• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2003.11a
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• pp.230-248
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• 2003

In this work an integrated model for paper plants combining wet-end and dry section is developed and a model predictive control scheme based on the plant model is proposed. Closed-loop process identification method is employed to produce a state-space model. Thick stock, filler flow, machine speed and steam pressure are selected as Input variables and basis weight, ash content and moisture content are considered as output variables. The desired output trajectory is constructed in the form of 1st-order dynamics. Results of simulations for control of grade change operations are compared with plant operation data collected during the grade change operations under the same conditions as in simulations. From the comparison, we can see that the proposed model predictive control scheme reduces the grade change time and achieves stable steady-state.

• Korean Chemical Engineering Research
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• v.46 no.6
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• pp.1043-1051
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• 2008

In polyolefin processes melt index (MI) is the most important controlled variable indicating product quality. Because of the difficulty in the on-line measurement of MI, a lot of MI estimation and correlation methods have been proposed. In this work a new dynamic MI estimation scheme is developed based on system identification techniques. The empirical MI estimation equation proposed in the present study is derived from the $1^{st}$-order dynamic models. Effectiveness of the present estimation scheme was illustrated by numerical simulations based on plant operation data including grade change operations in high density polyethylene (HDPE) processes. From the comparisons with other estimation methods it was found that the proposed estimation scheme showed better performance in MI predictions. Using the model predictive control method based on the present dynamic MI estimation model, MI values are estimated and compared with those of MI setpoints. From the numerical simulation of the proposed control system, it was found that significant reduction of transition time and the amount of off-spec during grade changes were achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.187-194
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• 2019

Traditional demand forecasting methods are difficult to meet the needs of companies due to rapid changes in the market and the diversification of individual consumer needs. In a diversified production environment, the right demand forecast is an important factor for smooth yield management. Many of the existing predictive models commonly used in industry today are limited in function by little. The proposed model is designed to overcome these limitations, taking into account the part where each model performs better individually. In this paper, variables are extracted through Gray Relational analysis suitable for dynamic process analysis, and statistically predicted data is generated that includes characteristics of historical demand data produced through ARIMA forecasts. In combination with the LSTM model, demand forecasts can then be calculated by reflecting the many factors that affect demand forecast through an architecture that is structured to avoid the long-term dependency problems that the neural network model has.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.73-82
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• 2019

In order to build a smart factory, building a CPPS (Cyber Physical Product System) is an important system that must be accompanied. Through the CPPS, it is the reality of smart factories to move physical factories to a digital-based cyber world and to intelligently and autonomously monitor and control them. But The existing CPPS architectures present only an abstract modeling architecture, and the research that applied the OPC UA Framework (Open Platform Communication Unified Architecture), an international standard for data exchange in the smart factory, as the basic system of CPPS It was insufficient. Therefore, it is possible to implement CPPS that can include both cloud and IoT by collecting field data distributed by CPPS architecture applicable to actual factories and concentrating data processing in a centralized In this study, we implemented CPPS architecture through central OPC UA Server based on OPC UA conforming to central processing OPC UA Framework, and how CPPS logical process and data processing process are automatically generated through OPC UA modeling processing We have proposed the CPPS architecture including the model factory and implemented the model factory to study its performance and usability.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.93-108
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• 2023

In manufacturing productivity analysis, understanding the intricate interplay among factors like facility performance, layout design, and workforce allocation within the production line is imperative. This paper introduces a simulation-based methodology tailored to food manufacturing, progressively expanding scenarios to analyze production enhancement. The target system is a food processing plant, encompassing production processes, including warehousing, processing, subdivision, packaging, inspection, loading, and storage. First, we analyze the target system and design a simulation model according to the actual layout arrangement of equipment and workers. Then, we validate the developed model reflecting the real data obtained from the target system, such as the workers' working time and the equipment's processing time. The proposed model aims to identify optimal factor values for productivity gains through incremental scenario comparisons. To this end, three stages of simulation experiments were conducted by extending the equipment and worker models of the subdivision and packaging processes. The simulation experiments have shown that productivity depends on the placement of skilled workers and the performance of the packaging machine. The proposed method in this study will offer combinations of factors for the specific production requirements and support optimal decision-making in the real-world field.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.346-355
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• 2020

This study proposed a new optimization-based decision model for an enterprise resource planning and production scheduling of a pharmaceutical intermediates manufacturing plant. To do this work, we first define the inflow and outflow information as well as the model structure, and develop an optimization model to minimize the production time (i.e., makespan) using a mixed integer linear programing (MILP). The unique feature of the proposed model is that the optimal process scheduling is established based on real-time resource logistics information using a radio frequency identification (RFID) technology, thereby theoretically requiring no material inventories. essential information for process operation, such as the required amount of raw materials and estimated arrival timing to manufacturing plant, is used as logistics constraints in the optimization model to yield the optimal manufacturing scheduling to satisfy final production demands. We illustrated the capability of the proposed decision model by applying the optimization model to two scheduling problems in a real pharmaceutical intermediates manufacturing process. As a result, the optimal production schedule and raw materials order timing were identified to minimize the makespan while satisfying all the product demands.

• Smart Media Journal
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• v.8 no.3
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• pp.95-103
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• 2019

The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.361-364
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• 2008

The Application Model in Wireless Sensor Networks(WSNs) consist of wireless sensor network based on sensor hardwares which is combined the micro-controller, chipset for wireless communication and sensors, middleware for dealing with data processing and user application for common service. Applications in WSN have been applied for environmental monitoring, smart factory and have concentrated the services based on remote monitoring applications which is difficult to watch the situation by human. In this paper, we described the construction model for applying for the Ubiquitous disaster prevention system and deal with its conformity. The proposed system includes the selecting the wireless sensor hardware, routing technique for u-Disaster Prevention, composition of middleware and web-interface for application services.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.1-11
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• 2009

This paper illustrates a case study of PLC logic simulation in a car manufacturing system. It was developed to simulate and verify PLC control program for automobile panel AS/RS. Because car models become varied, the complexity of supply problem is increasing in the car manufacturing system. To cope with this problem, companies use the AS (automated storage) and RS (retrieval system) but it has logical complexity. Industrial automated process uses PLC code to control the AS/RS, however control information and control codes (PLC code) are difficult to understand. This paper suggests a PLC simulation environment, using 3D models and PLC code with realistic data. Data used in this simulation is based on realistic 3D model and I/O model, using actual size and PLC signals, respectively. The environment is similar to a real factory; users can verify and test the PLC code using this simulation before the implementation of AS/RS. Proposed simulation environment can be used for test run of AS/RS to reduce implementation time and cost.