1. For each of the following equations determine the optimum response using a one-factor-at-a-time searching algorithm. Begin the search at (0,0) by first changing factor A , using a step-size of 1 for both factors. The boundary conditions for each response surface are 0 ≤ A ≤ 10 and 0≤ B ≤ 10. Continue the search through as many cycles as necessary until you find the optimum response. Compare your optimum response for each equation to the true optimum. Note: These equations are from Deming, S. N.; Morgan, S. L. Experimental Design: A Chemometric Approach , Elsevier: Amsterdam, 1987, and pseudo-three dimensional plots of the response surfaces can be found in their Figures 11.4, 11.5 and 11.14. (a) R = 1.68 + 0.24 A + 0.56 B – 0.04 A 2 – 0.04 B 2 \(\mu_\text{opt} = (3, 7)\) (b) R = 4.0 – 0.4 A + 0.08 AB \(\mu_\text{opt} = (10, 10)\) (c) R = 3.264 + 1.537 A + 0.5664 B – 0.1505 A 2 – 0.02734 B 2 – 0.05785 AB \(\mu_\text{opt} = (3.91, 6.22)\) 2. Use a fixed-sized simplex searching algorithm to find the optimum response for the equation in Problem 1c. For the first simplex, set one vertex at (0,0) with step sizes of one. Compare your optimum response to the true optimum. 3. Show that equation 14.1.3 and equation 14.1.4 are correct. 4. A 2 k factorial design was used to determine the equation for the response surface in Problem 1b. The uncoded levels, coded levels, and the responses are shown in the following table. Determine the uncoded equation for the response surface. A B A* B* response 8 8 +1 +1 5.92 8 2 +1 –1 2.08 2 8 –1 +1 4.48 2 2 –1 –1 3.52 5. Koscielniak and Parczewski investigated the influence of Al on the determination of Ca by atomic absorption spectrophotometry using the 2 k factorial design shown in the following table [Koscielniak, P.; Parczewski, A. Anal. Chim. Acta 1983 , 153 , 111–119]. [Ca2+] (ppm) [Al3+] (ppm) Ca* Al* response 10 160 +1 +1 54.92 10 0 +1 –1 98.44 4 16 –1 +1 19.18 4 0 –1 –1 38.52 (a) Determine the uncoded equation for the response surface. (b) If you wish to analyze a sample that is 6.0 ppm Ca 2 + , what is the maximum concentration of Al 3 + that can be present if the error in the response must be less than 5.0%? 6. Strange studied a chemical reaction using a 23 factorial design [Strange, R. S. J. Chem. Educ. 1990 , 67 , 113–115]. factor high (+1) level low (–1) level X: temperature 140oC 120oC Y: catalyst type B type A Z: [reactant] 0.50 M 0.25 M run X* Y* Z* % yield 1 –1 –1 –1 28 2 +1 –1 –1 17 3 –1 +1 –1 41 4 +1 +1 –1 34 5 –1 –1 +1 56 6 +1 –1 +1 51 7 –1 +1 +1 42 8 +1 +1 +1 36 (a) Determine the coded equation for this data. (b) If \(\beta\) terms of less than \(\pm 1\) are insignificant, what main effects and what interaction terms in the coded equation are important? Write down this simpler form for the coded equation. (c) Explain why the coded equation for this data can not be transformed into an uncoded form. (d) Which is the better catalyst, A or B? (e) What is the yield if the temperature is set to 125 o C, the concentration of the reactant is 0.45 M, and we use the appropriate catalyst? 