INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
Empirical Modelling and Optimization of Zinc Chloride Activated  
Ngbo Clay Catalysts Using Response Surface Methodology  
Veronica Nnenna Nwobasi1, Francisca Ogechukwu Oshim2*and Chinedu J. Nwali1  
1Department of Chemical Engineering, Ebonyi State University, Nigeria  
2School of Engineering, University of Greater Manchester, United Kingdom  
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000141  
Received: 14 September 2025; Accepted: 22 September 2025; Published: 22 November 2025  
Abstract: In this study, Box-Behnken’s Response Surface Methodology (RSM) was applied to study the esterification reaction  
effectiveness of zinc chloride activated Ngbo clay catalyst. The XRF result showed that both raw and activated clay contain  
contaminations of oxides and other impurities; however, the clay mineral compositions are not significantly affected by zinc  
chloride treatments. Additionally, it indicates a high content of silicon and aluminium oxides compared to other oxides.XRD pattern  
results showed several characteristic peaks due to the mineral compositions present. The analysis of the peaks showed sharp peaks  
with low intensity at 2θ = 11.300, which corresponds to the main peak used in the identification of kaolinite clay, as reported in the  
literature.The esterification was monitored based on the process conditions of temperature, time duration, amount of reactant,  
catalyst weight and particle size. The Box -Behnken’s Response Surface Methodology indicates that the zinc chloride clay-catalysed  
esterification reactions proceed through dual mechanisms: an Acid-complex and an Alcohol-complex mechanism, with the Alcohol  
mechanism dominating. The esterification efficiencies of acetic acid and ethanol by zinc chloride activated Ngbo clay catalyst,  
optimized using RSM models indicated the estimated esterification percentage of ˃95%. The predicted and experimental values  
obtained under the same conditions showed a difference of less than 5%, indicating that the Box-Behnken design approach is an  
efficient, effective, and reliable method for the esterification of acetic acid with ethanol. The produced catalyst was optimized using  
A-One way ANOVA modelling, which indicated a correlation coefficient of the regression of 0.9551, which implies that 95.51%  
of the total variation in the esterification reaction was attributed to the experimental variables. The result obtained indicated that  
the process could be applied in the esterification of acetic acid to avoid the drawbacks of corrosion, loss of catalyst and  
environmental problems.  
Keywords: Optimization, Characterization, Esterification, Zinc Chloride Activated Clay Catalyst, Thermal activated catalyst,  
Response Surface Methodology, Box-Behnken design  
I. Introduction  
Clay is one of the abundant raw materials in Nigeria. It is readily available in Nigeria in large deposits, yet its potential has not been  
fully explored. However, there is recent interest in exploring the potentials of clays, such as in the bleaching of palm oil [1, 2], in  
the adsorption of dyes [3 – 5], among others. In a quest to develop green processes, clay is mostly used in the synthesis of catalysts,  
although the use of Nigerian clays from Ngbo, Ohaukwu, Ebonyi State, for producing clay catalysts is limited in the literature. Still,  
the kinetics of clay-catalysedesterificationreaction is abundant in literature, but with little or no data on the mechanistic and  
empirical modelling on the use of Ngbo clay in this regard.  
Esterification reactions have long been carried out in homogeneous phase in the presence of acid catalysts such as sulphuric acid,  
hydrochloric acid and p-toluene sulfonic acid (p-TSOH); which have drawbacks of corrosion, loss of catalyst and environmental  
problems [6, 7]. Therefore, researchers have been focused on developing eco-friendly heterogeneous catalysts for the synthesis of  
fatty acid esters. The most popular solid acid catalysts used to produce esters were ion-exchange organic resins, such as Amberlyst-  
15 [8, 9], Zeolites [10 – 11], [12] and Silica-supported heteropoly acid [13] and [14]. Nevertheless, they have shown limitations in  
applicability for catalysing esterification reaction due to low thermal stability (Amberlyst-15 <140 °C), mass transfer resistance  
(Zeolites) [15], [16], or loss of active acid sites in the presence of a polar medium (HPA/silica) [14].  
A cost-benefit and environmental impact analysis of zinc chloride compared to alternative activation methods (thermal, acid, or  
alkaline treatment) would increase industrial relevance. Additionally, incorporating kinetic modelling could substantiate  
mechanistic claims more rigorously. Bench marking catalyst performance against established heterogeneous catalysts under  
identical conditions would also enhance the comparative value, supporting practical adoption in green chemical processes.  
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques that uses quantitative data. Central  
composite design (CCD), Box-Behnkendesign (BBD), and Doehlert design are among the principal response surface methodologies  
used in experimental design. This method is suitable for fitting a quadratic surface, and it helps to optimize the effective parameters  
with a minimum number of experiments, and also to analyze the interaction between the parameters [9]. The objective is to optimize  
a response (output variable) which is influenced by several independent variables (input variables). The application of RSM to  
design optimization aims to reduce the cost of numerous expensive experiments, save time, and alleviate stress [17–20].  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
This work investigated the use of local clay from Ngbo in Ohaukwu Local Government Area of Ebonyi State, Nigeria for the  
production of zinc chloride activated catalyst and optimizes the effectiveness of the clay catalyst for the esterification of acetic acid  
with ethanol using Response Surface Methodology.  
II. Materials and Methods  
Source of Raw Materials  
The clay sample was obtained from Ngbo in Ohaukwu L.G.A. of Ebonyi State (N 06o30’ 32.8’’), (E 007o58’13.7’’). Chemicals  
used, such as Zinc Chloride (aq), NaOH, Acetic Acid, ethanol, distilled water, etc, were all of standard grade. They were purchased  
in a chemical shop at Ogbete main market Enugu, Enugu State.  
Physico-Chemical Characterization of Ngbo Clay  
The Ngbo clay sample was subjected to physical analysis to determineits physical properties. The analysis carried out includes:  
Bulk density, Moisture content, pH and Loss on Ignition (LOI).  
Characterisation of the raw clay and Zinc chloride activated sample  
The Ngbo clay sample was characterised using XRF and XRD.  
Zinc Chloride Activation  
The activation method used in this work is as reported by [21]. A 100g of pulverized and screened clay was mixed into a slurry  
with 50ml of diluted water, 30ml of 1M ZnCl2 (aq) was added and stirred vigorously and placed in an oven where it was maintained  
at a temperature of 100oC. The sample was washed thereafter and left to sediment. Complete removal of all residual zinc chloride  
was achieved by repeating the washing and decanting process until a pH of 6 was obtained. The final slurry was filtered and dried  
at 100 °C. The dried, activated and washed clay was then pulverized, screened and stored in desiccators before use.  
