• The Journal of Bigdata
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• v.8 no.2
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• pp.83-95
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• 2023

Big data analytics and AI play a critical role in the development of smart plants. This study presents a big data platform for plant data and an 'AutoML platform' for AI-based plant O&M(Operation and Maintenance). The big data platform collects, processes and stores large volumes of data generated in plants using Hadoop, Spark, and Kafka. The AutoML platform is a machine learning automation system aimed at constructing predictive models for equipment prognostics and process optimization in plants. The developed platforms configures a data pipeline considering compatibility with existing plant OISs(Operation Information Systems) and employs a web-based GUI to enhance both accessibility and convenience for users. Also, it has functions to load user-customizable modules into data processing and learning algorithms, which increases process flexibility. This paper demonstrates the operation of the platforms for a specific process of an oil company in Korea and presents an example of an effective data utilization platform for smart plants.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.57-64
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• 2024

The introduction of Smart Factories has transformed manufacturing towards more objective and efficient line management. However, most companies are not effectively utilizing the vast amount of sensor data collected every second. This study aims to use this data to predict product quality and manage production processes efficiently. Due to security issues, specific sensor data could not be verified, so semiconductor process-related training data from the "SAMSUNG SDS Brightics AI" site was used. Data preprocessing, including removing missing values, outliers, scaling, and feature elimination, was crucial for optimal sensor data. Oversampling was used to balance the imbalanced training dataset. The SVM (rbf) model achieved high performance (Accuracy: 97.07%, GM: 96.61%), surpassing the MLP model implemented by "SAMSUNG SDS Brightics AI". This research can be applied to various topics, such as predicting component lifecycles and process conditions.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.396-407
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• 2020

As fossil fuels are depleted worldwide, alternative resources is required to replace fossil fuels, and biofuels are in the spotlight as alternative resources. Biofuels are produced from biomass, which is a renewable resource to produce biofuels or bio-chemicals. Especially, in order to substitute fossil fuels, the research focusing the biofuel (biodiesel) production based on CO₂ and biomass achieves more attention recently. To produce biomass-based biodiesel, the development of a supply chain network is required considering the amounts of feedstocks (ex, CO₂ and water) required producing biodiesel, potential locations and capacities of bio-refineries, and transportations of biodiesel produced at biorefineries to demand cities. Although many studies of the biomass-based biodiesel supply chain network are performed, there are few types of research handled the uncertainty in CO₂ supply which influences the optimal strategies of microalgae-based biodiesel production. Because CO₂, which is used in the production of microalgae-based biodiesel as one of important resources, is captured from the off-gases emitted in power plants, the uncertainty in CO₂ supply from power plants has big impacts on the optimal configuration of the biodiesel supply chain network. Therefore, in this study, to handle those issues, we develop the two-stage stochastic model to determine the optimal strategies of the biodiesel supply chain network considering the uncertainty in CO₂ supply. The goal of the proposed model is to minimize the expected total cost of the biodiesel supply chain network considering the uncertain CO₂ supply as well as satisfy diesel demands at each city. This model conducted a case study satisfying 10% diesel demand in the Republic of Korea. The overall cost of the stochastic model (US$ 12.9/gallon·y) is slightly higher (23%) than that of the deterministic model (US$ 10.5/gallon·y). Fluctuations in CO₂ supply (stochastic model) had a significant impact on the optimal strategies of the biodiesel supply network.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Clean Technology
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• v.29 no.4
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• pp.255-261
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• 2023

Power semiconductors are semiconductors used for power conversion, transformation, distribution, and control. Recently, the global demand for high-voltage power semiconductors is increasing across various industrial fields, and optimization research on high-voltage IGBT components is urgently needed in these industries. For high-voltage IGBT development, setting the resistance value of the wafer and optimizing key unit processes are major variables in the electrical characteristics of the finished chip. Furthermore, the securing process and optimization of the technology to support high breakdown voltage is also important. Etching is a process of transferring the pattern of the mask circuit in the photolithography process to the wafer and removing unnecessary parts at the bottom of the photoresist film. Ion implantation is a process of injecting impurities along with thermal diffusion technology into the wafer substrate during the semiconductor manufacturing process. This process helps achieve a certain conductivity. In this study, dry etching and wet etching were controlled during field ring etching, which is an important process for forming a ring structure that supports the 3.3 kV breakdown voltage of IGBT, in order to analyze four conditions and form a stable body junction depth to secure the breakdown voltage. The field ring ion implantation process was optimized based on the TEG design by dividing it into four conditions. The wet etching 1-step method was advantageous in terms of process and work efficiency, and the ring pattern ion implantation conditions showed a doping concentration of 9.0E13 and an energy of 120 keV. The p-ion implantation conditions were optimized at a doping concentration of 6.5E13 and an energy of 80 keV, and the p+ ion implantation conditions were optimized at a doping concentration of 3.0E15 and an energy of 160 keV.

