Optimization of polycondensation process of alkyd resin ...€¦ · alkyd polymers find use in most...

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( Received 27 November 2018; Accepted 12 December 2018; Date of Publication 13 December 2018 )

WSN 116 (2019) 62-90 EISSN 2392-2192

Optimization of polycondensation process of alkyd resin synthesis from modified Picralima nitida seed

oil suitable for surface coating of metal

J. O. Ezeugo

Department of Chemical Engineering, Chukwuemea Odumegwu Ojukwu University, Anambra State, Nigeria

E-mail address: [email protected]

Phone: +2348063873600

ABSTRACT

The potential of synthesizing an air drying alkyd resin through polycondensation of non-drying

Picralima nitida seed oil (PNSO) was investigated. The structural elucidation of the raw PNSO, de-

saturated PNSO and PNSO based alkyd resin were evaluated using FTIR. Design matrix and response

Surface methodology were used to model the process variables. Artificial neural network was further

used to examine the sensitivity analysis of the studied process variables. The results obtained showed

that modified PNSO has high drying rate in the presence of drying agent, exhibit excellent adhesion,

abrasion and chemical properties. Optimum responses of 82% conversion, viscosity of 269 cp and

molecular weight average of 4434 g/mol were predicted at a temperature of 260 ºC, time of 134 mins,

oil ratio of 0.286, catalyst concentration of 0.062 wt and a stirring rate of 595rpm. Correspondent

experimental results using the same optimal conditions gave optimum conversion of 83% of alkyd resin,

viscosity of 271cp and MWA of 4440 g/mol.

Keywords: Picralima nitida seed oil, Response surface methodology, alkyd resin, Artificial Neural

Network

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World Scientific News 116 (2019) 62-90

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1. INTRODUCTION

One of the emerging major themes in polymer science for the 21st century is the

production of sustainable green polymeric materials and chemicals from renewable resources

[1]. Seeds and fruits of plants are veritable sources of oil for domestic and industrial utility. The

lipid-based raw materials for paints are vegetable oils. Many vegetable oils and some animal

oils are ‘drying’ or ‘semi-drying’ and it is this property that accounts for the suitability of many

oils such as linseed, tung and some fish oils as the base of paints and other coatings. Vegetable

sources occupy an important position in the provision of individual raw materials for paint

production. This is because they are readily renewable resources and contain high levels of

unsaturated fatty acids; a well sought property for oil paint production. They are also

environmental-friendly, less expensive, easy to obtain using conventional extraction techniques

and produced easily in rural areas.

Although vegetable sources of raw materials are readily renewable, the utilization of

wholly inedible and ‘unuseful’ seeds as sources of industrial raw materials will help in

sustaining the high demand for industrial raw materials and reduce the environmental pollutions

usually caused by the indiscriminate dumping of such wastes [2].There are several potentially

useful topical plant materials that have been left unutilized due to inadequate knowledge of

their compositions. The seed of P. nitida epitomized such plant material. In view of the need to

find renewable sources of raw materials of quality for the paint industry, this work is a study of

the seed of Picralima nitida which is known to contain oils and is also wholly inedible [3].

According to [4], all seeds contain oils. With no competing food uses, attention is on P. nitida

seed. Picralima nitida (Akuama) is a well known plant in West Africa. The fruit is inedible.

The bark, leaves, stems and roots of the trees are used as local medicine for the treatment of

diseases [5,6]. Previous studies have shown that the seeds from these fruits contain oil which

have considerable nutritional value. This study is aimed at optimizing P. nitida seed oils for

polycondensation process of alkyd resin synthesis. It intends to maximize percentage yield of

alkyd resin oil based P. nitida via application of optimal independent variables and

subsequently reduce dependence on resin imports for oil paint production.

Firstly, Picralima nitida (P. nitida) is commonly known as Akuamma or pile plant. It

belongs to the tribe of the apocynaceae family. The plant is widely found in forest region of

west-central Africa, from Nigeria to Uganda. It even extends to Cameroon and Congo basin

(Iwu et al, 1993). P. nitida is an under storey tree which reaches up to 4-35 meters in height,

crown dense, trunk 5-60m diameter; cylindrical, the wood is pale-yellow, hard, elastic, fine-

grained and assuming a high polish. It bears white flowers (about 3 cm long) with ovoid fruits

which at maturity are yellowish in color. The leaves are broad (3-10 cm) and oblong (6-20 cm)

long with tough tiny lateral nerves of about 14 to 24 pairs [7]. P. nitida has widely varied

applications in Nigeria and indeed West Africa traditional medicine. The seedis used as

antipyretic, aphrodisiac, for the treatment of malaria [8]. The seed – decoction is given as an

enema while the crushed seed is taken orally for chest problems, pneumonia and for

gastrointestinal disorders [9]. The idea was to make alkyd with a high acid value which would

be neutralized by amines that could be soluble in water [10]. Alkyd and chemically modified

alkyd polymers find use in most types of liquid organic coatings for architectural, air-dry, and

baked industrial and maintenance coatings [11]. Alkyds are a special class of polyesters that

often have vegetable oil or fatty acids co-reacted into the polyester, and these compounds

provide the distinctive air-cure feature of many of these compounds [12].


