• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.670-673
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• 1987

The structure of chemical process has become increasingly complex, due to better management of energy and raw materials. As a consequence, the design of control systems for complete plants now constitutes the focal point of engineering interest, rather than controller designs for single processing units. Instead of traditional methods based on complex mathematical model, chemical processes are represented by structural array and cause-and-effect graph to apply non-numerical problem-solving techniques. A systematic logical procedure to synthesize alternatives of control system structure and some heuristic rules to select a feasible solution from the vast number of alternatives that are possible are considered in this study.

• Journal of the Korean Institute of Gas
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• v.11 no.2 s.35
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• pp.60-64
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• 2007

Chemical Process industry in Korea has over 30 year's of history and is likely to face potential incidents. The traditional risk analysis and control system in Chemical Process industry focuses on mechanical defects, overlooking the human performance control. Although development of automation technology and controlling technology was necessary, human decision factor is essential to preventing accidents in the Chemical Process. Almost all serious accidents take place when inappropriate humanperformance and mechanical defects of safety equipments simultaneously occurs. The AHRA(Advanced Human Reliability Analyzer) software has been developed to collect failure data and analyze human error probability (Reliability) in Chemical Process Industry in Korea. This paper describes the HRA analysis result of PIF(Performance Influencing Factor) evaluation, HEP(Human Error Probability) and root cause of accidents by applying a Chemical Process Industry related accident data. This analysis result should present a scheme that, by controlling human error factor other than putting safety management funds into the machinery in plants, can reduce cost and maximize the safety in Chemical Process Industry.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.322-326
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• 1991

A real time process control package was developed in an INTEL 80386 based PC and MS OS/2 environment using MS-C and MS-FORTRAN. RTACS(Real Time Advanced Control System), process control computer software for distributed or centralized architectures, is a package which meets functional requirements specified for typical continuous process applications like chemical processes. The package consists of 5 parts, which are DB(data base), OCF(Operator Console Functions), CL (Control logic Library), MSM(Multitasking and Scheduling, Manager) and UAI(User Applications Interface), based upon a table and function block architecture to improve the system performance.

• Korean Chemical Engineering Research
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• v.55 no.6
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• pp.767-770
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• 2017

Quadruple tank liquid level systems are popular in testing multivariable control systems for multivariable processes with positive or negative zeros. The liquid level system is nonlinear and it will help to illustrate the robustness of control systems. However, due to nonlinearity, it can be cumbersome to obtain process parameters for testing linear control systems. Perturbation sizes are limited for valid linearized process models, requiring level sensors with high precision. A simple method where the outlet orifice is replaced to a long tube is proposed here. The effluent flow rate becomes proportional to the liquid level due to the friction loss of long tube and the liquid level system shows near linear dynamics. It is applied to the quadruple tank system for easier experiments.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.331-336
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• 1989

Under the condition of stable inverse a billinear model predictive control method for SISO and MIMO system with time delay is derived. For processes subject to a bounded disturbance the proposed control method with a classical recursive adaptation algorithm was shown to be stable in the sense of the convergence of parameter estimates and the boundedness of the control error. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2600-2605
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• 2003

The PBL (Poly-butadiene Latex) production process is a typical batch process. Changes of the reactor characteristics due to the accumulated scaling with the increase of batch cycles require adaptive tuning of the PID controller being used. In this work we propose a tuning method for PID controllers based on the closed-loop identification and the genetic algorithm (GA) and apply it to control the PBL process. An approximated process transfer function for the PBL reactor is obtained from the closed-loop data using a suitable closed-loop identification method. Tuning is performed by GA optimization in which the objective function is given by ITAE for the setpoint change. The proposed tuning method showed good control performance in actual operations.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.875-878
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• 1988

In this work it is shown how the methods used in chemical engineering for the analysis and simulation of processes may be applied to the actual phosphoric acid plant. Attention has been focused on the dihydrate process for which the necessary fundamental experimental data and plant operation data are available. The results of the simulation have shown that a reasonable description of the process at hand is possible by the proposed method. However, because of the complexity of the process, of the limited basic experimental data reported in literature, and the limitations of mathematics, the model was somewhat idealized and gave a reliable representation of the influence of only a few of the variables that affect the performance of the plant.