METHOD FOR ASCERTAINING A PRODUCT COMPOSITION FOR A MIXED CHEMICAL PRODUCT

Abstract

The invention relates to a method for ascertaining a product composition for a mixed chemical product, a series of feature values, which numerically describe feature values in each case of a descriptor of the particular mixed product, being provided, for each first product composition of a plurality of first product compositions, in the case of a plurality of first product compositions for a particular mixed chemical product, each first product composition being characterised by a numerical product distribution for describing the proportions of components of the first product composition, the series of feature values for each mixed product being mapped by first bijective mapping onto a series of mapped feature values, a series of test feature values, which numerically describe in each case a behaviour property of the particular mixed product, being provided for a plurality of second product compositions for a particular mixed chemical product, the series of test feature values for each mixed product being mapped by second bijective mapping onto a series of mapped test feature values, at least the first or the second mapping including a modification, each series of mapped test feature values of a second product composition being assigned to a series of modified feature values of a first product composition, a correlation matrix being ascertained by multivariate analysis of the associated series, a target requirement profile being predefined for describing at last one behaviour property of a target mixed product and a target descriptor profile for describing descriptors of a target product composition being determined on the basis of the target requirement profile and the correlation matrix.

Claims

1.-15. (canceled)

16. A method of ascertaining a product composition for a mixed chemical product, wherein a multitude of feature values, each of which numerically describes a descriptor of the particular mixed product, is provided for each of a multitude of first product compositions for a particular mixed chemical product, wherein each first product composition is characterized by a numerical product distribution for description of proportions of components of the first product composition, wherein the series of feature values for each mixed product is mapped onto a series of mapped feature values by a first bijective mapping, wherein a series of test feature values, each of which numerically describes a behavior property of the particular mixed product, is provided for a multitude of second product compositions for a particular mixed chemical product, wherein the series of test feature values for each mixed product is mapped onto a series of mapped test feature values by a second bijective mapping, wherein at least the first or second mapping includes a variation, wherein each series of mapped test feature values of a second product composition is assigned to a series of varied feature values of a first product composition, wherein a multivariate analysis of the assigned series determines a correlation matrix, wherein a target profile of requirements for description of at least one behavior property of a target mixed product is defined and, on the basis of the target profile of requirements and the correlation matrix, a target descriptor profile for description of descriptors of a target product composition is determined.

17. The method as claimed in claim 16, wherein the determining of the target descriptor profile comprises, based on a comparison of the target descriptor profile with the feature values of the first product compositions of the multitude, determining a first product composition of the multitude as starting product composition and varying the product distribution of the starting product composition on the basis of the feature values of the remaining first product compositions of the multitude to obtain the target product composition.

18. The method as claimed in claim 16, wherein the series of varied test feature values of a first product composition is assigned to that series of varied feature values of a second product composition in which the second product composition is essentially identical to the first product composition.

19. The method as claimed in claim 16, wherein the series of feature values for each first product composition is provided on a first computer system on which the first bijective mapping is executed, in that the series of test feature values for each second product composition is provided on a second computer system on which the second bijective mapping is executed, and in that the first computer system and the second computer system are encompassed by a respectively disjoint intranet, preferably in that the determination of the target descriptor profile is performed at least partly on the first computer system.

20. The method as claimed in claim 19, wherein the multivariate analysis is executed on a third computer system encompassed by an intranet that is disjoint from the respective intranet of the first computer system and the second computer system.

21. The method as claimed in claim 19, wherein the product distribution and the series of feature values for each first product composition are stored by data encapsulation in the first computer system with respect to the second computer system, and in that the series of test feature values for each second product composition is stored with data encapsulation in the second computer system with respect to the first computer system.

22. The method as claimed in claim 19, wherein the series of varied feature values for each first product composition is transmitted from the first computer system to a target computer system in a disjoint intranet, preferably to the second computer system or the third computer system.

23. The method as claimed in claim 16, wherein, in a calculation model provided preferably in the first computer system, input of feature values for description of a particular descriptor results in output of a product distribution of a product composition for a mixed product for approximation of the feature values, and in that, preferably in the first computer system, the target descriptor profile is input into the calculation model for output of the target product composition.

24. The method as claimed in claim 23, wherein the calculation model is ascertained at least partly by multivariate analysis, preferably executed in the first computer system, of the series of feature values of each first product composition with respect to the product distribution of this product composition.

25. The method as claimed in claim 16, wherein, for each first product composition of the multitude, the series of feature values is ascertained at least partly, by a calculation based on the corresponding product distribution, preferably in that the calculation is based on a physical calculation model based on the product distribution.

26. The method as claimed in claim 16, wherein the first bijective mapping comprises a transformation of coordinates from the series of feature values to the series of varied feature values, and/or in that the second bijective mapping comprises a transformation of coordinates from the series of test feature values to the series of varied test feature values.

27. The method as claimed in claim 16, wherein the first bijective mapping comprises a one-dimensional bijective sub-mapping for each individual descriptor.

28. The method as claimed in claim 16, wherein the first bijective mapping or the second bijective mapping is a constant and strictly monotonous function with a continuously varying derivative.

29. A method of producing a mixed chemical product from a product composition, wherein the product composition has been ascertained by the method as claimed in claim 16.

30. A mixed chemical product, wherein the mixed chemical product has been produced by the method as claimed in claim 29.

Description

[0114] Further details, features, configurations, aims and advantages of the present invention are elucidated hereinafter with reference to the drawing. The drawing shows:

[0115] FIG. 1 by way of example, a strong positive correlation of the urea content as descriptor with the modulus of elasticity as behavior property,

[0116] FIG. 2 by way of example, a negative correlation of elongation at break as behavior property associated with the correlation of FIG. 1,

[0117] FIG. 3 by way of example, the derivative of the variation in modulus of elasticity as behavior property between two product compositions and the prediction of the variation in modulus of elasticity in the case of a varied product composition,

[0118] FIG. 4 by way of example, the derivative of the variation in elongation at break as behavior property between the two product compositions of FIG. 3 and the prediction of the variation in elongation at break in the case of the varied product composition of FIG. 3, and

[0119] FIG. 5 three computer systems for execution of a working example of the proposed method of ascertaining a product composition for a mixed chemical product.

[0120] The invention is also elucidated hereinafter with reference to tables. The tables show: Table A: various polyurethanes as product compositions with product distributions, taken from W. Panwiriyarat et al., J. Polym. Environ. 21, 807-815 (2013), Table 1, top of p. 809. The molar ratios of the four composition constituents are specified here: IPDI: isophorone diisocyanate with a molar mass of 220; PCL: polycaprolactonediol prepared from ethylene glycol and caprolactone with a molar mass of 530; HTNR: an unsaturated rubber diol having a molar mass of 1700 with an average of 23.6 double bonds and BDO: butanediol with a molar mass of 90.

