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Optimum design of integrated liquid recovery plants by variable population size genetic algorithm
Author(s) -
Mehrpooya Mehdi,
Vatani Ali,
Mousavian S. M. Ali
Publication year - 2010
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20359
Subject(s) - mathematical optimization , genetic algorithm , variable (mathematics) , optimal design , population , process (computing) , profit (economics) , process design , computer science , operating point , process engineering , engineering , mathematics , process integration , economics , microeconomics , demography , sociology , mathematical analysis , electrical engineering , machine learning , operating system
Increase in the price of energy sources as well as economic problems have caused cryogenic natural gas plants to become more complex and efficient. After selecting the process configuration, the flow rate, pressure, and temperature of the process fluid streams are determining factors which should be tuned in order to find the optimum condition. Products specification and operating costs of the plant are two significant parameters which should be considered in an optimal design. Moreover, process design limitations contribute to the problem being more difficult. This paper shows how the optimal operating point in an integrated NGL recovery plant can be found through solving a complex constrained optimization problem. A Variable Population size Genetic Algorithm (VPGA) was used for optimization. As well, the role of VPGA algorithm parameters in solving the process design problems is investigated in this study. The analysis showed that the VPGA method has better performance compared to the general GA methods. The plant‐wide net profit increases 12493360 $/year only by changing the selected operating conditions to its optimal value.