Assessing Waste Cooking Oils for the Production of Quality Biodiesel

Subscriber access provided by Washington University | Libraries

Biofuels and Biomass

Assessing waste cooking oils for the production of quality biodiesel using an electronic nose and a stochastic model. Adriano Francisco Siqueira, Igor Gomes Vidigal, Mariana Pereira Melo, Domingos Sávio Giordani, Pollyanna Souza Batista, and Ana Lucia Gabas Ferreira Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b04230 • Publication Date (Web): 19 Feb 2019 Downloaded from http://pubs.acs.org on February 24, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

1

Assessing waste cooking oils for the production of quality

2

biodiesel using an electronic nose and a stochastic model

3

Adriano F. Siqueiraa, Igor G. Vidigalb, Mariana P. Meloa, Domingos S. Giordanib,

4

Pollyanna S. Batistab, Ana L. G. Ferreiraa*

5

a

6

Universidade de São Paulo, 12602-810, Lorena, SP, Brazil

7

b

8

de São Paulo, 12602-810, Lorena, SP, Brazil

Departamento de Ciências Básicas e Ambientais, Escola de Engenharia de Lorena,

Departamento de Engenharia Química, Escola de Engenharia de Lorena, Universidade

*Corresponding

author.

E-mail address: [email protected] (A.L.G. Ferreira): Tel. +55 12 3159 5315 ACS Paragon Plus Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

9

Page 2 of 25

ABSTRACT

10

Around 1% of waste cooking oil (WCO) is currently recycled to make biodiesel in Brazil,

11

mainly because used oils can acquire physicochemical characteristics that render them

12

unsuitable as raw materials. In order to make biofuel production from waste oils and fats

13

more efficient and economically feasible, it is important to develop simple, rapid and low-

14

cost methods for testing the quality of WCOs. With the objective of establishing the

15

applicability of stochastic modelling of e-nose profiles in assessing the suitability of WCO

16

for biodiesel production, the synthesized biodiesel samples from 36 pre-used frying oils,

17

obtained from domestic and commercial premises, were analyzed regarding ester content,

18

acidity index, density, viscosity and iodine index. Olfactory profiles of the WCO sources

19

were obtained using a Cyranose® chemical vapor-sensing instrument and interpreted by

20

application of stochastic modelling and quadratic discriminant analysis. The predictive

21

model obtained by stochastic analysis exclusively from the olfactory profiles of the

22

samples of WCO allowed the latter to be classified according to their ability to generate

23

biodiesel that would be compliant with standard specifications and with an overall

24

accuracy greater than 80%. Our results demonstrated that stochastic modeling is a

25

promising tool for predicting the quality of biodiesel based only on the WCO olfactory

26

profiles and its origin, since it allows qualitative assessments of the principal biodiesel

27

properties and eliminates the need for complex and time-consuming laboratory tests.

28

Keywords: Biofuel, electronic nose, stochastic model, waste cooking oil

2 ACS Paragon Plus Environment

Page 3 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

29 30

Energy & Fuels

1.

Introduction The production and consumption of fossil fuels worldwide has increased

31

remarkably since the discovery of petroleum but during the past two decades the scenario

32

has changed as a consequence of global economic crises and increased awareness of the

33

effects of climate change. In this context, the search for new sources of energy has

34

gathered apace with research and development of biofuels, including biodiesel, gaining

35

priority in many emerging countries for environmental, economic, social and strategic

36

reasons1,2.

37

Biodiesel was introduced into Brazil in 2002 and it has since become mandatory to

38

add a proportion of the biofuel to all diesel fuels sold in the country. Conventional diesel

39

oil must currently contain 10% of biofuel, but this is expected to increase to 15% in future

40

years3. The main challenge to the economical and sustainable production of biodiesel is the

41

supply of raw material, since this factor impacts highly on the final cost. Soybean oil is the

42

main raw material for biodiesel production in Brazil, followed by bovine fat and other fatty

43

materials, principally frying oil, cottonseed oil and palm oil. However, the disadvantages

44

of vegetable oils are that they require large agricultural areas and more than 60 days for

45

cultivation, harvesting and processing, and there is the possibility that their production

46

could jeopardize the availability of raw material for food preparation4.

