4. Neural network development

The control strategies used in this work are DIC and IMC method. In order to develop and analyze the controller performance for the debutanizer column, there are two criteria for advanced process control which are the set point changes and disturbances changes applied to the column. The set point changes is the step increases for the temperature and the disturbances changes is by introducing a disturbance of the column feed temperature. The performance of the composition are used based on using a neural network estimator.

#### 4.1. Set point changes

the inputs of the neural net is the manipulated variable reboiler and reflux. The outputs predicted are the future predictions of top and bottom temperatures are switched with the manipulated variables. The sequence of the inputs of the network needs to be maintained. The training procedure outlined in this book is called inversed modeling. y(k + 1) is the required set

> �<sup>1</sup> ypð Þ <sup>k</sup> <sup>þ</sup> <sup>1</sup> ; ypð Þ<sup>k</sup> ; ypð Þ <sup>k</sup> � <sup>1</sup> ; u kð Þ; u kð Þ � <sup>1</sup> h i

In this case the manipulated variable reboiler and reflux flow rate are the output variable which are used in inverse model. The one-step ahead prediction of the control output, mv2 (k) and mv3 (k) is performed inconformity with that of the forward model. The one-step ahead control action application in the control strategies involving the neural network-based strategies.

The training and validation data set are predicted for inverse model for the networks are similar to that used for forward modeling. Nevertheless, inverse model will have different

In this case, p is the input to the neural network inverse temperature given by the vector

mv1ð Þ<sup>k</sup> mv1ð Þ <sup>k</sup> � <sup>1</sup> mv2ð Þ <sup>k</sup> � <sup>1</sup> mv3ð Þ <sup>k</sup> � <sup>1</sup> f kð Þ f kð Þ � <sup>1</sup> Ttopð Þ <sup>k</sup> <sup>þ</sup> <sup>1</sup> Ttopð Þ<sup>k</sup> Ttopð Þ <sup>k</sup> � <sup>1</sup> �

After simplifying the weights and biases values by pruning the neural network structure Eq. (10) can further be simplified in order to give the inverse temperature below in a form of

(9)

(10)

point. The network representation of the inverse is finally given below

�<sup>1</sup> represents the inverse map of the forward model.

u kð Þ¼ f

148 Advanced Applications for Artificial Neural Networks

input and output configuration.

Tbotð Þ <sup>k</sup> <sup>þ</sup> <sup>1</sup> Tbotð Þ<sup>k</sup> Tbotð Þ� <sup>k</sup> � <sup>1</sup> <sup>T</sup>

The inverse model for temperature is as follows

Figure 3. Forward and inverse models to control temperature.

where f

equation

First the top temperature is increased from 30 to 58�C. The bottom temperature is increased from 60 to 137�C. The starting point for the top temperature is 30�C and for bottom temperature is 60�C. This is because the starting point temperature mentioned here is based on the experience of the engineers to maintain and control that particular temperature. Figures 4 and 5 show the fluctuation of the top and bottom temperature due to set point changes. There are three types of control strategies implemented for the control strategies which are the IMC, DIC and PID controller. It can be seen that IMC and DIC show similar trends with small error, no overshoot and fast settling time and straight goes to the set point. The settling time for top and bottom temperatures fluctuation is at 200 min. The IMC and DIC method gives the least fluctuations for the set point changes. The fluctuations during step point changes for the PID controller does not give good results because it has large overshoot and small decay ratio. The settling time for PID also shows large value compared to the IMC and DIC methods. The PID controller also produces some offset when there are changes made for set point changes. This applies to the top and bottom temperatures, respectively. Table 1 shows the PID tuning for the column. Table 2 shows the performance of the controller to control the top and bottom temperature. The results indicate that IMC equation gives the optimum performance as the Integral absolute error (IAE), Integral square error (ISE) and Integral time weighted error (ITAE) values is the smallest compared to the result of the controller. Figures 6 and 7 show the fluctuation of the manipulated variables to control temperature. The neural network would be able to predict the manipulated variable for reboiler and reflux accurately compared to PID

Figure 4. Set point top temperature.



Table 1. PID tuning.

#### Advanced Process Control http://dx.doi.org/10.5772/intechopen.70704 151


Table 2. Controller performance during set point changes.

Figure 6. Manipulated variable temperature neural network.

Figure 7. Manipulated variable temperature PID.

Figure 5. Set point bottom temperature.

Table 1. PID tuning.

Parameter Kc Ti Td Top temperature 0.71 1.41 20 Bottom temperature 1.76 3.25 15 Top composition 137.32 3.26 10 Bottom compositon 87.36 3.26 5

Figure 4. Set point top temperature.

150 Advanced Applications for Artificial Neural Networks

controller. Therefore the performance of neural network is better. The fluctuations of the manipulated variable for the reboiler and reflux are very important to see how the controller calculates the error for a control system. The fluctuations for reboiler and reflux flow rate for temperature based on PID show similar trends as time progresses. The units for the calculated IA, ISE and ITAE are dimensionless.

