• Korean Chemical Engineering Research
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• v.52 no.4
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• pp.475-480
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• 2014

In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines for connecting equipment. However, what is the lacking of considerations in previous researches is to handle the multi floor processes considering the safety distances for domino impacts on a complex plant. The mathematical programming formulation can be transformed into MILP (Mixed Integer Linear Programming) problems as considering safety distances, maintenance spaces, and economic benefits for solving the multi-floor plant layout problem. The objective function of this problem is to minimize piping costs connecting facilities in the process. However, it is really hard to solve this problem due to complex unequality or equality constraints such as sufficient spaces for the maintenance and passages, meaning that there are many conditional statements in the objective function. Thus, it is impossible to solve this problem with conventional optimization solvers using the derivatives of objective function. In this study, the PSO (Particle Swarm Optimization) technique, which is one of the representative sampling approaches, is employed to find the optimal solution considering various constraints. The EO (Ethylene Oxide) plant is illustrated to verify the efficacy of the proposed method.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.209-223
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• 2020

Executions of SCM in a chemical company of which divisions produce petrochemicals, compounds, batteries, IT material and medicine directly affect their own profit. Execution level of SCM or optimization is very important. This work presents activities of SCM and optimization of inefficient issues in several industrial divisions using mathematical optimization method. The meaning is not only academic research but also making a useful tool which active partner deals with in his work. It is explained how to do beforehand and afterward optimization problem. The benefits are mentioned in the sections. The first of examples would be cover supply plan optimization, optimal profit business plan, and scheduling of a stretching process of polarizer based on minimizing raw material loss in polarizer production. The second example would be cover the optimization of production/packaging plans to maximize productivity of Poly Olefin processes, and the third example is minimization of transition loss in the production of battery electrodes. The fourth example would be cover scheduling of vessel approaching to berth. Because transportation of large portion of raw material and products of petrochemical industry is dealt with vessel, scheduling of vessel approaching to berth is important at the shore of large difference of tide. The final example would be scheduling problem to minimization of change over time of ABS semi products.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.7
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• pp.364-370
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• 2016

In this paper, the technique that determines efficient process combinations for the ultrapure water production was studied. The ultrapure water is one of the industrial water used in industrial activity and required in the advanced technology integrated industry. It is produced by combined process including filtration, ion exchange processes, the reverse osmosis (RO) process, degassing (DG) process and UV-oxidation (UVox) process. An ultrapure water production process consists of 15-20 different water treatment unit process. In this study, a pilot plant was built and operated to research the design parameters for the individual process. Through the pilot plant operation, 19 effective combinations were optimized among various processes. And then, 11 of them satisfied the final quality of the ultrapure water. The stability and economic feasibility were evaluated about the final 11 process combinations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.17-24
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• 2018

The HEC-HMS model was applied to identify the rainfall-runoff processes for the Anseongchun basin, where the lower part of the stream has been damaged severely by tropical storms in the past. Modeling processes include incorporating with the SCS-CN model for loss, Clark's UH model for transformation, exponential recession model for baseflow, and Muskingum model for channel routing. The parameters were calibrated through an optimization technique using a trial and error method. Sensitivity analysis after calibration was performed to understand the effects of parameters, such as the time of concentration, storage coefficient, and base flow related constants. Two storm water events were simulated by the model and compared with the corresponding observations. Good accuracy in predicting the runoff volume, peak flow, and the time to peak flow was achieved using the selected methods. The results of this study can be used as a useful tool for decision makers to determine a master plan for regional flood control management.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.6
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• pp.504-509
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• 2016

Production processes of double wall bellows can be roughly categorized into two steps. In the first step, inner and outer bellows are made of STS316L in austenite stainless steel due to their excellent formability and corrosion resistance. In the second step, the double wall bellows are manufactured using the welding method with both the inner and outer bellows. The microstructure and defects of each weldment are observed to ensure the reliability of bellows since weldment is a highly vulnerable part, which can crack and fracture when bellows are formed or used. In this study, optimum welding conditions were derived from the analysis of microstructure and inspection of weldment of bellows that were produced using various welding procedure. Moreover, the mechanical properties were evaluated through hardness measurement of substrate, weldment and the heat-affected zone.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.131-136
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• 2013

Multi-core processors can improve parallelism of application processes and thus can enhance the system throughput. Researchers have recently revealed that the processor affinity is an important factor to determine network I/O performance due to architectural characteristics of multi-core processors; thus, many researchers are trying to suggest a scheme to decide an optimal processor affinity. Existing schemes to dynamically decide the processor affinity are able to transparently adapt for system changes, such as modifications of application and upgrades of hardware, but these have limited access to characteristics of application behavior and run-time information that can be collected heuristically. Thus, these can provide only sub-optimal processor affinity. In this paper, we define meaningful system variables for determining optimal processor affinity and suggest a tool to gather such information. We show that the implemented tool can overcome limitations of existing schemes and can improve network bandwidth.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.5
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• pp.296-300
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• 2021

The performance of devices has been improved with fine processes from planar to three-dimensional transistors (e.g., FinFET, NWFET, and MBCFET). There are some problems such as a short channel effect or a self-heating effect occur due to the reduction of the gate-channel length by miniaturization. To solve these problems, we compare and analyze the electrical and thermal characteristics of FinFET and GAAFET devices that are currently used and expected to be further developed in the future. In addition, the optimal structure according to the Fin shape was investigated. GAAFET is a suitable device for use in a smaller scale process than the currently used, because it shows superior electrical and thermal resistance characteristics compared to FinFET. Since there are pros and cons in process difficulty and device characteristics depending on the channel formation structure of GAAFET, we expect a mass-production of fine processes over 5 nm through structural optimization is feasible.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.26-33
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• 2024

There has been a recent surge of interest in the Digital Twin technology. The Digital Twin is technique for optimizing objects by simulating physical phenomena or objects through computer-based simulations. Currently, single Digital Twin is being developed to optimize processes limited to specific fields, but there is a limitation in that the independent Digital Twins cannot analyze the vast and complex processes of the real world. To overcome this, the concept of federated Digital Twin has been introduced. To date, the federated Digital Twin research has primarily focused on how to optimize macroscopic objects such as cities. However, by leveraging the interconnected nature of twins, existing implementations of the single Digital Twins can be modularized. In this study, we define the concepts and interrelationships of the single Digital Twin and the federated Digital Twin from a functional perspective related to process optimization and design a modularization technique for the single Digital Twin using the federated Digital Twin. Furthermore, this study aims to discuss the proposed methodology's efficacy by designing a model applying modularization to a real-world fabric manufacturing case.

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.187-193
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• 2014

One of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture the Pareto Front, approximate criteria (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to the springback and failure criteria. To optimize these two criteria, a global optimization algorithm was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria shape optimization of highly non-linear mechanical systems.