7. Pharmaceutical tablets coated with lactose often develop a brown discoloration. The primary factors that affect the discoloration are temperature, relative humidity, and the presence of a base acting as a catalyst. The following data have been reported for a 2 3 factorial design [Armstrong, N. A.; James, K. C. Pharmaceutical Experimental Design and Interpretation , Taylor and Francis: London, 1996 as cited in Gonzalez, A. G. Anal. Chim. Acta 1998 , 360 , 227–241]. Empty DataFrame Columns: [(factor, X: benzocaine, Y: temperature, Z: relative humidity), (high (+1) level, present, 40oC, 75%), (low (–1) level, absent, 25oC, 50%)] Index: [] Empty DataFrame Columns: [(run, 1, 2, 3, 4, 5, 6, 7, 8), (X*, –1, +1, –1, +1, –1, +1, –1, +1), (Y*, –1, –1, +1, +1, –1, –1, +1, +1), (Z*, –1, –1, –1, –1, +1, +1, +1, +1), (color (arb. unit), 1.55, 5.40, 3.50, 6.75, 2.45, 3.60, 3.05, 7.10)] Index: [] (a) Determine the coded equation for this data. (b) If \(\beta\) terms of less than 0.5 are insignificant, what main effects and what interaction terms in the coded equation are important? Write down this simpler form for the coded equation. 8. The following data for a 2 3 factorial design were collected during a study of the effect of temperature, pressure, and residence time on the % yield of a reaction [Akhnazarova, S.; Kafarov, V. Experimental Optimization in Chemistry and Chemical Engineering , MIR Publishers: Moscow, 1982 as cited in Gonzalez, A. G. Anal. Chim. Acta 1998 , 360 , 227–241]. Empty DataFrame Columns: [(factor, X: temperature, Y: pressure, Z: residence time), (high (+1) level, 200oC, 0.6 MPa, 20 min), (low (–1) level, 100oC, 0.2 MPa, 10 min)] Index: [] Empty DataFrame Columns: [(run, 1, 2, 3, 4, 5, 6, 7, 8), (X*, –1, +1, –1, +1, –1, +1, –1, +1), (Y*, –1, –1, +1, +1, –1, –1, +1, +1), (Z*, –1, –1, –1, –1, +1, +1, +1, +1), (% yield, 2, 6, 4, 8, 10, 18, 8, 12)] Index: [] (a) Determine the coded equation for this data. (b) If \(\beta\) terms of less than 0.5 are insignificant, what main effects and what interaction terms in the coded equation are important? Write down this simpler form for the coded equation. (c) Three runs at the center of the factorial design—a temperature of 150 o C, a pressure of 0.4 MPa, and a residence time of 15 min—give percent yields of 8%, 9%, and 8.8%. Determine if a first-order empirical model is appropriate for this system at \(\alpha = 0.05\). 9. Duarte and colleagues used a factorial design to optimize a flow-injection analysis method for determining penicillin [Duarte, M. M. M. B.; de O. Netro, G.; Kubota, L. T.; Filho, J. L. L.; Pimentel, M. F.; Lima, F.; Lins, V. Anal. Chim. Acta 1997 , 350 , 353–357]. Three factors were studied: reactor length, carrier flow rate, and sample volume, with the high and low values summarized in the following table. Empty DataFrame Columns: [(factor, X: reactor length, Y: carrier flow rate, Z: sample volume), (high (+1) level, 1.3 cm, 1.6 mL/min, 100 \(\mu\)L), (low (–1) level, 2.0 cm, 2.2 mL/min, 150 \(\mu\)L)] Index: [] The authors determined the optimum response using two criteria: the greatest sensitivity, as determined by the change in potential for the potentiometric detector, and the largest sampling rate. The following table summarizes their optimization results. Empty DataFrame Columns: [(run, 1, 2, 3, 4, 5, 6, 7, 8), (X*, –1, +1, –1, +1, –1, +1, –1, +1), (Y*, –1, –1, +1, +1, –1, –1, +1, +1), (Z*, –1, –1, –1, –1, +1, +1, +1, +1), (\(\Delta E\) (mV), 37.45, 31.70, 32.10, 27.30, 39.85, 32.85, 35.00, 32.15), (sample/h, 21.5, 26.0, 30.0, 33.0, 21.0, 19.5, 30.0, 34.0)] Index: [] (a) Determine the coded equation for the response surface where \(\Delta E\) is the response. (b) Determine the coded equation for the response surface where sample/h is the response. (c) Based on the coded equations in (a) and in (b), do conditions that favor sensitivity also improve the sampling rate? (d) What conditions would you choose if your goal is to