#### 4.2. Disturbances test

Figures 8 and 9 show the fluctuations for the top and bottom temperatures due to disturbances. The disturbances introduced to the debutanizer column are the feed temperature. Similar trends are observed for DIC and IMC methods for the top and bottom temperatures because of disturbances. The neural network control performs well compared to PID controller because there is no overshoot, fast settling time and small error. The PID controller gives unacceptable results as they perform with high overshoot, some offset and large error. This also applies to the top and bottom temperatures. Table 3 shows the performance of the controller to control the top and bottom temperatures. Results indicate that IMC equation gives the optimum performance as the values of IAE, ISE and ITAE are the smallest compared to other controller. Figures 10 and 11 show the fluctuation of the manipulated variable to control temperature. The neural network would be able to predict the manipulated variable for reboiler and reflux accurately compared to PID controller. Therefore the performance of neural network is better. The fluctuation of the manipulated variable for the reboiler and reflux flow rate is very important in order to see how the controller calculates the error for a given control system. The fluctuations for reboiler and reflux flow rate for temperature based on PID shows similar trends as time progresses.

Figure 8. Disturbances top temperature.

Figure 9. Disturbances bottom temperature.

controller. Therefore the performance of neural network is better. The fluctuations of the manipulated variable for the reboiler and reflux are very important to see how the controller calculates the error for a control system. The fluctuations for reboiler and reflux flow rate for temperature based on PID show similar trends as time progresses. The units for the calculated

Figures 8 and 9 show the fluctuations for the top and bottom temperatures due to disturbances. The disturbances introduced to the debutanizer column are the feed temperature. Similar trends are observed for DIC and IMC methods for the top and bottom temperatures because of disturbances. The neural network control performs well compared to PID controller because there is no overshoot, fast settling time and small error. The PID controller gives unacceptable results as they perform with high overshoot, some offset and large error. This also applies to the top and bottom temperatures. Table 3 shows the performance of the controller to control the top and bottom temperatures. Results indicate that IMC equation gives the optimum performance as the values of IAE, ISE and ITAE are the smallest compared to other controller. Figures 10 and 11 show the fluctuation of the manipulated variable to control temperature. The neural network would be able to predict the manipulated variable for reboiler and reflux accurately compared to PID controller. Therefore the performance of neural network is better. The fluctuation of the manipulated variable for the reboiler and reflux flow rate is very important in order to see how the controller calculates the error for a given control system. The fluctuations for reboiler and reflux flow rate for temperature based on PID shows similar trends as time

IA, ISE and ITAE are dimensionless.

152 Advanced Applications for Artificial Neural Networks

4.2. Disturbances test

progresses.

Figure 8. Disturbances top temperature.


Table 3. Controller performance during disturbance changes.

#### 4.3. Estimator neural network

The neural network estimator used in the IMC and DIC method is to estimate and monitor the top and bottom compositions. Figures 12 and 13 show the fluctuations for the top and bottom compositions which are due to set point changes. For the neural network estimator for IMC for top composition are favorable than DIC method. This is due to the settling time to settle to the required set point for the composition is fastest. This could conclude that both IMC and DIC method perform better compared to the conventional PID controller. This is because the error is small with no overshoot. The results for PID controller are unacceptable because of large overshoot, large error and longer settling time. For the bottom composition fluctuations, the IMC and DIC methods show similar trends. Both methods show better fluctuations compared to PID controller. Figure 14 shows the fluctuation of the manipulated variable for composition.

Figure 10. Manipulated variable temperature neural network disturbances.

Figure 11. Manipulated variable temperature PID disturbances.

Figures 15 and 16 show the fluctuations for the top and bottom compositions due to disturbances. For the top composition for neural network controller for IMC and DIC methods, it could be concluded that the IMC trend shows similar results to the DIC method. The settling time for the required set point for the composition is similar. Both IMC and DIC methods are superior in comparison to the conventional PID controller. This is because the error is small with no overshoot. The results for PID controller are unacceptable that are due to large overshoot, large error and longer time to settle. For the bottom composition fluctuations, the

Figure 12. Neural network estimator for the top composition.

Figure 13. Neural network estimator for the bottom composition.

Figures 15 and 16 show the fluctuations for the top and bottom compositions due to disturbances. For the top composition for neural network controller for IMC and DIC methods, it could be concluded that the IMC trend shows similar results to the DIC method. The settling time for the required set point for the composition is similar. Both IMC and DIC methods are superior in comparison to the conventional PID controller. This is because the error is small with no overshoot. The results for PID controller are unacceptable that are due to large overshoot, large error and longer time to settle. For the bottom composition fluctuations, the

Figure 10. Manipulated variable temperature neural network disturbances.

154 Advanced Applications for Artificial Neural Networks

Figure 11. Manipulated variable temperature PID disturbances.

Figure 14. Manipulated variable compositions for PID.

Figure 15. Top composition disturbances.

IMC and DIC methods show similar trends. Both methods show better fluctuations compared to PID controller. Figure 17 shows the fluctuation of the manipulated variable for composition PID which is due to disturbances.

Figure 16. Bottom composition disturbances.

Figure 17. Manipulated variable compositions PID due to disturbances.

#### Acknowledgements

IMC and DIC methods show similar trends. Both methods show better fluctuations compared to PID controller. Figure 17 shows the fluctuation of the manipulated variable for composition

PID which is due to disturbances.

Figure 15. Top composition disturbances.

Figure 14. Manipulated variable compositions for PID.

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The authors would like to acknowledge PETRONAS for providing the required data and information for the research. I would like to acknowledge University Malaya for providing the grant for the research (PS107/2010B).
