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Empirical models obtained through second-order (Taylor series approximation) regression provided the optimal bioleaching conditions.
All the empirical models obtained using response surface methodology were considered predictive.
Using the empirical models obtained with the statistical analysis of the results, we determine the scale invariants for the system.
Industrial applications of model predictive control rely mostly on linear empirical models obtained by employing time series analysis approaches.
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The deterministic models require an enormous number of operating environment information and huge computational effort, while semi-empirical models obtained as their empirical modifications have a moderate complexity.
Empirical models were obtained for a and b as functions of operating conditions.
Empirical models were obtained for relating physicochemical properties and hydrogen production as functions of palladium and titanium dioxide content.
Physical models rely on basic laws of physics whereas empirical models are obtained by fitting an empirical equation to experimental data.
The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (α-diversity), but also areas with high species turnover (β-diversity).
The results from [1] suggest that the LIvE method, which relies on an empirical propagation model obtained from the statistical analysis of a large number of measurements and highly dependent on the adaptation to a specific geographical area of interest, outperforms most often considered direct methods IDW, IDW2, and Kriging.
The elastic modulus of intact rock is used for many rock engineering projects, such as tunnels, slopes, and foundations, but due to the requirements of high-quality core samples and associated sophisticated test equipment, instead the use of empirical models to obtain this parameter has been an attractive research topic.
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