• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.587-591
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• 2014

Experiments for a control method that enhances the yield to produce high quality red ginsengs have been performed. In the first steaming process of a series of processes to produce red ginsengs from raw ginsengs, there occur several undesirable defects on ginsengs such as cracks of ginseng body, inside cavity and inside white. These defects lead to deterioration in product qualities. Therefore an improved control method that minimizes these undesirable defects is needed in order to increase the yield of high quality red ginsengs. Until these days, the steaming process control methods such as controlling the steaming temperature and/or pressure have been studied. However, such control methods are not adequate enough to minimize the undesirable defects in steamed ginsengs. On the other hand, in this experiment, we suggest a control method that minimizes the undesirable defects through a weight control of steamed ginsengs, keeping the steaming temperature at $96{\sim}99^{\circ}C$ as usual. Experiments with the weight control show that amount of cracks on the steamed ginseng body can be reduced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1812-1816
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• 2011

This research developed chemical flow control system(CFCS) essential for spinner equipment in nano semiconductor manufacturing process under the 100nm to prevent complex process defect due to missing spread after chemical injection. The devices developed in this research, which can be swiftly replaced in case abnormal state element changes or wafer manufacturing defect occurs, are anticipated to improve module yield as well as real-time monitoring on the state element. In addition, as a result of mounting H/W and S/W system to control detailed operation sequence in production line and executing performance check and verification, we can be exactly detected in five abnomal process type.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1178-1183
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• 2006

This paper is concerned with the modeling and identification of pH neutralization process as nonlinear chemical system. The pH control has been applied to various chemical processes such as wastewater treatment, chemical, and biochemical industries. But the control of the pH is very difficult due to its highly nonlinear nature which is the titration curve with the steepest slope at the neutralization point. We apply SVM which have become an increasingly popular tool for machine teaming tasks such as classification, regression or detection to model pH process which has strong nonlinearities. Linear and radial basis function kernels are employed and each result has been compared. So SVH based on kernel method have been found to work well. Simulations have shown that the SVM based on the kernel substitution including linear and radial basis function kernel provides a promising alternative to model strong nonlinearities of the pH neutralization but also to control the system.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.05a
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• pp.217-220
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• 2001

In the shearing process the burr or rollover must be minimized in order to improve the quality of product. The burr size can be minimized by control of several process parameters. But removal of all burrs are impossible. Most mechanical type deburring methods (vibrating bowls, rotating barrels, shot blasting, for example.) will remove large burrs, other methods use chemical (electro-chemical deburring) or heat (thermal energy deburring). The electro-chemical deburring process removes burrs by the deplating method. Electro-chemical deburring equipment is requires a small capital investment than other methods(mechanical or thermal methods). Electro-chemical deburring method need to many parameters for control such as a time, voltage and concentration of electrolyte. In this paper shows relations of these parameters by experiment.

• Korean Chemical Engineering Research
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• v.50 no.5
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• pp.815-824
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• 2012

Extractive distillation system can be used when the components to be separated have close boiling points or form azeotropes. Extractive distillation is one of the most important and widely used separation methods in chemical process industry. The main disadvantage of the distillation is its high-energy requirements. Thermally coupled distillation system (TCDS) can provide significant savings in energy consumption and capital cost over the operation of sequences based on conventional distillation column. Despite such advantages of the thermally coupled distillation system, the process is not widely used in industry because control and operation of the column are difficult. In this study, we propose several control schemes for thermally coupled distillation system to overcome the difficulties and make the column stable when the process is confronted with feed disturbances. Profile position control scheme shows best control performance among the proposed control schemes.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1128-1136
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• 2007

This paper presents an application study of a model predictive control based commercial package PCTP to real chemical processes. The first case study concerns a product purity control of a splitter process which distillates styrene from undesired component ethyl-benzene produced from ethyl-benzene dehydrogenation reaction. The second case study is about a temperature control of ethyl-benzene dehydrogenation reactor and an excess oxygen control of the fired heater. Optimum control structure for MPC application is developed for each case study. The application results show a significant improvement in control performance and stability.

• Journal of the Korean Society of Safety
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• v.24 no.1
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• pp.31-36
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• 2009

This paper focuses on techniques of improving refinery reliability, availability, and profitability. Our team developed a corrosion control document(CCD) for processing of the crude distillation unit(CDU). Recent study shows the loss due to corrosion in US is around $276 billion. It's a big concern for both managers and engineers of refinery industry. The CCD consists of numerous parts namely damage mechanism(DM), design data, critical reliability variable(CRV), guidelines, etc. The first step in the development of CCD is to build material selection diagram(MSD). Damage mechanisms affecting equipments and process need to be chosen carefully based on API 571. The selected nine DM from API 571 are (1) creep/stress rupture, (2) fuel ash corrosion, (3) oxidation, (4) high temperature sulfidation, (5) naphthenic acid corrosion, (6) hydrochloric acid(HCL) corrosion, (7) ammonium chloride(salt) corrosion, (8) wet $H_2S$ corrosion, and (9) ammonia stress corrosion cracking. Each DM related to corrosion of CDU process was selected by design data, P&ID, PFD, corrosion loop, flow of process, equipment's history, and experience. Operating variables affecting severity of DM are selected in initial stage of CRV. We propose the guidelines for reliability of equipments based on CRV. The CCD has been developed on the basis of the corrosion control in refinery industry. It also improves the safety of refinery process and reduces the cost of corrosion greatly.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.344-349
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• 1990

Under the assumption that process input/output data are sufficiently rich to allow reasonable plant identification, a long-range predictive control method for SISO bilinear plant is derived. In order to ensure offset-free behaviour of the control method, a new bilinear CARIMA model with variable dead-time is introduced. Furthermore, to extend the maximum output prediction horizon, the future predicted outputs in the bilinear term are assumed to be equal to the known future set-points. With a classical recursive adaptation algorithm, the proposed control scheme is capable of stable control of bilinear plants with variable parameters, with variable dead-time, and with a model order which changes instantaneously. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.263-278
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• 2004

This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and objective, however, the algorithms and techniques available from these fields are not directly applicable to process control problems, and reformulations based on accurate understanding of these techniques are needed. We categorize the currently available approximate solution techniques fur dynamic programming and identify those most suitable for process control problems. Several open issues are also identified and discussed.