Parameter estimation of liquid chromatography model using quasi-oppositional differential evolution Reynier Hern´ andez Torres , Mirtha Irizar Mesa, Orestes Llanes Santiago, Lourdes M. Zumalac´ arregui de C´ ardenas Instituto Superior Polit´ ecnico Jos´ e Antonio Echeverr´ ıa (CUJAE), Marianao, La Habana, Cuba E-mail: reynier@electrica.cujae.edu.cu, mirtha@electrica.cujae.edu.cu, orestes@electrica.cujae.edu.cu, lourdes@quimica.cujae.edu.cu Leˆ oncio Di´ogenes T. Cˆ amara, Antˆ onio Jos´ e da Silva Neto Departamento de Engenharia Mecˆanica e Energia, Instituto Polit´ ecnico Universidade do Estado do Rio de Janeiro (UERJ), Nova Friburgo, RJ E-mail: dcamara@iprj.uerj.br, ajsneto@iprj.uerj.br Keywords: Liquid Chromatography; Opposition; Differential Evolution; Parameter Estimation Abstract: In many biotechnological processes, complex inverse problems related to the optimiza- tion, scalability and parameter estimation are present. Computational models of these processes are usually characterized by an absence of trustworthy parameter values. Chromatographic mod- els are an example of this problem. Usually, experimental data are used to estimate these missing parameters. Many optimization techniques have been applied to solve this problem. Metaheuris- tics are global optimization algorithms which seek and find good solutions with a reasonable computational cost. In this work, a recent algorithm, quasi-oppositional differential evolution, is applied to the problem of estimating parameters in liquid column chromatography. The effective- ness of this algorithm is analysed, through a comparison, with other basic algorithms (genetic algorithms, differential evolution, particle swarm optimization, ant colony optimization). It is utilized a case study of a single component (α-lactoalbumin), represented by the general rate model with five parameters. 1 Introduction Chromatography is a very important separation technique used in biotechnology. It is based on the structural differences and adsorption phenomena. In order to optimize and scale-up this process, it is important to have a good model and a set of parameters which characterize the system. To obtain the model parameters, it is necessary to employ methods that reduce the demands on cost and time in laboratory experiments. Simulation of chromatographic processes can be used to obtain different parameters that cannot be obtained experimentally. This paper is focused in the study and application of a novel algorithm, Quasi-Oppositional Differential Evolution, as optimization method to estimate parameters in a liquid chromatogra- phy process. A case study of estimating five parameters of the general rate model, modelling a single component (α-lactoalbumin) system, is used. The results of this algorithm is analysed and compared with others algorithm (Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)) previously applied. 577 ISSN 2317-3297