INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY (IJASMT )

E- ISSN 2489-009X
P- ISSN 2695-1908
VOL. 8 NO. 3 2022
DOI: https://doi.org/10.56201/ijasmt.v8.no3.2022.pg16.37


A Proposed Inscribed-Doehlert Design for three-factor Spherical N-Point Design: Variation in Model Parameter Estimation

Inamete Emem Ndah H., Maxwell Azubuike Ijomah & Dele Joshua Osahogulu


Abstract


This study considered and proposed three Factor Spherical N-point Design as applied to Variation in Model Parameter Estimation. Two quadratic models were considered: one contained all the parameters while the other model excluded the three interaction term s. The Box-Behnken, Central Composite Circumscribed, Central Composite Inscribed and Doehlert Designs were studied for these two quadratic models using the D-optimality criterion, Sum of Square Errors and Grand Means of the Designs. The study also proposed a 14-point Design from the Central Composite Inscribed and the Doehlert Designs. We found out that the determinant values of all the designs studied were higher for reduced model than for the full model and that the designs with smaller determinants usually produce larger Sum of Square Errors. We also ascertained that, as the centre points increase, the determinants of all the designs decrease for the full model, while the Box-Behnken Design has equal determinants for 1 and 2 centre points for the reduced model. The study found out that Box-Behnken Design was a better design for reduced model on the basis of Sum of Square Errors other than Central Composite Circumscribed design. Also, we found out that the reduced quadratic model was a better model for the three factor Spherical Second-Order Designs. The proposed design was found to be better than all the standard designs so far studied, because it gave smaller values of the sum of square errors and also better values of the AIC (Akaike Information Criterion) and SBC (Schwartz Bayesian Criterion) for both models and for all the 5 centre points added.


keywords:

Inscribed-Doehlert design, Box Behnken design, Doehlert design and Central Composite design


References:


Anup, K. D., &Saikat, D. (2018). Optimization of Extraction using Mathematical Models and
Computation.10(3), 75-106. DOI: 10.1016/B978-0-12-812364-5.00003-1

Atkinson, A.C. & Donev, A.N. (1992).Optimum experimental designs, Oxford. Clarendon.
Box, G.E.P.& Draper, N.R. (1963). The choice of a second order rotatable design, Biometrika, 50,
335–352.

Box, G.E.P. & Draper, N.R. (1975). Robust designs, Biometrika62, 347–352.

Box, G.E.P.& Draper, N.R. (1987).Empirical model-building and response surfaces, Wiley.

Box, G.E.P.& Hunter, J.S. (1957). Multifactor experimental designs for exploring response
surfaces, Annals of Mathematical Statistics, 28, 195–241. Cochran, W. and Cox, D.R.
(1957).Experimentaldesigns, John Wiley.

Chigbu, P.E., Ukaegbu, E.C. & Nwanya, J. C. (2009). On comparing the prediction variances of
some Central Composite Designs in Spherical regions: A Review. STATISTICA, anno
LXIX,4.

Dette, H. & Grigoriev, Y. (2014). Construction of efficient and optimal experiment design for
response surface models. The annals of statistics. V. 42,(4), 1635-1656. DOI:
10.1214/14-AOS1241.

Draper, N.R. (1982). Center points in second-order response surface designs.Technometrics24(2),
127–133.

Draper, N.R. & John, J.A. (1998). Response surface designs where levels of some factors are
difficult to change.Australian and New Zealand Journal of Statistics,40(4), 487–495.

Draper, N.R. & Lin, D.K.J. (1990). Small response-surface designs.Technometrics32, 187–194.

Frits, B. S., & David, C. M. (2018). 13 th International Symposium on process systems
Engineering. 44(3), 1-28

Giovannitti-Jensen, A. & Myers, R.H. (1989). Graphical Assessment of the Prediction Capability
of Response Surface Designs, Technometrics, 31, 2, 159-171.

Iwundu, M. P. & Albert-Udochukwuka, E. B. (2014). On the behaviour of D-optimal exact
designs under changing regression polynomials. International Journal of Statistics
andProbability,
3(4), 67-85.
doi:10.5539/ijsp.v3n4p67
URL:http://dx.doi.org/10.5539/ijsp.v3n4p67.

Iwundu, M. P. (2015) Optimal partially replicated cube, star and center runs in face-centered
central composite designs. International Journal of Statistics and Probability; 4(4),
doi:10.5539/ijsp.v4n4p1 URL: http://dx.doi.org/10.5539/ijsp.v4n4p1.

Iwundu, M. P. (2016a). Alternative Second-Order N-Point Spherical Response Surface
methodology designs and their efficiencies. International Journal of Statistics and
Probability;
5(4),22-30. doi:10.5539/ijsp.v5n4p22 URL:http://dx.doi.org/10.5539/ijsp.v5n4p22.