47

It is possible to reuse waste cooking oil (WCO) generated in food frying processes

48

for producing biodiesel. This is an attractive proposition because it comprises a renewable

49

resource, avoids disposal in the environment with the consequent devastating effects, and

50

represents an opportunity for businesses to increase revenue5-7. Approximately 7 million

51

tons of soybean oil were consumed in Brazil in 2017, and is the main source of WCO in

52

the country8. However, around 1% of WCO is currently recycled to make biodiesel in

53

Brazil, mainly because its physiochemical characteristics differ, depending on the use of 3 ACS Paragon Plus Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 25

54

the oil, mainly due to domestic or professional use that could render it unsuitable as a raw

55

material for biofuel production9,10. The characteristics of WCOs vary according to the

56

conditions of the frying process during which the oil may be degraded and modified with

57

respect to odor, color, viscosity and acidity. Although a number of reliable

58

physicochemical methods are available to monitor the quality of a WCO and its suitability

59

for biodiesel production, they are generally time consuming and demand large amounts of

60

solvents and chemical reagents. In order to make biofuel production from waste vegetable

61

oils more efficient and economically feasible, it is important to develop simple, rapid and

62

low-cost methods for testing the quality of biofuel and associating it with the

63

characteristics of WCOs. In this context, electronic noses (e-noses) have been employed

64

for quality control in many fields including those associated with agriculture, biomedicine,

65

cosmetics, food, military and the environment. E-noses are multisensor devices that mimic

66

the mammalian olfactory system and provide the aroma profile of sample mixtures without

67

identifying individual chemical species within the mixture11-13. In a recent publication13, we developed a stochastic model capable of obtaining

68 69

useful information from e-nose profiles. This information could be useful in assessing the

70

quality and suitability of WCO for producing biodiesel. In order to test this hypothesis, in

71

the present study we have: (i) used 36 WCO samples and biodiesel samples synthesized

72

therefrom, (ii) recorded the olfactory profiles of the WCOs using an e-nose, and (iii)

73

applied our stochastic model and quadratic discriminant analysis to the olfactory profiles in

74

order to identify WCOs that would be suitable for the generation of biodiesels compliant

75

with the specifications of the Brazilian Petroleum Authority (Agência Nacional de

76

Petróleo, Gás Natural e Biocombustíveis; ANP).

77

2.

Experimental


Page 5 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

78

Energy & Fuels

2.1.

Characterization of WCOs Thirty-six samples of WCOs generated by food frying were collected from

79 80

domestic (nd = 18) and commercial (nc = 18) premises located in the municipality of

81

Lorena, SP, Brazil. Samples were filtered through filter paper and homogenized prior to

82

analysis. Acidity and peroxide indices, determined according to the methods described by

83

the American Oil Chemists’ Society14,15, and density, recorded using an Anton Paar (Graz,

84

Austria) model DMA™ 35N EX digital density meter, were measured at 20 ºC with 2 mL

85

samples. Absolute fluid viscosity was determined as a function of shear stress rate at 40 ºC

86

with ~1 mL samples using a Brookfield Viscometers (Harlow, UK) model LVDVIII

87

viscometer and a CP-42 cone. Color was evaluated at 25 ºC using a HunterLab (Reston,

88

VA, USA) ColorQuest XE benchtop spectrophotometer and EasyMatch QC software. The

89

instrument was calibrated in the total transmittance mode for color assessment of

90

translucent liquids and configured for measurements using a D65 standard light source and

91

a 10° viewing angle (D65/10° color space). Assessments were performed using a 50 mm

92

path length cuvette in order to obtain values based on the CIELAB system according to the

93

color axes L *, a * and b *16. All analyses were performed in triplicate.

94

2.2.

