CC BY
13
Application of biosynthesized nano-catalyst for biodiesel synthesis and impact assessment of factors influencing the yield
C. P. Gandhi1, Rajni Garg2, Nnabuk Okon Eddy3
department of Mathematics, Rayat Bahra University, Mohali, Punjab, 140301, India 2Department of Chemistry, Rayat Bahra University, Mohali, Punjab, 140301, India
3Department of Pure and Industrial Chemistry, University of Nigeria, Nsukka, Enugu State, 410001, Nigeria [email protected], [email protected], [email protected]
DOI 10.17586/2220-8054-2021-12-6-808-817
This study presents a green process to synthesize biodiesel using biosynthesized nano-catalyst and a novel methodology for analyzing the impact of influencing factors - methanol/oil ratio, the concentration of nano-catalyst, temperature, and time of reactions- on the yield of biodiesel obtained by heterogeneous nano-catalyst-based transesterification reaction. Nano-calcium oxide (nano-CaO) was obtained as a heterogeneous nano-catalyst using waste eggshells. Waste cooking oil was treated with methanol and nano-catalyst at varying methanol: oil ratios (4 - 14:1), nano-catalyst concentration (0.5 - 2 %), time of reaction (3 - 4.5 hrs), and temperature (50 - 65 °C). The superiority and effectiveness of the proposed transester-ification indices were accomplished making use of Deluca and Termini fuzzy entropy weighted indexes to obtain optimal reaction conditions with 95.49 % biodiesel yield.
Keywords: nano-catalyst, biodiesel, transesterification, fuzzy entropy, neutrosophic entropy. Received: 17 September 2021
1. Introduction
Worldwide consumption of energy is progressing especially in the transportation sector which is solely dependent upon petroleum-based non-renewable resources of energy [1]. These resources are the primary source of increasing environmental pollution due to the emission of carbon dioxide from vehicular exhausts in addition to the greenhouse effect [2]. The resulting environmental degradation and the expected energy depletion require the continuous search for alternative energy resources if the quality of the environment must be maintained [3]. Consideration of the energy policy review indicates that India and several nations of the world are facing great challenges towards alternative and sustainable energy resources that are eco-friendly, less expensive, biodegradable, and easily available [4]. Consequently, the search for biofuels such as biogas, hydrogen cells, and biodiesel has attracted intense research interest because of the potential of green chemistry usage [5]. Biodiesel has emerged as a sulfur-free, bio-degradable, cost-effective, non-toxic, and more reliable option among the alternative sustainable fuels [6]. It is miscible in diesel at all proportions and is favored as a blending constituent of automotive fuel due to its very high cetane value and high lubricity [7]. It can be obtained as fatty acid alkyl esters employing transesterification of cheap resources viz oils and fat with alcohols (ethanol/methanol) or alkoxides and homogeneous or heterogeneous catalyst with the production of glycerol as a by-product [8]. Vegetable oil is commonly obtained from oil crops such as coconut, sunflower, canola, palm, mustard, soybean, flax, rapeseed, jatropha, hemp, and rice bran, etc. India has a great potential for production of biodiesel with vast production of oil crops in the wasteland and forest area. In the global society, efforts are ongoing toward reducing dependency on fossil fuels and reducing production costs for biodiesel by opting for low-cost and easy to obtain feedstock and catalysts [9]. Many researchers have used vegetable oils in the form of raw, non-edible and waste oils obtained from various oil processing units as well as from household and locally available food ventures [10].
It has been found that heterogeneous catalysts exhibit better performance compared to homogeneous catalysts. The major factor includes the high production cost during industrial production on large scale due to the involvement of the multistep purification process and difficulties to remove the byproducts such as glycerol formed during the reaction. Treatment of the effluent resulted from this process also adds to the cost [11]. Further, all the conventional feedstock including non-edible oils with high content of fatty acids can not be used in the homogeneous esterification due to the possibility of soap formation that leads to loss of yield and adds to the separation issue of byproducts. Hence, researchers are seeking economically viable and alternative methods for the production of biodiesel [12]. However, the greatest advantage of heterogeneous catalysts is the ease of separation while the disadvantage is limited activity and selectivity. Heterogeneous (solid) catalyst-based transesterification offers future hope to current challenges because solid catalysts are required in a lesser amount compared to conventional catalysts and can be easily separated and be reused. Also, the solid catalyst can be fabricated and tailored as per the requirement to increase the catalytic properties.
