Paper Title:
Speed Prediction of ESPCP System Based on Neural Network
  Abstract

ESPCP is a new way of well lifting. The speed optimization is the main way to improve economic index of ESPCP system. By analyzing the main factors of speed of ESPCP system and using the technology of artificial neural networks, established an artificial neural network model for output speed about oil viscosity, pressure difference of the pump two ends, magnitude of interference between stator and rotor as the input variables. Through the use of additional momentum and adaptive learning rate method to predict the learning samples, the results can fit experimental data. The results of recalling and forecasting are accurate. It is shown that the model is of high precision and reliability. It also provides a new calculation for ESPCP system speed prediction and optimization.

  Info
Periodical
Edited by
Ran Chen
Pages
3569-3573
DOI
10.4028/www.scientific.net/AMM.44-47.3569
Citation
X. Luo, S. J. Wang, X. R. Lv, H. Sun, "Speed Prediction of ESPCP System Based on Neural Network", Applied Mechanics and Materials, Vols. 44-47, pp. 3569-3573, 2011
Online since
December 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Qiang Luo, Qing Li Ren
Abstract:A three-layer back-propagation neural network model based on the non-linear relationship between the size of the SrTiO3 nanocrystalline and...
2497
Authors: Bo Zhao
Chapter 1: Material Engineering and its Application
Abstract:The artificial neural network model is used to predict the breaking elongation of polyester/cotton ring spinning yarn in this paper. In order...
108
Authors: Bing Hua Mo, Zi Nan Pan
Chapter 15: Materials Processing Technology
Abstract:A neural network model is established to predict the joint quality in resistance microwelding (RMW) of fine Cu wire and stainless steel thin...
1709
Authors: Bo Zhao
Chapter 1: Energy Materials and Material Applications with Analysis of Material Properties
Abstract:The filtration properties of melt blowing nonwovens are affected by the pore structure of nonwovens which is strongly related to the...
47
Authors: Lei Yang, Wei Dong Dai
Chapter 17: Transportation Engineering
Abstract:In this paper, genetic neural network is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time...
2866