A feedforward control strategy for distillation columns

doi:10.1016/S0954-1810(97)00002-2

Artificial Intelligence in Engineering

Volume 11, Issue 4, October 1997, Pages 405-412

https://doi.org/10.1016/S0954-1810(97)00002-2 Get rights and content

Abstract

The prime objective of this work is to demonstrate the potential of neural network modeling for advanced nonlinear control applications. In particular, for the case of a single composition distillation column, a model-based neural controller is developed to regulate the composition of the distillate stream. The neural controller relies on process inversion for the evaluation of the actuator action on the manipulated variable (reflux flowrate) to maintain the controlled variable (distillate composition) at the prescribed value.

The performance of the neural controller is assessed and compared with that of a conventional temperature control loop and of a neural inferential control structure. The neural controller by far outperforms the other two in terms of the response speed by which the upsetting loads are compensated.

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Citation Excerpt :
Their work indicated that the model was able to handle process time delay and disturbance. Baratti and Servida [10] have utilized an inverse model based neural controller to regulate the composition of the distillate stream in a distillation column. Research by Vieira et al. [11] had utilized neural network for temperature control of a kiln system.
Hydrolyzer is a commonly found unit operation in the splitting of crude palm oil into fatty acids and glycerol in the oleochemical industry of Malaysia. The control of this hydrolyzer has to be done carefully since efficiency in the control of this unit will affect the further yield of the process. At present conventional controllers such as the PID controller have been used to control the unit especially during startup and shutdown of the plant and under presence of disturbances. However experience shows that these PID controllers cannot efficiently handle random disturbance entering the plant. In this study, neural network have been applied as an alternative to cope with the nonlinear dynamics of the hydrolyzer. A mathematical model had been developed and used to simulate the dynamic responses of the temperatures when the controllers were applied into the system. Two types of control strategies namely, direct inverse controller (DIC) and internal model controller (IMC) were implemented, in simulation with actual industrial data, within the control system. The controllers were evaluated on the ability to track set-point and the ability to control the temperature when disturbances and noise appeared in the system. Based on the results, IMC was found to perform very well in the temperature control of the hydrolyzer during set-point tracking and disturbance tests. The responses generated by the IMC was much more stable as compared to the conventional controllers and when noise disturbance was taken into consideration, the IMC also performs better than the DIC controller.
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In modern chemical industries the purity of the distillate is the main objective and time to estimate the distillate composition is also the constraint. In the present paper, the Levenberg–Marquardt (LM) approach is proposed for predictive inferential control of distillation process. The developed estimator using LM approach predicts the composition of distillate using column pressure, reboiler duty, and reflux flow along with the temperature profile of the distillation column as inputs.
In complex chemical industries where the output depends on many parameters, Steepest Descent Back Propagation (SDBP) algorithm does not work properly for estimating the composition of distillate, which results in saturated outputs and differs from the desired results. To overcome such type of situation, LM approach is used in developed estimator. The estimated results are compared with the simulation results and it is observed that the results obtained from LM approach are significantly improved than the results obtained from SDBP algorithm. To enhance the accuracy of the estimated results, the pressure, reflux flow and heat input with temperature profile of the column are used as input to train the neural network.
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Adequate process monitoring and optimal control of distillation columns relies heavily on accurate and preferably on-line estimates of the product composition. Hence, inferring the product composition from easily accessible and abundantly available process measurements has become a key element for successful operation. This paper compares a hybrid soft sensor approach, based on the General Distillation Shortcut method introduced by Friedman in 1995, with a pure black box approach. On the basis of two industrial multicomponent distillation case studies, it can be concluded that the hybrid GDS approach outperforms the black box one if (i) a temperature measurement is available that is sensitive for the to be predicted concentration and (ii) if that concentration is present in a substantial amount with respect to the other components. The black box soft sensors do not suffer from that last drawback but, once again, their lack of extrapolative power is clearly illustrated.
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A feedforward control strategy for distillation columns

Abstract

Comput. Chem. Engng

Comput. Chem. Engng