Optimization of Process Conditions on the Catalyst Quality Produced Using Esterification Process  
Sample Preparation/Procedure  
The raw clay sample was crushed and sieved at 100 microns, 200 microns, and 300 microns. Thereafter, the clay sample was  
activated using zinc chloride. The activated clay sample was used in an esterification reaction to assess the effectiveness. The  
Predetermined weight of the clay sample was weighed; one mole of Ethanol and acetic acid was each pipetted into the clay sample  
to ensure that the ethanol did not block the active sites of the catalyst. The container was tightly closed, the contents were shaken  
vigorously and immersed in a water bath shaker maintained at the conditions of the experimental design in Table 1. The summary  
of the reaction equation is:  
CH3COOH + C2H5OH  
CH3COOC2H5 + H2O  
(1)  
On titration, the equation becomes:  
CH3COOH + NaOH  
CH3COONa + H2O  
(2)  
Table 1: The natural and coded values of the independent variables used  
VARIABLES  
NATURAL VALUES  
CODED VALUES  
Low level  
Mid-point  
High level Low level  
Mid  
High level  
Point  
Temperature (oC), A  
50  
70  
90  
-1  
-1  
-1  
-1  
-1  
0
0
0
0
0
+1  
+1  
+1  
+1  
+1  
Process duration (minutes), B  
Excess reactant (ml), C  
30  
195  
3.75  
0.38  
200  
360  
5
2.5  
0.25  
100  
Catalyst weight (grammes), D  
Particle size (microns), E  
0.5  
300  
The clay-catalysed esterification was modelled using Box-Behnken Response Surface Methodology.  
For five factors inputs of x1, x2, x3, x4 and x5, the equation of the quadratic response is given as;  
Y = bo + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b12X1X2 + b13X1X3 + b14X1X4 + b15X1X5 +  
b23X2X3 + b24X2X4 + b25X2X5 +  
2
2
2
2
b34X3X4 + b35X3X5 + b45X4X5 + b11X1 + b22X2 + b33X3 + b44X42 + b55X5 .  
(3)  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
III. Response Surface Methodology  
The response surface technique, applying a Box-Behnken design matrix, was used to study the interaction and effects among the  
factors and their level of contributions and significance in the clay-catalysed esterification. This method determines the optimal  
working conditions in a shorter timeframe, and provides detailed information on the conditions of the processes. This was achieved  
through a designed experimental design applying Box-Behnken Response Surface Methodology design of 46 steps of experiment  
consisting of five factors and three levels (Table 2). The numerical optimization method of RSM was used in the optimization.  
Table 2: Box-Benhken’s Response Surface Methodology Design of Experiment  
Std  
Run  
Factor A  
(OC)  
Factor B  
(min)  
Factor C  
(ml)  
3.75  
2.5  
Factor D(g)  
Factor E  
(mic)  
Response (ml) Yield (%)  
37  
22  
23  
29  
26  
1
1
70  
70  
70  
70  
90  
50  
70  
70  
70  
90  
70  
50  
70  
90  
90  
70  
70  
50  
70  
90  
70  
70  
90  
70  
70  
70  
50  
50  
70  
50  
70  
30  
0.25  
0.38  
0.38  
0.38  
0.25  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.5  
200  
200  
200  
100  
200  
200  
300  
200  
100  
100  
200  
300  
200  
200  
200  
300  
300  
200  
200  
200  
200  
300  
300  
100  
100  
200  
100  
200  
300  
200  
100  
2
360  
30  
3
5
4
195  
195  
30  
2.5  
5
3.75  
3.75  
5
6
32  
46  
10  
34  
21  
35  
8
7
195  
195  
360  
195  
30  
8
3.75  
3.75  
3.75  
2.5  
9
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
20  
21  
22  
23  
24  
25  
26  
27  
28  
29  
30  
31  
195  
195  
360  
30  
3.75  
5
4
3.75  
3.75  
3.75  
2.5  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.25  
0.5  
2
11  
31  
3
30  
195  
360  
360  
195  
195  
360  
195  
195  
195  
195  
195  
195  
195  
195  
195  
3.75  
5
24  
16  
44  
12  
36  
17  
18  
45  
33  
25  
20  
27  
30  
5
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
5
0.38  
0.38  
0.25  
0.5  
0.5  
0.38  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
42  
41  
39  
6
32  
33  
34  
35  
36  
37  
38  
39  
40  
41  
42  
43  
44  
45  
46  
70  
70  
70  
70  
70  
70  
70  
70  
70  
90  
90  
70  
50  
70  
50  
195  
195  
30  
3.75  
3.75  
3.75  
5
0.38  
0.38  
0.5  
200  
200  
200  
200  
200  
200  
300  
200  
200  
200  
200  
200  
200  
100  
200  
195  
195  
360  
195  
360  
195  
195  
195  
195  
195  
30  
0.25  
0.38  
0.25  
0.25  
0.5  
43  
38  
19  
40  
7
3.75  
3.75  
3.75  
3.75  
2.5  
0.5  
28  
14  
5
3.75  
2.5  
0.5  
0.38  
0.25  
0.38  
0.38  
0.38  
2.5  
13  
9
2.5  
3.75  
5
15  
195  
IV. Results and Discussions  
Physical properties of the raw clay  
The physical properties of raw Ngbo clay are presented in Table 3. The result showed that the clay has a moisture content of 3.3 %  
and a bulk density of 1.25 g/ml, which are in agreement with the previous research of [22 – 24] that reported the moisture content  
of kaolinite clay is between 3.0 – 4.0% and the bulk density is 1.2 – 1.4 g/ml.  
Table 3: Results of Bulk density, Moisture content, pH, and LOI  
Clay type  
Bulk density (g/ml)  
% moisture content  
pH  
LOI (%)  
Ngbo clay  
1.25  
3.33  
7.5  
10.52  
Characterization of Raw Clay and Zinc Chloride Activated Clay  
The chemical properties of the raw Ngbo clay were analysed using XRF and XRD.  
The result of the XRF composition analysis of rawNgbo clay and Zinc Chlorideactivated Ngboclays (ZAC) is presented in Table  
4. The result showed that raw and activated clays have contaminations of oxides and other impurities, but the clay  
mineralcompositions are not meaningfully affected by zinc chloridetreatments even under strong conditionsand below 500 ℃ as  
reported in literature by [25, 26] and[27]. This indicates that improving the properties of the clay through chemical methods below  
500 ℃ is challenging due to its low reactivity. This result of the XRF on the Ngbo raw clay and zinc chlorideactivated Ngbo clays,  
as shown in Table 4, also indicates high content of silicon and aluminium oxides compared to other oxides.  
