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Property Prediction and CAMD

Learn about property prediction methods and computer-aided molecular design in process design practice.

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Property Prediction and CAMD

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  1. Property Prediction and CAMD CHEN 4470 – Process Design Practice Dr. Mario Richard EdenDepartment of Chemical EngineeringAuburn University Lecture No. 21 – Property Prediction and Computer Aided Molecular Design April 2, 2013

  2. Property Prediction 1:2 • Motivation • Experiments are time-consuming and expensive. • How do we identify the components to investigate? • Components of similar molecular structure have been found to have similar properties. • Group Contribution Methods • Predominant means of predicting physical properties for new components. • Based on UNIFAC group descriptions • Large amounts of experimental property data has been fitted to obtain the contributions of individual groups.

  3. Property Prediction 2:2 • Examples and Software

  4. CAMD 1:3

  5. CAMD 2:3

  6. CAMD 3:3 • Application Examples • Water/phenol system: Toluene replacement • Separation of Cyclohexane and Benzene • Separation of Acetone and Chloroform • Refrigerants for heat pump systems • Heat transfer fluids for heat recovery and storage • and many others

  7. Aniline Case Study 1:7 • Problem Description • During the production of a pharmaceutical, aniline is formed as a byproduct. Due to strict product specifications the aniline content of an aqueous solution has to be reduced from 28000 ppm to 2 ppm. • Conventional Approach • Single stage distillation. • Reduces aniline content to 500 ppm. • Energy usage: 4248.7 MJ • No data is available for the subsequent downstream processing steps.

  8. Aniline Case Study 2:7 • Objective • Investigate the possibility of using liquid-liquid extraction as an alternative unit operation by identification of a feasible solvent • Reported Aniline Solvents • Water, Methanol, Ethanol, Ethyl Acetate, Acetone

  9. Aniline Case Study 3:7 • Performance of Solvent • Liquid at ambient temperature • Immiscible with water • No azeotropes between solvent & aniline and/or water • High selectivity with respect to aniline • Minimal solvent loss to water phase • Sufficient difference in boiling points for recovery • Structural and EH&S Aspects • No phenols, amines, amides or polyfunctional compounds. • No compounds containing double/triple bonds. • No compounds containing Si, F, Cl, Br, I or S

  10. Aniline Case Study 4:7 • Results of Solvent Search • No high boiling solvents found Also, higher and branched alkanes were identified as candidates

  11. Aniline Case Study 5:7 • Process Simulation

  12. Aniline Case Study 6:7 • Performance Targets and Results • Countercurrent extraction and simple distillation. • Terminal concentration of 2 ppm aniline in water phase. • Highest possible purity during solvent regeneration

  13. Aniline Case Study 7:7 • Validation of Minimum Cost Solution

  14. Oleic Acid Methyl Ester 1:3 • Problem Description • Fatty acid used in a variety of applications, e.g. textile treatment, rubbers, waxes, and biochemical research • Reported solvents: Diethyl Ether, Chloroform • Goal • Identify alternative solvents with better safety and environmental properties. Volatile Flammable Carcinogen

  15. Oleic Acid Methyl Ester 2:3 • Solvent Specification • Liquid at normal (ambient) operating conditions. • Non-aromatic and non-acidic (stability of ester). • Good solvent for Oleic acid methyl ester. • Constraints • Melting Point (Tm) < 280K • Boiling Point (Tb) > 340K • Acyclic compounds containing no Cl, Br, F, N or S • Octanol/Water Partition coefficient (logP) < 2 • 15.95 (MPa)½ < δ < 17.95 (MPa)½

  16. Oleic Acid Methyl Ester 3:3 • Database Approach (2 Candidates) • 2-Heptanone • Diethyl Carbitol • CAMD Approach (1351 Compounds Found) • Maximum of two functional groups allowed, thus avoiding complex (and expensive) compounds. • Formic acid 2,3-dimethyl-butyl ester • 3-Ethoxy-2-methyl-butyraldehyde • 2-Ethoxy-3-methyl-butyraldehyde • Calculation time approximately 45 sec on standard PC.

