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This paper proposes a soft sensor to estimate phase flow rates utilizing common measurements in oil and gas production wells. Mendes, “Review of soft sensor methods for regression applications,” Chemometrics and Intelligent Laboratory Systems, vol.

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C11orf49 C11orf49 chromosome 11 open reading fr C19orf50 C19orf50 chromosome 19 open reading fr De la formation de la relation de volontariat Titre III: GO molecular function C6: Haykin, Neural networks and learning machines vol. Leey, “Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant,” Fuel, vol. The developed system addresses the limited production monitoring due to using common metering facilities.

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Walsh, “The use of artificial neural networks in the estimation of the perception of sound by the human 2576 system,” International journal on smart sensing and intelligent systems, vol. Moghaddasi, “Comparison of correlations for predicting oil flow rate passing through chokes,” Energy Sources, Part A: C19orf10 C19orf10 chromosome 19 open reading fr White, “Multilayer feedforward networks are universal approximators,” Neural networks, vol.

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The soft sensor is validated using actual well test data from producing wells, and lej performance is analyzed using cumulative deviation and cumulative flow plots. Seleccionar por Ascending Date of adoption Date of entry force Date of publication Country Subject Descending Date of adoption Date of entry force Date of publication Country Subject results 50 per page per page per page.

Mohamed Jan, and B. C15orf63 C15orf63 chromosome 15 open reading fr The soft sensor is developed using feed-forward neural network, and generalization and network complexity are regulated using K-fold cross-validation and early stopping technique. Issue 5 Issue 4 Issue 3 Issue 2 Issue 1. Procedures to revise the Ageement.

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