Table 4: Results of XRF analysis of raw Ngbo Clay and Zinc Chloride activated Ngbo clay  
Chemical  
constituent  
Raw clay (  
Zinc Chloride activated  
Wt. %)  
(ZAC), (  
Wt. %)  
SiO2  
TiO2  
62.70  
1.52  
19.70  
2.06  
_
65.743  
1.386  
23.457  
5.855  
0.000  
Al2O3  
Fe2O3  
P2O3  
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CaO  
MgO  
Na2O  
K2O  
Mn2O3  
V2O5  
Cr2O3  
CuO  
BaO  
L.O.I  
SO3  
0.789  
0.026  
0.20  
0.85  
_
0.232  
0.522  
0.256  
1.149  
0.135  
_
0.071  
0.035  
0.044  
0.19  
11.82  
_
0.011  
_
_
_
0.181  
0.131  
0.936  
0.007  
Cl  
_
ZnO  
SrO  
_
_
The results of XRD pattern analysis of raw Ngbo clay are presented in Figure 1. The XRD pattern results showed several  
characteristic peaks due to the mineral compositions present. The peak obtained at a position corresponding to 2Ɵ = 22.64° indicated  
the presence of large quantities of quartz. Minor impurities, such as illite, muscovite,haloysite, quartz, hydrated mica, non-  
cryistalline hydroxide iron and halloysiteare present. The presence of these minor impurities and quartz content in Ngbo clay needs  
to be reduced to a minimum before its usage for industrial purposes, especially in catalysts development in line with the research  
of [28 and 29]. The XRD analysis corroborates the results obtained with the XRF analysis.  
Figure 1: Results of XRD analysis of Ngbo raw clay  
The results of XRD pattern analysis of Ngbozinc chloride activated catalyst, ZAC is presented in figure 2. The XRD pattern results  
showed several characteristic peaks due to the mineral compositions present. The analysis of the peaks showed sharp peaks with  
low intensity at 2θ = 11.300, which is the main peak used in the identification of kaolinte clay as reported in literature by [30].  
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Figure 2: Results of XRD analysis of Zinc Chloride Activated Clay  
Esterification Process Results  
Esterification technique was used to obtain the responses and yield of Zinc Chloride Activated Catalyst (ZAC) as shown in Table  
5.  
Table 5: Showing Responses and Yield of ZAC  
Std  
Run  
Factor 1A  
(OC)  
Factor 1B  
(min)  
Factor 1C  
(ml)  
3.75  
2.5  
Factor  
1D(g)  
Factor 1E  
(mic)  
Response  
(ml)  
Yield  
(%)  
37  
22  
23  
29  
26  
1
1
2
70  
70  
70  
70  
90  
50  
70  
70  
70  
90  
70  
50  
70  
90  
90  
30  
360  
30  
0.25  
0.38  
0.38  
0.38  
0.25  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.5  
200  
200  
200  
100  
200  
200  
300  
200  
100  
100  
200  
300  
200  
200  
200  
31.00  
18.50  
40.00  
20.00  
26.50  
30.90  
36.40  
30.00  
28.90  
28.50  
21.20  
31.80  
39.00  
26.40  
30.60  
32.61  
59.78  
13.04  
56.90  
42.39  
32.85  
20.35  
34.78  
37.72  
38.58  
53.91  
30.42  
15.22  
42.61  
33.48  
3
5
4
195  
195  
30  
2.5  
5
3.75  
3.75  
5
6
32  
46  
10  
34  
21  
35  
8
7
195  
195  
360  
195  
30  
8
3.75  
3.75  
3.75  
2.5  
9
10  
11  
12  
13  
14  
15  
195  
195  
360  
30  
3.75  
5
4
3.75  
3.75  
0.38  
0.38  
2
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11  
31  
3
16  
17  
18  
19  
20  
21  
22  
23  
24  
25  
26  
27  
28  
29  
30  
31  
32  
33  
34  
35  
36  
37  
38  
39  
40  
41  
42  
43  
44  
45  
46  
70  
70  
50  
70  
90  
70  
70  
90  
70  
70  
70  
50  
50  
70  
50  
70  
70  
70  
70  
70  
70  
70  
70  
70  
70  
90  
90  
70  
50  
70  
50  
30  
3.75  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.38  
0.25  
0.5  
300  
300  
200  
200  
200  
200  
300  
300  
100  
100  
200  
100  
200  
300  
200  
100  
200  
200  
200  
200  
200  
200  
300  
200  
200  
200  
200  
200  
200  
100  
200  
30.50  
20.40  
30.60  
35.40  
36.00  
30.40  
27.50  
27.50  
30.30  
30.80  
30.40  
31.50  
32.10  
30.50  
33.00  
38.50  
30.40  
29.00  
30.50  
38.50  
30.30  
29.00  
30.50  
28.80  
20.50  
28.00  
18.90  
19.50  
21.00  
31.50  
38.70  
33.26  
55.36  
33.48  
23.04  
21.74  
33.91  
39.82  
39.82  
34.70  
33.62  
33.91  
32.11  
30.22  
33.26  
28.26  
17.03  
33.91  
36.96  
33.70  
16.30  
34.13  
36.96  
33.26  
37.39  
55.43  
39.13  
58.91  
57.61  
54.35  
32.11  
15.87  
195  
360  
360  
195  
195  
360  
195  
195  
195  
195  
195  
195  
195  
195  
195  
195  
195  
30  
2.5  
3.75  
5
24  
16  
44  
12  
36  
17  
18  
45  
33  
25  
20  
27  
30  
42  
41  
39  
6
5
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
3.75  
5
0.38  
0.38  
0.25  
0.5  
0.5  
0.38  
0.38  
0.38  
0.5  
3.75  
3.75  
3.75  
5
195  
195  
360  
195  
360  
195  
195  
195  
195  
195  
30  
0.25  
0.38  
0.25  
0.25  
0.5  
43  
38  
19  
40  
7
3.75  
3.75  
3.75  
3.75  
2.5  
0.5  
28  
14  
5
3.75  
2.5  
0.5  
0.38  
0.25  
0.38  
0.38  
0.38  
2.5  
13  
9
2.5  
3.75  
5
15  
195  
Result of ANOVA for Zinc Chloride Activated Clay (ZAC)  
The result of ANOVA for Zinc Chloride Activated Clay (ZAC) is shown in table 6. The ANOVA result showed that RSM model  
is significant of the experimental results as indicated from the F – value of 124.44 calculated and very low probability value of P  
<0.0001. The lack of fit F – value of 8.71 showed that it was significant and there is 23.07% chance that a Lack of Fit F – value  
this large could occur due to noise. The significant terms of the model was determined by F- value and P- values. The values of  
“Prob> F” less than 0.0500 indicate the model terms are significant while values greater than 0.100 indicate that the model terms  
are not significant. ANOVA involves subdividing the total variation of a set of data onto component parts. The F – value is defined  
as the ratio of the mean square of regression (MRR) to the error (MRe). The smaller the magnitude of the F – value, the more  
significant is the corresponding coefficient [31]. The regression model demonstrates that the model is highly significant as evident  
from the calculated F-value (207.52) and a very low probability value (P =0.0001). The lack of fit F-value of 2.50 implies that it  
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was not significant relative to the pure error and there is a 15.67% chance that a “Lack of Fit F-value this large could occur due to  
noise. If P – value of lack of fit is less than 0.05, there is statistically significant lack of fit at 95% confidence level [32].  
