• Journal of the Korean Mathematical Society
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• v.49 no.3
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• pp.641-658
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• 2012

The goal of this paper is to develop a nonlinear observer-based control strategy for a multi-variables continuous stirred tank reactor (CSTR). A new robust nonlinear observer is constructed to estimate the whole process state variables. The observer is coupled with a nonlinear controller, designed based on the input-output linearization for controlling the concentration and reactor temperature. The closed loop system is shown to be globally asymptotically stable based on Lyapunov arguments. Finally, computer simulations are developed for showing the performance of the proposed controller.

• 한국가스학회:학술대회논문집
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• 2000.09a
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• pp.168-173
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• 2000

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1280-1286
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• 2004

A lot of industrial chemical processes contain certain input nonlinearities even though they are controlled by several linear controllers. Here we investigate a sequential loop closing identification method for MIMO Hammerstein nonlinear processes with diagonal nonlinearities. The proposed method separates the identification of the nonlinear static function from that of the linear subsystem by using a relay feedback test and a triangular type signal test. From 2 n activations for n n MIMO nonlinear processes, we sequentially identify the whole range of the nonlinear static function as well as the transfer function matrix of the linear subsystem.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• Journal of the Korean Chemical Society
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• v.56 no.4
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• pp.406-415
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• 2012

The corrosion inhibition of copper-nickel alloy by Ethylenediamine (EDA) and Diethylenetriamine (DETA) in 1.5M HCl has been investigated by weight loss technique at different temperatures. Maximum value of inhibitor efficiency was 75% at $35^{\circ}C$ and 0.2 M inhibitor concentration EDA, while the lower value was 4% at $35^{\circ}C$ and 0.01 M inhibitor concentration DETA. Two mathematical models were used to represent the corrosion rate data, second order polynomial model and exponential model respectively. Nonlinear regression analysis showed that the first model was better than the second model with high correlation coefficient. The reactivity of studied inhibitors was analyzed through theoretical calculations based on density functional theory (DFT). The results showed that the reactive sites were located on the nitrogen (N1, N2 and N4) atoms.

• 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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• 1997.10a
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• pp.1516-1519
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• 1997

With disseminationof Distributed Control Systems(DCS), the huge amounts of process operation data could have been available and led to figure out process behaviors better on the statistical basis. Until now, the statistical modeling technology has been susally applied to process monitoring and fault diagnosis. however, it has been also thought that these process information, extracted from statistical analysis, might serve a great opportunity for process operation improvements and process improvements. This paper proposed a general methodolgy for process operation improvements including data analysis, backing up the result of analysis based on the methodology, and the mapping physical physical phenomena to the Principal Components(PC) which is the most distinguished feature in the methodology form traditional statistical analyses. The application of the proposed methodology to the Balst Furnace(BF) process has been presented for details. The BF process is one of the complicated processes, due to the highly nonlinear and correlated behaviors, and so the analysis for the process based on the mathematical modeling has been very difficult. So the statisitical analysis has come forward as a alternative way for the useful analysis. Using the proposed methodology, we could interpret the complicated process, the BF, better than any other mathematical methods and find the direction for process operation improvement. The direction of process operationimprovement, in the BF case, is to increase the fludization and the permeability, while decreasing the effect of tapping operation. These guide directions, with those physical meanings, could save fuel cost and process operator's pressure for proper actions, the better set point changes, in addition to the assistance with the better knowledge of the process. Open to set point change, the BF has a variety of steady state modes. In usual almost chemical processes are under the same situation with the BF in the point of multimode steady states. The proposed methodology focused on the application to the multimode steady state process such as the BF, consequently can be applied to any chemical processes set point changing whether operator intervened or not.

• Korea-Australia Rheology Journal
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• v.19 no.1
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• pp.27-33
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• 2007

The co-extrusion of multi-layer films has been studied with the focus on its process stability. As in the single-layer film casting process, the productivity of the industrially important multi-layer film casting and the quality of thus produced films have often been hampered by various instabilities occurring in the process including draw resonance, a supercritical Hopfbifurcation instability, frequently encountered when the draw ratio is raised beyond a certain critical value. In this study, this draw resonance instability along with the neck-in of the film width has been investigated for a three-layer film casting using a varying width non-isothermal 1-D model of the system with Phan-Thien and Tanner (PTT) constitutive equation known for its robustness in portraying extensional deformation processes. The effects of various process conditions, e.g., the aspect ratio, the thickness ratio of the individual film layers, and cooling of the process, on the stability have been examined through the nonlinear stability analysis.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.1 s.109
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• pp.61-66
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• 2005

The grade change operations In paper mills exhibit inherent nonlinear dynamic characteristics. For this reason, the conventional model predictive control techniques based on linear process models are not adequate for the grade change operations. In this paper, a bilinear model for the nonlinear grade change processes was presented first and optimal input variables were calculated by using one-step-ahead predictive control method. Numerical simulations showed that the control performance lied within acceptable range and that the bilinear model predictive control scheme was highly promising control strategy for the grade change operations.

• 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.