Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

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


Year:
2013
Note:
This lecture was given at the School of Applied Sciences (HES) in Fribourg (Switzerland) in 4 hours in November 20, 2013.
Laboratories:




 Record created 2013-11-24, last modified 2018-09-13

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