IA data based, boiling point estimation using molecular weight and carbon chain with simple linear regression.

1. Which model is a better predictor, using molecular weight or number of carbon chain
2 or more independent variable (predictor)
Is boiling point associated with molecular weight and carbon chains.
Molecular weight or number of carbon chains – independent variables (predictor)
Boiling point of alkane – dependent variable (outcome)
Boiling point = x1 (molecular weight) + intercept
Boiling point = x1 (Number carbon chains) + intercept
Using Regression and Anova for analysis
Independent variable
Dependent variable
Is molecular weight or number carbon chains a good predictor
Independent variable
Dependent
variable
Data for b/p from CRC Handbook. Click here data
IA secondary data based – Simple linear regression analysis for boiling point estimation
B/point = x1 (molecular weight) + intercept
B/point = x1 (number of carbons) + intercept or
Research Question
Use 25 carbon chains for regression model
Use regression eqn to estimate the b/p for 25 carbon chain
Find the % error using expt values with predicted values.
Using molecular weight 3rd order as estimator for b/p.
Using carbon chain 3rd order as estimator for b/p.
Molecular
formula
Number
carbon
Molecular
weight
Boiling point
CH4 1 16 -161.5
C2H6 2 30 -89.42
C3H8 3 44.1 -42
C4H10 4 58.12 -1
C5H12 5 72.15 36
C6H14 6 86.18 69
C7H16 7 100.21 98.4
C8H18 8 114.23 114.23
C9H20 9 128.2 128.2
C10H22 10 142.28 142.28

2. • member differ by CH2 gp
• same functional group
• similar chemical properties
• chemical formula CnH2n+2
• end with ane
Class Functional gp Suffix Example Formula
Alkane C - C - ane ethane CnH2n+2
Homologous Series
Class Functional Suffix Example Formula
Alkene Alkenyl - ene ethene CnH2n
H H
‫׀‬ ‫׀‬
H - C – C – H
‫׀‬ ‫׀‬
H H
• member differ by CH2 gp
• same functional group
• similar chemical properties
• chemical formula CnH2n
• end with ene
H
‫׀‬
H - C – H
‫׀‬
H
H H H
‫׀‬ ‫׀‬ ‫׀‬
H - C – C – C – H
‫׀‬ ‫׀‬ ‫׀‬
H H H
H H H H
‫׀‬ ‫׀‬ ‫׀‬ ‫׀‬
H - C – C – C – C – H
‫׀‬ ‫׀‬ ‫׀‬ ‫׀‬
H H H H
Number
carbon
Word IUPAC
name
Structure formula Molecular
formula
1 Meth Methane CH4 CH4
2 Eth Ethane CH3CH3 C2H6
3 Prop Propane CH3CH2CH3 C3H8
4 But Butane CH3(CH2)2CH3 C4H10
5 Pent Pentane CH3(CH2)3CH3 C5H12
6 Hex Hexane CH3(CH2)4CH3 C6H14
7 Hept Heptane CH3(CH2)5CH3 C7H16
8 Oct Octane CH3(CH2)6CH3 C8H18
9 Non Nonane CH3(CH2)7CH3 C9H20
10 Dec Decane CH3(CH2)8CH3 C10H22
methane ethane propane butane
Saturated hydrocarbon (C – C single bond)
Number
carbon
IUPAC
name
Structure formula Molecular
formula
2 Ethene CH2CH2 C2H4
3 Propene CH2=CHCH3 C3H6
4 Butene CH2=CHCH2CH3 C4H8
5 Pentene CH2=CH(CH2)2CH3 C5H10
6 Hexene CH2=CH(CH2)3CH3 C6H12
7 Heptene CH2=CH(CH2)4CH3 C7H14
8 Octene CH2=CH(CH2)5CH3 C8H16
9 Nonene CH2=CH(CH2)6CH3 C9H18
10 Decene CH2=CH(CH2)7CH3 C10H20
H H
‫׀‬ ‫׀‬
C = C
‫׀‬ ‫׀‬
H H
H H H
‫׀‬ ‫׀‬ ‫׀‬
C = C – C - H
‫׀‬ ‫׀‬
H H
H H H H
‫׀‬ ‫׀‬ ‫׀‬ ‫׀‬
C = C – C – C - H
‫׀‬ ‫׀‬ ‫׀‬
H H H
Unsaturated hydrocarbon (C = C double bond)
H H H H H
‫׀‬ ‫׀‬ ‫׀‬ ‫׀‬ ‫׀‬
C = C – C – C – C - H
‫׀‬ ‫׀‬ ‫׀‬ ‫׀‬
H H H H
ethene propene butene pentene