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Figure 1. The general chemical structure of alkyd resin

The coatings made of long oil alkyd resins deliver properties such as ability to be air-

dryable and good interaction with polar substrates such as wood and steel [13]. In addition, the

coatings of conventional alkyd resin with any oil content or acid value are solvent based and

usually diluted in an organic solvent such as toluene, xylene and different oil cuts or a mixture

of these solvents [14].

Alkyd resins were very important paint in the past because of their strong strength, high

film hardness, and gloss retention [16]. They serve as the film-forming agent in some paints

and clear coatings [17]. Therefore alkyd resins are main product of poly-condensation reactions

between polycarboxylicacid and poly alcohol in present fatty acids or vegetable oils [18].This

kind of reaction had obtained by the following formula,

𝑃𝑜𝑙𝑦𝑐𝑎𝑟𝑏𝑜𝑥𝑦𝑙𝑖𝑐 𝑎𝑐𝑖𝑑𝑠 + 𝑃𝑜𝑙𝑦𝑎𝑙𝑐𝑜ℎ𝑜𝑙𝑠 + 𝐹𝑎𝑡𝑡𝑦 𝑎𝑐𝑖𝑑𝑠 𝐴𝑙𝑘𝑦𝑑 𝑟𝑒𝑠𝑖𝑛 + 𝐻2𝑂 … . . (1)

Poly-alcohols which are mainly used for condensational polymerization reactions of

alkyd resins are ethylene glycol, propylene glycol, diethylene glycol and pentaerythritol [19]

2. MATERIALS AND METHODS

2. 1. Sample collection and preparation

P. nitida fruit was plugged from its tree in nearby bush close to Ezeugos compound, Uke

in Idemili North Local Government Area, Nigeria. The pulps of the ripped fruits were consumed

and the seeds were cleaned by washing with distilled water and oven dried to constant weight

in a JP Selecta hot air at 100 ⁰C for 10 hours. The dried seeds were then ground and sieved with

a 450µm in order to increase the surface area prior to solvent extraction.


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2. 2. Design of experiments (DOE)

The system prediction through response surface methodology (RSM) study was

implemented on model-based calibration (MBC) v2.0 tool found in MATLAB 7.1 tool box.

The system analysis via MBC tool requires initial coding of the independent variables into the

solution algorithm. This involves using unique notations (or codes) such as; A, B, C…, x1, x2,

x3 ….etc., to represent the variables, and specifying the actual range of values of the variables

using a suitable DOE template. In the current study, five process variables including

temperature, time, oil ratio, catalyst concentration stirring rate coded A, B, C, D and E,

respectively, were considered as major operating factors affecting reaction progress and product

quality in a typical alkyd polycondensation process. A useful DOE template which minimizes

the required number of system iteration for full system assessment is the classical central

composite design (CCD). However, considering the number of independent variables in the

present study, fractional factorial design was implemented at the factorial levels of a standard CCD to further reduce the required number of iterations. This results in a useful experimental

design template called combined array (CA) which is also known to give reliable result with

minimized number of system iteration in studying multi-variate systems [20-23]. The proposed

CA for the current design problem requires performing sixteen distinct system iterations at the

factorial points, ten iterations at the axial points and four iterations at the center points, giving

a total of thirty different iterations of the system. This step was accomplished for the present

study applying the underlisted codes and data ranges:

𝐴: [−𝛼, 𝛼] → Temperature (˚C): [230, 270] 𝐵: [−𝛼, 𝛼] → Time (Mins.): [60, 180] 𝐶: [−𝛼, 𝛼] → Oil ratio: [0.1, 0.5] 𝐷: [−𝛼, 𝛼] → Catalyst Conc.: [0.02, 0.1] 𝐸: [−𝛼, 𝛼] → Stirring rate (rpm): [500, 700]

2. 3. Sensitivity Analysis and System Prediction via Artificial Neural Network

(ANN) Model

Table 1. Design template of alkyd resin from Avocado seed oil (PNSO)

Run Design

space

Independent variables Responses

A B C D E Y1 Y2 Y3

1 Factorial -1 -1 1 1 1 61 216 2438

2 Factorial 1 1 1 1 1 91 228 4890

3 Factorial 1 1 1 -1 -1 81 291 4995

4 Factorial -1 -1 1 -1 -1 38 162 693.9

5 Factorial 1 -1 -1 1 1 77 249 3620

6 Factorial 1 -1 1 -1 1 68 239 3088


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7 Factorial 1 -1 1 1 -1 70 273 4436

