• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.754-759
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• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.72-77
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• 2011

This study is to investigate the effect of the distillation temperature in ultra low sulfur diesel fuel on exhaust emissions in the low-temperature diesel combustion with 1.9L common rail direct injection diesel engine. Low temperature diesel combustion was achieved by adopting an external high EGR rate with a strategic injection control. The engine was operated at 1500 rpm 2.6 bar BMEP. The 90% distillation recovery temperature (T90) was $270^{\circ}C$ and $340^{\circ}C$ for the respective cetane number (CN) 30 and 55. It was found that there exists no distinctive discrepancy on exhaust emissions with regards to the different T90s. The high CN (CN55) fuels follow the similar trend of exhaust emissions as observed in CN30 fuels' except that high T90 fuel (CN55-T340) produced higher PM compared to low T90 fuel (CN55-T270). This may come from that high T90 plays an active role in aggravating the degree of fuel-air mixture preparedness before ignition.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.2
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• pp.54-58
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• 2022

This study aimed to investigate the anti-stress function of Aucklandiae Radix (AR) and Cyperi Rhizoma (CR). The essential oils used in the experiment were extracted from AR and CR using Steam Distillation Extraction and Super critical CO₂ extraction. To observe the effects of sample administration, we measured feed intake, leukocytes, red blood cells, hemoglobin, platelets, serum serotonin content, immobility time, climbing time, and swimming time in mice subjected to chronic restraint stress as behavioral changes. The average body weight of all experimental groups increased than the average body weight of the control group. The immobility and climbing times of experimental groups A and B administered with supercritical extraction samples were shorter than those of the other experimental groups and the control group, and the swimming time was longer. The serotonin content in the blood of all experimental groups decreased compared to the normal group, and the serotonin content of the control group was increased. The authors suggest that Korean herbal medicines AR and CR may be utilized as anti-stress flavoring agents based on the above results.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.301-304
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• 1997

Two options arising during implementation of an advanced model-based control system on a process with low-level loops are discussed. Strengths and deficiencies of the options are examined and methods to overcome the deficiencies are proposed. Simulation results of a CSTR and distillation column are presented to demonstrate the performance improvements.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.1
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• pp.73-87
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• 2016

Combining power generation and water production by desalination is economically advantageous. Most desalination projects use fossil fuels as an energy source, and thus contribute to increased levels of greenhouse gases. Environmental concerns have spurred researchers to find new sources of energy for desalination plants. The coupling of nuclear power production with desalination is one of the best options to achieve growth with lower environmental impact. In this paper, we will per-form a sensitivity study of coupling nuclear power to various combinations of desalination technology: {1} thermal (MSF [Multi-Stage Flashing], MED [Multi-Effect Distillation], and MED-TVC [Multi-Effect Distillation with Thermal Vapour Compression]); {2} membrane RO [Reverse Osmosis]; and {3} hybrid (MSF-RO [Multi-Stage Flashing & Reverse Osmosis] and MED-RO [Multi-Effect Distillation & Reverse Osmosis]). The Korean designed reactor plant, the APR1400 will be modeled as the energy production facility. The economical evaluation will then be executed using the computer program DEEP (Desalination Economic Evaluation Program) as developed by the IAEA. The program has capabilities to model several types of nuclear and fossil power plants, nuclear and fossil heat sources, and thermal distillation and membrane desalination technologies. The output of DEEP includes levelized water and power costs, breakdowns of cost components, energy consumption, and net saleable power for any selected option. In this study, we will examine the APR1400 coupled with a desalination power plant in the Kingdom of Saudi Arabia (KSA) as a prototypical example. The KSA currently has approximately 20% of the installed worldwide capacity for seawater desalination. Utilities such as power and water are constructed and run by the government. Per state practice, economic evaluation for these utilities do not consider or apply interest or carrying cost. Therefore, in this paper the evaluation results will be based on two scenarios. The first one assumes the water utility is under direct government control and in this case the interest and discount rate will be set to zero. The second scenario will assume that the water utility is controlled by a private enterprise and in this case we will consider different values of interest and discount rates (4%, 8%, & 12%).

