Browsing by Author "Santos, Lino O."
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- Application of MCSFilter to estimate stiction control valve parametersPublication . Amador, Andreia; Fernandes, Florbela P.; Santos, Lino O.; Romanenko, AndreyThe mitigation of the stiction phenomena in control valves is of paramount importance for efficient industrial plant operation. Mathematical models of sticky valves are typically discontinuous and highly nonlinear. A derivative-free optimization method is applied in the context of parameter estimation in order to determine the stiction parameters of a control valve. The method successfully determines the correct parameter set and compares favorably with a previous case study of this problem that used smooth function.
- Parameter estimation of a pulp digester model with derivative-free optimization strategiesPublication . Seiça, J.C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C.P.The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.
- Parameter estimation of the kinetic α -pinene isomerization model using the MCSfilter algorithmPublication . Amador, Andreia; Fernandes, Florbela P.; Santos, Lino O.; Romanenko, Andrey; Rocha, Ana Maria A.C.This paper aims to illustrate the application of a derivative- free multistart algorithm with coordinate search filter, designated as the MCSFilter algorithm. The problem used in this study is the parameter estimation problem of the kinetic α-pinene isomerization model. This is a well known nonlinear optimization problem (NLP) that has been investigated as a case study for performance testing of most derivative based methods proposed in the literature. Since the MCSFilter algorithm features a stochastic component, it was run ten times to solve the NLP problem. The optimization problem was successfully solved in all the runs and the optimal solution demonstrates that in all runs the MCSFilter provides a good quality solution.