8 Factorial -1 1 -1 -1 -1 60 215 2533

9 Factorial 1 1 -1 1 -1 78 280 4292

10 Factorial 1 1 -1 -1 1 77 260 3790

11 Factorial -1 1 1 -1 1 76 259 3886

12 Factorial -1 -1 -1 -1 1 30 140 500

13 Factorial -1 -1 -1 1 -1 30 140 500

14 Factorial -1 1 -1 1 1 63 230 2993

15 Factorial 1 -1 -1 -1 -1 42 177 1270

16 Factorial -1 1 1 1 -1 79 275 4640

17 Axial -α 0 0 0 0 51 180 1200

18 Axial Α 0 0 0 0 70 240 3336

19 Axial 0 -α 0 0 0 42 166 839

20 Axial 0 Α 0 0 0 82 273 4445

21 Axial 0 0 -α 0 0 55 199 1947

22 Axial 0 0 α 0 0 65 228 2921

23 Axial 0 0 0 -α 0 60 214 2111

24 Axial 0 0 0 Α 0 81 269 4294

25 Axial 0 0 0 0 -α 75 250 3830

26 Axial 0 0 0 0 Α 78 258 3850

27 Center 0 0 0 0 0 74 255 3980

28 Center 0 0 0 0 0 74 256 3980

29 Center 0 0 0 0 0 75 256 3986

30 Center 0 0 0 0 0 75 255 3988

where: A = Temperature; B = Time; C = Oil Ratio; D = Catalyst Conc.; E = Stirring rate

aliased to highest order interaction effect of the other process variables; Y1 = Conversion;

Y2 = Viscosity; Y3 = Molecular weight average (MWA).


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Sensitivity analysis was first conducted to determine the effectiveness of the parameters

by the constructed neural network model. In the analysis, the effects of different interactions of

the variables on the recorded conversion of acid functional group were studied and the results

were used as basis for evaluating the sensitivity of the neural network model. The performances

of the five interaction groups of; (one, two, three, four and five) variables were examined using

the LM (Levenberg-Marquardt) algorithm. Artificial neural network (ANN) model from

Toolbox V7.12 of MATLAB mathematical software is also constructed for prediction of the

system response. The effort is intended to compare the performance of the two popular

functional approximation methods in yielding adequate prediction of the studied alkyd

polycondensation process. The implementation followed the basic procedure for ANN model

(Ghazanfari et al, 2004; Haeri et al, 2000). The performance of ANN and RSM models were

statistically measured and compared by mean relative percent deviation (MRPD)

Picralima nitida seed oil extraction and synthesis of PNSO Oxy-polymerizable alkyd

resin were carried out following the published report of [24-26]

2. 4. Performance Evaluation of Modified Alkyd Resin PNSO

The properties and performance characteristics of the crude PNSO alkyd resin (Alkyd-

A), modified PNSO alkyd resin with PA only (Alkyd-B), modified PNSO alkyd resin with PA

and MA 1:1 (Alkyd-C) and modified PNSO alkyd resin with PA&MA 3:1 (Alkyd-D) of the

same class (50% oil) were to be evaluated in terms of their physico-chemico-mechanical

properties such as acid value, viscosity, colour, refractive index, chemical and abrasion

resistance, drying schedule, adhesion, impact, and scratch hardness test. There is no common

standard to compare alkyds resins as each alkyd resin has its own properties.

Alkyd resin that has acid number of less than 15 is suitable for application of paint,

according to literature (RMRDG, 2012). A higher acid value translates to reduced drying rate,

since acid group usually delay drying rate [27]. In addition, industrial products (such as paints)

formulated with alkyd resin with high acid value usually cause rusting or corrosion of substrate

surfaces.

3. RESULTS AND DISCUSSION

3. 1. Results

A percentage oil yield of 3.63% was obtained after extraction from the P. nitida seed

cake. (Uzoh and Onukwuli, 2018) reported a value of 50% for the avocado seed oil. The

difference in oil content could be attributed to differing climatic conditions, stage of

ripening/development of the fruit at the time of harvest and growth conditions.

3. 2. Statistical screening analysis of alkyd resin produced from the crude and modified

PNSO

To study the effects of the identified system parameters which include; temperature,

reaction time, oil ratio, catalyst concentration and stirring rate; A, B, C, D and E on the

molecular properties (conversion, viscosity and MW of PNSO modified alkyd resin for the

proposed method, a surrogate model of the system was derived from multi-regression analysis.

The global matrix equation (1) was fitted to the data provided by the combined array given in

Table 3 and the resulting models were adjusted in terms of the significant system variables to


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obtain the predictive model equation (2-3). The coefficients of determination R2 values of 0.89,

0.97 and 0.95 obtained for Y1, Y2 and Y3 based esterification processes respectively showed

that more than 85% of the overall system variability can be explained by the empirical models

of equations (2-3) which are specific cases of the general predictive equation derived for

individual investigations from the multivariate regression analyses implemented on Design

expert.