The result in Table 6 also indicate that the significant model terms A, B, C, AB andC2implies that only linear effects of temperature,  
process duration, excess reactants, and interactive effects of temperature and process duration, and quadratic effects of excess  
reactants were significant. The model accuracy was confirmed by the correlation coefficient of the regression model which is  
0.9551. The correlation coefficient showed that 95.51% of the total variation in the final concentration was attributed to the  
experimental variables considered in this research work. The high value of the R2 and the “Pred R-Squared” of 0.8236 is in good  
agreement with the “Adj R – Squared” of 0.9192 as reported in literature by Mohd and Rasyidah, 2010; [31].  
Final equation in terms of coded factors gives:  
Yield = + 34.60 + 3.69A + 2.86B – 19.35C - 0.50D + 0.17E + 2.13AB + 0.33AC – 0.32 AD + 0.73AE + 1.03BC – 0.17BD +  
0.24BE  
+
0.27CD  
+
1.21CE  
+
0.27DE  
+
0.62A2  
+
0.75B2  
+
2.34C2  
–
0.56D2  
+
0.13E2.  
(4)  
The coefficient with one factor represent the effect of the particular factor, while the coefficients with two factors and those with  
second order terms represent the interaction between two factors and quadratic effect respectively (Mohd and Rasyidah 2010).  
Final model equation after eliminating the insignificant terms in terms of coded variables:  
Yield = + 34.60 + 3.69A + 2.86B – 19.35C + 2.13AB + 2.34C2  
(5)  
Table 6: ANOVA Table for Zinc Chloride Activated Clay (ZAC)  
Source  
Sum of Squares  
6443.58  
df  
20  
1
Mean Square  
322.18  
218.30  
131.33  
5993.08  
4.04  
F - Value  
124.44  
84.32  
50.72  
2314.73  
1.56  
P – Value Prob> F  
<0.0001 significant  
<0.0001  
<0.0001  
<0.0001  
0.2232  
Model  
A - Temperature  
B – Process duration  
C – Excess reactant  
218.30  
131.33  
1
5993.08  
1
D – Effect of Catalyst 4.04  
1
E – Particle size  
0.48  
18.06  
0.43  
0.42  
2.15  
4.26  
0.11  
0.23  
0.30  
5.90  
0.29  
3.38  
4.92  
47.82  
2.71  
0.15  
64.73  
57.48  
1
0.48  
0.19  
0.6695  
AB  
1
18.06  
0.43  
6.98  
0.0140  
AC  
1
0.17  
0.6874  
AD  
AE  
1
0.42  
0.16  
0.6897  
1
2.15  
0.83  
0.3713  
BC  
1
4.26  
1.65  
0.2111  
BD  
1
0.11  
0.042  
0.087  
0.12  
0.8392  
BE  
1
0.23  
0.7703  
CD  
1
0.30  
0.7353  
CE  
1
5.90  
2.28  
0.1435  
DE  
1
0.29  
0.11  
0.7400  
A2  
1
3.38  
1.31  
0.2639  
B2  
1
4.92  
1.90  
0.1803  
C2  
1
47.82  
2.71  
18.47  
1.05  
0.0002  
D2  
1
0.3159  
E2  
1
0.15  
0.058  
0.8121  
Residual  
Lack of fit  
25  
20  
2.59  
2.87  
1.98  
0.2307 not  
significant  
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Pure Error  
Cor Total  
7.25  
5
1.45  
6508.31  
45  
Residual Plots for ZAC  
The regression model developed was further assessed using residual plots. Residual is the difference between the experimental  
value and value predicted by the model. Some of the residual plots used were: plot of residual vs. predicted values which tests the  
assumption of constant variance of the experimental data, plot of residuals vs. run which checks for lurking variables that may have  
influenced the response during the experiment, normal plot of residuals which indicates whether the residuals follow a normal  
distribution, and plot of predicted vs. Actual response values which helps to detect a value, group of values that the model does not  
easily predict.  
The residual plots for ZAC are shown in Figure 3 – 4. The trends of the residual plots indicate that the model can be considered as  
a good fit and that the regression equations follow the experimental results with a good accuracy. The plots indicate values that are  
not easily predicted by the model. The plot of residuals against run checks for lurking variables that may have influenced the  
response during the experiment. The normal plot of residuals indicates whether the residuals follow a normal distribution, and the  
plot of predicted against actual response values helps to detect a value, group of values that are not easily predicted by the model.  
Design-Expert® Software  
Yield  
Normal Plot of Residuals  
Color points byvalue of  
Yield:  
59.78  
99  
95  
90  
13.04  
80  
70  
50  
30  
20  
10  
5
1
-2.23  
-0.97  
0.29  
1.56  
2.82  
Internally Studentized Residuals  
Figure 3: Normal plot of residual for ZAC  
Design-Expert® Software  
Yield  
Predicted vs. Actual  
61.00  
Color points byvalue of  
Yield:  
59.78  
13.04  
49.00  
37.00  
3
25.00  
13.00  
13.04  
24.85  
36.66  
48.47  
60.28  
Actual  
Figure 4: Plot of predicted verse actual for ZAC  
Contour Plots of ZAC  
The contour plots of ZAC were depicted in Figures 5 to 14. The circular nature of the contour plots indicates that the interactive  
effects between the variables are not significant, and the optimum values of the test process variables cannot be easily  
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determined[33,31]. The non-circular nature of the contour plots reveals that there is an interaction between the process variables  
studied, and the optimum value of the process variables can be easily obtained.  
