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Previous results showed that there is an interaction effect between the process variables analysed on the mechanical properties evaluated.
A two-level factorial design is employed to quantify these effects and interactions between the process variables.
Regression model was used to determine the quantitative relationships between the process variables in terms of performance parameters.
This is due mostly to complex interactions between the process variables, nonlinearities that change with process conditions, and long speed-dependent time delays.
Whilst these parameters are important for describing physical or chemical relationships between the process variables, some of them only provide a marginal contribution to the input output behavior of the system.
A multivariate face-centered cube design (FCCD) was established to seek the functional relationship between the process variables (extraction time, agitation speed and solvent consumption) and the response (germination inhibition).
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These studies do not provide an insight how the process variables change between these alternate steady states.
Therefore, a standard RSM design (CCD) was used to identify the relationship between the response function (% removal) and the process variables (pH, biosorbent dose and contact time).
The paper also seeks to develop a statistically sound regression model that establishes the relationship between the factors (process variables) and the ethanol output (response variable).
At the second step, the relationship between MIcum and MIjns is used to compensate degradation of the prediction power for the regression model between the current process variables and MIcum instead of MIins.
The basis of a model-based diagram is a physical model that describes the functional relationships between the principal process variables (axes).
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