Paper Title:
Multi-ANN Predictive Control of Citrus Peel Supercritical Extraction Temperature
  Abstract

Considering nonlinearity, time-variation and inertia during temperature control of large supercritical extraction units, especially under the disturbance of system flow and pressure, a multi-artificial neural network (ANN) predictive control policy was proposed. It contains a radial basis function (RBF) ANN, aiming to approach nonlinear extraction temperature object and predicting output variable based on this model. There is also a back propagation (BP) ANN controller, seeking the optimal controlling signal by feedback correction and rolling optimization on purpose to overcome the time-variation and inertia. The experimental results indicate that this control strategy has excellent dynamic response performance, small steady state error and strong robustness.

  Info
Periodical
Advanced Materials Research (Volumes 236-238)
Edited by
Zhong Cao, Yinghe He, Lixian Sun and Xueqiang Cao
Pages
1472-1479
DOI
10.4028/www.scientific.net/AMR.236-238.1472
Citation
H. Y. Zhou, Q. Zhao, Z. H. Zhang, M. W. Tong, Z. H. Wang, "Multi-ANN Predictive Control of Citrus Peel Supercritical Extraction Temperature", Advanced Materials Research, Vols. 236-238, pp. 1472-1479, 2011
Online since
May 2011
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Price
$32.00
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