95

Synthesis and characterization of biodiesel Although production of biodiesel from high acid oils has been reported to be better

96

achieved from homogeneous acid or some specific heterogeneous catalysts, in this work

97

the more industrially employed basic homogeneous catalysis was used to evaluate the

98

biodiesel produced under the more usual conditions of production17. Transesterification of

99

a 100 g sample of a WCO was carried out at 60 ºC in a 300 mL jacketed reactor vessel

100

fitted with a mechanical stirrer set at 400 rpm. The catalyst, potassium hydroxide, was

101

dissolved in ethanol (9:1 alcohol: oil molar ratio) to form the alkoxide and subsequently 5 ACS Paragon Plus Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 25

102

added to the reactor vessel at a mass ratio of 1% catalyst to oil. After 2 h of reaction time,

103

the product mixture was transferred to a decantation funnel in order to separate the heavy

104

(glycerol and wash water) and light (ethyl esters or biodiesel) phases. The biodiesel was

105

washed with distilled water until the washings showed the same pH as distilled water and

106

then left to stand for 2 h. The biofuel was reduced to dryness at 80 ºC for 40 min in a rotary

107

evaporator, and drying was completed by the addition of anhydrous sodium sulfate.

108

Samples of biodiesel were assessed for quality compliance according to ANP

109

specifications regarding acidity index (≤ 0.5 mg KOH g-1), viscosity (3 – 6 mm2 s-1) and

110

density (850 – 900 kg m-3) as described above, while ester concentration (≥ 96.5%) was

111

determined by nuclear magnetic resonance (NMR) spectroscopy18 and iodine index was

112

calculated using the Hübl method19.

113

2.3.

Analysis of olfactory profiles of WCOs The olfactory profiles of the WCO samples were determined using a Sensigent

114 115

(Baldwin Park, CA, USA) Cyranose® 320 chemical vapor-sensing instrument, which

116

utilizes a NoseChip® array of 32 nanocomposite sensors and advanced pattern recognition

117

algorithms to detect substances based on the electrical resistances of the sensors. The

118

sensors act in response to the vapor headspace to which they are exposed, as explained in

119

the work of Giordani et al. (2008)11. The time during which the sensor was exposed to

120

vapor is large enough to reasonably assume that the concentration of volatile substances is

121

constant13. The WCO samples were stabilized at 23 ºC prior to analysis, and ten replicates

122

of each sample were analyzed to provide the olfactory profiles.

123

2.4.

Stochastic modeling


Page 7 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

The model used to extract information from the signals generated by the e-nose was

124 125

that proposed by Siqueira et al. (2018)13 and described by the stochastic differential

126

equation:

127

𝑑𝑋𝑡 = 𝑎 + 𝑒𝑘𝑡 𝑑𝑡 +

128

where 𝑋𝑡 is the electronic measurement of the e-nose signal at time t, 𝑊𝑡is the Brownian

129

motion to reproduce the signal noise, and a, b, c, k and p are model parameters.

130

The typical e-nose signal with respect to time may be described by the average signal plot

131

(corresponding to the solid line in Fig. 1), which shows an initial rapid increase in signal

132

followed by a linear behavior, called “plateau”. In the stochastic model, 1/k indicates the

133

approximate time when the signal reaches the “plateau”, 𝑎 represents the slope of the

134

“plateau” (which can be positive, negative or zero), and b is the signal strength at the start

135

of the “plateau”. The model parameters 𝑐 and 𝑝 are related to the widths of the 95%

136

confidence intervals (CI95) of the e-nose measurements (corresponding to the two dashed

137

lines in Fig.1). The larger the 𝑐 value, the greater the variability of the signal, whereas a

138

larger 𝑝 value indicates lower variability of the signal noise. The estimators of the model

139

parameters have been described by Siqueira et al. (2018)13, and these authors have shown

140

that the combination (𝑎 + 𝑏𝑘) may be proportional to the concentration of volatile

141

substances present in the sensor region.

(

𝑏𝑘

)

𝑐

142 143

𝑑𝑊𝑡

(𝑡 + 1)𝑝

(1)

(FIGURE 1 NEAR HERE)

2.5.

Statistical analyses

144

Quadratic discriminant analysis (Box's M-test, p value < 0.0001) with cross-

145

validation was employed to examine the possibility of predicting compliance of a biodiesel

146

with ANP specifications based on the origin of a WCO and the parameters of its olfactory


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

147

profile. All analyses were performed using R software version 3.2.5 (R Foundation,

148

Vienna, Austria) and Minitab version 16 (Minitab, State College, PA. USA).

149

3.

150

3.1.

