Analysis and Identification of Distributed Parameter Model for Wood Drying Systems

Abstract

A new approach based on the decomposition, in position-time space, of operating functions (OFs) is proposed and various criteria of parameter identification like the output least squares (OLS) and the regularized least squares (RLS) are formulated and incorporated in the framework. Several optimization algorithms such as the Gauss-Newton, conjugate gradient and Levenberg-Marquardt are diligently modified and computed in order to search the performed algorithm to identify, in space and time, the numerical fields of the model parameters. Because the sensitivity of the criteria to the design variables plays a very important role in the optimization problem, various techniques such as finite difference method and direct differentiation method are tested. The enhanced performance of the model with the newly identified OFs is proved by a higher conformity of its predictions with the real data of drying wood systems (DWSs).

Keywords:

• Characteristic curves
• Constrained equations
• Differential partial equations
• Dynamic models
• Operating functions
• Optimization algorithms
• Sensitivity
• Separated variables technique

Acknowledgments

Finally, the proposed mathematical model was tested and validated in the Canadian industry with the cooperation of KDL Lumber Inc., on different specimens of wood. So, their technical support is gratefully acknowledged. This work is supported by the Canadian National Science and Engineering Research Council (NSERC).