𝑅1 = 75.20 + 7.70𝐴 + 10.95𝐵 + 5.32𝐶 + 4.89𝐷 + 3.04𝐸 − 3.12𝐴𝐵 − 2.28𝐴𝐶 + 1.16𝐴 +1.14𝐴𝐸 − 0.87𝐵𝐶 − 2.83𝐵𝐷 − 2.89𝐵𝐸 − 3.41𝐴2 − 2.92𝐵2 − 3.46𝐶2 − 0.92𝐷2 + 0.49𝐸2

(2)

𝑅2 = 253.45 + 19.86𝐴 + 27.24𝐵 + 12.87𝐶 + 10.89𝐷 + 1.07𝐸 − 12.79𝐴𝐵 − 7.58𝐴𝐶 −5.88𝐴𝐸 − 7.26𝐵𝐶 − 10.87𝐵𝐷 − 10.78𝐵𝐸 − 7.87𝐶𝐸 − 6.08𝐷𝐸 − 9.85𝐴2 − 7.46𝐵2 − 9𝐶2

(3)

𝑅3 = 3867.66 + 686.21𝐴 + 945.25𝐵 + 479.87𝐶 + 475.74𝐷 + 78.55𝐸 − 272.94𝐴𝐵− 65.94𝐴𝐸 − 239.44𝐵𝐷 − 227.94𝐵𝐸 − 173.18𝐶𝐸 − 106.19𝐷𝐸− 341.99𝐴2 − 248.43𝐵2 − 300.49𝐶2 − 108.28𝐷2 + 51.01𝐸2

(4)

where: Y1, Y2 and Y3 are the predicted values of the dependent variables investigated. The

coefficients A, B, C, D and E are main linear effects of the independent process variables. AB,

AC, AE, BC, BD, BE, CE and DE represent the linear interaction effects between the various

independent variables. A2, B2 and C2 are the quadratic effects of the respective process

variables.

The estimated coefficient terms revealed that quadratic interactions of system variables

showed negative effects while the main linear and first order interactive effects are positive.

Higher order interaction terms are not significant in the model. The “Predicted R-Squared” of

0.87, 0.97 and 0.95 are in reasonable agreement with the respective “Adjusted R-Squared” of

0.89, 0.94 and 0.96; and the Model F-values of 16.53, 28.71 and 27.65 further indicated that

the models are significant.

There is only a 0.01% probability that the “Model F- values’’ this large could occur due

to noise. P-values of less than 0.05 indicated that the model terms are significant. “Adequacy

Precision” measures the signal to noise ratio (SN). A ratio greater than 4 is desirable. SN values

13.88, 17.46 and 17.272 indicated adequate signal to noise ratios. This model can be used to

navigate the design space.

The ANOVA results derived from the predictive models showed that the main linear

effects due to individual control factors coded A, B, C and D respectively are significant

variables indicated with the observed P-values < 0.05 in the numerical analysis.

This is equally true with the linear interaction effects between temperature and time

(AB),time and catalyst concentration (BD), time and stirring rate (BE) and (for Y2 analysis)

AB, AC, AE, BC, BD, BE, CE and DE are valid while AB, BD, BE and CE is valid for Y3. The

quadratic effects of temperature, time and molar ratio denoted by A2, B2 and C2 respectively

are significant for the molecular properties (Y1, Y2 and Y3) shown on Table 2.


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Table 2. ANOVA response surface reduced order quadratic model (RSROQM) in terms

of only the significant process parameters.

SOURCE

F-Value P-Value

Y1 Y2 Y3 Y1 Y2 Y3

Model 19.51 28.87 21.58


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Conversion

(Y1)

Std. Dev. 6.32 R-Squared 0.8969

Mean 66.06 Adj R-Squared 0.8426

C.V.% 9.57 Pred R-Squared 0.7424

Press 1897.26 Adeq R-Squared 13.880

Viscosity (Y2)




Press 12243.47 Adeq R-Squared 17.460

Molecular

weight average

(Y3)




Press 1.01x107 Adeq R-Squared 17.272

DF: Model = 9, Residual = 20, Total = 29.

3. 3. Interaction effects of process variables

The combined effects of adjusting the process variables within the design space were

monitored using 3-D surface plots. Every significant interaction effects on the system response

between two independent variables were analyzed in phases. The results are presented in Figs.

(2- 4).

