of intelligent measuring of the void fraction regardless of the liquid phase changes without the need for recalibration. To train AI, a data set for different liquid phases is required. Although it is possible to obtain the required data from experiments, it is time-consuming and also incorporates its own specific safety laboratory consideration, particularly working with flammable liquids such as gasoline, oil and gasoil. So, COMSOL Multiphysics software was used to model a homogenous regime of two-phase flow with five different liquid phases and void fractions. To validate the simulation geometry, initially an experimental setup including a concave sensor to measure the capacitance by LCR meter for the case that water used as the liquid phase, was established. After validation of the simulated geometry for concave sensor, a ring sensor was also simulated to investigate the best sensor type. It was found that the concave type has a better sensitivity. Therefore, the concave type was used to measure the capacitance for different liquid phases and void fractions inside the pipe. Finally, simulated data were used to train a Multi-Layer Perceptron (MLP) neural network model in MATLAB software. The trained MLP model was able to predict the void fraction independent of the liquid phase density changes with a Mean Absolute Error (MAE) of 1.74.

Measuring the void fraction of different multiphase flows in various fields such as gas, oil, chemical, and petrochemical industries is very important. Various methods exist for this purpose. Among these methods, the capacitive sensor has been widely used. The thing that affects the performance of capacitance sensors is fluid properties. For instance, density, pressure, and temperature can cause vast errors in the measurement of the void fraction. A routine calibration, which is very grueling, is one approach to tackling this issue. In the present investigation, an artificial neural network (ANN) was modeled to measure the gas percentage of a two-phase flow regardless of the liquid phase type and changes, without having to recalibrate. For this goal, a new combined capacitance-based sensor was designed. This combined sensor was simulated with COMSOL Multiphysics software. Five different liquids were simulated: oil, gasoil, gasoline, crude oil, and water. To estimate the gas percentage of a homogeneous two-phase fluid with a distinct type of liquid, data obtained from COMSOL Multiphysics were used as input to train a multilayer perceptron network (MLP). The proposed neural network was modeled in MATLAB software. Using the new and accurate metering system, the proposed MLP model could predict the void fraction with a mean absolute error (MAE) of 4.919.

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Nowadays Quantum-dot Cellular Automata (QCA) is one of the new technologies in nanoscale which can be used in future circuits. Most digital circuits are implemented with CMOS technology, but CMOS has some problems like power consumption and circuit size. So, for solving these problems a new method (QCA) is presented. It is clear that converters play a crucial role in the digital world. So, due to the aforementioned point, in this paper, two digital code converters, containing an excess-3 to decimal, and a decimal to excess-3 code converter are presented. The tile method is used to design proposed circuits in quantum-dot cellular automata (QCA) nanotechnology. The tile method gives a unique block for the majority and NOT gates. This property facilitates integration. Both of the proposed code converters have 1.75 clock cycles delay and have an energy dissipation of about 100meV. In the excess-3 code to decimal converter, 516 cells are used, which occupy an area equal to 0.43µm 2 also in the decimal to excess-3 code converter. 321 cells are used, which occupy an area equal to 0.28 µm 2 .

In this paper, two digital code converters are presented, excess-3 to decimal, and decimal to excess-3. The tile method is used to design proposed circuits in quantum-dot cellular automata (QCA) nanotechnology. The tile method gives a unique block for the majority and NOT gates. This property facilitates integrating circuits and since the NOT gate is not used in the tile method, the proposed circuits can do their work as fast as possible. Both of the proposed code converters has 1.75 clock cycles delay and have an energy dissipation of about 100meV. In the excess-3 code to decimal converter 516 cells are used, which occupy an area equal to 0.43µm2 also in the decimal to excess-3 code converter. 321 cells are used, which occupy an area equal to 0.28 µm2.