首页|Rzeszow University of Technology Reports Findings in Artificial Intelligence (Me asuring volume fractions of a three-phase flow without separation utilizing an a pproach based on artificial intelligence and capacitive sensors)
Rzeszow University of Technology Reports Findings in Artificial Intelligence (Me asuring volume fractions of a three-phase flow without separation utilizing an a pproach based on artificial intelligence and capacitive sensors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Rzeszow , Poland, by NewsRx journalists, research stated, "Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Vario us combinations of them can be expected and gas-oil-water is one of the most com mon flows." The news reporters obtained a quote from the research from the Rzeszow Universit y of Technology, "Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utiliz ed to ascertain the volumetric proportion of each phase. Sensors based on measur ing capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for mea suring in the process. Besides, at the present moment, Artificial intelligence ( AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, s tratified, and homogeneous. In this paper, volume fractions in a gas-oil-water t hree-phase homogeneous regime are measured. To accomplish this objective, an Art ificial Neural Network (ANN) and a capacitance-based sensor are utilized. To tra in the presented network, an optimized sensor was implemented in the COMSOL Mult iphysics software and after doing a lot of simulations, 231 different data are p roduced. Among all obtained results, 70 percent of them (161 data) are awarded t o the train data, and the rest of them (70 data) are considered for the test dat a. This investigation proposes a new intelligent metering system based on the Mu ltilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid's water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method's considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be c onsidered for future works."
RzeszowPolandEuropeArtificial Inte lligenceEmerging TechnologiesMachine Learning
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