optimize both sensitivity and sampling rate? 10. Here is a challenge! McMinn, Eatherton, and Hill investigated the effect of five factors for optimizing an H 2 -atmosphere flame ionization detector using a 2 5 factorial design [McMinn, D. G.; Eatherton, R. L.; Hill, H. H. Anal. Chem. 1984 , 56 , 1293–1298]. The factors and their levels were Empty DataFrame Columns: [(factor, A: H2 flow rate, B: SiH4, C: O2 + N2 flow rate, D: O2/N2 ratio, E: electrode height), (high (+1) level, 1460 mL/min, 20.0 ppm, 255 mL/min, 1.36, 75 (arb. unit)), (low (–1) level, 1382 mL/min, 12.2 ppm, 210 mL/min, 1.19, 55 (arb. unit))] Index: [] The coded (“+” = +1, “–” = –1) factor levels and responses, R , for the 32 experiments are shown in the following table run A* B* C* D* E* R run.1 A*.1 B*.1 C*.1 D*.1 E*.1 R.1 1 – – – – – 0.36 17 – – – – + 0.39 2 + – – – – 0.51 18 + – – – + 0.45 3 – + – – – 0.15 19 – + – – + 0.32 4 + + – – – 0.39 20 + + – – + 0.25 5 – – + – – 0.79 21 – – + – + 0.18 6 + – + – – 0.83 22 + – + – + 0.29 7 – + + – – 0.74 23 – + + – + 0.07 8 + + + – – 0.69 24 + + + – + 0.19 9 – – – + – 0.60 25 – – – + + 0.53 10 + – – + – 0.82 26 + – – + + 0.60 11 – + – + – 0.42 27 – + – + + 0.36 12 + + – + – 0.59 28 + + – + + 0.43 13 – – + + – 0.96 29 – – + + + 0.23 14 + – + + – 0.87 30 + – + + + 0.51 15 – + + + – 0.76 31 – + + + + 0.13 16 + + + + – 0.74 32 + + + + + 0.43 (a) Determine the coded equation for this response surface, ignoring \(\beta\) terms less than \(\pm 0.03\). (b) A simplex optimization of this system finds optimal values for the factors of A = 2278 mL/min, B = 9.90 ppm, C = 260.6 mL/min, and D = 1.71. The value of E was maintained at its high level. Are these values consistent with your analysis of the factorial design. 11. A good empirical model provides an accurate picture of the response surface over the range of factor levels within the experimental design. The same model, however, may yield an inaccurate prediction for the response at other factor levels. For this reason, an empirical model, is tested before it is extrapolated to conditions other than those used in determining the model. For example, Palasota and Deming studied the effect of the relative amounts of H 2 SO 4 and H 2 O 2 on the absorbance of solutions of vanadium using the following central composite design [Palasota, J. A.; Deming, S. N. J. Chem. Educ. 1992 , 62 , 560–563]. run drops of 1% H2SO4 drops of 20% H2O2 1 15 22 2 10 20 3 20 20 4 8 15 5 15 15 6 15 15 7 15 15 8 15 15 9 22 15 10 10 10 11 20 10 12 15 8 The reaction of H 2 SO 4 and H 2 O 2 generates a red-brown solution whose absorbance is measured at a wavelength of 450 nm. A regression analysis on their data yields the following uncoded equation for the response (absorbance \(\times\) 1000). \[R = 835.90 - 36.82 X_1 - 21.34 X_2 + 0.52 X_1^2 + 0.15 X_2^2 + 0.98 X_1 X_2 \nonumber\] where X 1 is the drops of H 2 O 2 , and X 2 is the drops of H 2 SO 4 . Calculate the predicted absorbances for 10 drops of H 2 O 2 and 0 drops of H 2 SO 4 , 0 drops of H 2 O 2 and 10 drops of H 2 SO 4 , and for 0 drops of each reagent. Are these results reasonable? Explain. What does your answer tell you about this empirical model? 12. A newly proposed method is tested for its single-operator characteristics. To be competitive with the standard method, the new method must have a relative standard deviation of less than 10%, with a bias of less than 10%. To test the method, an analyst performs 10 replicate analyses on a standard sample known to contain 1.30 ppm of analyte. The results for the 10 trials are 1.25 ppm, 1.26 ppm, 1.29 ppm, 1.56 ppm, 1.46 ppm, 1.23 ppm, 1.49 ppm, 1.27 ppm, 1.31 ppm, and 1.43 ppm. Are the single operator characteristics for this method acceptable? 