화학공학소재연구정보센터
학회 한국공업화학회
학술대회 2021년 봄 (05/12 ~ 05/14, 부산 벡스코(BEXCO))
권호 25권 1호
발표분야 [화학공정] AI 기반 공정시스템 기술
제목 Self-optimizing control of a natural gas liquefaction process
초록 Producing liquefied natural gas (LNG) is a highly energy intensive process, as required liquefaction temperature is around -160 oC at atmospheric pressure. In this study, we propose a novel energy-efficient operation strategy. The key idea is to trace the drifting optimal operating points by employing RTO, while minimizing the energy loss caused by imperfect control and unrenewed steady-state operating points during RTO intermission by designing the most energy-efficient SOC variables under two-layered operation framework. Special attention was paid to loss evaluation step to accurately consider the implementation loss so that the designed system can be seamlessly applied to the actual process with guaranteed optimality of energy efficiency. The proposed strategy is validated by applying to 100 ton-per-day industrial LNG plant. In the case study, warm end delta temperature is evaluated as the most promising variable, resulting in the lowest energy loss.
저자 원왕연
소속 경희대
키워드 Liquefied natural gas; Process control; Optimization; Energy efficiency
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