151

Page 8 of 25

Results and discussion Characteristics of WCOs and biodiesels produced therefrom Knowledge of the physicochemical properties of a WCO is essential in order to

152

assess the suitability of the material for the production of biodiesel, since these

153

characteristics influence the transesterification reaction and, consequently, the quality of

154

the biofuel so formed20. The acidity indices of the WCO samples were highly variable and

155

ranged from 0.39 to 12.59 mg KOH g-1 (Table 1). High acidity indices result from

156

oxidation and hydrolysis reactions that oils undergo during the frying process or storage21.

157

According to some studies, acidity indices above 1 mg KOH g-1 may cause deactivation of

158

the catalyst, thus impeding the transesterification reaction and resulting in a reduction in

159

the yield of esters, the generation of large amounts of soap, and the production of low

160

quality biodiesel22. Considerable variability was also observed in the peroxide indices of

161

the WCO samples, which ranged from 1.98 to 71.82 meq kg-1, indicating that many

162

samples had undergone considerable oxidative degradation. The kinematic viscosity of

163

WCO samples varied between 33.15 and 61.84 mm2 s-1, values that were much higher than

164

those (29.80 - 47.80 mm2 s-1) reported previously by Tiosso et al.20. However, the oil

165

samples evaluated in the present study had all been subjected to the complex chemical

166

reactions that occur during the frying process.

167 168

(TABLE 1 NEAR HERE)

Regarding the color of WCO samples, substantial variations were observed in the

169

measurements on all CIELAB axes, with lightness L* ranging from 0.05 to 68.98 (where 0

170

indicates extremely dark and 100 completely colorless), while the chromaticity coordinates 8 ACS Paragon Plus Environment

Page 9 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

171

a* and b* ranged from -5.79 to 31.45 and from 0.04 to 70.92, respectively. Although the

172

chromaticity coordinates can assume positive or negative values, the great majority of

173

positive values recorded in the present study indicate that the color of the WCOs tended to

174

towards the red (a*) and yellow (b*) sections of the chroma scales.

175

According to Lobo et al.21, it is possible to obtain information about the quality of a

176

biodiesel, including the selection of appropriate raw material, the production and storage

177

process, and the nature of its emissions, through application of a range of analytical

178

methods. In the present study, samples of biodiesel synthesized from WCOs were assessed

179

according to the main descriptors included in ANP specifications, namely ester content,

180

acidity index, density, viscosity and iodine index.

181

The ester content of a biodiesel is particularly important since it is associated with

182

the efficiency of the transesterification reaction. The synthesized biodiesels exhibited ester

183

contents that varied between 0 and 100%, although 75% of the samples contained at least

184

96.5% of esters and complied with ANP specifications23-24 (Table 1). The acidity indices of

185

the synthesized biodiesels varied between 0.14 and 6.3 mg KOH g-1 with 59% of the

186

samples exhibiting values higher than the acceptable limit of 0.5 mg KOH g-1. Increased

187

acidity suggests the presence of water in the reaction medium, since acids are formed by

188

hydrolytic reactions, and this is associated with a reduction in the quality of biodiesel

189

because it causes corrosion and accelerates aging in diesel engines21.

190

The iodine index of a biodiesel indicates the degree of unsaturation of the biofuel, a

191

factor that influences density, viscosity and oxidative stability21. Values of the iodine index

192

of the synthesized biodiesels varied between 72.84 and 113.37 g I2 100 mg-1 with 100% of

193

the samples exhibiting values up to the acceptable limit of 120 g I2 100 mg-1 (Table 1). The

194

densities of biodiesel samples were in the range 810 – 930 kg m-3 with 91% presenting

195

values within the acceptable limits of 850 to 900 kg m-3. According to Basso et al.25,


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

196

density influences the efficiency of atomization of the diesel in the fuel injector of the

197

engine.

Page 10 of 25

Viscosity not only affects atomization efficiency of the biofuel but also its

198 199

distribution in the engine and is, therefore, an important variable for monitoring the

200

conversion yield of the transesterification process. A reduced viscosity suggests a high

201

conversion yield into esters26, whereas increased viscosity indicates that the WCO was

202

incompletely transesterified or that the biodiesel was not adequately purified. The presence

203

of contaminants, residual soaps and products of oxidative degradation are further factors

204

responsible for increased viscosity21. The kinematic viscosities of the synthesized

205

biodiesels tested in the present study varied widely in the range 4.67 – 57.53 mm2 s-1,

206

although 81% of the samples presented values within the acceptable limits of 3 to 6 mm2

207

s-1.

