Pipe size sensitivity in pressure relief networks using genetic algorithms
Bočna strana članka
Glavni sadržaj članka
Apstrakt
This paper utilizes a stochastic optimization approach using genetic algorithms, for conducting rigorous pipe size sensitivity assessments onto the design of pressure relief networks. By sampling high performance candidates, only the finest options can survive. The pressure relief network system that was investigated in this work was previously reported in literature. The problem is constrained and involves minimizing a cost objective function that evaluates the overall network performance, in which the best pipe size combination should be selected for each segment within the network. The overall goal of this paper was to seek cost-effective designs for the pressure relief piping system by exploring different ranges of pipe diameters that are available for each segment in the network and comparing how the overall design of the system is affected, when the number of pipe size options to select from is varied.
Detalji članka
Broj časopisa
Rubrika
Kada je rukopis prihvaćen za objavlјivanje, autori prenose autorska prava na izdavača. U slučaju da rukopis ne bude prihvaćen za štampu u časopisu, autori zadržavaju sva prava.
Na izdavača se prenose sledeća prava na rukopis, uklјučujući i dodatne materijale, i sve delove, izvode ili elemente rukopisa:
- pravo da reprodukuje i distribuira rukopis u štampanom obliku, uklјučujući i štampanje na zahtev;
- pravo na štampanje probnih primeraka, reprint i specijalnih izdanja rukopisa;
- pravo da rukopis prevede na druge jezike;
- pravo da rukopis reprodukuje koristeći fotomehanička ili slična sredstva, uklјučujući, ali ne ograničavajući se na fotokopiranje, i pravo da distribuira ove kopije;
- pravo da rukopis reprodukuje i distribuira elektronski ili optički koristeći sve nosioce podataka ili medija za pohranjivanje, a naročito u mašinski čitlјivoj/digitalizovanoj formi na nosačima podataka kao što su hard disk, CD-ROM, DVD, Blu-ray Disc (BD), mini disk, trake sa podacima, i pravo da reprodukuje i distribuira rukopis sa tih prenosnika podataka;
- pravo da sačuva rukopis u bazama podataka, uklјučujući i onlajn baze podataka, kao i pravo prenosa rukopisa u svim tehničkim sistemima i režimima;
- pravo da rukopis učini dostupnim javnosti ili zatvorenim grupama korisnika na osnovu pojedinačnih zahteva za upotrebu na monitoru ili drugim čitačima (uklјučujući i čitače elektonskih knjiga), i u štampanoj formi za korisnike, bilo putem interneta, onlajn servisa, ili putem internih ili eksternih mreža.
Kako citirati
Reference
Rangaiah GP. Stochastic global optimization: techniques and applications in chemical engineering: Advances in Process Systems Engineering, World Scientific. 2010. ISBN - 978-981-4299-20-6, https://doi.org/10.1142/7669
Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 1, fundamentals. University Computing. 1993; 15(2):56-69.
Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 2, research topics. University Computing. 1993;15(4):170-81.
Fogel LJ, Owens AJ, Walsh MJ. Intelligent decision making through a simulation of evolution. Syst Res Behav Sci. 1966. 11 (4):253-72.
Rechenberg I. Evolution strategy: Natures way of optimization. In: Optimization: Methods and applications, possibilities and limitations, 106-126. Springer. 1989.
Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence: MIT press. 1992.
Mitchell M. An introduction to genetic algorithms. MIT press. 1998.
Lee K, Tsai J, Liu T, Chou J. Improved genetic algorithm for mixed-discrete-continuous design optimization problems. Eng Optim. 2010; 42 (10):927-41.
Duan X, Wang GG, Kang X, Niu Q, Naterer G, Peng Q. Performance study of mode-pursuing sampling method. Engineering Optim. 2009; 41(1):1-21.
Kuo J, Wang Y, Seng WL. A hybrid neural–genetic algorithm for reservoir water quality management. Water Res. 2006;40 (7):1367-76.
Wu L, Chang W, Guan G. Extractants design based on an improved genetic algorithm. Ind Eng Chem Res. 2007; 46 (4):1254-8.
Veith TL, Wolfe ML, Heatwole CD. Optimization procedure for cost effective BMP placement at a watershed scale. JAWRA 2003; 39 (6):1331-43.
Alnouri SY, Stijepovic MZ, Linke P, El-Halwagi M. Optimal design of spatially constrained interplant water networks with direct recycling techniques using genetic algorithms. Chem Eng Trans 39: 2014; 457-462.
Alnouri SY, Linke P, El-Halwagi M. Optimal interplant water networks for industrial zones: Addressing interconnectivity options through pipeline merging. AIChE J. 2014: 60 (8):2853-74.
Tung Ching, Hsu S, Liu C, Li S. Application of the genetic algorithm for optimizing operation rules of the LiYuTan reservoir in Taiwan. JAWRA. 2003; 39 (3):649-57.
Ravandi E. Nezamabadi-Pour GH, Monfared AE, Jaafarpour AM. Reservoir Characterization by a Combination of Fuzzy Logic and Genetic Algorithm. Pet Sci Technol. 2014. 32 (7):840-847.
Kovačič M, Rožej U, Brezočnik M. Genetic Algorithm Rolling Mill Layout Optimization. Mat er Manuf Process. 2013. 28(7):783-7.
Nath NK, Mitra K. Optimisation of suction pressure for iron ore sintering by genetic algorithm. Ironmak Steelmak. 2004. 31 (3):199-206.
Ghaedi M, Ebrahimi AN, Pishvaie MR. Application of genetic algorithm for optimization of separator pressures in multistage production units. Chem Eng Comm. 2014; 201 (7):926-38.
Ng W, Mak KL, Zhang YX. Scheduling trucks in container terminals using a genetic algorithm. Eng Optim. 2007. 39 (1): 33-47.
Mahdi E, Nasser K, Gharbia M. Optimization of Flare Header Platform Design in a Liquefied Natural Gas Plant. Proceedings of the 2nd Annual Gas Processing Symposium. 2010. 359-67.
Murtagh BA. An approach to the optimal design of networks. Chem Eng Sci 1972; 27 (5):1131-41.
Dolan WB, Cummings PT, LeVan MD. Process optimization via simulated annealing: application to network design. AIChE J. 1989; 35 (5):725-36.
Cheng W, Mah RS. Optimal design of pressure relieving piping networks by discrete merging. AIChE J. 1976; 22 (3):471-6.
Cardoso MF, Salcedo RL, de Azevedo SF. Nonequilibrium Simulated Annealing: A Faster Approach to Combinatorial Minimization. Ind Eng Chem Res. 1994; 33 (8):1908-18
Costa AL, de Medeiros JL, Pessoa FL. Optimization of pressure relief header networks: A linear programming formulation. Comput Chem Eng. 2000; 24 (1):153-6.
Alnouri SY, Linke P, El-Halwagi M. Synthesis of industrial park water reuse networks considering treatment systems and merged connectivity options. Comput Chem Eng. 2016; 91:289-306.