• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.263-268
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• 2001

A new control scheme s proposed for the control of reactive batch distillation (RBD) column. A nonlinear wave model captures the essential dynamic behavior of the RBD process. The proposed control scheme is based on both Generic Model Control(GNC) and nonlinear wave model. The control scheme uses a profile position of the column as a controlled variable. Ethanol esterification process using RBD is chosen as an example process. Tight control of the distillate purity is obtained with the use of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.105-107
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• 1991

A chemical process model represented by partial differential equations was studied as one of nonlinear distributed parameter control problems. Using an optimal control theory in the form of maximum principles based on nonlinear integral equations, an algorithm to solve the problem was developed and coded into a computer program.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.639-643
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• 1997

This paper presents how nonlinear PID control algorithms can be applied on chemical processes for a more stable operation and perfect automation. A pass balance controller is designed to balance the exiting temperatures of a heater and a heat exchange network. The proposed controller has gain-varying integral action and deals with the operational constraints in an efficient manner. Also, the use of a PID gap controller is proposed to maximize energy saving and operation stability and to minimize operator intervention in operation of air fan coolers. The proposed controller adjusts the opening of a louver automatically in such a way that it keeps the air fan pitch position within the desired range. All these nonlinear PID controllers have been implemented on the distributed control system (DCS) for good reliability and operability. Operator acceptance was very high and the implemented controllers have shown good performance and high service factor still now on. The proposed methodology can be directly applied to similar processes without any modification.

• Korea-Australia Rheology Journal
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• v.13 no.4
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• pp.179-187
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• 2001

As part of continuing efforts to investigate nonlinear dynamics and stability of film casting process, our earlier results obtained by Lee et al. (2001b) have been extended in the present study to cover the film casting of both extension thickening and extension thinning fluids. The same instability mechanism and draw resonance criterion previously derived have been found valid here, and a rather complex dynamic behavior of film width in contrast to that of film thickness has also been confirmed. The effect of fluid viscoelasticity on draw resonance, however, exhibits opposite results depending on whether the fluid is extension thickening or thinning, i.e., it stabilizes film casting in the former while destabilizing in the latter. The encapsulation extrusion method which recently has been successfully employed to stabilize industrially important paper coating process, has been theoretically explained in the present study as to why such stabilization is possible.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.48-53
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• 1993

In this paper, it is shown that chaotic nonlinear chemical process can be controlled based on the Poincare map based control algorithm. An isothermal autocatalytic CSTR, which has chaotic dynamics, is successfully controlled and period 2 orbit is generated in a normal chaotic region with small perturbation of the control parameter.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.49-52
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• 1999

A subspace-based identification method of the Wiener model, consisting of a state-space linear block and a polynomial static nonlinearity at the output, is used to retrieve from discrete sample data the accurate information about the nonlinear dynamics. Wiener model may be incorporated into model predictive control (MPC) schemes in a unique way which effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC. The control performance is evaluated with simulation studies where the original first-principles model for a continuous MMA polymerization reactor is used as the true process while the identified Wiener model is used for the control purpose. On the basis of the simulation results, it is demonstrated that, despite the existence of unmeasured disturbance, the controller performed quite satisfactorily for the control of polymer qualities with constraints.

• ICROS
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• v.2 no.1
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• pp.59-71
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• 1996

In the last decade, the model predictive control methods have enjoyed many industrial applications with successful results. Although the general predictive control methods for nonlinear chemical processes are not yet formulated, the promising features of the model predictive control methods attract attentions of many researchers who are involved with difficult but important nonlinear process control problems. Recently, the class of bilinear model has been introduced as an useful tool for examining many nonlinear phenomena. Since their structural properties are similar to those of linear models, it is not difficult to develop a robust adaptive model predictive control method based on bilinear model. We expect that the model predictive control method based on bilinear model will expand its region in the world of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.934-938
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• 1989

This paper considers the model predictive control (MPC) problems in nonlinear processes or systems. The MPC method determines the control law such that the predicted output based on the identified process model is equal to the reference output which consists of both the process output at current time and the setting value called as the command generator. In this paper, the nonlinear MPC software for a chemical reactor is developed and analized from the point of view of practical applications.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.