[0121] Table B shows the behavior properties of the products from table A, taken from W. Panwiriyarat et al., J. Polym. Environ. 21, 807-815 (2013), Table 2, p. 811 with modulus of elasticity in MPa (“YoungMod”), breaking stress in MPa (“TensileStr”), elongation at break in percent (“EaB”), tear strength in N/mm.sup.2 (“TearStr”) and Shore A hardness—unitless—(“Shore A”)

[0122] Table 1a: For polyurethanes 3 to 14 the descriptors: hard segment content in percent by weight (“HardSeg”), urea content in equivalents/kg (“Urea”), urethane content in equivalents/kg (“Urethane”), ester content in equivalents/kg (“Ester”), double bond content in equivalents/kg (“Doublebond”), butanediol content in equivalents/kg (“BDO”), and the performance properties: P.sub.i: modulus of elasticity in MPa (“YoungMod”), breaking stress in MPa (“TensileStr”), elongation at break in percent (“EaB”), tear strength in N/mm.sup.2 (“TearStr”) and Shore A hardness—unitless—(“Shore A”).

[0123] For the calculation of the hard segment content, the proportions by weight of all diisocyanates (IPDI here) and diols (butanediol here) were based on/calculated on the basis of the total weight and reported in percent.

[0124] For the calculation of the urea content, the isocyanate excess of the isocyanate groups that have not reacted with alcohol groups was calculated. These react with ambient water to give carbamic acid and with elimination of carbon dioxide to give amine that reacts rapidly with a further isocyanate to give urea. Thus, two excess isocyanate groups give rise to one urea group. The molar amount of urea groups is then based on/calculated on the basis of one kilogram of total product.

[0125] The calculation of the urethane content is made here from the alcohol groups present in deficiency. Each reacts to give a urethane group, and so the molar amount of the alcohol groups per kg of total product gives the urethane content in equivalents/kg.

[0126] The calculation of the ester content is made via the ester groups present in the polycaprolactone (“PCL”) of 2.05 ester groups per equivalent of polycaprolactone of 265 g. Thus, the ester content is calculated from the amount of ester equivalents in the amount of the polycaprolactone used, based on one kilogram of total product.

[0127] The calculation of the double bond content is made via the double bonds present in the unsaturated polybutadienediol (“HTNR”) of 23.6 double bond groups per molar mass of 1700 g/mol of HTNR, at an equivalent weight of 850 g. Thus, the double bond content is calculated from the amount of double bond equivalents in the amount of the HTNR used, based on one kilogram of total product.

[0128] The calculation of the butanediol content is made via the amount of butanediol (“BDO”) with an equivalent weight of 45 g, based on one kilogram of total product.

[0129] Table 2a: Analogous to table 1a, except that all values have been scaled on a scale from 0 to 1 in which the maximum value—feature value or test feature value—in each column (i.e. of each descriptor and each behavior property) has been determined and each individual value has been divided by this maximum value.

[0130] Table 3a: Analogous to table 1a, except that all values have first been squared and then been scaled on a scale from 0 to 1 in which the maximum value in each column (i.e. of each squared descriptor and each squared behavior property) has been determined and each individual squared feature value or test feature value has been divided by this maximum squared value.

[0131] Table 4a: Analogous to table 1a: The feature values have been retained and the decadic logarithm has been calculated from the test feature values.

[0132] Table 5a: Analogous to table 1a; in this case, the arithmetic average and the variance of the feature values of a descriptor and of the test feature values of a behavior property have first been determined. The arithmetic average is the sum total of all individual values divided by the number of individual values. The variance is calculated from a sum total over all squares of the differences of individual values minus the arithmetic average thereof, and the sum is then divided by the number of individual values. Table 5a gives results from the quotient of individual values minus arithmetic average divided by the square root of the variance (student distribution).

[0133] Table 5a-2: As table 5a, except with generically named descriptors M1-M6 and behavior properties P1-P5.

TABLE-US-00001 TABLE A IPDI PCL HTNR BDO Polyurethane 3 1.25 1.00 0.00 0.00 Polyurethane 4 1.50 1.00 0.00 0.00 Polyurethane 5 2.00 1.00 0.00 0.00 Polyurethane 6 2.25 1.00 0.00 0.00 Polyurethane 7 1.25 0.50 0.50 0.00 Polyurethane 8 1.25 0.35 0.35 0.30 Polyurethane 9 1.25 0.25 0.25 0.50 Polyurethane 10 1.25 0.50 0.00 0.50 Polyurethane 11 1.25 0.35 0.15 0.50 Polyurethane 12 1.25 0.15 0.35 0.50 Polyurethane 13 1.25 0.00 0.50 0.50

TABLE-US-00002 TABLE B Young Mod TensileStr EaB TearStr Shore A Polyurethane 3 0.5 18.9 916 16.9 42 Polyurethane 4 1.2 22.6 488 22.8 42 Polyurethane 5 3.9 54.5 392 56.2 45 Polyurethane 6 7.9 53.6 351 109.1 48 Polyurethane 7 0.3 3.5 824 7.5 21 Polyurethane 8 1.0 14.1 703 13.8 32 Polyurethane 9 1.4 14.9 590 38.6 47 Polyurethane 10 2.5 21.7 493 65.4 57 Polyurethane 11 2.2 24.3 506 38.3 56 Polyurethane 12 1.9 14.2 518 30.8 51 Polyurethane 13 1.2 9.1 605 20.9 40

TABLE-US-00003 TABLE 1a Name HardSeg Urea Urethane Ester Doublebond BDO YoungMod TensileStr EaB TearStr ShoreA Polyurethane 3 34.37 0.31 2.48 5.08 0.00 0.00 0.50 18.90 916.00 16.90 42.00 Polyurethane 4 38.59 0.58 2.32 4.75 0.00 0.00 1.20 22.60 488.00 22.80 42.00 Polyurethane 5 45.59 1.03 2.05 4 21 0.00 0.00 3.90 54.60 392.00 56.20 45.00 Polyurethane 6 48.52 1.21 1.94 3.98 0.00 0.00 7.90 53.60 351.00 109.10 48.00 Polyurethane 7 19.93 0.18 1.44 1.47 16.88 0.00 0.30 3.50 824.00 7.50 21.00 Polyurethane 8 28.07 0.23 1.84 1.32 15.16 0.55 1.00 14.10 703.00 13.80 32.00 Polyurethane 9 36.65 0.28 2.27 1.17 13.35 1.14 1.40 14.90 590.00 38.60 47.00 Polyurethane 10 54.90 0.43 3.40 3.49 0.00 1.70 2.50 21.70 493.00 65.40 57.00 Polyurethane 11 42.27 0.33 2.62 1.88 9.24 1.31 2.20 24.30 506.00 38.30 56.00 Polyurethane 12 32.35 0.25 2.01 0.62 16.50 1.00 1.90 14.20 518.00 30.  51.00 Polyurethane 13 27.51 0.21 1.71 0.00 20.04 0.  1.20 9.10 605.00 20.00 40.00 indicates data missing or illegible when filed

