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- 저자 : 맹좌영(Jwa-young Maeng) (검색결과 3 건)
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실시간 자동차 배출 입자 측정을 위한 MEMS기반 초소형 미세 입자 측정칩
맹좌영(Jwa-Young Maeng),박동호(Dongho Park),황정호(Jungho Hwang),김용준(Yong-Jun Kim) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
In this paper, we report design, fabrication and characterization of a MEMS based particle detection chip for real time monitoring of engine exhaust gas. The chip is designed on the basis of aerosol technology and is realized using micromachining processes. The complete device is composed of two parts, one is a micro virtual impactor for the classification of particles according to their sizes and the other is a micro corona discharger for measuring the particle concentration. The integrated chip is used for classifying bigger and smaller size particles and finally determining the number concentration by measuring the current carried by charged particles. In this experiment, the cut-off diameter of the particle was 550 ㎚. Since conventional particle detection equipments are relatively big in size and expensive as well, the proposed device will be the first step to the realization of low-cost and real-time PM₁ monitoring.
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보일러 연소 모델의 자동 갱신 기능을 가지는 화력발전소 보일러 연소 최적화 기법 적용 결과
나상건(Sang-Gun Na),이정식(Jung-Sik Lee),맹좌영(Jwa-young Maeng) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
In this study, a combustion optimization technique with a function of automatically updating the boiler combustion model is introduced. Combustion optimization technique controls the combustion of a boiler in a thermal power plant to reduce pollutants and increase combustion efficiency, ultimately reducing fuel consumption, increasing boiler efficiency, and reducing the cost of treating pollutants. Multi-Layer Perceptron (MLP), an artificial neural network model, was used for the boiler combustion model, and Particle Swarm Optimization (PSO) was used for the optimization algorithm. Boiler combustion model is managed by model management module that automatically creates/selects/updates/deletes. This model management module was able to continuously update the model by using the operation data generated in real time. In addition, an output controller that converts the optimum combustion control value derived by this technique into a stable control signal value of the power plant controller was added to enable safe connection with the power plant controller. This technique confirmed the performance by linking with actual control in the form of S/W solution of edge computing server for thermal power plants in India. The plant has a capacity of 660 MW and the boiler is tangential firing type. In addition, the performance of this technique was verified by linking the control to the opposite firing boiler of the domestic USC 1000 MW thermal power plant.
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보일러 연소 모델의 자동 갱신 기능을 가지는 화력발전소 보일러 연소 최적화 기법 적용 결과
나상건(Sang-Gun Na),이정식(Jung-Sik Lee),맹좌영(Jwa-young Maeng) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
In this study, a combustion optimization technique with a function of automatically updating the boiler combustion model is introduced. Combustion optimization technique controls the combustion of a boiler in a thermal power plant to reduce pollutants and increase combustion efficiency, ultimately reducing fuel consumption, increasing boiler efficiency, and reducing the cost of treating pollutants. Multi-Layer Perceptron (MLP), an artificial neural network model, was used for the boiler combustion model, and Particle Swarm Optimization (PSO) was used for the optimization algorithm. Boiler combustion model is managed by model management module that automatically creates/selects/updates/deletes. This model management module was able to continuously update the model by using the operation data generated in real time. In addition, an output controller that converts the optimum combustion control value derived by this technique into a stable control signal value of the power plant controller was added to enable safe connection with the power plant controller. This technique confirmed the performance by linking with actual control in the form of S/W solution of edge computing server for thermal power plants in India. The plant has a capacity of 660 MW and the boiler is tangential firing type. In addition, the performance of this technique was verified by linking the control to the opposite firing boiler of the domestic USC 1000 MW thermal power plant.
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