• International journal of advanced smart convergence
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• v.7 no.1
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• pp.42-47
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• 2018

Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.

• Journal of the Korean Society of Systems Engineering
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• v.14 no.2
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• pp.8-15
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• 2018

In traditional Document Based Configuration Management(DBCM) environment, changes in a system's configurations are hard to be reflected to existing engineering documents. This nature of DBCM triggers unconformities of system configurations which could become great risks. Model-based Configuration Management(MBCM) has been introduced to solve the problem of DBCM by managing system's configurations through an unified model. Therefore, it is important to model engineering documents in a general modeling language, down to low-level information items to develop traceability and flexibility of a system's engineering information. So, in the research, to explore the possibility of Model-based Approach(MBA) in the field of configuration management, a development of a systems requirement document model using SysML based Views & Viewpoints concept has been studied.

• Korean Journal of Computational Design and Engineering
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• v.21 no.1
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• pp.78-89
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• 2016

When a plant owner requests plant 3D CAD models in the format that a shipbuilding company does not use, the shipyard manually re-models plant 3D CAD models according to the owner's requirement. Therefore, it is important to develop a technology to compare the re-modeled plant 3D CAD models with original ones and to quantitatively evaluate similarity between two models. In the previous study, we developed a graphic user interface (GUI)-based comparison system where a user evaluates similarity between original and re-modeled plant 3D CAD models for piping design at the level of unit. However, an offshore plant consists of thousands of units and thus a system which compares several plant 3D CAD models at unit-level without human intervention is necessary. For this, we developed a new batch model comparison system which automatically evaluates similarity of several unit-level plant 3D CAD models using an extensible markup language (XML) file storing file location and name data about a set of plant 3D CAD models. This paper suggests system configuration of a batch-mode-based comparison system and discusses its core functions. For the verification of the developed system, comparison experiments for offshore plant 3D piping CAD models using the system were performed. From the experiments, we confirmed that similarities for several plant 3D CAD models at unit-level were evaluated without human intervention.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.389.1-389
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• 2001

• Journal of Ocean Engineering and Technology
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• v.30 no.6
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• pp.526-534
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• 2016

This study had the goal of designing onboard structures for a pre-pilot mining test (PPMT), which is required for the commercialization of the deep-sea mining industry. This PPMT is planned to validate the performance of a hydraulic lifting system and verify the concept of operating through a moon-pool in the east sea, Korea. All of the onboard equipment and facility were designed by KRISO. Because the test was performed at the first development, it is difficult to determine what risk will occur in the facility. Therefore, risk-based design is required in the facility for the PPMT, which includes the facility layout, failure mode and effect analysis (FMEA), and risk reduction plan. All of the expected performances of the lifting system itself and the onboard facilities were qualitatively validated using the risk-based design.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.167-178
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• 1994

For the identification of the optimal operating conditions of phosphoric acid plant, an intelligent simulation was performed based on the dissolution reaction of phosphate rock. A phosphoric acid plant consists of three main processes : ball-mill grinding process, rock reaction process and slurry filteration process. The grinding and filteration processes are relatively simple processes and most of the simulation works are on the reaction process. The practical operation data of phosphoric acid plant at Namhae Chemical Corp. were utilized in the simulation. The operation of the phosphoric acid plant is highly dependent on the heuristics of operators and so the expert system technology was employed. The operation of phosphoric acid plant varies with the origin of phosphate rock. Results of the simulation showed the optimal values of major process variables and optimal operating conditions. The knowledgebase for the expert system was constructed based on the interview with the experienced plant operators.

• Nuclear Engineering and Technology
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• v.41 no.3
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• pp.237-246
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• 2009

Digitalization of not only non-safety but also safety-grade I &C systems with full computerized Main Control Room (MCR) is the recent trend of I&C systems of nuclear power plants (NPP) around the world, while plant maintenance has been shifting from traditional time based maintenance to condition based maintenance. In order to cope with the new trend of operation and maintenance in NPP, a concept of online distributed diagnostic system for both plant operation and maintenance has been proposed in order to further improve both the plant efficiency and the work environment of plant operation staff members by organizational learning. In this respect, the research subjects of human machine interface (HMI) for the online distributed diagnostic system are also discussed for supporting the plant personnel at both MCR and local working places in the plant by the application of advanced ICT (Information and Communication Technologies).

• Proceedings of the Plant Resources Society of Korea Conference
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• 2006.05a
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• pp.12-12
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• 2006

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.525-530
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• 2003

This paper focuses on design of nonlinear power plant controller using immune based multiobjective fuzzy approach. The thermal power plant is typically regulated by the fuel flow rate, the spray flow rate, and the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature. the change of the dynamic characteristics in the steam-turbine system. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. These parameters tuned by multiobjective based on immune network algorithms could be used for the tuning of nonlinear power plant.