2. ka = a bit 5. k_a= a Many authors have developed empirical correlations to predict kla. In this project, we will use

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2 Ka A Bit 5 K A A Many Authors Have Developed Empirical Correlations To Predict Kla In This Project We Will Use 1 (155.24 KiB) Viewed 19 times

2. ka = a bit 5. k_a= a Many authors have developed empirical correlations to predict kla. In this project, we will use experimental data to fit different correlations to the measured kua and determine 1. kama (c) oh nap 3. k a/n Q./V.) = a(Pc/n/Qc) 4. kaV/D = appe OG a where: Pc = overall gassed power dissipation Vi = total volume of liquid media in the bioreactor Vs = superficial gas velocity. N = impeller rotational speed. D = bioreactor diameter. n = number of impellers. Qc = volumetric gas flow rate at half liquid height a = constants of the different kua correlations. B = exponential constants of the different kue correlations. You, as a bioprocess engineer are required to develop a correlation to predict kua for a new biotechnology pilot-plant where new bioreactors have been installed and are under the process of testing. validating, and clearing for use in manufacturing. The new plant bench-scale and pilot plant scale bioreactors were tested for kla and the complete data set is shown in Table 1 below. Table 1. Experimental values of volumetric oxygen transfer coefficient, la, as a function of bioreactor characteristics and operating variables. N," Vi, dm Paw, Wim ka, V, mms 4.873 8.33 Ou.com 198.966 149 227 8 2989 Run D, mmn 1 228 2 2 228 2 3 228 2 1544 8.33 3.655 8 1540 3212 1582 6.66 4.873 8 1007 198.966 149.227 4 228 2 6.66 3.655 8 1700 924 5 228 2 6.66 2.437 99.49 8 1881 900 5 228 2 5.00 4.873 198 966 697 574 7 228 2 5.00 3.655 149.227 8 a 8 8 749 533 8 228 2 5.00 2437 828 474 99.498 200.001 12.50 4.421 10 4304 2307 9 10 240 240 2 2 2 2 12.50 2.947 133.319 10 4763 1599 1563 240 12.50 2.211 100003 10 5118 11 12 240 2 8.33 4.421 200.000 10 1355 796 13 240 8.33 2.947 10 1499 765 2 2 133 319 100.003 14 240 8.33 10 615 2.211 4.421 1611 187 15 240 4.16 10 166 200.001 133 319 16 240 2 2.947 10 208 132 4.16 4.16 17 240 2 2.211 100.023 10 223 114 18 315 1 12.83 6.230 30 1779 635 485.511 485 511 19 1 13.66 6.230 2129 757 315 315 30 30 20 1 17.50 6.230 430e 1284 485.511 833.430 21 525 2 7.50 3.850 80 6135 1345 1555 22 525 2 6.66 5.390 1166 B02 80 4032 23 525 2 . 6.66 3.850 80 4385 1078 833.430 1483.289 24 525 2 5.00 6.852 80 1672 910 25 525 2 5.00 3.850 833430 80 1931 665 25 525 2 3.33 80 608 269 3.850 2.310 27 525 3.33 691 202 80 80 2 2 2 1.66 3.850 84 28 29 71 525 1000 833.430 500.00 833.430 10194.400 7279 070 4367 599 12.980 1000 3778 1091 30 1000 2 12.00 12.00 12.00 9.268 1000 4110 877 31 1000 5.561 1000 4670 609 Run D.mm ni Va mums Qa.com V. dm PV. Wim kua, N, 8.16 1000 2 10194.468 1000 1261 467 32 33 34 12.980 9.266 1000 8.16 7279.070 1000 1372 423 1000 8.16 5.561 4357.595 1000 1559 222 35 2 2 2 2 2 2 2 5.50 12.980 10194.468 1000 408 238 1000 1000 1000 36 5.50 9.268 7279.070 1000 177 37 5.561 4367.500 1000 444 505 2647 5.50 10.00 122 38 1000 2 11.740 9220 574 1000 630 39 2 5.565 1000 3190 490 1000 1000 10.00 8.16 40 2 11.740 1486 4367 500 9220 574 9220 574 4367 500 1000 1000 427 131 41 1000 5.50 11.740 481 2 2 42 1000 5.50 5.561 580 81 1000 2250 43 990 3 2.50 48.710 37495.436 1172 568 44 990 3 2.08 48.710 266 2250 2250 232 155 45 2.08 24.350 316 990 990 37495.435 18743.869 11246.321 37495.435 46 3 2.08 14.610 2250 359 120 47 990 3 3.50 48.710 2250 386 655 779 48 3 24.350 18743.869 217 990 990 990 3.50 3.50 2250 2250 49 14.610 11246.321 885 173 3 3 50 2.08 48.710 37495.435 2250 148 203 51 990 3 3 2.08 24.350 18743.869 2250 176 122 Using linear regression techniques, you are required to a. Find the appropriate values for constants a and ß of all five (5) correlations shown above. b. For all correlations you should present the ANOVA tables, residual plots and analyses that probe your best use and practice of Minitab. Present all of the above Minitab outputs in a single page for each correlation (total of 5 pages of Minitab analyses to shorten length of document). You can add extra pages for the results discussion c. Your analysis must contain a procedure to statistically justify which of the five (5) correlations is the best to predict ka for all the bioreactors of the new pilot-plant. It is recommended to use some kind of tabulation to make your points easier to explain, such as: Correlation R ANOVA Residual Plot Results Results Etc (You can devise a better format, table above is just a suggestion.)