• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.474-478
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• 1986

The phenomenon of a continuous Steel-making process was studied with a set of collected equilibrium data for the steel-oxidation reactions. Mass and Heat balances were also established. Mass transfer constants which are physically unmeasurable but escential for the simulation study in the steel-making process were calculated from the experimental data using an optimization technique. Based on these data various operating conditions and process characteristics were examined.

• 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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• 1999.10a
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• pp.53-56
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• 1999

The control performance of a chemical process is determined by process structure as well as the performance of controllers. Therefore, the concept of“controllability”should be introduced in the early design stage. Structural information makes controllability assessment possible by giving insights on the pathways of disturbances in the process. In this study, a simple procedure to evaluate controllability is suggested to screen out design alternatives using relative order analysis and structural decomposition. The effectiveness of the proposed method was validated by comparing the results with the case of dynamical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.519-524
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• 2005

A numerical process to simulate SiO2 dry etching with inductively coupled C2F6 plasmas has been constructed using a commercial CFD code as a first step to design a run-to-run control system. The simulator was tuned to reasonably predict the reactive ion etching behavior and used to investigate the effects of plasma operating variables on the etch rate and uniformity. The relationship between the operating variables and the etching characteristics was mathematically modeled through linear regression for future run-to-run control system design.

• 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.

• Current Photovoltaic Research
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• v.7 no.2
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• pp.33-37
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• 2019

In this paper, we compared the performance of $Cu(In,Ga)(S,Se)_2$ (CIGSSe) thin film solar cell with CdS buffer layer deposited by irradiating 365 nm UV light with 8 W power in Chemcial Bath Deposition (CBD) process. The effects of UV light irradiation on the thin film deposition mechanism during CBD-CdS thin film deposition were investigated through chemical and electro-optical studies. If the UV light is irradiated during the solution process, the hydrolysis of Thiourea is promoted even during the same time, thereby inhibiting the formation of the intermediate products developed in the reaction pathway and decreasing the pH of the solution. As a result, it is suggested that the efficiency of the CdS/CIGSSe solar cell is increased because the ratio of the S element in the CdS thin film increases and the proportion of the O element decreases. This is a very simple and effective approach to control the S/O ratio of the CdS thin film by the CBD process without artificially controlling the process temperature, solution pH or concentration.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.46.3-46
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• 2002

Most of the process control algorithms in practice are based on the finite dimensional control theory. However, many chemical processes are described by partial differential equations (PDE's) and are infinite dimensional in nature due to spatial variation. Especially when the convection is dominant and thus diffusion can be ignored, chemical processes that are described by a system of first order hyperbolic PDE's. Such processes include tubular reactors, fixed bed reactors and pressure swinging adsorption. Conventionally such infinite dimensional systems described by PDE's are controlled by finite dimensional controllers that are designed through finite dimensional reduction of the process m...

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.419-424
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• 1992

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.22-30
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• 2022

In order to prevent chemical accidents caused by human error, a chemical accident prevention and response training program using advanced technology was developed. After designing a virtual process based on the previously built pilot plant, chemical accident response contents were developed. A part of the pilot facility was remodeled for content realization and a remote control function was given. In addition, a DCS program that can control facilities in a virtual environment was developed, and chemical process operator training (OTS) that can finally respond to virtual chemical accidents was developed in conjunction with AR. Through this, trainees can build driving skills by directly operating the device, and by responding to virtual chemical accidents, they can develop emergency response capabilities. If the next-generation OTS like this study is widely distributed in the chemical industry, it is expected to greatly contribute to the prevention of chemical accidents caused by human error.