Mauro Fernandi

The paper illustrates innovative ways of using the CARSO (Computer Aided Response Surface Optimiz... more 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.

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Mauro: A Novel Strategy for Optimizing Mixture Properties

Applied Mathematics, 2012

The paper illustrates an innovative procedure for experimental design in mixture analysis. It rel... more 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.

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Mixture Optimization by the CARSO Procedure and DCM Strategies

Applied Mathematics, 2014

The paper illustrates a new way of using the CARSO procedure for response surfaces analyses deriv... more 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.

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Experimental Design for Optimizing a Mixture of Materials plus an Evaporating Solvent

Applied Mathematics, 2015

The paper illustrates a further new way of using the CARSO procedure for response surfaces analys... more 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.

format_quoteThe experimental designs with three or four variables yielded nearly identical gammas, indicating solvent inclusion is unnecessary for optimal mixture performance.format_quote

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