3. Class Functional group/name Examples
alkene C = C Alkenyl ethene
alkyne C ≡ C Alkynyl ethyne
alcohol OH Hydroxyl ethanol
ether C – O - C Ether methoxymethane
ketone O
‖
C – C - C
Carbonyl propanone
aldehyde CHO Aldehyde ethanal
Carboxylic
acid
COOH Carboxyl ethanoic acid
ester O
‖
C – O -R
Ester ethyl ethanoate
amide O
‖
C – NH2
Amide propanamide
amine NH2 Amine ethanamine
nitrile C ≡ N Nitrile propanenitrile
Class Functional gp Suffix Example Formula
Alkane C - C - ane ethane CnH2n+2
Homologous Series
carbon IUPAC
name
Structure formula Molecular
formula
Boiling
point
1 Methane CH4 CH4 Gas
2 Ethane CH3CH3 C2H6 Gas
3 Propane CH3CH2CH3 C3H8 Gas
4 Butane CH3(CH2)2CH3 C4H10 Gas
5 Pentane CH3(CH2)3CH3 C5H12 Liquid
6 Hexane CH3(CH2)4CH3 C6H14 Liquid
Physical properties
• Increase RMM / molecular size
•RMM increase ↑ - Van Der Waals forces stronger ↑
↓
Melting /boiling point increases ↑
(Increasing polarisability ↑)
London dispersion forces/temporary dipole ↑
1 2 3 4 5 6 7 8 9 10
number carbons – RMM ↑
150
100
50
0
-50
-100
-150
-200
b/p
increase ↑
boiling point
room temp
gas
liquid
Homologous Series
number
Carbons / RMM ↑
1 2 3 4 5 6 7 8 9 10
boiling point
boiling point increase with increase carbon atoms
alcohol
alkane
alkene
alkyne
London dispersion force
(temporary dipole)
H2 bonding
carboxylic acid > alkane/alkene/alkyne
alcohol
carboxylic acid

4. Number
carbon
Molecular
weight b/p
Predicted
poly fit
3rd order
1
2
3
4
5 72.15 36
6 86.18 69
7 100.21 98.4
8 114.23 125.6
9 128.2 151
10 142.28 174.1
11 156.31 196
12 170.33 216.2
13 184.37 234
14 198.39 253.6
15 212.42 270.6
16 226.41 286.9
17 240.4 302
18 254.5 317
19 268.5 330
20 282.5 343
21 296.6 363
22 310.6 368
23 324.6 379
24 338.6 391
25 352.7 403
IA secondary data based – Simple linear regression analysis for boiling point estimation
Research Question
Use 25 carbon chains for regression model
Use regression eqn to estimate the b/p for 25 carbon chain
Find the % error using expt values with predicted values.
Using molecular weight 3rd order as estimator for b/p.
Using carbon chain 3rd order as estimator for b/p.
Using molecular weight 3rd order as estimator for b/p
Number
carbon
Molecular
weight b/p
Predicted
poly fit
3rd order
1
2
3
4
5 72.15 36
6 86.18 69
7 100.21 98.4
8 114.23 125.6
9 128.2 151
10 142.28 174.1
11 156.31 196
12 170.33 216.2
13 184.37 234
14 198.39 253.6
15 212.42 270.6
16 226.41 286.9
17 240.4 302
18 254.5 317
19 268.5 330
20 282.5 343
21 296.6 363
22 310.6 368
23 324.6 379
24 338.6 391
25 352.7 403
Using carbon chain 3rd order as estimator for b/p