TABLE-US-00004 TABLE 2a Name HardSeg Urea Urethane Ester Doublebond BDO YoungMod TensileStr EaB TearStr ShoreA Polyurethane 3 0.6260 0.2550 0.7277 1.0000 0.0000 0.0000 0.0633 0.3462 1 0000 0.1549 0.7368 Polyurethane 4 0.7029 0.4773 0.6807 0.9358 0.0000 0.0000 0.1519 0.4139 0.5328 0.2090 0.7368 Polyurethane 5 0.8304 0.8460 0.6031 0.8290 0.0000 0.0000 0.4937 1.0000 0.4279 0.5151 0.7895 Polyurethane 6 0.8838 1.0000 0.5708 0.7845 0.0000 0.0000 1.0000 0.9817 0.3832 1.0000 0.8421 Polyurethane 7 0.3630 0.1479 0.4219 0.2899 0.8423 0.0000 0.0380 0.0641 0.8996 0.0687 0.3684 Polyurethane 8 0.5113 0.1898 0.5414 0.2606 0.7  0.3235 0.1266 0.2582 0.7675 0.1265 0.5614 Polyurethane 9 0.6676 0.2340 0.6677 0.2295 0.6662 0.6706 0.1772 0.2729 0.6441 0.3538 0.8246 Polyurethane 10 1.0000 0.3505 1.0000 0.6872 0.0000 1.0000 0.3165 0.3974 0.5382 0.5995 1.0000 Polyurethane 11 0.7699 0.2699 0.7700 0.3705 0.4610 0.7706 0.2785 0.4451 0 5524 0.3511 0.9825 Polyurethane 12 0.5893 0.2065 0.5893 0.1215 0.8234 0.5882 0.2405 0.2601 0.5655 0.2823 0.8947 Polyurethane 13 0.5011 0.1756 0.5012 0.0000 1.0000 0.5000 0.1519 0.1667 0.6605 0.1833 0.7018 indicates data missing or illegible when filed

TABLE-US-00005 TABLE 3a Name HardSeg Urea Urethane Ester Doublebond BDO YoungMod TensileStr EaB TearStr ShoreA Polyurethane 3 0.3919 0.0650 0.5295 1.0000 0.0000 0.0000 0.0040 0.1198 1.0000 0.0240 0.5429 Polyurethane 4 0.4941 0.2278 0.4633 0.8757 0.0000 0.0000 0.0231 0.1713 0.2838 0.0437 0.5429 Polyurethane 5 0.6896 0.7157 0.3637 0.6873 0.0000 0.0000 0.2437 1.0000 0.1831 0.2654 0.6233 Polyurethane 6 0.7811 1.0000 0.3258 0.6155 0.0000 0.0000 1.0000 0.9637 0.1468 1.0000 0.7091 Polyurethane 7 0.1318 0.0219 0.1780 0.0841 0.7095 0.0000 0.0014 0.0041 0.8092 0.0047 0.1357 Polyurethane 8 0.2614 0.0360 0.2931 0.0679 0.5723 0.1047 0.0160 0.0667 0.5890 0.0160 0.3152 Polyurethane 9 0.4457 0.0548 0.4459 0.0527 0.4438 0.4497 0.0314 0.0745 0.4149 0.1252 0.6799 Polyurethane 10 1.0000 0.1228 1.0000 0.4723 0.0000 1.0000 0.1001 0.1580 0.2897 0.3593 1.0000 Polyurethane 11 0.5928 0.0728 0.5929 0.1373 0.2125 0.5938 0.0776 0.1981 0.3051 0.1232 0.9652 Polyurethane 12 0.3472 0.0426 0.3473 0.0148 0.6779 0.3460 0.0578 0.0676 0.3198 0.0797 0.8006 Polyurethane 13 0.2511 0.0308 0.2512 0.0000 1.0000 0.2500 0.0231 0.0278 0.4362 0.0336 0.4925

TABLE-US-00006 TABLE 4a Name HardSeg Urea Urethane Ester Doublebond BDO YoungMod TensileStr EaB TearStr ShoreA Polyurethane 3 34.37 0.31 2.48 5.08 0.00 0.00 −0.30 1.28 2.96 1.23 1.62 Polyurethane 4 38.69 0.58 2.32 4.75 0.00 0.00 0.08 1.35 2.  1.3  1.62 Polyurethane 5 45.69 1.03 2 05 4.21 0.00 0.00 0.59 1.74 2.  1.75 1.65 Polyurethane 6 48.62 1.21 1.94 3.98 0.00 0.00 0.90 1.73 2.  2.04 1.68 Polyurethane 7 19.93 0.18 1.44 1.47 16.88 0.00 −0.52 0.54 2.92 0.88 1.32 Polyurethane 8 28.07 0.23 1.84 1.32 16.16 0.55 0.00 1.15 2.65 1.14 1.61 Polyurethane 9 36.65 0.28 2.27 1.17 13.35 1.14 0.15 1.17 2.77 1.59 1.67 Polyurethane 10 54.90 0.43 3.40 3.49 0.00 1.70 0.40 1.34 2.69 1.82 1.76 Polyurethane 11 42.27 0.33 2.62 1.88 9.24 1.31 0.34 1.39 2.70 1.58 1.75 Polyurethane 12 32.35 0.25 2.01 0.62 16.50 1.00 0.28 1.15 2.71 1.49 1.71 Polyurethane 13 27.61 0.21 1.71 0.00 20.04 0.85 0.08 0.96 2.78 1.30 1.60 indicates data missing or illegible when filed

TABLE-US-00007 TABLE 5a Name HardSeg Urea Urethane Ester Doublebond BDO YoungMod TensileStr EaB TearStr ShoreA Polyurethane 3 −0.2859 −0.4478 0.5707 1.4816 −1.0415 −0.9837 −0.8212 −0.2506 2.0265 −0.7546 −0.1740 Polyurethane 4 0.1467 0.3648 0.2536 1.2911 −1.0415 −0.9837 −0.4794 −0.0167 −0.5591 −0.5448 −0.1740 Polyurethane 5 0.8643 1.7129 0.2695 0.9742 −1.0415 −0.9837 0.8390 2.0069 −1.1390 0.6424 0.1282 Polyurethane 6 1.1646 2.2761 −0.4874 0.8421 −1.0415 −0.9837 2.7921 1.9437 −1.3867 2.5229 0.4304 Polyurethane 7 −1.7662 −0.8396 −1.4920 −0.6257 1.0797 −0.9837 −0.9189 −1.2245 1.4707 −1.0887 −2.2892 Polyurethane 8 −0.9317 −0.6863 −0.6856 −0.7128 0.8636 −0.0751 −0.5771 −0.5542 0.7397 −0.8648 −1.1813 Polyurethane 9 −0.0522 −0.5246 0.1664 −0.8052 0.6361 0.8996 −0.3818 −0.5036 0.0571 0.0168 0.3297 Polyurethane 10 1.8186 −0.0987 2.4075 0.5533 −1.0415 1.8247 0.1554 −0.0736 −0.5289 0.9695 1.3369 Polyurethane 11 0.5239 −0.3936 0.8560 −0.3867 0.1195 1.1805 0.0089 0.0908 −0.4503 0.0061 1.2362 Polyurethane 12 −0.4930 −0.6252 −0.3626 −1.1257 1.0319 0.6683 −0.1376 −0.5479 −0.3778 −0.2605 0.7326 Polyurethane 13 −0.9891 −0.7381 −0.9570 −1.4862 1.4768 0.4205 −0.4794 −0.8704 0.1477 −0.  444 −0.3754 indicates data missing or illegible when filed