Iwundu, M. P. (2016a). Alternative second-order n-point spherical response surface methodology
designs and their efficiencies. International Journal of Statistics and Probability; 5(4),
22-30.

Iwundu, M.P. & Onu, O.H. (2017). Preferences of equiradial designs with changing axial
distances, design sizes and increase center points and their relationship to the N-point
central composite design: International Journal of Advanced Statistics and Probability,
5 (2), 77-82.

Khuri, A. I. & Cornel, J. A. (1996). Response Surface: Design and Analysis. Second Edition.
Marcel Dekker, Inc.

Khuri, A.I. (1988). A measure of rotatability for response-surface designs, Technometrics,30, 95–
104.

Lucas, J. M. (1976) response surface design is the best: A performance comparison of several
types of quadratic response surface designs in symmetric regions. Technometrics, 18,
411-417.

Lucas, J.M. (1976). Which response surface design is best: a performance comparison of several
types of quadratic response surface designs in symmetric regions.Technometrics 18(4),
411–417.

Magangi O. J. (2018). Construction of modified optimal second order rotatable designs. 20(8),
2467-2478.

Montgomery, D.C. (1991).Design and analysis of experiments. John Wiley.

Myers, R.H. (1971).Responsesurfacemethodology. Edwards Brothers.

Myers, R. H., Montgomery, D. C. & Anderson-Cook, C. M. (2009). Response surface
methodology: Process and product optimization using designed experiments. 3 rd Edition.
John Wiley & Sons, Inc.

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2007).Response surface
methodology: Process and product optimization using designed experiments. 3rd
Edition.John Wiley & Sons, Inc.

Myers, R. H., Vinning, G. G., Giovannitti-Jensen, A. & Myers, S. L. (1992). Variance
Dispersion properties of Second-order response surface designs. Journal of quality
technology, 24, 1-11.

Nalimov, V.V., Golikova, T.I. & Mikeshina, N.G. (1970). On practical use of the concept of D-
optimality,Technometrics12(4), 799–812.

Nitin, V. &Vivek, K. (2019).Application of Box-Behnken design for the optimization of
cellulase production under solid-state fermentation. SpringerLink.1(1733).
Nwobi, F.N., Okoroafor, A.C. & Onukogu, I.B. (2001). Restricted second-order designs on one
and two concentric balls, Statistica,LXI(1), 103–112.

Onukogu, I. B. & Iwundu, M. P. (2007).A Combinatorial Procedure for constructing D-optimal
designs.Statistica, 67 (4), 415-423.

Oyejola, B. A. & Nwanya, J. C. (2015).Selecting the right central composite design.Journal of
Statistics and Applications, 5(1), 21-30.

Oyejola, B. A., &Nwanya, J. C. (2015). Selecting the right central composite design.Journal of
Statistics and Applications, 5(1), 21-30.

Pazman, A. (1986).Foundations of optimal experimental designs,D. Reidel Publishing Company.
Rady, E. A., Abd El-Monsef, M. M. E. & Seyam, M. M. (2009). Relationship among several
optimality criteria. interstat.statjournals.net>YEAR>articles.

Robert, W. M., (2009). New Box-Behnken Design.Knoxville, TN 37996-0532, U.S.A.

Satriani, A. P., Rusli, D., Mohamad, Y. M. & Osman H. (2013).Application of Box-Behnken
design in optimization of glucose production from oil palm empty fruit bunch cellulose.
International Journal of Polymer Science.5(4), 29-33.
Sergio, L. C., Walter, N. L., Cristina, M. Q., Benicio, B. N, & Juan, M. B. (2004). Doehlert
matrix: Achemometric tool for analytical chemistry –review. Talanta,63(4),1061-1067.

Shruti, S. R., & Padma, T., (2017).Selection of a design for response surface School of
Biosciences and Technology. Vellore Institute of Technology.263(2), 638-646.

Suliman, R. (2017). Response surface methodology and its application in optimizing the
efficiency of organic solar cells, university Open PRAIRIE: Open Public Research
Access Institutional Repository and Information Exchange. 7(2). 2232-2241

Ukaegbu, E.C. & Chigbu, P.E. (2015). Graphical evaluation of the prediction capabilities of
partially replicated orthogonal central composite designs.Quality and Reliability
Engineering International,31, 707 – 717.

Verdooren, (2017).Use of Doehlert Designs for Second-order Polynomial Models. Springer
Nature, 5(2), 62-67.

Wagna, P. C., Diogenes, R. G., Alete, P. T., Antonio, C. S., &Coata, M. G. (2008). Use of
Doehlert design for optimizing the digestion of beans formulti-element determination by
inductively coupled plasma optical emission spectrometry. Journal of the Brazilian
chemical society, 19(1).

Wardrop, D. M. (1985). Optimality criteria applied to certain responses suface designs. 86(1), 1-20

William, G. W. & Alain, G. (2018). Cell Culture media in Bio processing. 24(5), 116-121.


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