5. Predicted b/p for carbon 10 – MW of 142.3
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(142.3)3 – 0.0064(142.3)2 + 3.0855(142.3) – 153.76 = 173
Research Question
Which model, molecular weight model, 3rd order, better estimator for b/p.
Which model, carbon chain model, 3rd order, better estimator for b/p.
y = 6E-06x3 - 0.0064x2 + 3.0855x - 153.76
R² = 0.9998
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400
b/p
molecular weight
molecular weight vs b/p
y = 0.0171x3 - 1.2705x2 + 43.433x - 155.23
R² = 0.9992
0
50
100
150
200
250
300
350
400
450
0 5 10 15 20 25 30
b/p
number of carbon chains
number of carbon chains vs b/p Predicted b/p for carbon 10
3rd order fit, y = 0.0171x3 – 1.2705x2 + 43.433x – 155.23
b/p=0.0171(10)3 – 1.2705(10)2 + 43.433(10) – 155.23 =169
Predicted b/p for carbon 26 – MW of 366.7
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(366.7)3 – 0.0064(366.7)2 + 3.0855(366.7) – 153.76 = 413
Predicted b/p for carbon 30 – MW of 422.8
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(422.8)3 – 0.0064(422.8)2 + 3.0855(422.8) – 153.76 = 460
Predicted b/p for carbon 26
3rd order fit, y = 0.0171x3 – 1.2705x2 + 43.433x – 155.23
b/p=0.0171(26)3 – 1.2705(26)2 + 43.433(26) – 155.23 = 416
Predicted b/p for carbon 30
3rd order fit, y = 0.0171x3 – 1.2705x2 + 43.433x – 155.23
b/p=0.0171(30)3 – 1.2705(30)2 + 43.433(30) – 155.23 = 466
Using molecular weight 3rd order as estimator for b/p
Using carbon chain 3rd order as estimator for b/p

6. Using molecular weight 3rd order as estimator for b/p Using carbon chain 3rd order as estimator for b/p
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
(% error)
1
2
3
4
5 72.15 36
6 86.18 69
7 100.21 98.4
8 114.23 125.6
9 128.2 151
10 142.28 174.1 173 (0.5%)
11 156.31 196
12 170.33 216.2
13 184.37 234
14 198.39 253.6
15 212.42 270.6
16 226.41 286.9
17 240.4 302
18 254.5 317
19 268.5 330
20 282.5 343
21 296.6 363
22 310.6 368
23 324.6 379
24 338.6 391
25 352.7 403
26 366.7 412 413 (0.2%)
27 378.8 422
28 392.7 431
29 406.8 440
30 422.8 449.7 460 (2%)
Number
carbon
Boiling
point
predicted
poly fit
3rd order
(% error)
1
2
3
4
5 36
6 69
7 98.4
8 125.6
9 151
10 174.1 169 (2.9%)
11 196
12 216.2
13 234
14 253.6
15 270.6
16 286.9
17 302
18 317
19 330
20 343
21 363
22 368
23 379
24 391
25 403
26 412 416 (1%)
27 422
28 431
29 440
30 449.7 466 (3.6%)
% error =
(𝑬𝒙𝒑𝒕 𝒗𝒂𝒍𝒖𝒆 −𝑷𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅 𝒗𝒂𝒍𝒖𝒆)
𝑬𝒙𝒑𝒕 𝒗𝒂𝒍𝒖𝒆
x 100%
% error =
(𝟏𝟕𝟒.𝟏 −𝟏𝟕𝟑)
𝟏𝟕𝟒.𝟏
x 100% = 0.5%
% error =
(𝑬𝒙𝒑𝒕 𝒗𝒂𝒍𝒖𝒆 −𝑷𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅 𝒗𝒂𝒍𝒖𝒆)
𝑬𝒙𝒑𝒕 𝒗𝒂𝒍𝒖𝒆
x 100%
% error =
(𝟏𝟕𝟒.𝟏 −𝟏𝟔𝟗)
𝟏𝟕𝟒.𝟏
x 100% = 2.9%