Various solid catalysts including metal oxide and alumina/silica supported metal oxide catalysts have been explored by researchers [13]. Several reports are available with usage of CaO as a heterogeneous nano-catalyst. Animal eggshells are a rich source of calcium carbonate that can be easily converted to nano-CaO. Throughout the world, eggshells of chicken, duck, and snails are discarded as waste and their improper disposal leads to environmental pollution that results in various health hazards. Hence, the use of waste eggshells for the generation of nano-CaO as a heterogeneous nano-catalyst is an eco-friendly step [14]. Likewise, edible oil is processed in various food processing units especially in the kitchen and restaurants for frying purposes. After frying, edible oil becomes unfit for consumption and is discarded and is known as waste cooking oil (WCO). This oil is rich in free fatty acids and may be successfully employed to obtain biodiesel using nano-CaO based heterogeneous transesterification [15].
Production of biodiesel is dependent upon many factors including methanol/oil ratio, temperature, the concentration of nano-catalyst, time of reaction, extent of stirring, etc. [16]. Subsequently, Yi et al. approached the enduring Karl's Pearson's coefficient formula and designed the optimal reaction conditions responsible for transesterification reaction [17]. However, the correlation coefficient formula has some limitations as it can be only used for studying interrelation and thus leads to a non-significant curvilinear relationship between two variables. The fuzzy set theory proposed by Zadeh has been proven as an indispensable tool for optimizing energy-related problems [18]. Ovchin-nikov elaborated many impressions of fuzzy sets (FSs), one of which corresponds to the representation of membership degree or grade of the underlying FS [19]. Since then, FSs have been extended into a variety of equivalents and more quantified information is provided by the neutrosophic set (NS) contribute in comparison with the existing fuzzy set theory [20]. Moreover, the NS theory has the potential of predicting optimal reaction conditions under heterogeneous catalyzed transesterification reactions. To handle and solve these problematic issues, an effort is accomplished in this pathway by establishing a novel neutrosophic entropy-based methodology which can play a crucial role for the classification of influencing factors (i) methanol/oil ratio (ii) concentration of nano-catalyst (iii) temperature and (iv) time of reactions, responsible for optimal biodiesel yield, obtained by heterogeneous nano-catalyst-based transesterification reaction. The underlying neutrosophic entropy-based methodology provides remarkable yield information confirming the optimum parameters. The results of this underlying study are superseded as below.
Section 2 provides the details of the materials and procedures employed in performing the reaction. Section 3 introduces the projected methodology for constructing fuzzy entropy weighted waste cooking oil transesterification index (FCOTI), single-valued neutrosophic entropy weighted waste cooking oil transesterification index (SCOTI), and Deluca and Termini fuzzy entropy-based weighted waste cooking oil transesterification index (ECOTI) respectively. Section 4 provides the details for the characterization of the prepared nano-catalyst and biodiesel. It also provides the applicability and remarkability of the underlying methodology by identifying the most influencing reaction set, responsible for the optimum biodiesel yield whereas Section 5 finally summarizes the study. The basic terminology of fuzzy and neutrosophic entropy measures, required for the successive progress of the underlying study has been given in supplementary information.
2. Materials and methods
2.1. Materials
WCO, procured from the resident food vendors, was utilized as resource material for the reaction. Waste chicken eggshells, collected from the local food junctions, were used to develop the heterogeneous nano-catalyst. All the chemicals of analytical grade were acquired from Merck, Mumbai, and were utilized without any additional processing or purification.