208

3.2.

209

Olfactory profiles of WCOs and stochastic analyses Sensors 17, 18 and 28 of the Cyranose® 320 chemical vapor-sensing instrument

210

were selected for the assessment of olfactory profiles because they exhibited typical signal

211

acquisition behavior (cf. Fig. 1) as described by Siqueira et al.13. Of the 360 e-nose profiles

212

analyzed, i.e. 10 profiles for each of the 36 WCOs, a total of 317 exhibited acceptable

213

goodness-of-fit to the stochastic model as shown by the CI95 and R-squared values (>

214

0.976) presented in Fig. 2.

215


216 217

Mean values of the model parameters 1/k, p and a + bk of the olfactory profiles

218

produced by sensors 17, 18 and 28 (Fig. 3) revealed distinct differences between WCO

219

samples that generated biodiesels that were compliant with ANP specifications (group

220

BD1) and those that generated biodiesels that were non-compliant (group BD0). Thus, 10 ACS Paragon Plus Environment

Page 11 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

221

olfactory signals emitted by sensors 17 and 28 for the BD0 group of WCOs tended to reach

222

the plateau of the signal/time plot more rapidly than those of the BD1 group as

223

demonstrated by the smaller 1/k values (Fig. 3a and 3b). Moreover, the signals emitted by

224

sensors 18 and 28 for the BD0 group exhibited more variability in comparison with those

225

of the BD1 group as demonstrated by the smaller 𝑝 values (Fig. 3c and 3d). Finally, the

226

mean concentration of volatile substances around sensor 28 (as determined by 𝑎 + 𝑏𝑘) was

227

higher in the BD0 group compared with the BD1 group (Fig. 3e). One possible explanation

228

for these results is the presence of moisture in BD0 samples, which generated more rapid

229

and variable signals and a higher concentration of volatiles around the sensors. According

230

to Sabudak & Yildiz6, the presence of water in a WCO promotes the formation of free fatty

231

acids that hinder the production of biodiesel using the method employed in this study. The

232

presence of free fatty acid requires pretreatment in the biofuel process in order to reduce

233

this compound5,27. Therefore, the technique proposed here can help in reducing the volume

234

of residual oil destined to the pretreatment, decreasing their costs.

235 236


3.3.

Characteristics of synthesized biodiesels according to stochastic modeling

237

The prediction of obtaining quality biodiesel from the OGR, even before the

238

production process begins, is extremely important, both economically and environmentally.

239

In Siqueira et al.13 the stochastic model presented in (1) was used to analyze the olfactory

240

profile produced by an electronic nose, which enabled the origin of the OGR to be identified,

241

residential neighborhoods and commercial restaurants. The aim of the present study is to

242

predict the quality of the biodiesel that will be produced from WCO.

243

Quadratic discrimination analysis with cross-validation was employed in order to

244

examine the possibility of predicting whether a synthesized biodiesel would be compliant

245

with ANP specifications based on the parameters of the olfactory profile (provided by e11 ACS Paragon Plus Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 25

246

nose sensors 17, 18 and 28) and the origin (domestic or commercial) of the WCO. The

247

stochastic parameters that were significant for discriminant analysis are identified in Table

248

2, from which it may be observed that their distribution was balanced between the sensors.

249

(TABLE 2 NEAR HERE)

250

The confusion matrices obtained by quadratic discriminant analyses of 317 e-nose

251

profiles for the parameters ester content, acidity index, density and viscosity are presented

252

in Table 3. The overall accuracy for ester content was around 80%, with 82% of profiles of

253

WCOs that produced biodiesel with compliant ester content (≥ 96.5%) identified correctly

254

and 76% of the non-compliant profiles rightly classified. For acidity index, the overall

255

accuracy was 80%, with 87% of compliant profiles (synthesized biodiesel with ≤ 0.5 mg

256

KOH g-1) and 75% of non-compliant profiles identified correctly. Regarding density, the

257

overall accuracy was 92%, with 93% of compliant profiles (synthesized biodiesel in range

258

850 – 900 kg m-3) and 80% of non-compliant profiles rightly classified. For viscosity, the

259

overall accuracy was 89%, with 91% of compliant profiles (synthesized biodiesel in range

260

3 – 6 mm2 s-1) and 80% of non-compliant profiles identified correctly. Finally, for all

261

descriptors of biofuel quality, the overall accuracy was 84%, with 79% of compliant

262

profiles (synthesized biodiesel with all descriptors according to ANP specifications) and

263

86% of non-compliant profiles identified correctly (synthesized diesel non-compliant with

264

respect to one or more ANP specifications).

