An Adaptive Neuro-Fuzzy Approach to Control a Distillation Column

In this paper we use a control strategy that enhances a fuzzy controller with self-learning capability for achieving the control of a binary methanol-propanol distillation column. An Adaptive-Network-based Fuzzy Inference System (ANFIS) architecture extended to cope with multivarible systems has been used. This allows the tuning of parameters both of the membership functions and the consequents in a Sugeno-type inference system. To satisfy the control objectives the backpropagation gradient descent through the plant method is applied, hence identification of the plant dynamics is also needed. The performance of the resulting neuro-fuzzy controller under different reference settings for the concentration of methoanol demonstrates the stabilisation of the concentration profiles in the column, leading to an effective methanol composition control.