Most optimization models require rigorous optimization processes using numerical methods since closed-form solutions are rarely found.
Existing SOC methods rely on known process models and are developed based on linearization of original nonlinear process models, which requires rigorous knowledge of the plant and may result in large losses due to the locality inherited from linearization.
Finding an optimal retrofit for existing plants is a very challenging problem, since it often requires rigorous process models for describing the currently operating process and consequently the corresponding optimization problems typically become very complex.
However, a shear model satisfying these three principles usually requires rigorous computational effort.
However, it requires rigorous validation of the computational models and their underlying data [ 93, 94].
A willingness to experiment requires rigorous discipline.
Intervention research requires rigorous conceptualization and assessments.
This approach requires rigorous multiple testing correction.
Answering the questions requires rigorous experiment design.
CAS are complex in nature and requires rigorous tool support.
