Lecture-Selection Part 1

CHEMICAL PRODUCT DESIGN

1

“SELECTION”

Last time…

Concluded the second stage of the Chemical

Product Design 4-step procedure:

• Needs: what needs should the product fulfil? What

specifications do these needs imply?

• Ideas: what products/concepts could satisfy these needs?

• Selection: which ideas are most promising?

• Manufacture: how do we turn the design into a commercial product?

This time…

Introduction to the selection phase

Selection using thermodynamics

• Group contribution models

• Solubility parameters

• Exercise

Introduction

The ideas phase and particularly

the screening process left us

around 5 really good ideas

with a lot of potential.

The selection phase involves

more detailed scientific

scrutiny of the good ideas.

We use all scientific tools at our disposal, and do

research and experiments

100 ideas

20 better ideas

5 really good ideas

Introduction

We also evaluate risks, and

take steps to mitigate them

With these tools we try to

make a decision on which

product to manufacture

We can produce a selection

matrix to help us make

that decision

5 really good ideas

1 workable really good idea

“We’ve considered every potential risk, except the risks of avoiding all risks.”

Selection using science

Two fundamental areas:

• Thermodynamics (the science of the possible)

• Kinetics (the science of the how quickly?)

Also make use of a number of other skills and

techniques in many areas of science and

engineering

Selection by thermodynamics

Often we wish to make improved products by removing

harmful or toxic chemicals in existing processes

Eg. CFCs were used as coolants in

fridges for a long time

When they were discovered to

be harmful to the ozone layer

in the 1980s, they were slowly

phased out

Replacement chemicals had to be

found with similar properties

eg. HCFCs

Other examples:Solvents, Asbestos, BPA in baby bottles etc.

Ingredient substitutions

• Main aim: to duplicate product’s properties

• Most common effort: search for less volatile & less

toxic solvents, example:

– Methylene chloride (CH2Cl2) used for fine chemical

manufacturing is carcinogenic

– Acetone (CH3COCH3) useful in lab – more toxic than

methanol

• Option:

– Replace with solvent which is non-volatile, non-toxic

& cheap

– Equal performance with additional benefits: safety

& cheapness

First step

How to predict the properties of the

potential molecules with out doing

experiments?

Property Prediction Methods

• 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.

• Property Prediction

– Physical properties of molecules depends on their topological structure

– Data keeps increasing- so are the models!!!

Property Prediction Methods

Group Contribution Methods

– Physical properties can be estimated from molecular

building blocks.

– 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.

UNIFAC UNIQUAC Functional-group Activity Coefficients

– Originally developed for predicting activity coefficients in

non-ideal systems.

– Functional groups in the molecules involved in liquid

mixtures are used to estimate activity coefficients.

– Equivalent to UNIQAC model with further breaking

down into the constituent groups.

Eg. UNIQUAC METHANOL+ WATER

UNIFAC ETHANOL: CH3 CH2 AND OH

UNIFAC – Two terms are involved in UNIFAC model

Combinatorial: Individual contributions from molecular groups. Accounts for the deviation from ideality because of the differences in molecular shapes.

Residual: Contribution from interactions between the groups. Correct for the change in interacting forces between molecules upon mixing.

– UNIFAC groups have extended applications in property prediction models such as group contribution methods.

rci lnlnln

Group contribution models

In the Group Contribution Method (GCM), the property of

a compound is estimated as a summation of the

contributions of molecular groups present in the molecular

structure. A correction term has been added to account

for interactions between the groups.

The property estimation model in GCM is

k

kk

i

ii CNCNXf )(

f(X) Function of property X

i Index for group

N Number of groups

C Group contribution

k Index for second order group (interaction)

β Binary variable to check the existence of second order group

Higher order groups

Important

If one building block is completely overlapped by another

one, choose the bigger one

Example

CH3CO-CH2-CH2-CH3

CH3CO is a building block in group contribution/UNIFAC

tables. So, do not break that into CH3 and CO.