2.2. Collection of reaction sets
A heterogeneous nano-catalyst based on waste eggshells was developed by the procedure given in literature [21]. Methanol has been used as a solvent and co-reactant. After washing, drying, and grinding, the eggshells were calcined to obtain nano-CaO as a nano-catalyst. The nano-catalyst was characterized by FTIR (Model Perkin Elmer Spectrum 400 FT-IR Spectrometer) operating with a resolution of 2 cm-1 (range 4000 - 400 cm-1) and XRD (Model PANalyt-ical X'Pert Pro) operating at a voltage of 45 kV with a current of 35 mA using Cu-Ka radiation (K = 1.5406 A) and 26 ranging from 20 - 80° at a continuous speed of 0.045° per min. Waste cooking oil was subjected to filtration for the removal of any insoluble impurities and was pre-treated with anhydrous sodium sulfate before heating at 100 ° C. The reaction was executed in a batch reactor supported with a thermostatic water bath and magnetic stirrer by varying methanol: oil ratio (4 - 14:1), nano-catalyst concentration (0.5 - 2 %), time of reaction (3 - 4.5 hrs.), and temperature (50 - 65 ° C) as per reaction sets and a constant stirring was maintained.
After the separation of glycerol, the by-product, the % yield of biodiesel was determined using the standard equation [22]. The biodiesel so obtained was characterized by FTIR (Model Perkin Elmer Spectrum 400 FT-IR
Spectrometer) and GC-MS analysis (Model THERMO Scientific Trace 1300GC) operating at conditions as shown in Table 1.
Table 1. GC-MS Conditions for the analysis of essential oils
GC Condition
Injection Temperature 280 °C
Injection Mode Split
Column Flow (Helium) 1.21 mL/min
Split Ratio 5
Injection Volume 1 y«L
MS Conditions (EI mode at 70 eV)
Ion Source Temperature 200 °C
Interface Temperature 280 °C
Solvent Cut Time 2.2 min
Start Time 2.5 min
End Time 55 min
Start m/z 40
Endm/z 700
Oven Temperature Program
Rate (°C/min) Temperature (°C) Hold Time (min)
60 1
5 220 2
5 280 8
3. Normalization of monitored biodiesel yield
Suppose the number of parameters (influencing factors) under study is denoted by "n". Let the number of reaction sets to be optimized is denoted by "m". Let r— denote the monitored biodiesel yield of the jth influencing factor at ith reaction set. To diminish the errors created by the various influencing factor, it becomes essential for us to normalize each monitored biodiesel yield. If V- denotes the normalization construction function for a typical jth influencing factor at a particular ith reaction set, then:
r— — min r aa
Vji =---, (1)
max r— — min r—
where j runs over 1 to n and i over 1 to m.
3.1. Determination of fuzzy and neutrosophic entropy weights
In the existing literature of Information theory, many fuzzy entropy (FE) measures have been investigated and characterized by researchers, but with some faults and limitations. De Luca and Termini suggested the first nonadditive FE measure as follows [23]:
1
HF (CFS) = - logm ^ [^C ) l0g ) + (1 - ^ l0g (1 - ^
(2)
where m is a fixed natural number.
The fuzzy entropy H^ (C^) has been found capable of optimizing the uncertainty problems associated with various aspects of the environment, pollution, and energy-saving resources. Unfortunately, the FE measure is facing a major drawback with assumption 0 x log 0 = 0. Due to this reason, this fuzzy entropy faced intrinsic conflicts and hence indicated uncertainty and elusiveness in the quality evaluation. To overcome this problematic situation and to meet the exigency, we have been able to establish Neutrosophic fuzzy entropy (NTFE) and applied it for identifying
the most influencing reaction sets responsible for the optimal bio-diesel yield. Let Tji denote the amount of fuzziness (concentration) based on the truth membership degree of jth persuading factor at the ith reaction set. Then:
T
T ji
Vji
n '
E j
j=i
(3)
where j runs over 1 to n and i over 1 to m.
(a) The weights Wj of jth influencing factors at ith reaction set can be determined as follows. Let m be the number of reaction sets, then:
1 - Hi
n :
E j
where
HI
log m
i=1
E (j log j + (1 - j log (1 - j
(4)
(5)
(b) The weights Wji of jth influencing factors at ith reaction set employing the proposed trigonometric FE measure (1) can be determined as follows. Let m be the number of reaction sets, then:
W2 ji
1 - Hi
n '
y H2. / ji j=i J
(6)
where
H2 = - tan
- E
m
7 j=i
tan
+ ^(1 - Tji)
tan
v^+1
(c) Let Fji = 1 - Vji and Iji = 1 - Vji - Fji denote the amount of fuzziness (concentration) based on falsity
and indeterminacy membership degree of j influencing factor at i reaction set respectively. Then, the weights wj3) of jth influencing factors at ith reaction set employing the proposed Single Valued Neutrosophic
Entropy (SVNE) measure can be determined as follows. Let m be the number of reaction sets, then:
where
W(3) = 1 - Н?