The overall system behavior is characterized by various degrees of curvature which

reflect the levels of uncertainties associated with every interaction of the process variables. The

observed trends suggested that by proper adjustment of the system variables within the sampled

space, a valid optimal was attained. The results conformed largely to what is already known for

alkyd polycondensation processes [28-32]

Nevertheless, one noticeable unusual result may be the apparent linear (one-directional)

response observed in the system response with respect to D and E axes. This does not reflect a

typical behavior of batch alkyd polycondensation processes. Particle congestion which occurs

at high catalyst concentration.

The consistent linear behavior implies that no definite optimal solution could be obtained

for (D and E).


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(a)

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(e), B and E, (f), B and D on conversion of acid functional group



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Figure 3. Interaction effects between: (a) A and B, (b) A and C, (c) A and E, (d) B and C,

(e) B and D, (f) B and E, (g) C and D, (h) C and E, (i) D and E on viscosity.

Fig. 2. (a) and (b) reveal some basic routes to optimum progress of reaction for the studied

alkyd polymerization process which requires tuning the key control variables including reaction

time, temperature or oil ratio to some values well above their center points while catalyst

concentration and stirring rate are fixed at their mean values. This framework stands as a viable

solution especially where oil as a major raw material is relatively available. Alternatively,

optimum conversion of acid functional group could be achieved by setting reaction time and

temperature at their mean values while either catalyst concentration, Fig. 2. (c) or steering rate,

Fig. 2.(d) is reviewed upwards.

By and large, the latter solution may be less economically viable since it possibly leads

to increased material/operational cost [33]. The most economically viable solution in terms of

achieving optimal progress of reaction may be traced to the possibility of reaching optimum

conversion of acid functional group either with low catalyst concentration demonstrated in

Figure 2 (e) or with low stirring rate presented in Figure 2 (f) by mere allowing a relatively high

reaction period. Hence reaction time seems to be a very important process variable whose

overall effects on the system responses must be investigated in details to enhance selection of

the most desirable optimal solution.

In a typical alkyd monitoring scheme, reactor performance is usually measured in terms

of the commercial values of the resulting molecular properties of the product including cold-

viscosity and/or molecular weight average. Thus these molecular properties are usually

monitored (preferably on-line) such that the final product lies substantially within specifications



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34-36]. As part of the present modeling and optimization scheme, data obtained for cold-

viscosity and MWA at various conditions were analyzed and the effects of every significant

interaction of the variables on system response were evaluated systematically. The results of

this study are presented in Figure 2 for cold-viscosity and Figure 3 for MWA. In coating

industry alkyd resin whose cold-viscosity and MWA values lie in the range (243-600 CP) and

(4000-10000 g/mol.) satisfy most commercial needs (Fujita and Kishimoto, 1951). The results

presented in Fig. 2 highlight various ways to achieve this target.

Figures 2 (a-g) represent a typical behavior in which increasing the value of the system

variables initially leads to a corresponding increase in the value of the observed response before

an optimum value is attained, after which the trend is reversed. In Fig. 2 (c, f, h, and.i) high

viscosity response is recorded at low stirring speed by reviewing upwards the temperature, the

reaction time, the oil ratio and the catalyst concentration, respectively. For some economic

reasons, it is usually more elegant to pursue a good result with moderate oil ratio and reduced

catalyst concentration.

Thus, an important observation made in this regard is the possibility of obtaining high

viscous resin with low catalyst concentration and mean oil ratio presented in Fig. 2.(c) and (e)

by mere allowing relatively increased reaction time. This point again highlights the reaction

time as an important factor that must be monitored closely to control both reaction progress and

product quality without unnecessarily increasing the cost of operating the batch. The results

obtained for MWA properties of the system at various operation conditions are presented in

Fig. 3. However, Fig. 3(a) highlights the futility of operating the reactor for short reaction time

using low reactor temperature.

(a)


Design points above predicted value4995

500

X1 = A: AX2 = B: B


90

100

110

120

130

140

150

240

245

250

255

260

0

1000

2000

3000

4000

5000

R3

A: AB: B

3625.86


-80-



500

X1 = A: AX2 = E: E


550

570

590

610

630

650

240

245

250

255

260

0

1000

2000

3000

4000

5000

R3

A: AE: E

3625.86

+

(b)

(c)



500

X1 = B: BX2 = D: D


0.04

0.05

0.06

0.07

0.08

90

100

110

120

130

140

150

0

1000

2000

3000

4000

5000

R3

B: BD: D

3625.86


-81-



500

X1 = B: BX2 = E: E


550

570

590

610

630

650

90

100

110

120

130

140

150

0

1000

2000

3000

4000

5000

R3

B: BE: E

3625.86

(d)

(e)

Figure 3. Interaction between: (a) A and B, (b) A and D, (c) B and D, (d) B and E,

(e) C and E, on molecular weight average



500

X1 = C: CX2 = E: E

Actual FactorsA: A = 260B: B = 150D: D = 0.04

550

570

590

610

630

650

0.2

0.25

0.3

0.35

0.4

0

1000

2000

3000

4000

5000

R3

C: CE: E

3625.86


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With mean reaction time and moderate temperature a good result in MWA properties

could be achieved; either with increased catalyst concentration as described in Fig. 3(b) or with

high oil ratio as shown in Fig. 3 (e). However, both options may lead to increased material cost

making them less economically viable. Some good alternative routes to desirable response are

demonstrated in Fig. 3(a) and Fig. 3(d) where good MWA properties are rather obtained with

low catalyst concentration and low stirring rate, respectively, by mere increasing the reaction

time.