265 266

(TABLE 3 NEAR HERE)

Our results demonstrate that stochastic modeling is a promising tool in the

267

prediction of biodiesel quality based on the olfactory profile of a WCO, since it allows

268

qualitative assessments of the parameters of interest. Application of our stochastic model

269

in association with quadratic discriminant analysis presented a predicting quality of

270

biodiesel, according to ANP specifications, based on the profiles provided by an e-nose

271

device. The predictive model described herein is efficient and has the advantage of 12 ACS Paragon Plus Environment

Page 13 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

272

eliminating the need for complex and time-consuming laboratory tests. The development

273

of e-noses for specific applications such as biodiesel production would greatly improve the

274

efficiency and economic viability of the processes while allowing the diversification of raw

275

materials to include other types of food waste, thus engendering a positive impact on both

276

society and the environment.

277 278 279

4.

Conclusions Our results demonstrate that stochastic modeling is a promising tool in the

280

prediction of biodiesel quality based on the olfactory profile of a WCO, since it allows

281

qualitative assessments of the parameters of interest. Application of our stochastic model

282

in association with quadratic discriminant analysis presented a predicting quality of

283

biodiesel, according to ANP specifications, based on the profiles provided by an e-nose

284

device. The predictive model described herein is efficient and has the advantage of

285

eliminating the need for complex and time-consuming laboratory tests. The development

286

of e-noses for specific applications such as biodiesel production would greatly improve the

287

efficiency and economic viability of the processes while allowing the diversification of raw

288

materials to include other types of food waste, thus engendering a positive impact on both

289

society and the environment.

290

Author Information

291

Corresponding author

292

*Ana Lucia Gabas Ferreira, E-mail: [email protected], Department of Basic and

293

Environmental Sciences, Engineering School of Lorena, University of São Paulo, Brazil.

294

Acknowledgements 13 ACS Paragon Plus Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

295

This work was financed by Fundação de Amparo à Pesquisa do Estado de São Paulo

296

(FAPESP; grant number 2014/25001-2) and in part by the Coordenação de

297

Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

298

References

299

1.

C.A.G. Garcez, J.N.S. Vianna, Brazilian biodiesel policy: social and environmental considerations of sustainability. Energy 2009, 34, (5), 645-654.

300 301

Page 14 of 25

2.

A.E. Atabani, A.S. Silitonga, I.A. Badruddin, T.M.I. Mahlia, H.H. Masjuki, S.

302

Mekhilef, A comprehensive review on biodiesel as an alternative energy resource

303

and its characteristics. Renew. Sust. Energ. Rev. 2012, 16, (4), 2070-2093.

304

3.

Brazilian National Agency of Petroleum, Natural Gas and Biofuels, Biodiesel,

305

January 2019, ANP, Brasília, 2019.

306

http://www.anp.gov.br/biocombustiveis/biodiesel (Accessed 29 January 2019) (In

307

Portuguese).

308

4.

H.M. Mahmudul, F.Y.Hagos, R. Mamat, A.A. Adam, W.F.W. Ishak, R. Alenezi,

309

Production, characterization and performance of biodiesel as an alternative fuel in

310

diesel engines – a review. Renew. Sust. Energ. Rev. 2017, 72, 497–509.

311

5.

Sahar, S. Sadaf, J. Iqbal, I. Ullah, H. N. Bhatti, S. Nouren, Habib-Ur-Rehman, J.

312

Nisar, M. Iqbal, Biodiesel production from waste cooking oil: An efficient

313

technique to convert waste into biodiesel. Sustainable Cities and Society. 2018, 41,

314

220–226.

315 316

6.

T. Sabudak, M. Yildiz, Biodiesel production from waste frying oils and its quality control. Waste Manag. 2010, 30, (5), 799–803.