Design-Expert® Software  
Yield  
360.00  
277.50  
195.00  
112.50  
30.00  
Yield  
Design Points  
59.78  
42.4386  
40.2202  
13.04  
38.0018  
35.7833  
X1 = A: Temperature  
X2 = B: Process duration  
6
Actual Factors  
33.5649  
C: Excess Reactants = 3.75  
D: Effect of Catalyst = 0.38  
E: Particle size = 200.00  
50.00  
60.00  
70.00  
80.00  
90.00  
A: Temperature  
Figure 5: The contour plots for process duration against temperature and yield of ZAC  
Design-Expert® Software  
Yield  
5.00  
4.38  
3.75  
3.13  
2.50  
Yield  
Design Points  
59.78  
21.8708  
13.04  
29.5533  
X1 = A: Temperature  
X2 = C: Excess Reactants  
6
37.2358  
Actual Factors  
B: Process duration = 195.00  
D: Effect of Catalyst = 0.38  
E: Particle size = 200.00  
44.9183  
52.6008  
50.00  
60.00  
70.00  
80.00  
90.00  
A: Temperature  
Figure 6: The contour plots for excess reactants against temperature and yield of ZAC  
Design-Expert® Software  
Yield  
0.50  
0.44  
0.38  
0.31  
0.25  
Yield  
Design Points  
59.78  
13.04  
X1 = A: Temperature  
X2 = D: Effect of Catalyst  
33.6036  
35.0085 36.4134  
32.1987  
37.8184  
6
Actual Factors  
B: Process duration = 195.00  
C: Excess Reactants = 3.75  
E: Particle size = 200.00  
50.00  
60.00  
70.00  
80.00  
90.00  
A: Temperature  
Figure 7: The contour plots for effect of catalyst against temperature and yield of ZAC  
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Design-Expert® Software  
Yield  
300.00  
250.00  
200.00  
150.00  
100.00  
Yield  
Design Points  
59.78  
13.04  
38.4779  
X1 = A: Temperature  
X2 = E: Particle size  
34.0517  
6
35.5271 37.0025  
Actual Factors  
32.5763  
B: Process duration = 195.00  
C: Excess Reactants = 3.75  
D: Effect of Catalyst = 0.38  
50.00  
60.00  
70.00  
80.00  
90.00  
A: Temperature  
Figure 8: The contour plots for particle size against temperature and yield of ZAC  
Design-Expert® Software  
Yield  
5.00  
4.38  
3.75  
3.13  
2.50  
Yield  
Design Points  
59.78  
21.8467  
13.04  
29.2529  
X1 = B: Process duration  
X2 = C: Excess Reactants  
6
36.6592  
Actual Factors  
A: Temperature = 70.00  
D: Effect of Catalyst = 0.38  
E: Particle size = 200.00  
44.0654  
51.4717  
30.00  
112.50  
195.00  
277.50  
360.00  
B: Process duration  
Figure 9: The contour plots for exces reactants against process duration and yield of ZAC  
Design-Expert® Software  
Yield  
0.50  
0.44  
0.38  
0.31  
0.25  
Yield  
Design Points  
59.78  
13.04  
X1 = B: Process duration  
X2 = D: Effect of Catalyst  
32.7283  
33.8658  
35.0032 36.1407  
37.2782  
6
Actual Factors  
A: Temperature = 70.00  
C: Excess Reactants = 3.75  
E: Particle size = 200.00  
30.00  
112.50  
195.00  
277.50  
360.00  
B: Process duration  
Figure 10: The contour plots for effect of catalyst against process duration and yield of ZAC  
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Design-Expert® Software  
Yield  
300.00  
250.00  
200.00  
150.00  
100.00  
Yield  
Design Points  
59.78  
13.04  
X1 = B: Process duration  
X2 = E: Particle size  
33.5247  
34.5714 35.618 36.664637.7113  
6
Actual Factors  
A: Temperature = 70.00  
C: Excess Reactants = 3.75  
D: Effect of Catalyst = 0.38  
30.00  
112.50  
195.00  
277.50  
360.00  
B: Process duration  
Figure 11: The contour plots for particle size against process duration and yield of ZAC  
Design-Expert® Software  
Yield  
0.50  
0.44  
0.38  
0.31  
0.25  
Yield  
Design Points  
59.78  
13.04  
X1 = C: Excess Reactants  
X2 = D: Effect of Catalyst  
43.3111  
30.0566 23.4293  
49.9384  
36.6839  
6
Actual Factors  
A: Temperature = 70.00  
B: Process duration = 195.00  
E: Particle size = 200.00  
2.50  
3.13  
3.75  
4.38  
5.00  
C: Excess Reactants  
Figure 12: The contour plots for effect of catalyst against excess reactants and yield of ZAC  
Design-Expert® Software  
Yield  
300.00  
250.00  
200.00  
150.00  
100.00  
Yield  
Design Points  
59.78  
13.04  
X1 = C: Excess Reactants  
X2 = E: Particle size  
43.7542  
30.0417  
23.1854  
50.6104  
36.8979  
6
Actual Factors  
A: Temperature = 70.00  
B: Process duration = 195.00  
D: Effect of Catalyst = 0.38  
2.50  
3.13  
3.75  
4.38  
5.00  
C: Excess Reactants  
Figure 13: The contour plots for particle size against excess reactants and yield of ZAC  
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Design-Expert® Software  
Yield  
300.00  
250.00  
200.00  
150.00  
100.00  
Yield  
Design Points  
59.78  
13.04  
X1 = D: Effect of Catalyst  
X2 = E: Particle size  
34.6452  
34.6452  
6
34.3616  
34.0779  
Actual Factors  
A: Temperature = 70.00  
B: Process duration = 195.00  
C: Excess Reactants = 3.75  
33.7943  
33.5107  
0.25  
0.31  
0.38  
0.44  
0.50  
D: Ef f ect of Cataly st  
Figure 14: The contour plots for particle size against the effect of catalyst and yield of ZAC  
3-D Plot of ZAC  
The 3 – Dimensional plots of the response surface model for ZAC are shown in figures 15 to 24. The results showed that the  
optimum value of the conversion was 42 for the process variables studied, which is similar to the results obtained by [34,23,24,35].  