• Clean Technology
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• v.27 no.4
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• pp.332-340
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• 2021

With the recent reinforcement of emission standards, it is necessary to make efforts to reduce NOx from air pollutant-emitting workplaces. The NOx reduction method mainly used in industrial facilities is selective catalytic reduction (SCR), and the most commercial SCR catalyst is the ceramic honeycomb catalyst. This study was carried out to reduce the NOx emitted from steel plants by applying De-NOx catalyst coated on metallic monolith. The De-NOx catalyst was synthesized through the optimized coating technique, and the coated catalyst was uniformly and strongly adhered onto the surface of the metallic monolith according to the air jet erosion and bending test. Due to the good thermal conductivity of metallic monolith, the De-NOx catalyst coated on metallic monolith showed good De-NOx efficiency at low temperatures (200 ~ 250 ℃). In addition, the optimal amount of catalyst coating on the metallic monolith surface was confirmed for the design of an economical catalyst. Based on these results, the De-NOx catalyst of commercial grade size was tested in a semi-pilot De-NOx performance facility under a simulated gas similar to the exhaust gas emitted from a steel plant. Even at a low temperature (200 ℃), it showed excellent performance satisfying the emission standard (less than 60 ppm). Therefore, the De-NOx catalyst coated metallic monolith has good physical and chemical properties and showed a good De-NOx efficiency even with the minimum amount of catalyst. Additionally, it was possible to compact and downsize the SCR reactor through the application of a high-density cell. Therefore, we suggest that the proposed De-NOx catalyst coated metallic monolith may be a good alternative De-NOx catalyst for industrial uses such as steel plants, thermal power plants, incineration plants ships, and construction machinery.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5179-5186
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• 2012

Miglitol, a well-known therapeutic intervention agents for diabetes, exhibits competitive inhibitory activity against ${\alpha}$-glucosidase and it is usually produced through three sequential steps including chemical and bioconversion processes. Gluconobactor oxydans (G. oxydans) belonging to acetic acid bacteria biologically, converts 1-deoxy-1-(2-hydroxyethylamino)-D-glucitol (P1) into a key intermidiate, 6-(2-hydroxyetyl) amino-6-deoxy-${\alpha}$-L-sorbofuranose (P2) by incomplete oxidation. In this study, we identified and optimized fermentation conditions of CK-2165, that was selected in soil samples by comparing the bioconversion yield. CK-2165 strain was found to be closely related to G. oxydans based on the result of phylogenetic analysis using 16S rDNA sequence. Utilization of API 20 kits revealed that this strain could use glucose, mannose, inositol, sorbitol, rhamnose, sucrose, melibiose, amygdalin and arabinose as carbon sources. The culture conditions were optimized for industrial production and several important factors affecting bioconversion rate were also tested using mycelial cake. Cell harvested at the late-stationary phase showed the highest bioconversion yield and $MgSO_4$ was critically required for the catalytic activity.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.42.2-42.2
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• 2010

