Physical parameter estimation in spatial heat transport models with an application to food storage

Parameter estimation plays an important role in physical modelling, but can be problematic due to the complexity of spatiotemporal models that are used for analysis, control and design in industry. In this paper we aim to circumvent these problems by using a methodology that approximates a model, or a part of a model, by a first-order plus dead time (FOPDT) approximation, explicit in the physical parameters. The FOPDT model with its physical parameters can be calibrated and validated to experimental data via an Output Error identification. The methodology is illustrated by a model of a temperature-controlled food storage room using experimental data. The complex part of the model is reduced to an accurate first-order model that has predictive power with respect to physical parameter variations. Moreover, this methodology allows one to test model adjustments for phenomena that were not considered in the physical model, in a relatively easy way.

► We present a method to estimate physical parameters for partial differential equation models. ► The method combines model reduction, parameter estimation and calibration. ► The method simplifies model calibration and validation considerably. ► We successfully test the method on a realistic model using real-world data.