  17. Property Based Design • Why Design Based on Properties? • Many processes driven by properties NOT components • Performance objectives often described by properties • Often objectives can not be described by composition • Product/molecular design is based on properties • Insights hidden by not integrating properties directly • Property Clusters • Extension to existing composition based methods • Reduces dimensionality of problem • Enables visualization of problem • Property estimation in molecular design via GC • Unifying framework for simultaneous solution

  18. Property Clusters 1:2 Property clusters are conserved surrogate properties described by property operators, which have linear mixing rules, even if the operators themselves are nonlinear. R.H.S.

  19. Feed Constraint Feasibility Region Analysis has shown that region boundary can be described by 6 unique points. Property Clusters 2:2 Feasibility Necessary Condition Match clustering target Sufficient Condition Match AUP value of sink

  20. Group Contribution Methods • Group Contribution Methods (GCM) allow for prediction of physical properties from structural information • 1st order, 2nd order, and 3rd order groups are utilized to increase the accuracy of the predicted properties

  21. Molecular Clusters 1:5

  22. Molecular Clusters 2:5

  23. C2 0,1 0,9 0,2 0,8 0,3 0,7 0,4 0,6 0,5 0,5 0,6 0,4 0,7 0,3 0,8 0,2 2 0,9 0,1 G2 C1 C3 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Feasibility Region G1 M1 G4 G3 Molecular Clusters 3:5 b1, the visualization arm, corresponds to the location of G1-G2 molecule

  24. Molecular Clusters 4:5 1:CH3 2: CH2 3: CH3N 4: COOH Molecular Synthesis CH3-(CH2)2-CH3N-COOH

  25. Molecular Clusters 4:5 1: CH3 2: CH2 3: CH3N 4: COOH 5: CH3N-COOH Molecular Synthesis CH3-(CH2)2-CH3N-COOH

  26. Molecular Clusters 4:5 1: CH3 2: CH2 3: CH3N 4: COOH 5: CH3N-COOH 6: CH3-CH2 MolecularSynthesis CH3-(CH2)2-CH3N-COOH

  27. Molecular Clusters 4:5 1: CH3 2: CH2 3: CH3N 4: COOH 5: CH3N-COOH 6: CH3-CH2 7: CH3-(CH2)2 MolecularSynthesis CH3-(CH2)2-CH3N-COOH

  28. Molecular Clusters 4:5 1: CH3 2: CH2 3: CH3N 4: COOH 5: CH3N-COOH 6: CH3-CH2 7: CH3-(CH2)2 Molecular Synthesis CH3-(CH2)2-CH3N-COOH

  29. Molecular Clusters 5:5 The location of each molecular formulation is unique and independent of group addition path Formulation of Butyl methyl ether CH3-CH2-CH2-CH2-CH3O

  30. Example: Molecular Synthesis • Blanket Wash Solvent Design • Solved as MINLP by Sinha and Achenie (2001) • Problem Statement • Design blanket wash solvent for phenolic resin printing ink • Molecules designedfrom 7 possible groups, with a max. chain length of 7 groups

  31. Blanket Wash Solvent 1:7 • Visualization limits problem to three properties • Heat of vaporization, boiling and melting temperatures are used, with vapor pressure and solubility used as final screening properties Property Prediction (GCM) Molecular Property Operators ,Y ref = 20 ,Y ref = 100 ,Y ref = 7

  32. Blanket Wash Solvent 2:7

  33. Blanket Wash Solvent 3:7

  34. Blanket Wash Solvent 4:7

  35. Blanket Wash Solvent 5:7 • Feasible formulations from Visual Synthesis • Application of feasibility conditions • All formulations satisfy the first two necessary conditions • M9-M11 fail to satisfy the AUP range of the sink