(7)
EH?,
j=1
H?=tan( m)E
j=1
3tan
2
v^+1
2 + Vji + Fji 3
tan
tan
\vv/2+ vj - j /
'_1 + Vji + Fji_'
(^/j + У Vji + Fji)
4 - y.. — F--
4 w Fji
tan
3 yji Fji
+ /Г-Fj") ( V2 - Vji - Fji)
. (8)
3.2. Calculation of relative sub-indices of each influencing factor at various reaction sets
Two types of sub-indices, absolute and relative, are being used by eminent researchers. Since absolute (or relative) sub-indexing approaches are fully independent (or dependent) on quality standards, the relative sub-indexing approach has been empowered in this study.
Let Qji = The relative sub-indices, Sj = The maximum monitored biodiesel yield, and cji =The monitored biodiesel yield of the jth influencing factor at ith reaction set consecutively. Then, the relative sub-indexing of each influencing factor concerning various reaction sets in this study has been assigned by:
Qji = j x 100,
Sji
where j runs over 1 to n and i over 1 to m.
1
2
2
2
3
3.3. Constructions of ECOTI, FCOTI and SCOTI
The existing Deluca-Termini fuzzy entropy, proposed fuzzy trigonometric entropy and SVNE weighted waste cooking oil transesterification indices (ECOTI, FCOTI and SCOTI) can be computed employing the following equations:
ECOTI at ith reaction set = ^ Wj-Q, (10)
j=i
FCOTI at ith reaction set = ^ WjQji, (11)
j=i n
SCOTI at ith reaction set = ^ WjiQji. (12)
j=i
The maximum ECOTI, FCOTI, or SCOTI score for a typical influencing factor at a particular reaction set is designated as the most influencing reaction set.
4. Results and discussion
4.1. Characterization of nano-catalyst
The X-ray diffraction patterns of nano-CaO have been plotted in Fig. 1. The narrow and intense peaks in the patterns can be attributed to the crystalline nature (calcite) of nano-CaO [24]. The diffraction patterns clearly show that the nano-catalyst showed characteristic peaks for CaO at 26 equal to 31.5, 37.5, 46.4, 51.1, 52.3, 55.2, and 58.1° [25]. The prepared nano-catalyst was also characterized using FTIR analysis as shown in Fig. 2. A band at 3267.67 cm-1 corresponded to the O-H stretching while two bands around 1439.41 and 438.43 cm-1 were attributed to Ca-O bending [26]. The FTIR features shown by the nano-catalyst in our study are consistent with the already reported data in the literature [27].
Fig. 1. X-ray Diffraction pattern for nano-CaO
4.2. Characterization of biodiesel
The biodiesel composition in terms of fatty acid methyl esters was identified by a pattern of mass fragmentation and the retention time of GCMS analysis. Fig. 3 shows the composition of the biodiesel produced under optimized conditions and confirms the completion of the transesterification reaction. The results found were consistent with previously reported results [28].
FIG. 2. FTIR spectrum for nano-CaO
Fig. 3. GCMS for biodiesel
Functional group analysis of biodiesel was explored by FTIR as represented in Fig. 4. The most intense peak at 1755.96 cm-1 was assigned to CO stretching in esters [29,30]. The absorption bands corresponding to the asymmetric bending of methyl groups of esters in the biodiesel were observed at 1639.41 cm-1 while that for stretching vibrations of methoxy groups were observed at 1239.85 cm-1 [31, 32]. The axial deformation of CH2 bond was indicated by absorption bands at 2911.66 cm-1 [28,33]. The absorption band at 2407.14, and 2170.60 cm-1 corresponded to stretching vibrations of C-O while O-H stretching vibration was observed as a weak absorption band at 3400.91 cm-1 [34]. The absorption bands at 628.59 cm-1 corresponded to CH2 and CH3 bending vibrations [28].