TABLE-US-00008 TABLE 5a-2 Name M1 M2 M3 M4 M5 M6 P1 P2 P3 P4 P5 Polyurethane 3 −0.2859 −0.4478 0.5707 1.4816 −1.0415 −0.9837 −0.8212 −0.2506 2.0265 −0.7546 −0.1740 Polyurethane 4 0.1467 0.3648 0.2536 1.2911 −1.0415 −0.9837 0.4794 −0.0167 −0.5591 −0.5448 −0.1740 Polyurethane 5 0.8643 1.7129 −0.2695 0.9742 −1.0415 −0.9837 0.8390 2.0069 −1.1390 0.6424 0.1282 Polyurethane 6 1.1646 2.2761 −0.4874 0.8421 −1.0415 −0.9837 2.7921 1.9437 −1.3867 2.5229 0.4304 Polyurethane 7 −1.7662 −0.8396 −1.4920 −0.6257 1.0797 −0.9837 −0.9189 −1.2245 1.4707 −1.0887 −2.2892 Polyurethane 8 −0.9317 −0.6863 −0.6856 −0.7128 0.8636 −0.0751 −0.5771 −0.5542 0.7397 −0.8648 −1.1813 Polyurethane 9 −0.0522 −0.5246 0.1664 −0.8052 0.6361 0.8996 −0.3818 −0.5036 0.0571 0.0168 0.3297 Polyurethane 10 1.8186 −0.0987 2.4075 0.5533 −1.0415 1.8247 0.1554 −0.0736 −0.5289 0.9695 1.3369 Polyurethane 11 0.5239 −0.3936 0.8560 −0.3867 0.1195 1.1805 0.0089 0.0908 −0.4503 0.0061 1.2362 Polyurethane 12 −0.4930 −0.6252 −0.3626 −1.1257 1.0319 0.6683 −0.1376 −0.5479 −0.3778 −0.2605 0.7326 Polyurethane 13 −0.9891 −0.7381 −0.9570 −1.4862 1.4768 0.4205 −0.4794 −0.8704 0.1477 −0.6444 −0.3754

[0134] Table 1b: Table 1a was read into R in csv file format. The following R version was used: R version 3.4.3 (Nov. 30, 2017)—“Kite-Eating Tree”, Copyright © 2017 The R Foundation for Statistical Computing, Platform: x86_64-w64-mingw32/x64 (64-bit). Subsequently, the “cor” command was used to calculate the Pearson correlation matrix-correlation matrix hereinafter.

[0135] For 11 formulations, with df (degree of freedom)=11-2=9, a critical Pearson correlation value of 0.602 is found. Correlation values>0.602 show an upward arrow (positive correlation); correlation values<−0.602 show a downward arrow (negative correlation); all intermediate values show no (statistical) correlation and hence a horizontal arrow.

[0136] Table 2b: Correlation matrix analogous to table 1b, except that table 2a was read in. The table is identical to table 1b.

[0137] Table 3b: Correlation matrix analogous to table 1b, except that table 3a was read in.

[0138] Table 4b: Correlation matrix analogous to table 1b, except that table 4a was read in.

[0139] Table 5b: Correlation matrix analogous to table 1b, except that table 5a was read in. The table is identical to table 1b.

[0140] Table 5b-2: As table 5b, except with generically named descriptors M1-M6 and test features P1-P5.

[0141] Tables 1b, 2b and 5b show correlation results that are the same except for rounding errors.

[0142] It has thus been shown that, in selected cases of the mapping of all feature values of all product compositions and of the mapping of all test feature values by a bijective, constant, strictly monotonous and selectively normalizing function, identical results can occur: specifically in table 1a->2a with a scale (=normalization) and in table 1a->5a with Student's distribution.

[0143] If table 1b is compared with 3b, slight differences are seen in the correlation coefficients, which are similar in terms of sign and magnitude, but in individual cases lead to a different assessment in terms of the criterion of the critical Pearson correlation value. (cf., for example: c(Pearson).sub.Urea-Eab=−0.694 (in table 1b) vs. c(Pearson).sub.Urea-Eab=−0.556 (in table 3b)). This can be explained by the better correlation by a linear function compared to a quadratic function.

[0144] It follows that the better Pearson correlation value in table 1b suggests that a more suitable bijective mapping maps the experimental data better than in table 3b.

[0145] Reference is made to an analogous example with c(Pearson).sub.Ester-TensileStr=0.600 (table 1b), c(Pearson).sub.Ester-TensileStr=0.457 (table 3b), c(Pearson).sub.Ester-TensileStr=0.619 (table 4b). It is found here that in table 4b with the decadic logarithm as bijective, constant, strictly monotonous function better results are found.

[0146] Thus, the positive and negative correlations from tables 1b-5b are considered collectively and the maximum correlation value (for positive values) or the minimum correlation value (for negative values) is chosen in each case and compared with the critical correlation value. In this way, an overall view is obtained (in table 3 here), and it becomes clear that various bijective, constant, strictly monotonous and selectively normalizing mappings or functions should be examined. This may be assisted by graph representation of the data. In qualitative terms, a good overview of which descriptors have a positive or negative correlation with which behavior properties is thus obtained.

TABLE-US-00009 TABLE 1b HardSeg Urea Urethane Ester Doublebond BDO HardSeg ↑ 1.000 ↑ 0.650 ↑ 0.751 .fwdarw. 0.566 ↓ −0.774 .fwdarw. 0.281 Urea ↑ 0.650 ↑ 1.000 .fwdarw. 0.025 ↑ 0.614 ↓ −0.693 .fwdarw. −0.43  Urethane ↑ 0.751 .fwdarw. 0.025 ↑ 1.000 .fwdarw. 0.420 .fwdarw. −0.577 .fwdarw. 0.578 Ester .fwdarw. 0.565 ↑ 0.614 .fwdarw. 0.420 ↑ 1.000 ↓ −0.  60 .fwdarw. −0.490 Doublebond ↓ −0.774 ↓ −0.593 .fwdarw. −0.577 ↓ −0.960 ↑ 1.000 .fwdarw. 0.281 BDO .fwdarw. 0.281 .fwdarw. 0.436 .fwdarw. 0.578 .fwdarw. −0.490 .fwdarw. 0.281 ↑ 1.000 YoungMod ↑ 0.650 ↑ 0.887 .fwdarw. 0.035 .fwdarw. 0.325 .fwdarw. −0.470 .fwdarw. −0.153 TensileStr ↑ 0.711 ↑ 0.864 .fwdarw. 0.125 .fwdarw. 0.600 ↓ −0.702 .fwdarw. −0.325 EaB ↓ −0.701 ↓ −0.694 .fwdarw. −0.212 .fwdarw. −0.127 .fwdarw. 0.335 .fwdarw. −0.191 TearStr ↑ 0.808 ↑ 0.520 .fwdarw. 0.297 .fwdarw. 0.363 .fwdarw. −0.553 .fwdarw. 0.059 ShoreA ↑ 0.805 .fwdarw. 0.272 ↑ 0.751 .fwdarw. 0.183 .fwdarw. −0.413 ↑ 0.605 YoungMod TensileStr EaB TearStr ShoreA HardSeg ↑ 0.650 ↑ 0.711 ↓ −0.701 ↑ 0.808 ↑ 0.805 Urea ↑ 0.887 ↑ 0.964 ↓ −0.694 ↑ 0.820 .fwdarw. 0.272 Urethane .fwdarw. 0.035 .fwdarw. 0.126 .fwdarw. −0.212 .fwdarw. 0.2  7 ↑ 0.751 Ester .fwdarw. 0.325 .fwdarw. 0.600 .fwdarw. −0.127 .fwdarw. 0.363 .fwdarw. 0.183 Doublebond .fwdarw. −0.470 ↓ −0.702 .fwdarw. 0.335 .fwdarw. −0.553 .fwdarw. −0.413 BDO .fwdarw. −0.153 .fwdarw. −0.326 .fwdarw. −0.191 .fwdarw. 0.059 ↑ 0.605 YoungMod ↑ 1.000 ↑ 0.859 ↓ −0.739 ↑ 0.850 .fwdarw. 0.408 TensileStr ↑ 0.858 ↑ 1.000 ↓ −0.695 ↑ 0.503 .fwdarw. 0.397 EaB ↓ −0.739 ↓ −0.695 ↑ 1.000 ↓ −0.746 ↓ −0.620 TearStr ↑ 0.850 ↑ 0.803 ↓ −0.746 ↑ 1.000 .fwdarw. 0.570 ShoreA .fwdarw. 0.408 .fwdarw. 0.397 ↓ −0.620 .fwdarw. 0.570 ↑ 1.000 indicates data missing or illegible when filed

