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Epistemic uncertainty results from incomplete knowledge about the process under study.
Furthermore, the desirability function was employed in order to optimize the process under study.
Besides, they provide advantages such as better parametric control of the process under study.
Many practical applications of animated computer simulation stop at the modeling and displaying of the process under study.
System identification techniques were employed to obtain a mathematical model that characterizes the dynamic behavior of the process under study.
The industrial process under study produces LDPE by high-pressure polymerization of ethylene in a tubular reactor using oxygen and organic peroxides as initiators.
In both cases, the identified model is characterized by a second order ARMAX structure which describes with very high precision the process under study.
Since this important issue has not attracted considerable attention over the past decade, the paper introduces and evaluates three techniques to assess the importance of each parameter to the overall input/output description of the process under study.
The latter extracts persistent features out of non-simultaneously recorded snapshots of stochastic events, thereby mitigating possible interpretation issues related to the turbulent nature of the process under study.
Valid information is obtained between the spectral bins of conventional DFT, scalloping error is greatly reduced, and amplitude and phase of Fourier components are more true to the process under study as with conventional DFT.
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Buckingham's π theorem is proposed to define non-dimensional parameters that allow suitably describing the relationships linking the physical variables involved in the process under-study to the fundamental dimensions.
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preparation under study
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