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A block diagram of a modified PI fuzzy control system (version 2)

A block diagram of a modified PI fuzzy control system (version 2)

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Fig. 1. Shows distillation column
Fig. 6. A block diagram of a modified PI fuzzy control system (version 2)
Fig. 7. Shows the fuzzy logic structure
Fig. 8. Membership functions for two inputs (version 1) of G11rWB
FUZZY CONTROLLER OF MODEL REDUCTION DISTILLATION COLUMN WITH MINIMAL RULES
Article
Full-text available
  • Jun 2020
In this paper the control of a binary distillation column is described. This control is done with fuzzy logic, one with PI- like fuzzy controller and the other with modified PI fuzzy controller, using the minimal rules for fuzzy processing. This work is focused on model reduction of Wood and Berry binary distillation column to get the best performa...
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... may consider the controller output not as a control signal, but as a change in the control signal. The block diagram for this system is given in Figure 6. It is clear that the gain factor í µí°¾í µí±– is used with the error input and í µí°¾í µí± with the change-of-error. ...

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Intelligent Control of an Industrial Debutanizer Column
Article
  • Jan 2022
  • CHEM ENG TECHNOL
A debutanizer column located at a refinery in Malaysia produces LPG as its top product and light naphtha as its bottom product. Control of the product compositions of the column is very important to maintain the desired purities. The current control design of debutanizer column is based on classical PID approach, which has been found to be less effective in controlling the variations in the column, as the process is characterized by strong nonlinear and uncertain dynamics. Hence, in this paper, intelligent control based on ANFIS is investigated and compared against ANN for the special case of limited training data of the industrial debutanizer column as due to some practical constraints, limited plant step tests data are available for the development of controllers. It is observed that ANFIS‐based controller outperforms the other control configurations for both set point tracking and disturbance rejection cases and has better generalization performance even when trained with limited data samples.
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