3. 4. Sensitivity Analysis and Reaction Prediction via ANN

The performances of the five interaction groups of; (one, two, three, four and five)

variables were examined using the LM (Levenberg-Marquardt) algorithm. The results are

presented in Table 3. The results show that while B (reaction time) is the most effective

parameter in the group of one variable, the interaction of A and B which shows relatively low

MSE value (0.299) has the greatest effect in the group of two variables. Similarly, the

interaction of five variables produced the most significant effect on the studied response with

the overall minimum MSE of 0.079. This suggests that the most accurate prediction of the

system via ANN model could be achieved by using the five independent variables in

formulation of the model.

3. 5. Reaction prediction using artificial neural network

The performance of the constructed artificial neural network model in yielding the

conversion of acid functional group (𝑌1), viscosity (𝑌2) and molecular weight average (𝑌3)were evaluated considering the highest order interaction of the system variables (ABCD E)

respectively.

The results are presented in Fig. (4-6) for 𝑌1, 𝑌2 and 𝑌3 respectively. The ANN results and that of equivalent RSM are compared with experimental data in Table 3 and 4.From the

comparative study, looking at the performances of the two functional approximation models it

seems that greater overall prediction accuracy is guaranteed by the RSM model. Further

analysis of the predictive efficiency of models using MRPD as shown in Table 5 clearly showed

that RSM is a better model for alkyd resin poly-condensation process. This may be because of

the limited number of experimental data used in the analysis. ANN generally performs better

when very large number of data points is used for training the network (Katarina et al, 2013).

Attempts to compare this observation overtly with earlier work from literature did not provide

much desire result. This is because there is scanty or no study on alkyd resin poly-condensation

process modeling using ANN and RSM.

However, the result contained in this essay are apparently different from literature;

methanol sis of sunflower oil using ANN and RSM in which there are 162 data points (Katarina

et al, 2013); RSM and ANN modeling of electro coagulation of copper from simulated

wastewater (Manpreet et al, 2011); optimization of recombinant of Oryza sativa non-symbiotic

hemoglobin using ANN and RSM. Optimization of biosorption process using ANN and RSM,

alkaline palm oil transesterification by RSM and ANN. From the foregoing, the prediction

accuracy was more prominent for ANN possibly because, it was built up from the experimental

results of RSM.


-83-

Table 3. Performance evaluation of the interaction of the process variables for the LM

algorithm with 5 neurons in the hidden layer for sensitivity analysis

No Interaction MSE 𝑅2 Gradient BLF

Group of one Variables

1 A 0.736 0.24 1.38 × 10−09 𝑌1 = 0.217𝑇 + 51.9 2 B 0.546 0.42 4.30 × 10−08 𝑌1 = 0.413𝑇 + 38.6 3 C 0.766 0.14 7.80 × 1004 𝑌1 = 0.15𝑇 + 56.9 4 D 0.836 0.09 3.40 × 1004 𝑌1 = 0.092𝑇 + 60.3 5 E 0.857 0.07 1.56 × 10−2 𝑌1 = 0.069𝑇 + 61.8

Group of two variables

6 A B 0.299 0.67 2.77 × 1000 𝑌1 = 0.682𝑇 + 20.5 7 A C 0.436 0.33 7.47 × 10−09 𝑌1 = 0.377𝑇 + 39.9 8 A D 0.758 0.28 1.30 × 10−10 𝑌1 = 0.179𝑇 + 55.6 9 A E 0.625 0.27 8.60 × 10−08 𝑌1 = 0.211𝑇 + 52.9 10 B C 0.386 0.54 1.59 × 10−09 𝑌1 = 0.475𝑇 + 37 11 B D 0.444 0.51 1.84 × 10−03 𝑌1 = 0.51𝑇 + 32.9 12 B E 0.439 0.48 9.23 × 10−12 𝑌1 = 0.597𝑇 + 26.9 13 C D 0.581 0.16 4.98 × 10−09 𝑌1 = 0.18𝑇 + 54.5 14 C E 0.629 0.13 2.35 × 10−11 𝑌1 = 0.167𝑇 + 58.2 15 D E 0.790 0.11 3.15 × 10−11 𝑌1 = 0.075𝑇 + 62.2