Page 15 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

317

Energy & Fuels

7.

Z. Yaakob, M. Mohammad, M. Alherbawi, Z. Alam, K. Sopian, Overview of the

318

production of biodiesel from waste cooking oil. Renew. Sust. Energ. Rev. 2013, 18,

319

184-193.

320

8.

Brazilian Association of Vegetable Oil Industries, January 2019, ABIOVE, São

321

Paulo, 2019.

322

http://www.abiove.org.br/site/index.php?page=statistics&area=MTAtMi0x

323

(Accessed 29 January 2019).

324

9.

Brazilian National Agency of Petroleum, Natural Gas and Biofuels, Statistic data,

325

January 2019, ANP, Brasília, 2019. http://www.anp.gov.br/dados-estatisticos

326

(Accessed 29 January 2019) (In Portuguese).

327

10.

A.S. César, D.E. Werderits, G.L.O. Saraiva, R.C.S Guabiroba. The potential of

328

waste cooking oil as supply for the Brazilian biodiesel chain. Renewable and

329

Sustainable Energy Reviews, 2017, 72, 246–253.

330

11.

D.S. Giordani, A.F. Siqueira, M.L.C.P. Silva, P.C. Oliveira, H.F. Castro,

331

Identification of the biodiesel source using an electronic nose. Energy Fuels 2008,

332

22, (4), 2743-2747.

333

12.

noses for food quality: a review. J. Food Eng. 2015, 144, 103-111.

334 335

A. Loutfi, S. Coradeschi, G.K. Mani, P. Shankar, J.B.B. Rayappan, Electronic

13.

A.F. Siqueira, M.P. Melo, D.S. Giordani, D.R.V. Galhardi, B.B. Santos, P.S.

336

Batista, A.L.G. Ferreira, Stochastic modeling of the transient regime of an

337

electronic nose for waste cooking oil classification. J. Food Eng. 2018, 221, 114–

338

123.

339 340

14.

American Oil Chemists’ Society, Official Methods of Analysis of the Association of Official Analytical Chemists, method 962.19, AOCS, Urbana, 16th ed, 1995.


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

341

15.

American Oil Chemists’ Society, Official Methods and Recommended Practices of the American Oil Chemists’ Society, AOCS, Urbana, 4th ed, 1990.

342 343

16.

J.D. Motta, A.J.M. Queiroz, R.M.F. Figueirêdo, K.S.M. Sousa, Índice de cor e sua

344

correlação com parâmetros físicos e físico-químicos de goiaba, manga e mamão.

345

Comun. Sci. 2015, 6, (1), 74–82.

346

17.

J. Nisar, R. Razaq, M. Farooq, M. Iqbal, R. Ali, M. Sayed, A. Shah, Enhanced

347

biodiesel production from Jatropha oil using calcined waste animal bones as

348

catalyst. Renewable Energy, 2017, 101, 111–119.

349

18.

E.J.M. Paiva, M.L.C.P. Silva, J.C.S. Barboza, P.C. Oliveira, H.F. Castro, D.S.

350

Giordani, Non-edible babassu oil as a new source for energy production-a

351

feasibility transesterification survey assisted by ultrasound. Ultrason. Sonochem.

352

2013, 20, (3), 833-838.

353

Page 16 of 25

19.

M.B. Dantas, A.R. Albuquerque, A.K. Barros, M.G. Rodrigues Filho, N.R.

354

Antoniosi Filho, F.S.M. Sinfrônio, R. Rosenhaim, L.E.B. Soledade, I.M.G. Santos,

355

A.G. Souza, Evaluation of the oxidative stability of corn biodiesel. Fuel. 2011, 90,

356

(2), 773–778.

357

20.

P.C. Tiosso, A.K.F. Carvalho, H.F. Castro, F.F. Moraes, G.M. Zanin, Utilization of

358

immobilized lipases as catalysts in the transesterification of non-edible vegetable

359

oils with ethanol. Braz. J. Chem. Eng. 2014, 31, (4), 839–847.

360

21.

analíticos. Quim. Nova. 2009, 32, (6), 1596–1608.

361 362

22.

