The paper illustrates innovative ways of using the CARSO (Computer Aided Response Surface Optimization) procedure for response surfaces analyses derived by DCM4 experimental designs in multivariate spaces. Within this...The paper illustrates innovative ways of using the CARSO (Computer Aided Response Surface Optimization) procedure for response surfaces analyses derived by DCM4 experimental designs in multivariate spaces. Within this method, we show a new feature for optimization studies: the results of comparing their quadratic and linear models for discussing the best way to compute the most reliable predictions of future compounds.展开更多
The paper illustrates a new way of using the CARSO procedure for response surfaces analyses derived from innovative experimental designs in multivariate spaces, based on Double Circulant Matrices (DCMs). We report a c...The paper illustrates a new way of using the CARSO procedure for response surfaces analyses derived from innovative experimental designs in multivariate spaces, based on Double Circulant Matrices (DCMs). We report a case study regarding a design based on a DCM for 4 variables. The final response surface model is obtained by the formerly developed CARSO method.展开更多
The paper illustrates a further new way of using the CARSO procedure for response surfaces analyses derived from experimental designs based on Double Circulant Matrices (DCMs). We report a case study regarding a desig...The paper illustrates a further new way of using the CARSO procedure for response surfaces analyses derived from experimental designs based on Double Circulant Matrices (DCMs). We report a case study regarding a design based on a mixture of three chemicals plus an evaporating solvent, in order to compare the relative reliability of designs based either on 3 only or on all 4 factors. We show that both designs give the same results, but the second is preferable because it represents the real situation at the beginning of the process, so that it is possible to know the required amount of solvent that should be used for each experiment. Obviously this applies to any number of factors using the correspondent DCM.展开更多
The paper illustrates an innovative procedure for experimental design in mixture analysis. It relies on D-optimal designs performed on the combinatorial explosion of five levels of components composition, keeping in m...The paper illustrates an innovative procedure for experimental design in mixture analysis. It relies on D-optimal designs performed on the combinatorial explosion of five levels of components composition, keeping in mind the requirements of Central Composite Designs. The final response surface model is obtained by the formerly developed CARSO method.展开更多
文摘The paper illustrates innovative ways of using the CARSO (Computer Aided Response Surface Optimization) procedure for response surfaces analyses derived by DCM4 experimental designs in multivariate spaces. Within this method, we show a new feature for optimization studies: the results of comparing their quadratic and linear models for discussing the best way to compute the most reliable predictions of future compounds.
文摘The paper illustrates a new way of using the CARSO procedure for response surfaces analyses derived from innovative experimental designs in multivariate spaces, based on Double Circulant Matrices (DCMs). We report a case study regarding a design based on a DCM for 4 variables. The final response surface model is obtained by the formerly developed CARSO method.
文摘The paper illustrates a further new way of using the CARSO procedure for response surfaces analyses derived from experimental designs based on Double Circulant Matrices (DCMs). We report a case study regarding a design based on a mixture of three chemicals plus an evaporating solvent, in order to compare the relative reliability of designs based either on 3 only or on all 4 factors. We show that both designs give the same results, but the second is preferable because it represents the real situation at the beginning of the process, so that it is possible to know the required amount of solvent that should be used for each experiment. Obviously this applies to any number of factors using the correspondent DCM.
文摘The paper illustrates an innovative procedure for experimental design in mixture analysis. It relies on D-optimal designs performed on the combinatorial explosion of five levels of components composition, keeping in mind the requirements of Central Composite Designs. The final response surface model is obtained by the formerly developed CARSO method.