Group of three variables

16 ABC 0.165 0.82 5.91 × 10−10 𝑌1 = 0.81𝑇 + 11.7 17 ABD 0.217 0.76 8.86 × 10−09 𝑌1 = 0.746𝑇 + 17 18 ABE 0.236 0.73 1.35 × 10−10 𝑌1 = 0.632𝑇 + 24 19 ACD 0.410 0.46 1.05 × 1001 𝑌1 = 0.49𝑇 + 32.9 20 ACE 0.584 0.37 5.42 × 10−12 𝑌1 = 0.336𝑇 + 42.9 21 ADE 0.613 0.30 2. 11 × 10−13 𝑌1 = 0.311𝑇 + 46.3 22 BCD 0.330 0.64 8. 28 × 10−10 𝑌1 = 0.601𝑇 + 26.4 23 BCE 0.385 0.58 9. 01 × 10−09 𝑌1 = 0.586𝑇 + 26.6 24 BDE 0.419 0.57 1. 06 × 10−11 𝑌1 = 0.541𝑇 + 30.4 25 CDE 0.642 0.20 1.42 × 10−12 𝑌1 = 0.204𝑇 + 52

Group of four variables

26 ABCD 0.095 0.86 1. 35 × 10−11 𝑌1 = 0.823𝑇 + 11.7 27 ABCE 0.186 0.76 3.23 × 10−12 𝑌1 = 0.663𝑇 + 23.1 28 ABDE 0.152 0.77 8. 42 × 10−11 𝑌1 = 0.752𝑇 + 15.4 29 ACDE 0.475 0.29 1. 69 × 10−10 𝑌1 = 0.173𝑇 + 63.1 30 BCDE 0.166 0.69 1.52 × 10−12 𝑌1 = 0.717𝑇 + 19.1

Group of five variables

31 ABCDE 0.079 0.88 1. 16 × 10−11 𝑌1 = 0.853𝑇 + 10.1


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0 5 10 15 200

1

2

3

4

Epoch

MS

E

TrainingValidationTest

20 40 60 80 10030

40

50

60

70

80

90

100

Experimentally measured conversion (%)

AN

N p

redic

ted c

onvers

ion (

%)

Data PointsBest Linear FitA = T

(a)

(b)

Figure 4. Predicted versus actual values of the conversion (b) performance of

the constructed ANN model in predicting the actual value of the conversion


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Figure 5. Predicted (A) versus actual (T) values of (a) the viscosity

Figure 6. Predicted (A) versus actual (T) values of (b) the molecular weight average.

100 150 200 250 300100

150

200

250

300

T

A

Best Linear Fit: A = (0.911) T + (25.2)

R = 0.962

Data Points

Best Linear Fit

A = T

0 2000 4000 60000

1000

2000

3000

4000

5000

T

A

Best Linear Fit: A = (0.938) T + (188)

R = 0.96

Data Points

Best Linear Fit

A = T


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Table 4. RSM and ANN results compared with the experimental data.

