Property Prediction and CAMD CHEN 4470 – Process Design Practice Dr. Mario Richard Eden Department of Chemical Engineering Auburn University Lecture No.

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Presentation transcript:

Property Prediction and CAMD CHEN 4470 – Process Design Practice Dr. Mario Richard Eden Department of Chemical Engineering Auburn University Lecture No. 10 – Property Prediction and Computer Aided Molecular Design February 14, 2006

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.

Property Prediction 2:2 Examples and Software

CAMD 1:3

CAMD 2:3

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

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 ppm to 2 ppm. Conventional Approach –Single stage distillation. –Reduces aniline content to 500 ppm. –Energy usage: MJ –No data is available for the subsequent downstream processing steps.

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

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 Aniline Case Study 3:7

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

Aniline Case Study 5:7 Process Simulation

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

Aniline Case Study 7:7 Validation of Minimum Cost Solution

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

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 (T m ) < 280K –Boiling Point (T b ) > 340K –Acyclic compounds containing no Cl, Br, F, N or S –Octanol/Water Partition coefficient (logP) < 2 –15.95 (MPa) ½ < δ < (MPa) ½

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.

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 Summary

Next Lecture – February 21 –Comments about first progress report –Product engineering and Six Sigma –Unless other topics need to be addressed? –Senior class picture (weather permitting) Possibly No Afternoon Meetings on February 16 –Continuation of medical exam in Montgomery –Will know for certain tonight, update once I know Team Evaluation Forms –Make sure every team member turns in a form! –Still a few missing. Other Business