International Journal of Electronics Signals and Systems


For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computation approach for solving such problems. In this work, a modified version of Differential Evolution (DE) algorithm [named Modified Differential evolution (MDE)] is used to solve a kinetic parameter estimation problem from chemical engineering field. The computational efficiency of MDE is compared with that of original DE and Trigonometric Differential Evolution (TDE). Results indicate that performance of MDE algorithm is better than that of DE and TDE.



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