Nonlinear Regression Modeling For Engineering Applications. Modeling, Model Validation, And Enabling Design Of Experiments

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Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products)

Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization

Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications

First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding

If the model does not fit the data, our understanding of the mechanism was wrong or incomplete

Patterns in the residuals can guide model improvement

The author has used the material in the undergraduate unit operations lab course and in advanced control applications.

The focus is on practical applications, with relevant methods supported by fundamental analysis

This book details methods of nonlinear regression, computational algorithms, model validation, interpretation of residuals, and useful experimental design

This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model

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