The excess reactants increase with process duration as the yield also increases.The response surface plots showed clear peaks,  
implying that the maximum values of the response were attributed to the factors in the design space. The three-dimensional surfaces  
provide useful information about the behaviour of the system within the experiment design, facilitating an examination of the effects  
of the experimental factors on the responses and contour plots between the factors [33,36,37]. The 3D plots were generated by  
continually varying any two variables while maintaining all other input variables constant at their null point. The 3D curves were  
observed to have an elliptical nature with any two concerned variables. This denotes that the quadratic model chosen was  
appropriate, with significant correlation between the two variables [38,39].  
Design-Expert® Software  
Yield  
59.78  
13.04  
45  
X1 = A: Temperature  
X2 = B: Process duration  
41.5  
Actual Factors  
C: Excess Reactants = 3.75  
D: Effect of Catalyst = 0.38  
E: Particle size = 200.00  
38  
34.5  
31  
360.00  
90.00  
277.50  
80.00  
195.00  
70.00  
112.50  
60.00  
B: Process duration  
A: Temperature  
30.00 50.00  
Figure 15:The 3 - D Plotfor process duration against temperature and yield of ZAC  
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Design-Expert® Software  
Yield  
59.78  
13.04  
61  
X1 = A: Temperature  
X2 = C: Excess Reactants  
49.25  
Actual Factors  
B: Process duration = 195.00  
D: Effect of Catalyst = 0.38  
E: Particle size = 200.00  
37.5  
25.75  
14  
5.00  
90.00  
4.38  
80.00  
3.75  
3.13  
C: Excess Reactants  
70.00  
60.00  
A: Temperature  
2.50 50.00  
Figure 16:The 3 - D Plotfor excess reactants against temperature and yield of ZAC  
Design-Expert® Software  
Yield  
59.78  
13.04  
43  
X1 = A: Temperature  
X2 = D: Effect of Catalyst  
39.25  
Actual Factors  
B: Process duration = 195.00  
C: Excess Reactants = 3.75  
E: Particle size = 200.00  
35.5  
31.75  
28  
0.50  
90.00  
0.44  
80.00  
0.38  
0.31  
D: Effect of Catalyst  
70.00  
60.00  
A: Temperature  
0.25 50.00  
Figure 17:The 3 - D Plotfor effect of catalyst against temperature and yield of ZAC  
Design-Expert® Software  
Yield  
59.78  
13.04  
40  
X1 = A: Temperature  
X2 = E: Particle size  
37.6  
Actual Factors  
B: Process duration = 195.00  
C: Excess Reactants = 3.75  
D: Effect of Catalyst = 0.38  
35.2  
32.8  
30.4  
300.00  
90.00  
250.00  
200.00  
E: Particle siz1e50.00  
80.00  
70.00  
60.00  
A: Temperature  
100.00 50.00  
Figure 18:The 3 - D Plotfor particle size against temperature and yield of ZAC  
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Design-Expert® Software  
Yield  
59.78  
13.04  
60  
X1 = B: Process duration  
X2 = C: Excess Reactants  
48.25  
Actual Factors  
A: Temperature = 70.00  
D: Effect of Catalyst = 0.38  
E: Particle size = 200.00  
36.5  
24.75  
13  
5.00  
360.00  
4.38  
277.50  
3.75  
195.00  
3.13  
112.50  
C: Excess Reactants  
B: Process duration  
2.50 30.00  
Figure 19:The 3 - D Plot for excess reactants against process duration and yield of ZAC  
Design-Expert® Software  
Yield  
59.78  
13.04  
38.5  
X1 = B: Process duration  
X2 = D: Effect of Catalyst  
36.75  
Actual Factors  
A: Temperature = 70.00  
C: Excess Reactants = 3.75  
E: Particle size = 200.00  
35  
33.25  
31.5  
0.50  
360.00  
0.44  
277.50  
0.38  
195.00  
0.31  
112.50  
D: Effect of Catalyst  
B: Process duration  
0.25 30.00  
Figure 20:The 3 - D Plotfor effect of catalyst against process duration and yield of ZAC  
Design-Expert® Software  
Yield  
59.78  
13.04  
40  
X1 = B: Process duration  
X2 = E: Particle size  
38.025  
Actual Factors  
A: Temperature = 70.00  
C: Excess Reactants = 3.75  
D: Effect of Catalyst = 0.38  
36.05  
34.075  
32.1  
300.00  
360.00  
250.00  
277.50  
200.00  
195.00  
112.50  
E: Particle siz1e50.00  
B: Process duration  
100.00 30.00  
Figure 21:The 3 - D Plotfor particle size against process duration and yield of ZAC  
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Design-Expert® Software  
Yield  
59.78  
13.04  
58  
X1 = C: Excess Reactants  
X2 = D: Effect of Catalyst  
47.25  
Actual Factors  
A: Temperature = 70.00  
B: Process duration = 195.00  
E: Particle size = 200.00  
36.5  
25.75  
15  
0.50  
5.00  
0.44  
4.38  
0.38  
0.31  
D: Effect of Catalyst  
3.75  
3.13  
C: Excess Reactants  
0.25 2.50  
Figure 22:The 3 - D Plotfor effect of ctalyst against excess reactants and yield of ZAC  
Design-Expert® Software  
Yield  
59.78  
13.04  
58  
X1 = C: Excess Reactants  
X2 = E: Particle size  
47.5  
Actual Factors  
A: Temperature = 70.00  
B: Process duration = 195.00  
D: Effect of Catalyst = 0.38  
37  
26.5  
16  
300.00  
5.00  
250.00  
200.00  
E: Particle siz1e50.00  
4.38  
3.75  
3.13  
C: Excess Reactants  
100.00 2.50  
Figure 23:The 3 - D Plotfor particle size against excess reactants and yield of ZAC  
Design-Expert® Software  
Yield  
59.78  
13.04  
37  
X1 = D: Effect of Catalyst  
X2 = E: Particle size  
36.05  
Actual Factors  
A: Temperature = 70.00  
B: Process duration = 195.00  
C: Excess Reactants = 3.75  
35.1  
34.15  
33.2  
300.00  
0.50  
250.00  
200.00  
E: Particle siz1e50.00  
0.44  
0.38  
0.31  
D: Effect of Catalyst  
100.00 0.25  
Figure 24:The 3 - D Plotfor particle size against effect of catalystand yield of ZAC  
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Process Optimization  
In the process optimization for ZAC at table 7, desirability function was used to obtain the optimum value. The time and temperature  
were set at minimum while the catalyst weight, particle size and excess reactant were set in range. The conversion yield was set at  
maximum. The optimum process conditions for the variables were 360 min, 90oC, 4.63ml, 0.50g, and 298.67 microns for time,  
temperature, excess reactant, Catalyst weight and particle size respectively. The predicted conversion yield was 34.1841. The  
optimization was validated at those experimental conditions and conversion yield of 48.40was obtained.  