13. A proposed gravimetric method was evaluated for its ruggedness by varying the following factors. 0 1 2 Factor A: sample size A = 1 g a = 1.1 g Factor B: pH B = 6.5 b = 6.0 Factor C: digestion time C = 3 h c = 1 h Factor D: number of rinses D = 3 d = 5 Factor E: precipitant E = reagent 1 e = reagent 2 Factor F: digestion temperature F = 50oC f = 60oC Factor G: drying temperature G = 100oC g = 140oC A standard sample that contains a known amount of analyte is carried through the procedure using the experimental design in Table 14.3.1 . The percentage of analyte actually found in the eight trials are as follows: R 1 = 98.9, R 2 = 98.5, R 3 = 97.7, R 4 = 97.0, R 5 = 98.8, R 6 = 98.5, R 7 = 97.7, and R 8 = 97.3. Determine which factors, if any, appear to have a significant affect on the response, and estimate the expected standard deviation for the method. 14. The two-sample plot for the data in Example 14.3.1 is shown in Figure 14.3.4 . Identify the analyst whose work is (a) the most accurate, (b) the most precise, (c) the least accurate, and (d) the least precise. 15. Chichilo reports the following data for the determination of the %w/w Al in two samples of limestone [Chichilo, P. J. J. Assoc. Offc. Agr. Chemists 1964 , 47 , 1019 as reported in Youden, W. J. “Statistical Techniques for Collaborative Tests,” in Statistical Manual of the Association of Official Analytical Chemists , Association of Official Analytical Chemists: Washington, D. C., 1975]. analyst sample 1 sample 2 1 1.35 1.57 2 1.35 1.33 3 1.34 1.47 4 1.50 1.60 5 1.52 1.62 6 1.39 1.52 7 1.30 1.36 8 1.32 1.33 Construct a two-sample plot for this data and estimate values for \(\sigma_\text{rand}\) and for \(\sigma_\text{syst}\). 16. The importance of between-laboratory variability on the results of an analytical method are determined by having several laboratories analyze the same sample. In one such study, seven laboratories analyzed a sample of homogenized milk for a selected aflatoxin [Massart, D. L.; Vandeginste, B. G. M; Deming, S. N.; Michotte, Y.; Kaufman, L. Chemometrics: A Textbook , Elsevier: Amsterdam, 1988]. The results, in ppb, are summarized below. lab A lab B lab C lab D lab E lab F lab G 1.6 4.6 1.2 1.5 6.0 6.2 3.3 2.9 2.8 1.9 2.7 3.9 3.8 3.8 3.5 3.0 2.9 3.4 4.3 5.5 5.5 4.5 4.5 1.1 2.0 5.8 4.2 4.9 2.2 3.1 2.9 3.4 4.0 5.3 4.5 (a) Determine if the between-laboratory variability is significantly greater than the within-laboratory variability at \(\alpha = 0.05\). If the between-laboratory variability is significant, then determine the source(s) of that variability. (b) Estimate values for \(\sigma_\text{rand}^2\) and for \(\sigma_\text{syst}^2\). 17. Show that the total sum-of-squares ( SS t ) is the sum of the within-sample sum-of-squares ( SS w ) and the between-sample sum-of-squares ( SS b ). See Table 14.3.2 for the relevant equations. 18. Eighteen analytical students are asked to determine the %w/w Mn in a sample of steel, with the results shown here. 0 1 2 3 4 5 0.26% 0.28% 0.27% 0.24% 0.26% 0.25% 0.26% 0.28% 0.25% 0.24% 0.26% 0.25% 0.29% 0.24% 0.27% 0.23% 0.26% 0.24% (a) Given that the steel sample is 0.26% w/w Mn, estimate the expected relative standard deviation for the class’ results. (b) Are the actual results consistent with the estimated relative standard deviation?