4.3. Identification of most influencing reaction set through ECOTI, FCOTI, and SCOTI
To predict the most influencing reaction set responsible for the optimum bio-diesel yield employing the proposed methodology has already been explained. The number of influencing factors in this study is four (n = 4) and the number of transesterification reaction sets is eleven (m = 11) for the concentration of nano-catalyst, temperature, and time of reaction, and ten (m = 10) for methanol/oil ratio. We have been able to evaluate Deluca and Termini [23] fuzzy
FIG. 4. FTIR spectrum for biodiesel
entropy-based weighted waste cooking oil transesterification index (ECOTI) employing equation (11) concerning each influencing factor at various reaction sets as summarized in Table 2. A careful analysis of the results depicted in Table 2 along with Fig. 5(a) reveals that, like the proposed fuzzy and SVNE measures, for the variation of (i) concentration of nano-catalyst (ii) temperature (iii) time of reaction and (iv) methanol:oil ratio, the reaction sets RS8, RS19, RS30, RS35 were found to be the most influencing ones owing to their maximum ECOTI scores. The desired TFE weighted waste cooking oil transesterification index (FCOTI) can be evaluated employing equation (12). The results have been depicted in Table 2 along with Fig. 5(b), revealing that for the variation of concentration of nano-catalyst, temperature, time of reaction, and methanol: oil ratio, the reaction sets RS8, RS19, RS30, RS35 were found to be the most promising ones owing to their maximum FCOTI scores. Following the proposed methodology and resulting equation (13), the desired trigonometric SVNE weighted waste cooking oil transesterification index (SCOTI) concerning each influencing factor at various reaction sets is summarized in Table 2. The subsequent analysis of the results depicted in Table 2 along with Fig. 5(c) reveals that for the variation of concentration of nano-catalyst, temperature, time of reaction, and methanol: oil ratio, the reaction sets RS8, RS19, RS30, RS35 were found to be the most influencing ones owing to their maximum ECOTI scores.
The previous discussion reveals that for the variation in methanol: oil ratio with variation in concentration of nano-catalyst from 0.5 - 2 %, the reaction set RS8 was found to be most influencing, owing to its maximum ECOTI, FCOTI, and SCOTI scores. Similarly, for the variation in methanol: oil ratio with variation in temperature of transesterification from 50 - 65 °C, the reaction set RS19 was found to be most effective owing to its maximum ECOTI, FCOTI, and SCOTI scores. Also, for the variation in methanol: oil ratio with variation in time of esterification from 3 - 4.5 hrs., the reaction set RS30 was found to be most effective owing to its maximum ECOTI, FCOTI, and SCOTI scores. These reaction sets involve the use of methanol: oil ratio as 11:1 which means that the optimized methanol: oil ratio for best yield of biodiesel is 11:1.
During the study of the effect of nano-catalyst concentration, temperature, and time of reactions at constant methanol: oil ratio, it was observed that the reaction set corresponding to the nano-catalyst concentration as 1 %, reaction temperature as 60 °C, and reaction time as 4.5 hrs., was found to be most effective, owing to its maximum ECOTI, FCOTI, and SCOTI scores resulting in 95.49 % biodiesel yield. It is therefore concluded that during the optimization analysis, the methanol: oil ratio has been the most influencing factor. This is due to the reason that for transesterification reaction, excess methanol is required for obtaining maximum yield (methanol being the co-reactant for the reaction). For a particular methanol: oil ratio, the temperature is the most influencing factor followed by nano-catalyst concentration and time of reaction owing to their highest ECOTI, FCOTI, and SCOTI scores. However, for the optimized value of nano-catalyst concentration and time of reaction, a slight decrease of reaction temperature 55 °C was observed followed by an increase of reaction temperature 65 ° C indicating that at the optimized value of nano-catalyst concentration and reaction time, temperature change can be afforded. However, all four factors are interdependent and can be suitably varied at particular methanol: oil ratios to obtain the best yield of biodiesel.