TABLE-US-00010 TABLE 2b HardSeg Urea Urethane Ester Doublebond BDO HardSeg ↑ 1.000 ↑ 0.647 ↑ 0.754 .fwdarw. 0.566 ↓ −0.774 .fwdarw. 0.281 Urea ↑ 0.647 ↑ 1.000 .fwdarw. 0.024 ↑ 0.612 ↓ −0.689 .fwdarw. −0.437 Urethane ↑ 0.754 .fwdarw. 0.024 ↑ 1.000 .fwdarw. 0.420 .fwdarw. −0.578 .fwdarw. 0.578 Ester .fwdarw. 0.566 ↑ 0.612 .fwdarw. 0.420 ↑ 1.000 ↓ −0.960 .fwdarw. −0.490 Doublebond ↓ −0.774 ↓ −0.689 .fwdarw. −0.575 ↓ −0.960 ↑ 1.000 .fwdarw. 0.281 BDO .fwdarw. 0.201 .fwdarw. −0.437 .fwdarw. 0.575 .fwdarw. −0.490 .fwdarw. 0.281 ↑ 1.000 YoungMod ↑ 0.650 ↑ 0.889 .fwdarw. 0.035 .fwdarw. 0.325 .fwdarw. −0.470 .fwdarw. −0.15  TensileStr ↑ 0.711 ↑ 0.963 .fwdarw. 0.129 .fwdarw. 0.600 ↓ −0.702 .fwdarw. −0.326 EaB ↓ −0.701 ↓ −0.694 .fwdarw. −0.214 .fwdarw. −0.128 .fwdarw. 0.336 .fwdarw. −0.191 TearStr ↑ 0.808 ↑ 0.820 .fwdarw. 0.300 .fwdarw. 0.363 .fwdarw. −0.553 .fwdarw. 0.059 ShoreA ↑ 0.805 .fwdarw. 0.270 ↑ 0.751 .fwdarw. 0.163 .fwdarw. −0.413 ↑ 0.606 YoungMod TensileStr EaB TearStr ShoreA HardSeg ↑ 0.650 ↑ 0.711 ↓ −0.701 ↑ 0.508 ↑ 0.805 Urea ↑ 0.889 ↑ 0.863 ↓ −0.694 ↑ 0.520 .fwdarw. 0.270 Urethane .fwdarw. 0.038 .fwdarw. 0.129 .fwdarw. −0.214 .fwdarw. 0.300 ↑ 0.751 Ester .fwdarw. 0.325 .fwdarw. 0.600 .fwdarw. −0.128 .fwdarw. 0.363 .fwdarw. 0.183 Doublebond .fwdarw. −0.470 ↓ −0.702 .fwdarw. 0.336 .fwdarw. −0.553 .fwdarw. −0.413 BDO .fwdarw. −0.153 .fwdarw. −0.326 .fwdarw. −0.191 .fwdarw. 0.059 ↑ 0.606 YoungMod ↑ 1.000 ↑ 0.859 ↓ −0.739 ↑ 0.950 .fwdarw. 0.406 TensileStr ↑ 0.859 ↑ 1.000 ↓ −0.695 ↑ 0.502 .fwdarw. 0.397 EaB ↓ −0.739 ↓ −0.695 ↑ 1.000 ↓ −0.746 ↓ −0.620 TearStr ↑ 0.950 ↑ 0.802 ↓ −0.746 ↑ 1.000 .fwdarw. 0.570 ShoreA .fwdarw. 0.40  .fwdarw. 0.397 ↓ −0.620 .fwdarw. 0.570 ↑ 1.000 indicates data missing or illegible when filed

TABLE-US-00011 TABLE 3b HardSeg Urea Urethane Ester Doublebond BDO HardSeg ↑ 1.000 .fwdarw. 0.552 ↑ 0.746 .fwdarw. 0.441 ↓ −0.759 .fwdarw. 0.498 Urea .fwdarw. 0.552 ↑ 1.000 .fwdarw. −0.119 .fwdarw. 0.467 .fwdarw. −0.540 .fwdarw. −0.355 Urethane ↑ 0.746 .fwdarw. −0.119 ↑ 1.000 .fwdarw. 0.307 .fwdarw. −0.574 ↑ 0.784 Ester .fwdarw. 0.441 .fwdarw. 0.467 .fwdarw. 0.307 ↑ 1.000 ↓ −0.839 .fwdarw. −0.334 Doublebond ↓ −0.759 .fwdarw. −0.540 .fwdarw. −0.574 ↓ −0.839 ↑ 1.000 .fwdarw. −0.027 BDO .fwdarw. 0.498 .fwdarw. −0.355 ↑ 0.784 .fwdarw. −0.334 .fwdarw. −0.027 ↑ 1.000 YoungMod .fwdarw. 0.504 ↑ 0.597 .fwdarw. −0.106 .fwdarw. 0.267 .fwdarw. −0.388 .fwdarw. −0.216 TensileStr .fwdarw. 0.572 ↑ 0.968 .fwdarw. −0.077 .fwdarw. 0.457 .fwdarw. −0.563 .fwdarw. −0.309 EaB ↓ −0.628 .fwdarw. −0.  .fwdarw. −0.190 .fwdarw. 0.036 .fwdarw. 0.249 .fwdarw. −0.292 TearStr ↑ 0.676 ↑ 0.853 .fwdarw. 0.125 .fwdarw. 0.274 .fwdarw. −0.468 .fwdarw. 0.009 ShoreA ↑ 0.765 .fwdarw. 0.153 ↑ 0.740 .fwdarw. 0.091 .fwdarw. −0.444 ↑ 0.731 YoungMod TensileStr EaB TearStr ShoreA HardSeg .fwdarw. 0.504 .fwdarw. 0.572 ↓ −0.628 ↑ 0.676 ↑ 0.765 Urea ↑ 0.897 ↑ 0.968 ↓ −0.556 ↑ 0.853 .fwdarw. 0.153 Urethane .fwdarw. −0.105 .fwdarw. −0.077 .fwdarw. −0.180 .fwdarw. 0.125 ↑ 0.740 Ester .fwdarw. 0.267 .fwdarw. 0.457 .fwdarw. 0.038 .fwdarw. 0.274 .fwdarw. 0.091 Doublebond .fwdarw. −0.388 .fwdarw. −0.563 .fwdarw. 0.249 .fwdarw. −0.468 .fwdarw. −0.444 BDO .fwdarw. −0.218 .fwdarw. −0.309 .fwdarw. −0.292 .fwdarw. 0.009 ↑ 0.731 YoungMod ↑ 1.000 ↑ 0.795 .fwdarw. −0.488 ↑ 0.986 .fwdarw. 0.210 TensileStr ↑ 0.795 ↑ 1.000 .fwdarw. −0.553 ↑ 0.757 .fwdarw. 0.203 EaB .fwdarw. −0.485 .fwdarw. −0.553 ↑ 1.000 .fwdarw. −0.546 .fwdarw. −0.598 TearStr ↑ 0.965 ↑ 0.757 .fwdarw. −0.545 ↑ 1.000 .fwdarw. 0.369 ShoreA .fwdarw. 0.210 .fwdarw. 0.203 .fwdarw. −0.586 .fwdarw. 0.359 ↑ 1.000 indicates data missing or illegible when filed

