ODE Model Parameter Identification Via Global Optimization Techniques

In this work we present our progress on the development of a globally optimal method of determining parameters from experimental data for systems described by ordinary differential equations. For cases where the ODE models cannot be analytically integrated, non-linear regression techniques are typically employed in the solution. These methods, however, can only guarantee local optimality of the solution, and also fail to ascertain whether or not the proposed problem parameterization is appropriate for the available data. To address these shortcomings, we propose a novel method for parameter identification that is guaranteed to identify the global optimum of the non-linear regression problem and is also able to deliver ranges for the model parameters for which the proposed model can describe the available data within a predetermined level of accuracy. The method is illustrated in a case study involving the Trambouze reaction scheme.