An improved self-adaptive membrane computing optimization algorithm and its applications in residue hydrogenating model parameter estimation

Abstract

In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing (ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.

Keywords

optimization algorithm / membrane computing / benchmark function / improved self-adaptive operator

References

[1] SuW-h, HuangH-z. Development and calibration of a reduced chemical kinetic model of n-heptane for HCCI engine combustion [J]. Fuel, 2005, 84(9): 1029-1040

[2] ZhangZ-y, HidajatK, AjayK R. Determination of adsorption and kinetic parameters for methyl tert-butyl ether synthesis from tert-butyl alcohol and methanol [J]. Journal of Catalysis, 2001, 200(2): 209-221

[3] JohnHAdaptation in natural and artificial systems [M], 1975Ann ArborUniversity of Michigan Press164-184

[4] GoldbergD EGenetic algorithms in search, optimization and machine learning [M], 1989New YorkAddison-Wesley28-33

[5] NouguesJ M, GrauM D, PuigjanerL. Parameter estimation with genetic algorithm in control of fed-batch reactors [J]. Chemical Engineering Processing, 2002, 41(4): 303-309

[6] IzadifarM, BaikO D. Application of genetic algorithm for determination of the effective diffusivity of andrographolide in plant materials [J]. Biochemical Engineering Journal, 2007, 33(2): 178-187

[7] MilaniG, MilaniF. Genetic algorithm for the optimization of rubber insulated high voltage power cables production lines [J]. Computers & Chemical Engineering, 2008, 32(12): 3198-3212

[8] JongK, ADAn analysis of the behavior of a class of genetic adaptive systems [D], 1975Ann Arbor, MI, USAUniversity of Michigan

[9] MorosR, KaliesH, RexH G, SchaffarczykS. A genetic algorithm for generating initial parameter estimations for kinetic models of catalytic process [J]. Computers & Chemical Engineering, 1996, 20(10): 1257-1270

[10] BallandL, MouhabN, CosmaoJ M, EstelL. Kinetic parameter estimation of solvent-free reactions: Application to esterification of acetic anhydride by methanol [J]. Chemical Engineering Processing, 2002, 41(5): 395-402

[11] BehrangM, NavidM, FarhangJ F. A hybrid GA-SQP optimization technique for determination of kinetic parameters of hydrogenation reactions [J]. Computers and Chemical Engineering, 2008, 32: 1447-1455

[12] HoS-y, ShuL-s, ChenJ-h. Intelligent evolutionary algorithms for large parameter optimization problems [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(6): 522-541

[13] PaunG. Computing with membranes [J]. Journal of Computer and System Sciences, 2000, 61(1): 108-143

[14] YangS-p, WangN. A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model [J]. International Journal of Hydrogen Energy, 2012, 37(10): 8465-8476

[15] GaoH-y, LiC-w. Membrane-inspired quantum bee colony optimization and its applications for decision engine [J]. Journal of Central South University, 2014, 21(5): 1887-1897

[16] HuangL, WangN. An optimization algorithm inspired by membrane computing [J]. Lecture Notes in Computer Science, 2006, 4222: 49-52

[17] FeiX-l, GevertB S. Kinetics of vanadyl etioporphyrin hydrodemetallization [J]. Journal of Catalysis, 2001, 200(1): 91-98

[18] LongF-x, GevertB S. Kinetic parameter estimation and statistical analysis of vanadyl etioprophyrin hydrodemetallization [J]. Computers & Chemical Engineering, 2003, 27(5): 697-700

[19] ZhaoW-x, ChenD-z, HuS-x. Differential fraction-based kinetic model for simulating hydrodesulfurization process of petroleum fraction [J]. Comput Chem, 2002, 26(2): 141-148

[20] YanX-f, ChenD-z, HuS-x. Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model [J]. Computers & Chemical Engineering, 2003, 27(10): 1393-1404

[21] XuX-k, ZhangL-c, WengH-x. Study on kinetics model for residue hydrofining [J]. Petrol Refinery Eng, 2005, 35: 44-48

[22] ZhangL-c, XuX-k, WengH-x. Hydrodemetallization kinetic model for Maoming’s residue hydrodesulfurization treatment unit [J]. Journal of Chemical Engineering of Chinese Universities, 2006, 20(2): 227-232

[23] ZhengQ-f, LiuH-z. Modified holographic research strategy and its application to optimization in chemical engineering [J]. CIESC Journal, 2006, 57(10): 2349-2354