• Korean Journal of Food Science and Technology
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• v.41 no.4
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• pp.369-373
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• 2009

${\alpha}$-Waxy corn starch was used as a feed for twin-screw extrusion in order to enhance starch liquefaction with added thermostable ${\alpha}$-amylase (derived from Bacillus licheniformis). The residence time distribution and starch liquefaction were investigated. The starch liquefaction was analyzed in terms of reducing sugar contents, molecular size from gel permeation chromatography (GPC), and microstructure from scanning electron microscopy (SEM). The use of ${\alpha}$-starch contributed to the production of more reducing sugar than the use of raw starch use alone. From GPC, the effect of ${\alpha}$- starch on the molecular size reduction was shown to be small. From SEM, irregular and damaged surface were observed on the extrudate from ${\alpha}$-starch, as compared to those from raw starch. The spread of residence time distribution curves was greater with feed of ${\alpha}$-starch than raw starch, indicating that ${\alpha}$-starch was hard to flow forward during extrusion. This could be improved by increasing the feed moisture content and barrel temperature of extruder.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.104-110
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• 2014

In order to study gross alpha analysis method using LSC, the efficiency tests with uranium standard materials were performed and then compared with the GPC method (US EPA 900.0 method) using 15 groundwater samples. For 15 groundwater samples, the average efficiencies of the GPC and LSC method were 7~11% and 90%, respectively. The average precisions of the GPC and LSC method were 16.16% and 6.00%, respectively. Also, The average standard deviations for 15 samples were 7.38 pCi/L and 2.95 pCi/L, respectively. The determination coefficient of the tested results by two methods was 0.9948. As a result, the LSC method tested in this study was applicable for the screening of the gross alpha and showed the advantages in the gross alpha measurement due to the simple measurement procedures.

• Journal of the Korean Wood Science and Technology
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• v.49 no.1
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• pp.1-13
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• 2021

This study was conducted to investigate the effects of the synthesis method, melamine content, and GPC parameters (such as flow rate, column-detector temperature, and sample injection temperature) on the molecular weight of melamine-urea-formaldehyde (MUF) resins. Two different synthesis methods were employed. In the first method, MUF-A resins were synthesized by simultaneously reacting urea, formaldehyde, and melamine (5%, 10%, and 20%) using the reaction of alkaline-acid-alkaline steps under controlled temperature and viscosity. In the second method, MUF-B resins were synthesized by first reacting melamine at the same levels with formaldehyde and then by adding urea. The highest weight average molecular weight (Mw) of MUF-A resins was found at 10% melamine content when the flow rate was 0.3 and 0.8 ml/min; Mw decreased slightly at 20% melamine content. The results showed that Mw increased with an increase in the melamine content when the flow rate was 0.5 and 1.0 ml/min. In addition, Mw was the highest when the flow rate, column-detector temperature, and injection temperature were 0.3 ml/min, 50℃, and 25℃, respectively. On the contrary, MUF-B resins had greater Mw and number average molecular weight (Mn) than MUF-A resins. Overall, Mw and Mn increased as the melamine content increased. The optimal GPC parameter for MUF resins was determined as follows: a flow rate of 0.5 ml/min, a column-detector temperature of 50℃, and a sample injection temperature of 50℃.

• Computers and Concrete
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• v.33 no.2
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Journal of the Korean Wood Science and Technology
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• v.45 no.4
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• pp.471-481
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• 2017

As the molecular weight (MW) of urea-formaldehyde (UF) resins had a great impact on their properties, this work was conducted to study effect of analytical parameters of gel permeation chromatography (GPC) on the MW measurement of UF resins. GPC parameters such as flow rate, column, detector temperature, and sample injection temperature were selected to compare number-average molecular weight (Mn), weight-average molecular weight (Mw), molecular weight distribution (MWD) and polydispersity index (PDI) of two UF resins with different viscosities. As expected, UF resin with higher viscosity resulted in greater Mn and Mw than those of low viscosity UF resin. When the flow rate increased, both Mn and Mw of UF resins decreased and MWD became narrower. By contrast, both Mn and Mw increased and MWD became wide when the column, detector, and sample injection temperature increased. The column, detector, and sample injection temperature of $50^{\circ}C$ at a flow rate of $0.5m{\ell}/min$ resulted in the highest MW and broadest MWD for the GPC analysis. These results suggest that the apparent molecular size or a hydrodynamic radius of UF resin molecules dissolved in the mobile phase affect to Mn, Mw and MWD.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.9-12
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• 2005

Molecular characteristics and rheological properties of cellulose acetate butyrate (CAB), cellulose acetate propionate (CAP) and nitrocellulose (GC-519) which are being widely used as propellants were investigated. Their weight-average molecular weight (Mw) and number-average molecular weight (Mn) were estimated via Gel Permeartion Chromatograpy (GPC). Cellulose derivatices were mixed with di-n-propyl adipate (DNPA) which acted as plasticizer in acetone, and then rheological properties of the mixture of cellulose derivatives and this plasticizer in acetone were investigated at $0^{\circ}C$ by rheometer.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.11
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• pp.985-993
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• 2006

An experiment to find out the removal mechanism of PEG(polyethyleneglycol) by using UV-enhanced $O_2$ GPC (gas phase cleaning) at low substrate temperature below $200^{\circ}C$ was executed under various process conditions, such as substrate temperature, UV exposure, and $O_2$ gas. The possibility of using $UV/O_2$ GPC as a low-temperature in-situ cleaning tool for organic removal was confirmed by the removal of a PEG film with a thickness of about 200 nm within 150 sec at a substrate temperature of $200^{\circ}C$. Synergistic effects by combining photo-dissociation and photo oxidation can only remove the entire PEG film without residues within experimental splits. In $UV/O_2$ GPC with substrate temperatures higher than the glass transition temperature, the substantial increase in the PEG removal rate can be explained by surface-wave formation. The photo-dissociation of PEG film by UV exposure results in the formation of end aldehyde by dissociation of back-bone chain and direct decomposition of light molecules. The role of oxygen is forming peroxide radicals and/or terminating the dis-proportionation reaction by forming peroxide.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• Elastomers and Composites
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• v.45 no.4
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• pp.286-290
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• 2010

Liquid-crystalline (LC) monomer, which was functionalized with a coumarin group on their extremity, was synthesized by UV light irradiation in their LC phases. LC monomer was converted into the dimers by the cycloaddition reaction of the coumarin group, and the LC phases were maintained after photodimerization reaction. The dimers showed LC phases in the wider temperature range than those of the corresponding monomer. Structures of the compound were identified by FT IR and $^1H$ NMR spectroscopies. Their phase transition temperatures and thermal stability were also investigated by differential scanning calorimetry (DSC), gel permeating chromatography (GPC) and polarized optical microscopy (POM). From optical polarizing microscopy, the prepared polymer shows enantiotropic liquid crystallinity with smectic and nematic textures.