7. Research Question
Use regression eqn to estimate the b/p for 10, 26 and 30 based on molecular weight.
Which model, molecular weight model, better estimator for b/p.
Which model, carbon chain model, better estimator for b/p.
Result showed molecular weight model is a better fit
% error, smaller compared to carbon chain model
% error increases as molecular weight increases
3rd order fit – % error changes from 0.5% to 0.2% to 2% as
carbon chain changes from 10 to 26 to 30
y = 6E-06x3 - 0.0064x2 + 3.0855x - 153.76
R² = 0.9998
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400
b/p
molecular weight
molecular weight vs b/p
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
(% error)
10 142.28 174.1 173 (0.5%)
26 366.7 412 413 (0.2%)
30 422.8 449.7 460 (2%)
y = 0.0171x3 - 1.2705x2 + 43.433x - 155.23
R² = 0.9992
0
50
100
150
200
250
300
350
400
450
0 5 10 15 20 25 30
b/p
number of carbon chains
number of carbon chains vs b/p
Number of
carbon b/p
predicted
poly fit
3rd order
(% error)
10 174.1 169 (2.9%)
26 412 416 (1%)
30 449.7 466 (3.6%)
Result showed carbon chain model is a weaker fit
% error, bigger compared to molecular weight model.
% error increases as carbon chains increases
3rd order fit – % error changes from 2.9% to 1% to 3.6% as
carbon chain changes from 10 to 26 to 30

8. Predicted b/p for carbon 35 – MW of 493
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(493)3 – 0.0064(493)2 + 3.0855(493) – 153.76 = 529
Polynomial 3rd order molecular weight model
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
5 72.15 36
6 86.18 69
7 100.21 98.4
8 114.23 125.6
9 128.2 151
10 142.28 174.1 173
26 366.7 412 413
30 422.8 449.7 460
35 493 490 529
y = 6E-06x3 - 0.0064x2 + 3.0855x - 153.76
R² = 0.9998
0
200
400
600
0 50 100 150 200 250 300 350 400
b/p
molecular weight
molecular weight vs b/p
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
(% error)
10 142.28 174.1 173 (0.5%)
26 366.7 412 413 (0.2%)
30 422.8 449.7 460 (2%)
35 493 490 529 (8%)
Polynomial 3rd order carbon chain model
Number of
carbon
Boiling
point
Predicted
poly fit
3rd order
5 36
6 69
7 98.4
8 125.6
9 151
10 174.1 169
26 412 416
30 449.7 466
35 490 541
Predicted b/p for carbon 35
3rd order fit, y = 0.0171x3 – 1.2705x2 + 43.433x – 155.23
b/p = 0.0171(35)3 – 1.2705(35)2 + 43.433(35) – 155.23 = 541
y = 0.0171x3 - 1.2705x2 + 43.433x - 155.23
R² = 0.9992
0
200
400
600
0 5 10 15 20 25 30
b/p
number of carbon chains
number of carbon chains vs b/p
Number
carbon b/p
predicted
poly fit
3rd order
(% error)
10 174.1 169 (2.9%)
26 412 416 (1%)
30 449.7 466 (3.6%)
35 493 541 (10%)

9. Predicted b/p for carbon 35 – MW of 493
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(493)3 – 0.0064(493)2 + 3.0855(493) – 153.76 = 529
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
5 72.15 36
6 86.18 69
7 100.21 98.4
8 114.23 125.6
9 128.2 151
10 142.28 174.1 173
26 366.7 412 413
30 422.8 449.7 460
35 493 490 529
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
(% error)
10 142.28 174.1 173 (0.5%)
26 366.7 412 413 (0.2%)
30 422.8 449.7 460 (2%)
35 493 490 529 (8%)
% error increases as molecular weight increases
3rd order fit – % error changes from 0.5% to 0.2% to 2% to 8%
as carbon chain changes from 10 to 26 to 30 to 35.
Polynomial 3rd order molecular weight model
Number
carbon b/p
predicted
poly fit
3rd order
5 36
6 69
7 98.4
8 125.6
9 151
10 174.1 169
26 412 416
30 449.7 466
35 490 541
Polynomial 3rd order carbon chain model
Predicted b/p for carbon 35
3rd order fit, y = 0.0171x3 – 1.2705x2 + 43.433x – 155.23
b/p = 0.0171(35)3 – 1.2705(35)2 + 43.433(35) – 155.23 = 541
Number
carbon b/p
predicted
poly fit
3rd order
(% error)
10 174.1 169 (2.9%)
26 412 416 (1%)
30 449.7 466 (3.6%)
35 493 541 (10%)
% error increases as carbon chains increases
3rd order fit – % error changes from 2.9% to 1% to 3.6% to 10%
as carbon chain changes from 10 to 26 to 30 to 35.