Method for the Production of Hydrophilic Polymers and Finishing Products Containing the Same Using a Computer-Generated Model

• US 20070260357A1
• Filed: 06/09/2005
• Published: 11/08/2007
• Est. Priority Date: 06/09/2004
• Status: Abandoned Application

• Current Assignee: Evonik Stockhausen GmbH (Evonik Degussa GmbH)
• Original Assignee: Evonik Stockhausen GmbH (Evonik Degussa GmbH)
• Inventors: Schmidt, Harald, Issberner, Jorg, Resch, Jorg
• Application Number:
  US11/570,165
• Publication Number:
  US 20070260357A1
• Time in Patent Office: Days
• US Class Current: 700/269
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 17/02 electric

Abstract

The invention relates generally to a process for producing a hydrophilic polymer, a prediction process, hygiene articles, and other chemical products, which comprise a hydrophilic polymer produced according to the process according to the invention, as well as the use of a polymer according to the invention in hygiene articles and further chemical products and the use of a computer-generated model for determination of different values and a process for production of hydrophilic polymer-comprising further processing products.

Claims

1. A process for producing a hydrophilic polymer in a production device, wherein a computer-generated model controls this production device.

2. The process according to claim 1, wherein the control occurs by determination of at least one process parameter and via at least one process value based upon the at least one process parameter.

3. The process according to claim 1, wherein the computer-generated model calculates the at least one process value.

4. The process according to claim 1, wherein this process occurs continuously.

5. The process according to claim 1, wherein the process is divided into at least two process steps.

6. The process according to claim 5, wherein in each of the at least two process steps, at least one step parameter is determined as a process parameter.

7. The process according to claim 6, wherein the at least one step parameter influences the at least one process value.

8. The process according to claim 5 with at least:

(a) an educt preparation step,

(b) a polymerization step,

(c) a first confectioning step,

(d) optionally a post-crosslinking step, and

(e) optionally a further confectioning step.

9. The process according to claim 1, wherein the control occurs by means of a store of experience assigned to at least one experience parameter.

10. The process according to claim 9, wherein the at least one experience parameter is at least one physical or chemical property of a hydrophilic polymer.

11. The process according to claim 10, wherein the experience parameter characterizes at least one property selected from:

P1 retention of an aqueous liquid,

P2 absorption of an aqueous liquid,

P3 absorption of an aqueous liquid against pressure,

P4 rate of absorption of an aqueous liquid,

P5 rate of absorption of an aqueous liquid against pressure,

P6 particle size distribution,

P7 residual monomer content,

P8 saline flow capacity,

P9 bulk density,

P10 pH value,

P11 flowability, and

P12 color.

12. The process according to claim 9, wherein the store of experience is manifested by the computer-generated model.

13. The process according to claim 9, wherein the store of experience is obtainable by a learning process.

14. The process according to claim 46, wherein the artificial neuronal network comprises at least one first artificial neurone and at least one further artificial neurone following the first artificial neurone.

15. The process according to claim 14, wherein in the first artificial neurone an input occurs by means of an input signal.

16. The process according to claim 14, wherein from the further artificial neurone an output occurs by means of an output signal.

17. The process according to claim 14, wherein the at least one process parameter correlates with at least one input signal of the first artificial neurone.

18. The process according to claim 14, wherein the at least one process value correlates with at least one output signal of the at least one further artificial neurone.

20. The process according to claim 1, wherein at least one G value contributes to the control of the production device.

21. Composites, hygiene articles, fibers, sheets, foams, formed bodies, soil improvers, flocculation additives, paper additives, textile additives, water treatment additives, or leather additives comprising a hydrophilic polymer made by a process according to claim 1.

22. Use of a hydrophilic polymer obtainable by a process according to claim 1 in composites, hygiene articles, fibers, sheets, foams, formed bodies, soil improvers, flocculation additives, paper additives, textile additives, water treatment additives, or leather additives.

