BSP-based support vector regression machine parallel framework

Zhang, Hong; Lei, Yongmei

doi:10.1109/icis.2013.6607862

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Optimization Of Svmr Parameters With Genetic Algorithms1

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2018

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“…Equation (6) can be solved using dual programming [14]. The goal is to build an objective function by adding a set of variables a i called Lagrange multipliers, where the dual formulation is given by…”

Section: Optimization Of Svmr Parameters With Genetic Algorithmsmentioning

confidence: 99%

Multiphase gas-flow model of an electrical submersible pump

Ricardo

Jiménez

Ferreira

et al. 2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Various artificial lifting systems are used in the oil and gas industry. An example is the Electrical Submersible Pump (ESP). When the gas flow is high, ESPs usually fail prematurely because of a lack of information about the two-phase flow during pumping operations. Here, we develop models to estimate the gas flow in a two-phase mixture being pumped through an ESP. Using these models and experimental system response data, the pump operating point can be controlled. The models are based on nonparametric identification using a support vector machine learning algorithm. The learning machine’s hidden parameters are determined with a genetic algorithm. The results obtained with each model are validated and compared in terms of estimation error. The models are able to successfully identify the gas flow in the liquid-gas mixture transported by an ESP.

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Section: Optimization Of Svmr Parameters With Genetic Algorithmsmentioning

confidence: 99%