TABLE-US-00012 TABLE 4b HardSeg Urea Urethane Ester Doublebond BDO HardSeg ↑ 1.000 .fwdarw. 0.650 ↑ 0.751 .fwdarw. 0.566 ↓ −0.774 .fwdarw. 0.281 Urea .fwdarw. 0.650 ↑ 1.000 .fwdarw. 0.025 ↑ 0.614 ↓ −0.693 .fwdarw. −0.436 Urethane ↑ 0.751 .fwdarw. 0.025 ↑ 1.000 .fwdarw. 0.420 .fwdarw. −0.577 ↑ 0.578 Ester .fwdarw. 0.566 ↑ 0.614 .fwdarw. 0.420 ↑ 1.000 ↓ −0.960 .fwdarw. −0.490 Doublebond ↓ −0.774 ↓ −0.693 .fwdarw. −0.577 ↓ −0.960 ↑ 1.000 .fwdarw. 0.281 BDO .fwdarw. 0.281 .fwdarw. −0.436 .fwdarw. 0.578 .fwdarw. −0.490 .fwdarw. 0.281 ↑ 1.000 YoungMod ↑ 0.790 ↑ 0.770 .fwdarw. 0.270 .fwdarw. 0.207 .fwdarw. −0.426 .fwdarw. 0.182 TensileStr ↑ 0.829 ↑ 0.819 .fwdarw. 0.407 ↑ 0.619 ↓ −0.756 .fwdarw. −0.087 EaB ↓ −0.736 ↓ −0.781 .fwdarw. −0.204 .fwdarw. −0.224 .fwdarw. 0.421 .fwdarw. −0.100 TearStr ↑ 0.903 ↑ 0.727 .fwdarw. 0.484 .fwdarw. 0.328 .fwdarw. −0.558 .fwdarw. 0.267 ShoreA ↑ 0.787 .fwdarw. 0.295 ↑ 0.714 .fwdarw. 0.202 .fwdarw. −0.421 .fwdarw. 0.554 YoungMod TensileStr EaB TearStr ShoreA HardSeg ↑ 0.790 ↑ 0.829 ↓ −0.736 ↑ 0.903 ↑ 0.787 Urea ↑ 0.770 ↑ 0.818 ↓ −0.781 ↑ 0.727 .fwdarw. 0.295 Urethane .fwdarw. 0.270 .fwdarw. 0.407 .fwdarw. −0.204 .fwdarw. 0.484 ↑ 0.714 Ester .fwdarw. 0.207 ↑ 0.619 .fwdarw. −0.224 .fwdarw. 0.328 .fwdarw. 0.202 Doublebond .fwdarw. −0.426 ↓ −0.756 .fwdarw. 0.421 .fwdarw. −0.558 .fwdarw. −0.421 BDO .fwdarw. 0.182 .fwdarw. −0.087 .fwdarw. −0.100 .fwdarw. 0.267 .fwdarw. 0.554 YoungMod ↑ 1.000 ↑ 0.836 ↓ −0.943 ↑ 0.943 ↑ 0.711 TensileStr ↑ 0.836 ↑ 1.000 ↓ −0.760 ↑ 0.833 ↑ 0.700 EaB ↓ −0.943 ↓ −0.760 ↑ 1.000 ↓ −0.866 ↓ −0.614 TearStr ↑ 0.943 ↑ 0.833 ↓ −0.866 ↑ 1.000 ↑ 0.800 ShoreA ↑ 0.711 ↑ 0.700 ↓ −0.614 ↑ 0.800 ↑ 1.000

TABLE-US-00013 TABLE 5b HardSeg Urea Urethane Ester Doublebond BDO HardSeg ↑ 1.000 .fwdarw. 0.647 ↑ 0.754 .fwdarw. 0.565 ↓ −0.774 .fwdarw. 0.281 Urea ↑ 0.647 ↑ 1.000 .fwdarw. 0.024 ↑ 0.612 ↓ −0.689 .fwdarw. −0.437 Urethane ↑ 0.754 .fwdarw. 0.024 ↑ 1.000 .fwdarw. 0.420 .fwdarw. −0.575 .fwdarw. 0.578 Ester .fwdarw. 0.566 ↑ 0.612 .fwdarw. 0.420 ↑ 1.000 ↓ −0.960 .fwdarw. −0.490 Doublebond ↓ −0.774 ↓ −0.689 .fwdarw. −0.578 ↓ −0.960 ↑ 1.000 .fwdarw. 0.281 BDO .fwdarw. 0.281 .fwdarw. −0.437 .fwdarw. 0.578 .fwdarw. −0.490 .fwdarw. 0.281 ↑ 1.000 YoungMod ↑ 0.650 ↑ 0.889 .fwdarw. 0.038 .fwdarw. 0.325 .fwdarw. −0.470 .fwdarw. −0.153 TensileStr ↑ 0.711 ↑ 0.963 .fwdarw. 0.129 ↑ 0.800 ↓ −0.702 .fwdarw. −0.326 EaB ↓ −0.701 ↓ −0.684 .fwdarw. −0.214 .fwdarw. −0.125 .fwdarw. 0.336 .fwdarw. −0.191 TearStr ↑ 0.608 ↑ 0.820 .fwdarw. 0.300 .fwdarw. 0.363 .fwdarw. −0.553 .fwdarw. 0.059 ShoreA ↑ 0.605 .fwdarw. 0.270 ↑ 0.751 .fwdarw. 0.183 .fwdarw. −0.413 ↑ 0.506 YoungMod TensileStr EaB TearStr ShoreA HardSeg ↑ 0.550 ↑ 0.711 ↓ −0.701 ↑ 0.808 ↑ 0.805 Urea ↑ 0.589 ↑ 0.983 ↓ −0.594 ↑ 0.820 .fwdarw. 0.270 Urethane .fwdarw. 0.038 .fwdarw. 0.129 .fwdarw. −0.214 .fwdarw. 0.300 ↑ 0.751 Ester .fwdarw. 0.325 .fwdarw. 0.500 .fwdarw. −0.128 .fwdarw. 0.363 .fwdarw. 0.183 Doublebond .fwdarw. −0.470 ↓ −0.702 .fwdarw. 0.336 .fwdarw. −0.553 .fwdarw. −0.413 BDO .fwdarw. −0.153 .fwdarw. −0.326 .fwdarw. −0.191 .fwdarw. 0.059 ↑ 0.606 YoungMod ↑ 1.000 ↑ 0.  ↓ −0.739 ↑ 0.950 .fwdarw. 0.408 TensileStr ↑ 0.  ↑ 1.000 ↓ −0.595 ↑ 0.803 .fwdarw. 0.397 EaB ↓ −0.739 ↓ −0.  ↑ 1.000 ↓ −0.746 ↓ −0.620 TearStr ↑ 0.950 ↑ 0.503 ↓ −0.746 ↑ 1.000 .fwdarw. 0.570 ShoreA .fwdarw. 0.408 .fwdarw. 0.397 ↓ −0.520 .fwdarw. 0.570 ↑ 1.000 indicates data missing or illegible when filed

