Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods
This lecture describes the following topics: <br><br> • Dynamic Models <br> - Conservation of Mass (Concentration Measurements) <br> - Conservation of Energy (Calorimetry) <br> - Beer’s Law (Spectroscopy) <br><br> • Integration of Dynamic Models <br> - Euler’s Method <br> - Runge-Kutta’s Methods (RK) <br><br> • Linear Regression Problems (OLS) <br> - Calibration-free Calorimetry and Spectroscopy <br><br> • Gradient-based Nonlinear Regression Methods (NLR) <br> - Steepest Descent Method <br> - Newton-Raphson and Newton-Gauss Methods (NG) <br> - Newton-Gauss Levenberg Marquardt Method (NGLM) <br><br> • References
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