46. The process of claim 1, wherein the computer-generated model is an artificial neuronal network.

19. A prediction process for predetermining at least one of the following G values

G1 a G process parameter,

G2 a G process value,

G3 a G experience parameter,

in connection with a hydrophilic polymer or its production or both, comprising the following steps:

V1 operating a production of a hydrophilic polymer, thereby

V2 determining at least one of the V values

i. a V process parameter,

ii. a V process value,

iii. a V experience parameter,

V3 processing of the at least one V value in a data processing unit to form a store of experience in the form of a computer-generated model, and

V4 providing at least one G value based upon this store of experience.

23. Use of an artificial neuronal network for determination of process values by means of a physical property of a hydrophilic polymer or of an absorbent composition comprising a hydrophilic polymer and at least one component different therefrom.

24. A process for producing a further processing product comprising a hydrophilic polymer in a further processing machine, comprising the process steps

providing

the hydrophilic polymer, and

at least one further processing component,

bringing into contact the hydrophilic polymer and the at least one further processing component to obtain a further processing product,

wherein a computer-generated model controls the further processing machine.

26. The process according to claim 47, wherein the computer-generated model of the production device and the computer-generated model of the further processing machine interact with each other.

27. The process according to claim 24, wherein the controlling occurs by determination of at least one W process parameter and by means of at least one W process value based upon this at least one W process parameter.

28. The process according to claim 24, wherein the computer-generated model calculates the at least one W process value.

29. The process according to claim 24, wherein this process occurs continuously.

30. The process according to claim 24, wherein in each of the at least two process steps respectively at least one W step parameter as W process parameter is determined.

31. The process according to claim 30, wherein the at least one W step parameter influences the at least one W process value.

32. The process according to claim 47, wherein the control occurs by means of a W store of experience assigned to at least one W experience parameter.

33. The process according to claim 32, wherein the at least one W experience parameter is at least one physical or chemical property of the further processing product.

34. The process according to claim 33, wherein the experience parameter characterizes at least one property selected from:

W1 Rewet,

W2 Leakage,

W3 Wicking,

W4 Absorption speed,

W5 Spreading of the liquid (“

Spreading”

in spread direction and area), and

W6 integrity in the dry or wet state.

35. The process according to claim 32, wherein the W store of experience is manifested through the computer-generated model.

36. The process according to claim 32, wherein the W store of experience is obtainable by means of a learning process.

37. The process according to claim 32, wherein the artificial neuronal network comprises at least one first artificial neurone and at least one further artificial neurone following the first artificial neurone.

38. The process according to claim 37, wherein in the first artificial neurone an input occurs by means of an input signal.

39. The process according to claim 37, wherein from the further artificial neurone an output occurs by means of an output signal.

40. The process according to claim 37, wherein the at least one W process parameter correlates with at least one input signal of the first artificial neurone.

41. The process according to claim 37, wherein the at least one W process value correlates with at least one output signal of the at least one further artificial neurone.

44. The process according to claim 24, wherein the further processing machine is a fiber spinning, fiber matrix, paper, core, wound dressing or diaper machine.

45. The process according to claim 24, wherein the further processing product is fibers, fiber matrices, paper, cores, wound dressings or diapers.

47. The process of claim 24, wherein the computer-generated model is an artificial neuronal network.

25. (canceled)

42. A prediction process for pre-determining at least one of the following WG values

WG1 a W process parameter or process parameter,

WG2 a W process value or process value,

WG3 a W experience parameter or experience parameter,

in connection with a hydrophilic polymer and/or a further processing product or production thereof or both, comprising the following steps:

V1 operating a production of a further processing product, thereby

V2 determining at least one of the WV values

i. a WV process parameter,

ii. a WV process value,

iii. a WV experience parameter,

V3 processing of the at least one WV value in a data processing unit to form a store of experience in the form of a computer-generated model,

V4 providing at least one WG value based upon this store of experience.

43. A prediction process for pre-determination of at least one of the following WG values

WG1 a W process parameter or process parameter,

WG2 a W process value or process value,

WG3 a W experience parameter or experience parameter,

in connection with a hydrophilic polymer and/or a further processing product or its production or both, wherein at least one WG value based upon an available store of experience is provided.