  36. Blanket Wash Solvent 6:7 • Feasible formulations from Visual Synthesis • Application of feasibility conditions • Checking property values with sink including Non-GC properties (VP, solubility), the sufficient conditions are satisfied for remaining formulations

  37. Blanket Wash Solvent 7:7 Candidate molecules M1-M7 identified visually by the developed method correspond to solutions found by the MINLP approach used by Sinha and Achenie (2001) Although valid formulation, heptane (M8) is flammable hence not an ideal solvent Cyclical compound Ethers Ethers MEK

  38. Integrated Design Approach Stream Properties & Unit Constraints ProcessDesign Clusters Process/ Product Design Calculations PropertyTargets ClustersM Molecular Formulations MolecularDesign Molecular Design

  39. Example – Integrated Design Stream Characterization Sulfur Content (S) Molar Volume (Vm) Vapor Pressure (VP) Objective To maximize the use of off-gas condensate and to minimize fresh solvent use to the degrease

  40. Metal Degreasing 1:9 • Degreaser Feed Constraints • Property Operator Mixing Rules , S ref = 0.5 wt% , Vmref = 80 cm3/mol , VP ref = 760 mmHg

  41. Metal Degreasing 2:9 Visualization of Process Design Problem VOC Condensation Data Sulfur content, density and vapor pressure data given for temperature range 480K-515K

  42. Metal Degreasing 3:9 Visualization of Process Design Problem Conditions Condenser operates @ 500 K Feed Solvent must have zero sulfur content C 2 0.1 0.9 0.2 0.8 POINT A 0.3 0.7 Point A & B dictate property constraint targets 0.4 0.6 0.5 0.5 0.6 0.4 DEGREASER 0.7 0.3 0.8 0.2 495 K POINT B 490 K 500 K 0.9 510 K 0.1 480 K 485 K 505 K CONDENSATE 515 K C C 3 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

  43. Metal Degreasing 4:9 Values from Process Design Visual Solution Molecular Property Constraints

  44. y = ref , 20 y = ref , 100 y = ref , 7 Metal Degreasing 5:9 Property Prediction (GCM) Molecular Property Operators Non-GC Property

  45. Metal Degreasing 6:9 Visualization of Molecular Design Problem Molecular Fragments G1: CH3 G2: CH2 G3: CH2O G4: CH2N G5: CH3N G6: CH3CO G7: COOH

  46. Metal Degreasing 7:9 Visualization of Molecular Design Problem Candidate Molecules M1 CH3-(CH2)5-CH3CO M2 CH3CO-(CH2)2-CH3CO M3 (CH3)3-(CH2)5-CH2N M4 CH3-(CH2)2-COOH M5 (CH3)2-CH3CO-CCL M6 -(CH2O)5- ring M7 CH3-(CH2)2-CH3N-COOH

  47. Metal Degreasing 8:9 • Formulations from Visual Design • Application of Feasibility Conditions • All formulations satisfy first two necessary conditions • M5 and M6 fail to satisfy sink AUP range • M3 and M7 did not match Non-GC property value • M1, M2 and M4 are valid solvent candidates

  48. Metal Degreasing 9:9 Solutions to Molecular Design Problem Maximization of Condensate 17.44 kg/min of condensate recycle is utilized 19.36 kg/min of 2,5-hexadione as fresh solvent Visualization of Process Design Solution

  49. Summary • Property Prediction and CAMD • Can generate data for the simulation software in order to solve novel problems • Allows for development of environmentally benign designs and components • Systematic approaches, do not rely on rules of thumb. • Utilizes process/product knowledge at selection level. • Expands solution space for solvent design/selection • Capable of identifying novel compounds not included in databases and/or literature • Methodology has been proven through numerous application studies • Powerful tool when used in an integrated framework

  50. Other Business • Next Lecture – April 4 • Product engineering and Six Sigma • SSLW pp. 662-678 • Progress Report No. 3 • Friday April 5 • Remember to fill out the team evaluation forms

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