Neuro-fuzzy controller for methanol recovery distillation column

Abstract EN

Distillation columns are widely used in chemical processes as separation systems in industries.

In order to gain better product quality and lower the energy consumption of the distillation column, an effective control system is needed to allow the process to be operated over larger operating ranges.

In this study Different control strategies were used to control the distillate and bottom compositions of a packed distillation column to separate the mixture of methanol (CH3OH) and water (H2O).

The tuning of control parameters were determined for PI and PID controllers using three different methods; Internal Model Control (IMC), Ziegler- Nichols (Z.N), and Cohen-Coon (PRC) to find the best values of proportional gain (KC), integral time (τI) and derivative time (τD).

The Internal Model Control (IMC) method gave better results than that of the other two methods thus it was recommended to be the tuning method in this work.

The low values of ITAE of 61.3 for distillate product composition and 54 for bottom composition were obtained which represent the adaptive neuro-fuzzy inference system (ANFIS) method and assure the feasibility of this method as a control strategy among other methods (conventional feedback controllers (PI, PID), artificial neural network (ANN), adaptive fuzzy logic and PID fuzzy logic controllers).