TABLE 2. FCOTI, SCOTI and ECOTI Score for (i) Methanol: Oil Ratio (MR) with the variation of nano-catalyst (Cat), temperature (Temp.) and time of reactions
FCOTI Score
RSs Nano-catalyst RSs Temp. RSs Time RSs MR
RS1 0.0000 RS12 0.0000 RS23 0.0000 RS34 357.83
RS2 305.80 RS13 307.83 RS24 294.78 RS35 367.57
RS3 312.72 RS14 316.63 RS25 302.84 RS36 361.33
RS4 320.19 RS15 324.50 RS26 310.46 RS37 360.21
RS5 327.81 RS16 331.51 RS27 316.71 RS38 357.39
RS6 342.00 RS17 343.71 RS28 330.05 RS39 364.27
rs7 350.00 RS18 350.09 RS29 337.19 RS40 364.15
RSs 359.44 RS19 359.58 RS30 347.12 RS41 0.0000
RS9 356.95 RS20 358.13 RS31 345.09 RS42 347.07
RS 10 354.30 RS21 356.35 RS32 341.65 RS43 359.97
RS11 352.80 RS22 354.67 RS33 339.19
SCOTI Score
RSs Nano-catalyst RSs Temp. RSs Time RSs MR
RS1 0 RS12 0 RS23 0 RS34 358.73
RS2 305.8 RS 13 307.82 RS24 294.78 RS35 367.57
RS3 312.73 RS14 316.63 RS25 302.84 RS36 362.71
RS4 320.21 RS15 324.5 RS26 310.47 RS37 360.64
RS5 327.83 RS16 331.51 RS27 316.73 RS3S 356.22
RS6 342.06 RS17 343.72 RS2S 330.11 RS39 365.22
rs7 349.99 RS\s 350.07 RS29 337.2 RS40 363.09
RSs 359.44 RS19 359.58 RS30 347.12 RS41 0
RS9 357.3 RS20 358.28 RS31 345.83 RS42 347.22
RS 10 354.52 RS21 356.37 RS32 342.35 RS43 360.55
RS11 353.01 RS22 354.71 RS33 340.02
ECOTI Score
RSs Nano-catalyst RSs Temp. RSs Time RSs MR
RS1 0.0000 RS12 0 RS23 0.0000 RS34 357.51
RS2 305.78 RS13 307.82 RS24 294.79 RS35 367.57
RS3 312.68 RS14 316.63 RS25 302.82 RS36 360.97
RS4 320.15 RS15 324.5 RS26 310.44 RS37 360.13
RS5 327.78 RS16 331.51 RS27 316.68 RS3S 357.70
RS6 341.97 RS17 343.71 RS2S 330.03 RS39 364.36
rs7 350.00 RS\s 350.09 RS29 337.19 RS40 364.09
RSs 359.44 RS19 359.58 RS30 347.12 RS41 0.0000
RS9 356.95 RS20 358.13 RS31 345.08 RS42 346.96
RS10 354.29 RS21 356.35 RS32 341.61 RS43 359.86
RS11 352.79 RS22 354.67 RS33 339.11
FIG. 5. Identification of Most Influencing Reaction Sets Employing a) ECOTI; b) FCOTI; c) SCOTI
5. Conclusion
This study investigates the synthesis of biodiesel using bio-synthesized nano-CaO as a nano-catalyst and employs a novel trigonometric fuzzy and SVNE-based methodology for evaluating the impact of various influencing factors (i) methanol/oil ratio (ii) concentration of nano-catalyst (iii) temperature and (iv) time of reaction on the biodiesel yield obtained through a heterogeneous nano-catalyst-based transesterification reaction. The proposed trigonometric entropy measures are deployed for constructing fuzzy entropy weighted waste cooking oil transesterification index (FCOTI) and SVNE weighted waste cooking oil transesterification index (SCOTI) respectively. The maximum FCOTI or SCOTI score concerning each influencing factor at various reaction sets indicated that the optimum methanol: oil ratio under the variation of nano-catalyst concentration from 0.5 - 2 % and the temperature variation from 50-65 °C comes out to be 11:1 to provide the maximum yield of biodiesel. Further, out of the next three parameters, the effect of temperature is more prominent to obtain the best yield of biodiesel followed by nano-catalyst and time of reaction. If nano-catalyst and time are suitably controlled within the range of optimum values, the reaction temperature doesn't significantly affect the reaction yield. The classification of various influencing factors obtained through the proposed FCOTI and SCOTI coincides with the existing Deluca and Termini fuzzy entropy-based weighted waste cooking oil transesterification index (ECOTI). This confirms that the optimum parameters are nano-catalyst concentration value of 1 %, reaction temperature as 60 °C, methanol: oil ratio as 11:1, and reaction time as 4.5 hrs.
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