Transparent conducting oxides (TCOs) are of significant importance for their applications in various devices, such as light-emitting diodes, thin-film solar cells, organic light-emitting diodes, liquid crystal displays, and so on. In order for TCOs to contribute to the performance improvement of these devices, TCOs should have high transmittance and good electrical properties simultaneously. Sn-doped $In_2O_3$ (ITO) is the most commonly used TCO. However, indium is toxic and scarce in nature. Thus, ZnO has attracted a lot of attention because of the possibility for replacing ITO. In particular, group III impurity-doped ZnO showed the optoelectronic properties comparable to those of ITO electrodes. Al-doped ZnO exhibited the best performance among various doped ZnO films because of the high substitutional doping efficiency. However, in order for the Al-doped ZnO to replace ITO in electronic devices, their electrical and optical properties should further significantly be improved. In this connection, different ways such as a variation of deposition conditions, different deposition techniques, and post-deposition annealing processes have been investigated so far. Among the deposition methods, RF magnetron sputtering has been extensively used because of the easiness in controlling deposition parameters and its fast deposition rate. In addition, when combined with post-deposition annealing in a reducing ambient, the optoelectronic properties of Al-doped ZnO films were found to be further improved. In this presentation, we deposited Al-doped ZnO (ZnO:$Al_2O_3$ = 98:2 wt%) thin films on the glass and sapphire substrates using RF magnetron sputtering as a function of substrate temperature. In addition, the ZnO samples were annealed in different conditions, e.g., rapid thermal annealing (RTA) at $900^{\circ}C$ in $N_2$ ambient for 1 min, tube-furnace annealing at $500^{\circ}C$ in $N_2:H_2$=9:1 gas flow for 1 hour, or RTA combined with tube-furnace annealing. It is found that the mobilities and carrier concentrations of the samples are dependent on growth temperature followed by one of three subsequent post-deposition annealing conditions.

• Korean Chemical Engineering Research
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• v.51 no.2
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• pp.226-232
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• 2013

To produce biobutanol, fermentation processes using clostridia that mainly produce acetone, butanol and ethanol are used. In this work, a dynamic model describing the metabolic reactions in an acetone-butanol-ethanol (ABE)-producing clostridium, Clostridium acetobutylicum ATCC824, was proposed. To estimate the 58 kinetic parameters of the metabolic network model with experimental data obtained from a batch fermentor, we used an efficient optimization method combining a genetic algorithm and the Levenberg-Marquardt method because of the complexity of the metabolism of the clostridium. For the verification of the determined parameters, the developed metabolic model was evaluated by experiments where genetically modified clostridium was used and the initial concentration of glucose was changed. Consequently, we found that the developed kinetic model for the metabolic network was considered to describe the dynamic metabolic state of the clostridium sufficiently. Thus, this dynamic model for the metabolic reactions will contribute to designing the clostridium as well as the fermentor for higher productivity.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.5
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• pp.720-732
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• 2001

Skeletal muscle is of major economic importance since it is finally converted to meat for consumers. The increase in meat production with low costs of production may be achieved by optimizing muscle growth, whereas a high meat quality requires, among other factors, the optimization of intramuscular glycogen and fat stores. Thus, research in energy metabolism aims at controling muscle metabolism, but also liver and adipose tissue metabolism in order to optimize energy partitioning in favour of muscles. Liver is characterized by high anabolic and catabolic rates. Metabolic enzymes are regulated by nutrients through short-term regulation of their activities and long-term regulation of expression of their genes. Consequences of liver metabolic regulation on energy supply to muscles may affect protein deposition (and hence growth) as well as intramuscular energy stores. Adipose tissues are important body reserves of triglycerides, which result from the balance between lipogenesis and lipolysis. Both processes depend on the feeding level and on the nature of nutrients, which indirectly affect energy delivery to muscles. In muscles, the regulation of rate-limiting nutrient transporters, of metabolic enzyme activities and of ATP production, as well as the interactions between nutrients affect free energy availability for muscle growth and modify muscle metabolic characteristics which determine meat quality. The growth of tissues and organs, the number and the characteristics of muscle fibers depend, for a great part, on early events during the fetal life. They include variations in quantitative and qualitative nutrient supply to the fetus, and hence in maternal nutrition. During the postnatal life, muscle growth and characteristics are affected by the age and the genetic type of the animals, the feeding level and the diet composition. The latter determines the nature of available nutrients and the rate of nutrient delivery to tissues, thereby regulating metabolism. Physical activity at pasture also favours the orientation of muscle metabolism, towards the oxidative type. Consequently, breeding systems may be of a great importance during the postnatal life. Research is now directed towards the determination of individual tissue and organ energy requirements, a better knowledge of nutrient partitioning between and within organs and tissues. The discovery of new molecules (e. g. leptin), of new molecular mechanisms and of more powerful techniques (DNA chips) will help to achieve these objectives. The integration of the different levels of knowledge will finally allow scientists to formulate new types of diets adapted to sustain a production of high quality meat with lower costs of production.