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2014 | OriginalPaper | Chapter

Decision Parameter Optimization of Beam Pumping Unit Based on BP Networks Model

Authors : Xiao-hua Gu, Zhi-qiang Liao, Sheng Hu, Jun Yi, Tai-fu Li

Published in: Fuzzy Information & Engineering and Operations Research & Management

Publisher: Springer Berlin Heidelberg

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Abstract

Beam pumping unit is the most popular oil recovery equipment. One of the most common problems of beam pumping unit is its high energy consumption due to its low system efficiency. The main objective of this study is modeling and optimization a beam pumping unit using Artificial Neural Network (ANN). Among the various networks and architectures, multilayer feed-forward neural network with Back Propagation (BP) training algorithm was found as the best model for the plant. In the next step of study, optimization is performed to identify the sets of optimum operating parameters by Strength Pareto Evolutionary Algorithm-2 (SPEA2) strategy to maximize the oil yield as well as minimize the electric power consumption. Forty-nine sets of optimum conditions are found in our experiments.

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Metadata
Title
Decision Parameter Optimization of Beam Pumping Unit Based on BP Networks Model
Authors
Xiao-hua Gu
Zhi-qiang Liao
Sheng Hu
Jun Yi
Tai-fu Li
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-38667-1_2

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