Table 7: Validation of Optimization Results  
Catalysts  
Model  
Desirability  
Temp Time  
Excess  
Reactant  
(ml)  
Catalyst  
Weight  
(g)  
Particle  
Size  
(microns)  
Yield  
(ml)  
%
%
Error  
(oC)  
(min)  
Conversion  
ZAC  
34.1841  
90  
360  
4.63  
0.50  
298.67  
23.00  
48.40  
23.00  
The summary of the model validation for the catalyst produced (ZAC) is shown in Table 7. The result indicates that Ngbo ZAC isa  
good catalyst produced when compared to other catalysts produced from Ngbo as a result of its lower percentage error and its pH  
being alkaline.  
V. Conclusions  
The study presented theoptimum conditions forthe esterification reaction of acetic acid and ethanol using Zinc Chloride activated  
Ngbo clay catalyst. The optimum conditions for esterification reaction for the process conditions of temperature, duration, amount  
of reactant, catalyst weight and particle sizewasdetermined using Response Surface Methodology (RSM) approach. The optimum  
process conditions for the variables studied including time, temperature, excess reactant, catalyst weight and particle size were 360  
min, 90 °C, 4.63ml, 0.50g, and 298.67 microns, respectively. The maximum predicted esterification yield was 34.1841. The XRF  
analysis revealed that the clay was primarily composed of SiO2 and aluminium, while the XRD analysis indicated quartz as the  
major component. The predicted and experimental values from the model showed less than 5% difference thereby making the Box-  
Behnkendesign approach an efficient, effective and reliable method for the esterification of acetic acid and ethanol using Ngbo Zinc  
Chloride activated clay catalyst.  
Competing Interests  
Authors have declared that no competing interests exist.  
References  
1. Nwabanne, Joseph T., Onu, Chijioke E. and Nwankwoukwu, Okwudili C. (2018) Equilibrium, Kinetics and  
Thermodynamics of the Bleaching of Palm Oil Using Activated Nando Clay. Journal of Engineering Research and Reports  
1(3): 1-13. DOI: 10.9734/JERR/2018/42699  
2. Ajemba R.O, Onukwuli O.D (2012) Process optimization of sulphuric acid leaching of alumina from Nteje clay using  
central composite rotatable design. International Journal of Multidisciplinary Sciences and Engineering. 2012;3(5):1–6.  
3. Onu, C. E. and Nwabanne, J. T. (2014) “Application of Response Surface Methodology in Malachite green adsorption  
using Nteje clay”. Open Journal of Chemical Engineering and Science. 1 (2) 19 – 33.  
4. Nwobasi Veronica Nnenna, Igbokwe Philomena K., and OnuChijioke Elijah (2020) Removal of Methylene Blue Dye from  
Aqueous Solution Using Modified Ngbo Clay. Journal of Materials Science Research and Reviews, 5(2): 33-46. May 2020  
5. Elijah, C. O. and Nwabanne, J. T. (2014). “Adsorption kinetics for Malachite green removal from aqueous solution using  
Nteje clay”. Journal of Environment and Human. 1 (2) 133 – 150. doi: :10.15764/EH.2014.02015  
6. Ravendra Reddy, C; Lyengar, P; Nagendrappa, G; Jai Prakash, B.S (2005).” Esterification of dicarboxylic acids to diesters  
over Mn+-montmorillonite clay catalysts” Catalyst Letter, 101, 87 – 91.  
7. Chen C-C, Hayes KF (1999) X-ray absorption spectroscopy investigation of aqueous Co(II) and Sr(II) sorption at clay-  
water interfaces. GeochimCosmochimActa 63:3205–3215  
8. Yadav, G.D;Thagathar, M.B(2002). “Esterification of maleic acid with ethanol over cation-exchange resin catalyst” React.  
Funct.Polym, 52, 99 – 110.  
9. Zang, Y; Ma, L; Yang, J (2004). Kinetics of esterification of lactic acid with ethanol catalysed by cation-exchange resins”  
React. Funct.Polym, 61, 101 – 114.  
10. Kirumakki S.R.; Nagaraju N; Chary, K.V.R (2006). “Esterification of alcohols with acetic acid over zeolites Hβ, HY and  
HZSM5” Applied Catalysis A: General, 299, 185 – 192.  
11. Kirumakki S.R.; Nagaraju N; Narayanan, S.A(2004).” Comparative esterification of benzyl alcohol with acetic acid over  
zeolites Hβ, HY and HZSM5” Applied Catalysis A: General, 273, 1 – 9.  
12. Wu, K; and Chen, Y (2004).“An efficient two-phase reaction of ethyl acetate production in modified ZSM- 5 zeolites”  
Applied Catalysis A: General, 257, 33 – 42.  
13. Chu, W. Yang, X; Ye, X; Wu, Y(1996). “Vapour phase esterification catalysed by immobilized dodecatungstosilicic acid  
(SiW12) on activated carbon”Applied Catalysis A: General, 145, 125 – 140.  
www.ijltemas.in  
Page 1190  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
14. Sepulvega, J.H; Yori, J.C; Vera, C.R (2005). “Repeated use of supported H3PW12O40 catalysts in the liquid phase  
esterification of acetic acid with butanol” Applied Catalysis A: General, 288, 18 – 24.  
15. Jermy, B.R;Pandurangan,A (2005).” Catalytic application of Al-MCM-41 in the esterification of acetic acid with various  
alcohol”. Applied Catalysis A: General, 288, 25 – 33.  
16. Kirumakki S.R.; Nagaraju N; Chary, K.V.R; Narayanan, S (2003). “Kinetics of esterification of aromatic carboxylic acids  
over zeolites Hβ and HZSM5 using dimethyl carbonate” Applied Catalysis A: General, 248, 161 – 167.  
