• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

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 cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.4
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• pp.257-262
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• 2003

A straightforward and effective method is presented for immobilizing enzymes on a microchip platform without chemically modifying a micro-channel or technically microfabricating a column reactor and fluid channel network. The proposed method consists of three steps: the reconstitution of a nitrocellulose (NC) membrane on a plane substrate without a channel network, enzyme immobilization on the NC membrane, and the assembly of another substrate with a fabricated channel network. As a result, enzymes can be stably and efficiently immobilized on a microchip. To evaluate the proposed method, two kinds of enzymatic reaction are applied: a sequential two-step reaction by one enzyme, alkaline phosphatase, and a coupled reaction by two enzymes, glucose oxidase and peroxidase, for a glucose assay.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.2
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• pp.225-232
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• 2012

A chemical reactor network (CRN) was developed for a lean premixed gas turbine combustor to predict the emission of pollutants such as NOx and CO. In this study, the predictions of NOx and CO emissions from lean premixed methane-air combustion in the gas turbine were carried out using CHEMKIN and a GRI 3.0 methane-air combustion mechanism, which includes the four NO formation mechanisms for various load conditions. The calculated results were compared with experimental data obtained from a modified test combustor to validate the model. The contributions of the four NO pathways were investigated for various load conditions. The effects of nonuniformity of the mass flux and of the equivalence ratio of the injector on the NOx formation were investigated, and a method of reducing the pollutant formation was suggested for the development of a sub-10 ppm gas turbine combustor.

• 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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.12
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• pp.1257-1264
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• 2011

This paper presents 3D simulation by STAR-CCM+ for lean premixed combustion in a stationary gas turbine combustor with separate pilot and main nozzles. The constant for the source term in the flame area density transport equation was modified to account for a low global equivalence ratio and validated against measurement data. A Partially-premixed Coherent Flame Model(PCFM) involves propagation of a laminar premixed flame with the predicted flame surface density and equilibrium assumption in the burned gas with spatial inhomogeneity. The conditions for cooling by radiation and convection are considered for accurate determination of the heat flux on the wall. A parametric study is of the pilot-fuel-to-total-fuel-ratio is carried out. A chemical reactor network (CRN) was constructed on the basis of the 3D simulation results and compared against measurements of NOx.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1174-1179
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• 2003

Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained learning data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.4
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• pp.460-466
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• 2004

Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained teaming data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.328-339
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• 2023

Methanol steam reforming (MSR) is a promising method for hydrogen supplying as a critical step in hydrogen fuel cell commercialization in mobile applications. Modelling and understanding of the reactor behavior is an attractive research field to develop an efficient reformer. Three-layer feed-forward artificial neural network (ANN) and Box-Behnken design (BBD) were used to modelling of MSR process using the Cu-SiO₂ aerogel catalyst. Furthermore, impacts of the basic operational variables and their mutual interactions were studied. The results showed that the most affecting parameters were the reaction temperature (56%) and its quadratic term (20.5%). In addition, it was also found that the interaction between temperature and Steam/Methanol ratio is important on the MSR performance. These models precisely predict MSR performance and have great agreement with experimental results. However, on the basis of statistical criteria the ANN technique showed the greater modelling ability as compared with statistical BBD approach.

• IE interfaces
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• v.24 no.2
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• pp.156-163
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• 2011

The radiopharmaceutical synthesis for PET (positron emission tomography) is composed of chemical reactions using automated synthetical equipment. Due to the radioactive material, the automated equipment is being frequently developed to replace human operators who conduct dangerous, repetitive and dexterous operations. As to operation, the manipulating program is commonly coded using the spread sheet while the whole actuators are mapped in every step. The process map (program) is changed according to such parameters as temperature of reactor, keeping time, mixture sequence and amount of reagent. In cases of customizing the automated synthetical equipment or developing the new radiopharmaceuticals, lots of experiments should be conducted and the programming mistake is not allowed as it can lead abnormal control of the equipment to leak the radioactive materials. The exact process map has depended on trial and error manner. Thus, this study developed the methodology to tabulate the synthetical process to convert the process map automatically while the synthetical module formation is represented by a network model. The proposed method is validated using the actual radiopharmaceutical synthetical procedure.

• Korean Chemical Engineering Research
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• v.52 no.6
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• pp.720-726
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• 2014

PEM fuel cell vehicles have been getting much attraction due to a sort of highly clean and effective transportation. The onboard fuel processor, however, is inevitably required to supply the hydrogen by conversion from some fuels since there are not enough available hydrogen stations nearby. A lot of studies have been focused on analyses of ATR reactor under the assumption of thermo-neutral condition and those of the optimized process for the minimization of energy consumption using thermal efficiency as an objective function, which doesn't guarantee the maximum hydrogen production. In this study, the analysis of optimization for 100 kW PEMFC onboard fuel processor was conducted targeting various fuels such as gasoline, LPG, diesel using newly defined hydrogen efficiency and keeping simply synthesized heat exchanger network regardless of external utilities leading to compactness and integration. Optimal result of gasoline case shows 9.43% reduction compared to previous study, which shows the newly defined objective function leads to better performance than thermal efficiency in terms of hydrogen production. The sensitivity analysis was also done for hydrogen efficiency, heat recovery of each heat exchanger, and the cost of each fuel. Finally, LPG was estimated as the most economical fuel in Korean market.