| 학회 | 한국화학공학회 |
| 학술대회 | 2020년 가을 (10/14 ~ 10/16, e-컨퍼런스) |
| 권호 | 26권 1호, p.205 |
| 발표분야 | 공정시스템 |
| 제목 | Machine learning based estimation of decision variables for SMR natural gas liquefaction process |
| 초록 | Abstract Natural gas (NG) is considered as relatively clean energy source in comparison with coal and oil. The transportation of NG over long distances is carried out in liquid form i.e., liquefied natural gas (LNG). However, liquefaction of NG is an energy intensive process and a small change in the feed conditions can cause the process infeasible. To make the process feasible again there is need an optimization which requires lot of computational cost. Therefore, to solve this problem support vector regression based machine learning model is used to estimate the decision variables at which the process becomes feasible again. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A2B6001566) and by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A6A1031189). |
| 저자 | 카딜킨자, 민성웅, Muhammad Abdul Qyyum, 이문용 |
| 소속 | 영남대 |
| 키워드 | 공정시스템 |
| 원문파일 | 초록 보기 |