TABLE-US-00014 TABLE 5b-2 M1 M2 M3 M4 M5 M6 M1 ↑ 1.000 ↑ 0.647 ↑ 0.754 .fwdarw. 0.566 ↓ −0.774 .fwdarw. 0.281 M2 ↑ 0.647 ↑ 1.000 .fwdarw. 0.024 ↑ 0.612 ↓ −0.669 .fwdarw. −0.437 M3 ↑ 0.754 .fwdarw. 0.024 ↑ 1.000 .fwdarw. 0.420 .fwdarw. −0.578 .fwdarw. 0.578 M4 .fwdarw. 0.566 ↑ 0.612 .fwdarw. 0.420 ↑ 1.000 ↓ −0.980 .fwdarw. −0.490 M5 ↓ −0.774 ↓ −0.689 .fwdarw. −0.578 ↓ −0.960 ↑ 1.000 .fwdarw. 0.281 M6 .fwdarw. 0.281 .fwdarw. −0.437 .fwdarw. 0.578 .fwdarw. −0.490 .fwdarw. 0.281 ↑ 1.000 P1 ↑ 0.650 ↑ 0.669 .fwdarw. 0.038 .fwdarw. 0.325 .fwdarw. −0.470 .fwdarw. −0.153 P2 ↑ 0.711 ↑ 0.963 .fwdarw. 0.129 ↑ 0.800 ↓ −0.702 .fwdarw. −0.326 P3 ↓ −0.701 ↓ −0.694 .fwdarw. −0.214 .fwdarw. −0.128 .fwdarw. 0.336 .fwdarw. −0.191 P4 ↑ 0.806 ↑ 0.820 .fwdarw. 0.300 .fwdarw. 0.363 .fwdarw. −0.553 .fwdarw. 0.059 P5 ↑ 0.605 .fwdarw. 0.270 ↑ 0.751 .fwdarw. 0.183 .fwdarw. −0.413 ↑ 0.606 P1 P2 P3 P4 P5 M1 ↑ 0.650 ↑ 0.711 ↓ −0.701 ↑ 0.808 ↑ 0.805 M2 ↑ 0.889 ↑ 0.963 ↓ −0.694 ↑ 0.820 .fwdarw. 0.270 M3 .fwdarw. 0.038 .fwdarw. 0.129 .fwdarw. −0.214 .fwdarw. 0.300 ↑ 0.751 M4 .fwdarw. 0.325 .fwdarw. 0.600 .fwdarw. −0.128 .fwdarw. 0.363 .fwdarw. 0.183 M5 .fwdarw. −0.470 ↓ −0.702 .fwdarw. 0.336 .fwdarw. −0.553 .fwdarw. −0.413 M6 .fwdarw. −0.153 .fwdarw. −0.326 .fwdarw. −0.191 .fwdarw. 0.059 ↑ 0.806 P1 ↑ 1.000 ↑ 0.859 ↓ −0.739 ↑ 0.950 .fwdarw. 0.406 P2 ↑ 0.859 ↑ 1.000 ↓ −0.695 ↑ 0.803 .fwdarw. 0.397 P3 ↓ −0.739 ↓ −0.695 ↑ 1.000 ↓ −0.746 ↓ −0.620 P4 ↑ 0.950 ↑ 0.803 ↓ −0.746 ↑ 1.000 .fwdarw. 0.570 P5 .fwdarw. 0.408 .fwdarw. 0.397 ↓ −0.620 .fwdarw. 0.570 ↑ 1.000

TABLE-US-00015 TABLE 3 YoungMod TensileStr EaB TearStr ShoreA HardSeg ↑ 0.790 ↑ 0.829 ↓ −0.736 ↑ 0.903 ↑ 0.805 Urea ↑ 0.897 ↑ 0.968 ↓ −0.781 ↑ 0.853 .fwdarw. 0.295 Urethane .fwdarw. 0.270 .fwdarw. 0.407 .fwdarw. −0.214 .fwdarw. 0.484 ↑ 0.751 Ester .fwdarw. 0.325 ↑ 0.619 .fwdarw. 0.038 .fwdarw. 0.363 .fwdarw. 0.202 Doublebond .fwdarw. −0.470 ↓ −0.756 .fwdarw. 0.421 .fwdarw. −0.558 .fwdarw. −0.444 BDO .fwdarw. 0.182 .fwdarw. −0.326 .fwdarw. −0.292 .fwdarw. 0.267 ↑ 0.731

[0147] The following conclusions thus arise from table 3: [0148] Modulus of elasticity (“YoungMod”) as behavior property is determined by the hard segment content (“HardSeg”) and the urea content (“Urea”)—each descriptors. Since both have a positive correlation, a higher hard segment and a higher urea content here means a higher modulus of elasticity. [0149] The same applies to the behavior property of breaking stress (“TensileStr”), which additionally correlates positively with the ester content descriptor, while the double bond content descriptor (“Doublebond”) correlates negatively. [0150] The behavior property of elongation at break (“EaB”) shows anti-proportional behavior, like the modulus of elasticity. [0151] The behavior property of tear strength (“TearStr”) is correlated with the modulus of elasticity. [0152] The behavior property of Shore A hardness (“Shore A”) correlates positively with the hard segment content (“HardSeg”), the urethane content (“Urethane”) and the descriptor of butanediol content (“BDO”), and is thus the sole behavior property considered that depends on the “BDO” descriptor.

[0153] The following dependences that affect optimization of the behavior properties are thus apparent: [0154] Modulus of elasticity (“YoungMod”), elongation at break (“EaB”) and tear strength (“TensileStr”) affect one another. [0155] Tear strength (“TensileStr”) can be increased by a high ester content and low double bond content (or else conversely lowered). [0156] Shore A hardness (“Shore A”) is increased by high urethane content and high butanediol content.

[0157] The dependences of the behavior properties with respect to the descriptors are comprehended by evaluation of the correlation matrix and can then be adapted to target test feature values based on behavior properties according to a target profile of requirements of a target mixed product, for example in an application in paints, adhesives, sealing compounds, casting resins and foams. Suitable combinations of behavior properties can be inferred directly or even extrapolated from the datasets.

[0158] In the manner described, the method proposed enables systematic performance of product developments, and permits the developer of paints, adhesives, sealing compounds, casting resins and foams to find a targeted course of action and clear instructions for action for development of these products. In addition, it has been shown that behavior properties are dependent on particular (individual or multiple) descriptors, which permits buildup of knowledge for future tasks.

