TY  - JOUR
T1  - Computed Multi-Variable Control Applied on Viscosity Control by Means of In-line Mixing Control Using Neural Networks
AU - Ferreiro Garcia, Ramon AU - Catoira, Alberto DeMiguel AU - Rolle, Jose Luis Calvo 
JO  - Research Journal of Applied Sciences
VL  - 5
IS  - 4
SP  - 274
EP  - 282
PY  - 2010
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2010.274.282
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2010.274.282
KW  - In-line mixing
KW  -computed variable control
KW  -functional approximation
KW  -feedforward neural networks
KW  -conjugate gradient algorithm
KW  -multivariable control
AB  - Model-based functional approximation techniques are being used on in-line mixing processes affected by controlled variables which depend strongly on complementary physical variables. The aim for such type of nonlinear control problems is to compute the proportions of input product flow rates yielding a final product thus satisfying as much physical properties as manipulated input flow rates exists. The core of the contribution is based on functional approximation implemented by means of backpropagation neural networks associated to the computed multivariable control strategy on a viscosity control problem.
ER  -