Da Ros, S;
SCHWAAB, M;
PINTO, JC;
(2017)
Parameter Estimation and Statistical Methods.
In:
Reference Module in Chemistry, Molecular Sciences and Chemical Engineering.
Elsevier
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Abstract
Parameter estimation procedures are very important in the chemical engineering field for development of mathematical models, since design, optimization, and advanced control of chemical processes usually rely on mathematical models, which in turn depend on parameter values (and respective uncertainties) obtained with help of available experimental data. For this reason, the parameter estimation problem is introduced here and discussed in terms of its three fundamental steps: the definition of the objective function, the minimization of the objective function, and the statistical analysis of the obtained results.
| Type: | Book chapter |
|---|---|
| Title: | Parameter Estimation and Statistical Methods |
| DOI: | 10.1016/B978-0-12-409547-2.13918-6 |
| Publisher version: | https://doi.org/10.1016/B978-0-12-409547-2.13918-6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Confidence region, Deterministic algorithms, Experimental design, Experimental fluctuations, Maximum likelihood, Model building, Numerical problems, Objective function, Parameter estimation, Prediction uncertainty, Statistical analysis, Stochastic algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10041904 |
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