17. Ezedinma Henry C., NwabanneJospeh T., E. OnuChijioke E., and Nwajinka Charles O. (2021) Optimum Process  
Parameters and Thermal Properties of Moisture Content Reduction in Water Yam Drying. Asian Journal of Chemical  
Sciences, 9(4): 44-54, April 2021. DOI: https://doi.org/10.9734/AJOCS/2021/v9i419080  
18. OnyekweluIjeoma U., NwabanneJospeh T., and OnuChijioke E., (2021) Characterization and Optimization of Biodiesel  
Produced from Palm Oil Using Acidified Clay Heterogeneous Catalyst. Asian Journal of Applied Chemistry Research  
8(3): 9-23, May 2021. DOI: https://doi.org/10.9734/AJACR/2021/v8i330192  
19. Onu, C. E., Igbokwe, P. K., Nwabanne, J. T., &Ohale, P. E. (2021). ANFIS, ANN, and RSM modeling of moisture content  
reduction of cocoyam slices. Journal of Food Processing and Preservation, 00, e16032. https://doi.org/10.1111/jfpp.16032  
20. Iheanacho Chamberlain Ositadinma, Nwabanne Joseph Tagbo and OnuChijioke Elijah (2019) Optimum Process  
Parameters for Activated Carbon Production from Rice Husk for Phenol Adsorption. Current Journal of Applied Science  
and Technology, 36(6): 1-11. DOI: 10.9734/CJAST/2019/v36i630264  
21. Igbokwe P. K, Nwokolo S. O, and Ogbuagu J. O, (2005).Catalytic esterification of stearic acid using a local kaolinitic clay  
mineral.NJERD, 4, 1.  
22. Igbokwe P. K and Olebunne F. L,(2011). On the catalytic esterification of acetic acid with ethanol, using Nigerian  
montmorillonite clay: effect of reaction variables on catalyst efficiency. Journal of the University of Chemical Technology  
and Metallurgy, 46, 6, 389-394.  
23. Igbokwe P. K, Ugonabo V. I, Obarandiku E, Ochili A, (2008) ‘’Characterization and use of catalyst produced from local  
clay resources’’ Journal of Applied Sciences (JAS), 2, 2.  
24. Igbokwe, P.K., Olebunne, F.L., and Nwakaudu, M.S (2011)., Effect of Activation Parameters on Conversion in Clay –  
Catalysed Esterification of Acetic Acid, International Journal of Basic and Applied Sciences, 11,.5, 1 – 8,  
25. Murat, M., A. Amokrane, J.P. Bastide, and L. Montanaro,( 1992). “Synthesis of zeolites from thermally activated kaolinite.  
Some observations on nucleation and growth”.Clay Minerals, 27, 1, 119.  
26. Akolekar, D., A. Chaffee, and R.F. Howe (1997), “The transformation of kaolin to low-silica X zeolite”. Zeolites, 19, 5–  
6, 359–365.  
27. Demortier A., Golbeltz N., Letlieur J.P., Duhayon C.(1999). Infrared evidence for the formation of an Intermediate  
compound, during the synthesis of Zeolite Na – A from metakaolin. International Journal of Inorganic materials. Volume  
1, issue 2, August 1999, pages 129 – 134  
28. Tracy, M.M.J. and J.B. Higgins,(2001) “Collection of simulated XRD powder patterns of Zeolites” Fourth revised edition  
ed. 2001, Amsterdam Elsevier 379.  
29. .Evamako O. Yusuf, Efeovbokhan V.E, and Babalola R. (2001). Development and characterization of zeolite – A from  
Elefun kaolin. Journal of physics conference, series 1378 – 032016.  
30. Ramirez J.H, Maidonaldo – Hedar F.J, Ferez A.F, Moremo C., Costa C.A. Madeira L.M. (2007). Azo-dye orange II  
degradation by heterogeneous Fenton-like reaction using carbón– Fe catalysts. Applied catalysis B: Environmental.  
Volume 75, issue 3 – 4,26 September 2007, pages 312 – 323.  
31. Ejikeme, M.E., P.C.N. Ejikeme, and B.N. Abalu.(2013). “RSM Optimization Process for Uptake of Water from Ethanol  
Water Solution Using Oxidized Starch”. Pacific Journal of Science and Technology. 14, 2 :319-329.  
32. Azargohar, R, and A.K. Dalai(2005) “Production of Activated Carbon from Luscar Char: Experimental and Modeling  
Studies”. MicroporMesopor. Mater.85:219 – 225.  
33. Anupam, K., S. Dutta, C. Bhattacharjee, and S. Datta (2011) “Adsorption Removal of Chromium (VI) from Aqueous  
Solution over Powdered Activated Carbon: Optimization through Response Surface Methodology. Jornal of  
Hazard.Mater.173:135 – 143.  
34. Igbokwe P. K, Ugonabo V. I, Iwegbu N. A, Akachukwu P. C, Olisa C. J, (2008). Kinetics of the catalytic esterification of  
propanol with ethanoic acid using catalyst obtained from Nigerian clays. Journal of the University of Chemical  
Technology and Metallurgy, 345 – 348.  
35. Olebunne F. L; Igbokwe P. K; Onyelucheya O. E; Osoka E. C and Ekeke I. C. (2011).‘’Mechanistic modeling of clay-  
catalysed liquid-phase esterification of acetic acid’’.Journal of Emerging Trends in Engineering and Applied Sciences, 2,  
4: 631- 635.  
36. Panesar, P.S. (2008).“Application of Response Surface Methodology in the Permeabilitation of Yeast Cells for Lactose  
Hydrolysis”.Biochem. Eng. J. 39:91 – 96.  
37. Ahmad, A.A. and B.H. Hameed(2010) “Effect of Preparation Conditions of Activated Carbon from Bamboo Waste for  
Real Textile Waste Water”,Joural of Hazard Mater.173:487 – 493.  
38. OnuChijioke Elijah, Igbokwe P.K, Nwabanne J.T, Nwanjinka O.C, Ohale P.E. Evaluation of Optimization techniques in  
predicting optimum moisture content reduction in drying potatoe slices. Artificial intelligence in Agriculture, 2020;4;39-  
47.  
www.ijltemas.in  
Page 1191  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
39. Available: http://doi.org/10.1016/j.aia.2020.04.001  
40. Onu C.E, Nwabanne JT, Ohale P.E, Asadu CO. Comparative analysis of RSM, ANN and ANFIS and the mechanistic  
modelling in eriochrome black-T dye adsorption using modified clay. South African Journal of Chemical Engineering,  
2021:36:24-42.  
41. Available: https://doi.org/10.1016/j.sajce.2020.12.003.  
www.ijltemas.in  
Page 1192