• Applied Biological Chemistry
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• v.39 no.5
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• pp.368-373
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• 1996

Korean general medicinal herbs-sasam, gilkyung, jakyak, danggwi, hwangki, and chunkung-were added In the normal brewing procedure as a raw material or in the distilling procedure as a packing material. The distillates from the former procedure and those from the latter procedure were compared in quality and distillation properties. As distillation proceeded, pH of the medicinal herb wine distillate and the control(not added herbs) distillates were decreased, whereas that of the herb packing distillate was increased slowely of $0.05{\sim}0.97$ during $1{\sim}4$ fractions and decreased remarkably of $0.92{\sim}0.98$ afterward. Average pH was the highest of 5.70 in jakyak and lowest of 4.37 In gilkyung. Absorbances of the herb Packing distillate were decreased rapidly of $0.60{\sim}1.59$ in the $1{\sim}4$ fractions but slowely of $0.19{\sim}0.54$ in the next fractions. During distillation both fractional alcohol concentration of the distillates and distillation rate were decreased. Their values were decreased more slowly than the control. Distillation rates of medicinal herb wine distillate were varied by medicinal herb varieties and alcohol concentration of fermented wine. Danggwi and control showed the highest average distillation rate as $0.12\;m{\ell}/sec$ and gilkyung the lowest value as $0.073\;m{\ell}/sec$. Maximum concentration of index component, paeoniflorin of jakyak was observed as 293 mg% in the 5th fraction of herb packing distillate and decrusin of danggwi as 3514 mg% In the 1st fraction of herb packing distillate. The extraction rate was 41.3% for paeoniflorin and 20.5% for decrusin. From sensory evaluation, the highest overall Qualify was observed in the medicinal herb wine distillate of hwangki added wine, the next in those of danggwi and jakyak added wine.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1553-1556
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• 1997

In the paper, we porpose a new mehtod of extending PLS(Partial Least Squares) regressiion method to nonlinear framework and apply it to the estimation of product compositions in high-purity distillation column. There have veen similar efforets to overcome drawbacks of PLS by using nonlinear-mapping ability of meural networks, however, they failed to show great improvement over PLS since they focused only in capturing nonlinear functional relationship between input data, not on nonlinear correlation inthe data set. By incorporating the structure of Robust Auto Associative Networks(RAAN) into that of previous nonlinear PLS, we can handle nonlinear correlation as well as nonlinear functional relationship. The application result shows that the proposed method performs better than previous ones even for nonlinearities caused by changing operating conditions, limited observations, and existence of meas-unrement noises.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1109-1114
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• 1992

Modern chemical processes are becoming more complicated. The sophisticated chemical processes have needed the fault diagnosis pxpert systems that can detect and diagnose the fault diagnosis expert systems that can detect and diagnose the faults of some processes and give and advice to the operator in the event of process faults. We present the Time-Delay Neural Network(TDNN) approach for on-line fautl diagnosis. The on-line fault diagnosis system finds the exact origin of the fault of which the symptom is propagated continuously with time. The proposed method has been applied to a pilot distillation column to show the merits and applicability of the TDNN.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Korean Journal of Food Science and Technology
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• v.53 no.5
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• pp.593-600
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• 2021

In this study, the vacuum distillation process for producing non-alcoholic red wine was optimized via response surface methodology. As a result of optimizing the responses (alcohol content, yield) for independent variables (operating time, boiling point, and temperature difference), 1% alcohol content and 81.15% yield were obtained at an operating time of 24.5 min, boiling point of 65℃, and temperature difference of 8℃. To investigate the physicochemical and sensory properties, non-alcoholic wines with different boiling points (bp 25℃, bp 45℃, and bp 65℃) and a blended wine (4.2% of control wine added) were prepared. As the boiling point increased, the alcohol content decreased, and CI (color intensity) and Hue increased. Blended wine exhibited the highest value and bp 65℃ showed the lowest value in terms of sensory properties. In conclusion, distillation at a low boiling point and blending control wine could be used to prepare non-alcoholic wine with a high preference.