Multivariable control of a pulsed liquid-liquid extraction column by neural networks

Modelling and control of chemical process systems ave usual applications of artificial neural networks that have been explored so far with success. This paper deals with the potential application of neural networks to the multivariable control of a solvent extraction pilot plant. The pilot plant to be controlled is a pulsed liquid-liquid extraction column, which presents a non-linear behaviour and time-varying dynamics. Previous works have shown that the column could be maintained in its optimal behaviour by means of the control of conductivity by action on the pulse frequency. A given product specification can be obtained by the control of the product concentration in the outlet stream by acting on the solvent feed-flow rate. Owing to interactions between one variable and the other, a two input-two output control scheme has been developed and implemented. Promising experimental results have been obtained by using neural networks as an alternative tool for online control of chemical plant with dynamic changes.