An integration based optimization approach for parameter estimation in dynamic models

Abstract

A common problem in model verification is to determine the values of model parameters that provide the best fit to measured data, based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently nonconvex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. As the need for a user-interactive parameter estimation software, especially for identifying kinetic parameters, was needed; in this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

Control vector parameterization approach in optimization of alternating aerobic–anoxic systems

“…The approach proposed here, which is practically a version of control vector parameterization [12], was tested previously on a number of literature problems [2,13], and found to be effective. Since optimization is carried out in the space of the decision variables, discretization of only the control variables becomes sufficient.…”

“…Given the initial conditions and a given set of control parameters, the differential algebraic equation (DAE) system was solved by a DAE solver at each iteration. Yuceer et al [2] developed a software program, called PARES, to make parameter estimation using different optimization methods and emphasized the importance of the initial guesses on the results. There appears to be two similar works [3,4] available in the current literature, where the model was based upon ASM1 [5].…”

Estimation of Kinetic Parameters for Five Lump Fluids Catalytic Cracking Riser Reactor

“…The objective of the optimization is the use of specific methods to ascertain the best parameters and efficient solution to a design or reaction process. This solution technique is one of the best quantitative tools in industrial decision making [5].…”