S/N

Independent variables

A B C D E

𝑌1(%) Actual RSM

ANN

𝑌2(𝑐𝑃) Actual RSM

ANN

𝑌3(𝑔/𝑚𝑜𝑙) Actual RSM

ANN

1a

2c

3a

4b

5a

6a

7a

8c

9a

10b

11a

12a

13a

14c

15a

16b

17a

18a

19a

20c

21a

22b

23a

24a

25a

26c

27a

28b

29a

30

240 90 0.4 0.08 650

260 150 0.4 0.08 650

260 150 0.4 0.04 550

240 90 0.4 0.04 550

260 90 0.2 0.08 650

260 90 0.4 0.04 650

260 90 0.4 0.08 550

240 150 0.2 0.04 550

260 150 0.2 0.08 550

260 150 0.2 0.04 650

240 150 0.4 0.04 650

240 90 0.2 0.04 650

240 90 0.2 0.08 550

240 150 0.2 0.08 650

260 90 0.2 0.04 550

240 150 0.4 0.08 550

270 120 0.3 0.06 600

230 120 0.3 0.06 600

250 180 0.3 0.06 600

250 60 0.3 0.06 600

250 120 0.5 0.06 600

250 120 0.1 0.06 600

250 120 0.3 0.1 600

250 120 0.3 0.02 600

250 120 0.3 0.06 700

250 120 0.3 0.06 500

250 120 0.3 0.06 600

250 120 0.3 0.06 600

250 120 0.3 0.06 600

250 120 0.3 0.06 600

62

91

81

38

77

68

70

60

78

77

76

30

30

63

42

79

71

51

83

43

66

55

81

61

74

75

74

75

75

75

64

86

80

40

77

66

69

63

79

75

77

34

37

64

46

80

76

45

85

41

71

50

84

64

81

68

74

74

74

74

61

79

78

37

72

71

75

65

78

78

76

34

40

77

48

78

78

48

79

36

77

63

77

59

76

69

73

73

73

73

227

229

291

163

250

240

274

216

280

261

260

141

141

231

178

276

241

181

274

167

229

210

271

215

259

251

256

256

256

256

216

225

280

161

253

231

270

219

281

257

258

145

145

236

174

274

253

174

278

169

243

191

275

231

255

251

253

253

253

253

217

231

267

176

255

242

267

244

279

270

257

145

156

267

185

276

267

186

278

165

232

205

268

214

248

242

264

264

264

264

2440

4890

4990

693

3621

3089

4436

2533

4292

3790

3886

501

505

2993

1270

4640

3336

1210

4445

839

2921

1947

4294

2111

3850

3830

3980

3980

3986

3988

2328

4759

4825

735

3746

3016

4089

2815

4111

3564

3463

392

746

3216

1229

4467

3803

1058

4695

840

2956

1637

4681

2777

4140

3825

3729

3729

3729

3729

2712

4146

4913

0795

3571

3062

3507

3041

4199

4167

3818

0340

0715

4066

1441

4281

3864

1302

4552

313

3117

1717

4191

2112

3818

4311

3869

3869

3869

3869

a -Training data set; b -Validating data set; c -Testing data set of the ANN mode

Table 5. Mean Relative Percent Deviation (MRPD) for Artificial Neural Net Work (ANN)

MRPD (%)

RSM ANN

Conversion % 3.13 ±4.18

Viscosity CP 2.91 ±3.11

Molecular weight average g/mol 7.01 ±9.24


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3. 6. Optimization process

Significant economic benefit may be derived by optimizing the molecular properties

which relates to the end-use properties of PNSO modified alkyd resin during synthesis. These

molecular properties are required to lie within some desired optimal in the parameter design

space. As anticipation of a typical manufacturing process, the characterization-control-

optimization algorithm base on a 25-1 FFA adequately guaranteed the details process analysis

and optimization of the PNSO modified alkyd resin molecular properties. Of particular interest

in the end-use properties is the drying time of the alkyd which has been highlighted as a critical

issue associated with drying oil. Other advantages from the chosen optimization process are

through reduced reaction time and temperature which in effect minimized the overall cost of

the synthesis simultaneously.

The processing of high quality resin from PNSO, with short drying time reduced material

cost, improved color (appearance), viscosity, molecular weight and high commercial

importance necessarily require optimization of the poly-condensation process with particular in

focus on the molecular properties as the output responses. This was achieved in the present

study through formulation of a global optimization criteria based on RSM upon which the

necessary trade-off of system variables were implemented. Such systematic compromise was

particularly important in the process described above since the system responses show peak

value at non-unique locations within the variable design space. The entire exercise aided by

numerical optimization tool function of the design expert statistical software (trial version 9)

used for the experimental analysis. Equations (1-3) were solved for the best solution(s) such

that the response Y, X and Z were maximized. No unique solution was attainable.

The various solutions obtained were assessed based on their contribution to maximum

responses and other necessary economic consideration. From 20 best optimal solution obtained

as shown in Table 6 (a) credible optimum solution of 82.184% fractional conversion, viscosity

of 269.444 CP and MW (av) of 4434.213 predicted at temperature of 260 ºC, time of 134.043

min, oil ratio of 0.286, catalyst ratio of 0.062 and stirring rate of 595.385rpm at 1.00 desirability

were selected based on economic consideration and necessary trade-off. A repeated

correspondence investigation performed following the predicted optimal conditions recorded

83% fractional conversion, viscosity of 271CP and MW (av) of 4440 g/mol for the studied PNSO

modified alkyd resin. This figure represents 0.65% average maximum prediction error.

Table 6. Optimum values of process parameters for maximum responses

Process Parameters Optimum Values

Temperature (ºC) A 260.000

Time (mins) B 134.043

Oil Ratio C 0.286

Catalyst Conc. (wt.)D 0.062

Stirring Rate (rpm) 595.385

Conversion (%)Y1 82.184

Viscosity (CP) Y2 269.444

Molecular Weight Average (g/mol) Y3

4434.213


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4. CONCLUSION

An auto-oxidative alkyd resin was synthesized from the chemically modified Picralima

nitida seed oil. The optimum yield of the P. nitida seed oil was determined through the

application of central composite design matrix Via response surface methodology. The optimal

conditions of the process parameters which gave the optimum responses were established

Acknowledgements

The authors sincerely acknowledged the staff and management of National Center for Energy Research and

Development, University of Nigeria Nsukka (UNN). Isa Yakubu of National Research Center for Chemical

Technology, Zaria, Nigeria. Our gratitude also goes to the staff and managements of Kappas Biotechnology,

Ibadan, Nigeria. However, we wish to state that no fund was received in the course of executing this research

work.

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