[0159] If tables 5a and 5a-2 and tables 5b and 5b-2 are considered in a comparative manner, the descriptors are cited there as M1-M6 and the behavior properties as P1-P5 in tables 5a-2 and 5b2. The content of the correlation matrices in tables 5b and 5b-2 is identical, and so it has been shown that, given additional coding of the descriptors and the behavior properties and the use of normalizing mappings (the student distribution here, for example), the assignment to particular parameters is not known, nor is it known what kind of parameter is involved. The numerical values that have been altered by the bijective mapping also means that it is not possible for the person skilled in the art to infer the correlation relationships to the descriptors or the behavior properties. But the content of the correlation matrix as instructions for action for the respective side (being aware either solely of descriptors or, on the other hand, solely of the behavior properties) is conserved.

[0160] It is to be shown hereinafter that it is possible by this course of action to calculate rationally derivable proposals for new product distributions of the product compositions of the examples given in table 1. For this purpose, reference is made firstly to table 4 in which the weight ratios of the product distributions from table 1 are given.

TABLE-US-00016 TABLE 4 IPDI PCT HTNR BDO Polyurethane 3 34.16 65.84 0.00 0.00 Polyurethane 4 38.37 61.63 0.00 0.00 Polyurethane 5 45.36 54.64 0.00 0.00 Polvuresaate 6 48.29 51.71 0.00 0.00 Polyurethane 7 19.78 19.06 61.15 0.00 Polyurethane 8 25.40 17.14 54.97 2.49 Polyurethane 9 31.34 15.10 48.43 5.13 Polyurethane 10 47.01 45.30 0.00 7.69 Polvurethane 11 36.16 24.39 33.53 5.92 Polyurethane 12 27.65 7.99 59.83 4.52 Polvurethane 13 23.50 0.00 72.65 3.85

[0161] For the calculation of table 4 from table 1, the equivalent weights of the four components IPDI: 110 g/mol, PCL: 265 g/mol, HTNR 850 g/mol and BDO 45 g/mol were used. These were multiplied by the equivalents figures given in table 1 and then converted to percent.

[0162] As already inferred from table 3, the important mechanical properties of modulus of elasticity and elongation at break are typical behavior properties to be optimized. The urea content descriptor and the hard segment content descriptor determine both behavior properties—except that the behavior property of modulus of elasticity does so in an inverse manner to the behavior property of elongation at break. In general, such a development task presents a problem to the developer since it is supposed that all he can do is seek a compromise.

[0163] In order then to achieve an improvement in both behavior properties, a graph representation is particularly suitable in a multivariate analysis. It can be important here to give greater consideration to the identification of the descriptors (here the urea content “Urea”) as important influencing factor on these two behavior properties. For instance, FIG. 1 shows modulus of elasticity (“YoungMod”) plotted against urea content (“Urea”), while FIG. 2 shows elongation at break (“EaB”) plotted against urea content (“Urea”). Both graphs show a defined target region marked in gray for which a new product distribution of the product composition of IPDI/PCL/HTNR/BDO is being sought and which thus constitutes a target profile of requirements for the mixed target product being sought. By comparison of the product compositions/product distributions within the gray target region, it becomes clear that no product from the selection of PU3 to PU13 fulfills the target profile of requirements described by the two target behavior properties.

[0164] FIG. 1 shows the strong positive correlation of urea content (“Urea”) with modulus of elasticity (“YoungMod”), for example from the example series PU3 to PU6 (PU=polyurethane). This was achieved in the example series by an increasing excess of equivalents of IPDI—shown by the vectors of PU3 to PU6—but with loss of elongation at break (“EaB”), as clearly shown by FIG. 2. The same applies to the example series PU7-PU9, where an increasingly higher use of BDO that likewise leads to a higher urea content through the low equivalents ratio likewise results in a higher modulus of elasticity—but at a lower level—which then also leads to a loss in elongation at break (“EaB”).

[0165] Using the same raw materials, PU10 is a possible starting point since this is already within the range of modulus of elasticity, but an increase in elongation at break is still required. The variation in the urea content from PU3 to PU4 leads to a small gain in modulus of elasticity and a high loss in elongation at break. Therefore, the reversed course of action should result in a small loss in modulus of elasticity and a large gain in elongation at break. In this way, a target descriptor profile is obtained. Proceeding from PU10, this should lead into the target region.

[0166] It is in turn possible to conclude the product composition from this descriptor profile. The change in the product distribution from PU4 to PU3 is the reduction in the equivalents ratio of isocyanate to alcohols. It follows as an instruction for action that, proceeding from the product composition PU10, the equivalents ratio should be reduced further by estimation from the graph with reference to the identification of the target feature value of the urea content descriptor “Urea”, or, in other words, the correlation matrix is utilized to alter the behavior properties in order to arrive via the descriptors at a target product composition of the mixed target product.

[0167] This is shown in the form of a graph in FIGS. 3 and 4, wherein the projection of the vectors in the same direction of the variation in behavior properties from PU3 to PU4 is used, but inverted in direction and roughly halved in length, in order to arrive within the target region shaded in gray. This already results in the new target composition based on PU10 which is shown in table A2. Polyurethanes 3, 4 and 10 are cited again as reference; the new mixed target product polyurethane 10-new shows the target behavior property with a graphically estimated urea content of 0.33. The equivalents ratio is calculated in such a way that the urea content is attained, such that the projection of the vectors in FIG. 6 still leads into the gray-shaded target region. The equivalents ratio of IPDI is thus chosen at 1.189 (which results in a urea content of 0.33) and, as shown above and elucidated for table 4, the new target distribution is calculated (see table 4-new).

TABLE-US-00017 TABLE A2 Derivation of the new equivalents ratios for the new target composition Polyurethane 10-new (see also FIGS. 6 and 7) IPDI PCL HTNR BDO Polyurethane 3 1.25 1.00 0.00 0.00 Polyurethane 4 1.50 1.00 0.00 0.00 Polyurethane 10 1.25 0.50 0.00 0.50 Polyurethane 10-new 1.19 0.50 0.00 0.50

TABLE-US-00018 TABLE 4-new Result of the new product distribution of polyurethane 10-new IPDI PCL HTNR BDO Polyurethane 10-new 45.76 46.36 0.00 7.87

[0168] It has thus been shown that a graph analysis of the variations in behavior properties in relation to descriptors that have been identified beforehand as being of statistical relevance is a suitable course of action for predicting new product compositions and their product distributions. The term “projection” derives from the vectors that result from the graph assessment in the dimension space of behavior properties with respect to descriptors.

[0169] FIG. 5 shows a first 1, a second 3 and a third 5 computer system for execution of the proposed method of ascertaining a product composition for a mixed chemical product. The first computer system 1 is in a dedicated intranet 2, the second computer system 3 is likewise in a dedicated intranet 4, and the third computer system 5 is also in a dedicated intranet 6, with each intranet 2, 4, 6 being disjoint from the respective others. The three respectively disjoint intranets 2, 4, 6 may also each be referred to as first intranet 2, as second intranet 4 and as third intranet 6.

[0170] All three computer systems 1, 3, 5 are connected by the general internet 7, and there exist technical protective measures that enable exchange of information (especially: mapped feature values and mapped test feature values) between supplier and customer solely via their specific access