Common properties with GC models

Property Property function Group contribution terms

Normal melting point, Tm 0exp mm TT jm

j

jim

i

i TMTN 21

Normal boiling point, Tb 0exp bb TT jb

j

jib

i

i TMTN 21

Critical temperature, Tc 0exp cc TT jc

j

jic

i

i TMTN 21

Viscosity, η ln(η) 21 j

j

ji

i

i MN

Standard enthalpy of formation, Hf 0ff HH jf

j

jif

i

i HMHN 21

Standard enthalpy of vaporization, Hv 0vv HH jv

j

jiv

i

i HMHN 21

Standard enthalpy of fusion, Hfus 0fusfus HH jfus

j

jifus

i

i HMHN 21

Acentric factor, w Cawb

/exp 21 j

j

ji

i

i wMwN

Liquid molar volume, Vm dVm 21 mj

j

jmi

i

i VMVN

Surface Tension, σ σ 21 j

j

ji

i

i MN

Example

Estimate the boiling point of CH3(CH2)3OH

Property model

Group contributions

Adjusted parameter, Tb0= 222.543 K

CH3 CH2 OH

0.8491 0.7141 2.5670

Calculated value: 382 K Measured value: 391 K

Target property

How to look for a good solvent without

doing many experimental works?

Problem

Solubility parameters

A useful tool in selection using thermodynamics

is the use of Hildebrand solubility parameters

This is based on the idea of chemical potentials. For an

ideal solution,

x is the activity coefficient, RT ln(x) is energy of mixing

But most solutions are non-ideal! In this case

w is a measure of how non-ideal the solution is.

solventsolutesolnin solute ln xRT

2

solutesolventsolutesolnin solute ln xxRT w


The Margules equation approximates w to

where Vsolute is the molar volume of the solute, and dsolvent

and dsolute are the Hildebrand solubility parameters.

Ideal solutions have dsolvent = dsolute and w = 0.

The more different dsolvent and dsolute are, the less miscible

they are – the worse the solvent.

2

solutesolventsolutesolnin solute ln xxRT w

2

solutesolventsolute ddw V


dsolvent is known for most solvents.

However, in most cases we don’t know what dsolute is!

A good way to look for similar properties is to substitute a

working solvent with a known d for one with a similar d.

2

solute

2

solutesolventsolutesolventsolutesolnin solute ln xVxRT dd


Example: An alternative solvent to Methanol

2

solute

2

solutesolventsolutesolventsolutesolnin solute ln xVxRT dd

Solvent Hildebrand solubility

parameter d

Methanol 29.61

Ethanol 26.13

Propanol 24.45

Ethylene glycol 33.7

Propylene glycol 29.52


The Hildebrand parameter d was later refined

by Charles Hansen by splitting it into three

parts:

dD for the dispersion

dP for the polar interactions

dH for the hydrogen bonding

These are known as the Hansen solubility parameters

2

H

2

P

2

D

2 dddd

Solvent Hildebrand

solubility

parameter

d

Hansen

dispersion

dD

Hansen

polar

dP

Hansen

hydrogen

dH

Cyclohexane (C6H12) 16.8 16.8 0 0.2

Hexachloroethane (C2Cl6) 22.5 22.0 4.7 0

Hydrazine (N2H4) 18.7 14.2 8.3 8.9

Benzene (C6H6) 18.5 18.4 0 2


Some more examples:

• Cyclohexane – non-polar, almost no hydrogen bonding

• Hexachloroethane – polar but no hydrogen

• Sometimes better to use Hansen parameters!

Hansen solubility parameters

Using the Hansen parameters is a good way of predicting

the effectiveness of solvents.

The distance of the solvent from the solubility sphere of the

polymer

with the radius of interaction of the polymer R0.

If the ratio is less than 1, then the polymer is probably

soluble in the polymer.

2p

H

s

H

2p

P

s

P

2p

D

s

Da 4 dddddd R

0

a

R

R

Estimation of Hansen parameters

by Van Krevelen method

• Hansen parameters can be estimated from molecular

groups by Van Krevelen’s method

• Fdi, Fji, and Ehi values for a selection of molecular

building blocks are available.

• Vm is obtained from group contribution model.

Comparison of solubility

• Estimate the Hansen solubility parameter for the solute.

• Estimate the Hansen solubility parameter for the

potential solvents.

• Estimate the radius of interaction for all solvents.

• The best solvent is the one with lowest radius of

interaction.


A very useful technique based on thermodynamics

for comparing and selecting solvents

Hildebrand parameters very quick and easy to use

Hansen parameters more sophisticated

Can use solubility parameters to make predictions

and pre-selection of solvents and processes

Must check the final results with experiments

Documents

Lecture-Selection Part 1