Abstract
In this paper, an optimization strategy on the basis of the particle swarm optimization (PSO) method is proposed to determine the optimal recipe offline for the gasoline blending process. An octane number model is proposed for optimization. Furthermore, the proposed strategy has been applied onsite at a gasoline production line in Nanjing, China. The results show that the optimized recipes are able to improve the first-time success rate for the blending process and significantly decrease the quality giveaways and blending cost. The stability of the gasoline production has been improved as well.
This work is supported by Shanghai PostDoc Fund (No. 10R21412200), National Science Fund for Distinguished Young Scholars (60625302), National 863 plans (2008AA042902), Shanghai scientific and technological project (09DZ1120400), the special funds of Shanghai College basic scientific research operating expenses, Shanghai Leading Academic Discipline Project (B504) and National Nature Science Foundation of China (No.60804029).
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References
DeWitt, C.W., Lasdon, L.S., Waren, A.D., Brenner, D.A., Melhem, S.A.: Omega: an improved gasoline blending system for Texaco. Interfaces 19, 85–101 (1989)
Healey, W.C., Maasen, C.W., Peterson, R.T.: A new approach to blending octanes. In: Proc. 24th Meeting API Refining Division, vol. 39, pp. 132–136 (1959)
Singh, A., Forbes, J.F., Vermeer, P.J., Woo, S.S.: Model-based real-time optimization of automotive gasoline blending operations. Journal of Process Control 10, 43–58 (2000)
Murty, B.S.N., Rao, R.N.: Global optimization for prediction of blend composition of gasolines of desired octane number and properties. Fuel Processing Technology 85, 1595–1602 (2004)
Gary, J.H., Handwerk, G.E.: Petroleum refining technology and economics, 3rd edn. Marcel Dekker, New York (1994)
Magoulas, K., Marinos-Kouris, D., Lygeros, A.: Instructions are given for building a gasoline blending LP. Oil and Gas Journal 4, 32–37 (1988)
Diaz, A., Barsamian, J.A.: Meeting changing fuel requirements with on-line optimization. Hydrocarbon Processing, 71–76 (1996)
Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. 6th Int. Symp. on Micro Machine and Human Science, pp. 39–43. IEEE Service Center, Piscataway (1995)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. 5th Int Conf. on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)
Clerc, M.: The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Proc. ICEC, Washington, DC, pp. 1951–1957 (1999)
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Cheng, H., Zhong, W., Qian, F. (2011). An Application of the Particle Swarm Optimization on the Gasoline Blending Process. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_45
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DOI: https://doi.org/10.1007/978-3-642-23220-6_45
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