365

A.N. Phan, T.M. Phan, Biodiesel production from waste cooking oils. Fuel. 2008, 87, (17-18), 3490–3496.

363 364

I.P. Lôbo, S.L.C. Ferreira, R.S. Cruz, Biodiesel: parâmetros de qualidade e métodos

23.

C. Brunschwig, W. Moussavou, J. Blin, Use of bioethanol for biodiesel production. Progress in Energy and Combustion Science. 2012, 38, (2), 283–301.


Page 17 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

366

Energy & Fuels

24.

N. Nisar, S. Mehmood, H. Nisar, S. Jamil, Z. Ahmad, N. Ghani, A. Adeyemi, R.

367

Wassem, A. Abdul, S. Rashid, M. Iqbal, M. Abbas, Brassicaceae family oil methyl

368

esters blended with ultra-low sulphur diesel fuel (ULSD): Comparison of fuel

369

properties with fuel standards. Renewable Energy. 2018, 117, 393-403.

370

25.

R.C. Basso, A.J.A. Meirelles, E.A.C. Batista, Densities and viscosities of fatty acid

371

ethyl esters and biodiesels produced by ethanolysis from palm, canola, and soybean

372

oils: Experimental data and calculation methodologies. Ind. Eng. Chem. Res. 2013,

373

52, (8), 2985–2994

374

26.

R.A. Usmanov, S.V. Mazanov, A.R. Gabitova, L.K. Miftakhova, F.M. Gumerov,

375

R.Z. Musin, I.M. Abdulagatov, The effect of fatty acid ethyl esters concentration on

376

the kinematic viscosity of biodiesel fuel. J. Chem. Eng. Data 2015, 60, (11), 3404–

377

3413.

378

27.

Y. Zhang, M.A. Dubé, D.D. McLean, M. Kates, Biodiesel production from waste

379

cooking oil: Economic assessment and sensitivity analysis. Bioresour. Technol.

380

2003, 90, 229-240.

381 382 383 384 385 386 387 388 389 390


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 25

391 392

Figure legends Fig. 1. Typical behavior of an e-nose signal. The individual data points represent the discrete signals acquired from the e-nose, the solid line represents the average signal plot, while the dashed lines represent the 95% confidence intervals calculated according to equation 1. Fig. 2. Interval plots of the 95% confidence intervals of mean R2 values representing the goodness-of-fit to the stochastic model of olfactory profiles obtained from e-nose sensors 17, 18 and 28. Fig. 3. Interval plots of the 95% confidence intervals of means of the variables 1/k, p and a + bk, established from olfactory profiles produced by sensors 17, 18 and 28, showing distinct differences between samples of waste cooking oil that generated compliant biodiesels (group BD1) and those that did not (group BD0).


Page 19 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Figure 1

393 394 395 396 397 398 399 400 401


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 25

Figure 2


Page 21 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

(a)

(b)

(c)

(d)

(e)

Figure 3 402


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

403

Page 22 of 25

Graphical Abstract

404

405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420


Page 23 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 1 Characteristics of waste cooking oil and biodiesel synthesized therefrom. ANPa descriptors Acidity index (mg KOH g-1) Peroxide index (meq kg-1) Density (kg m-3) Viscosity (mm2 s-1) Color L* a* b* Ester content (%) Iodine index (g I2 100 mg-1) a Brazilian

Waste cooking oil mean ± SDb 1.78 ± 2.56 18.36 ± 16.15 917 ± 6.4 42.69 ± 7.62

Synthesized biodiesel mean ± SD 0.91 ± 1.14 NA c 880 ± 20 8.66 ± 10.71

No. of biodiesel samples compliant with ANP nb (%) 14 (39%) NA 29 (81%) 33 (92%)

43.36 ± 13.68 6.93 ± 8.25 49.37 ± 13.80 NA NA

NA NA NA 90.51 ± 24.73 99.11 ± 9.96

NA NA NA 27 (75%) 36 (100%)

Petroleum Authority (Agência Nacional de Petróleo, Gás Natural e

Biocombustíveis). b

standard deviation.

c

not applicable.


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 25

Table 2 Results of quadratic discriminant analyses with cross-validation showing parameters of the stochastic model that were significant for predicting the main descriptors of synthesized biodiesels included in the specifications of the Brazilian Petroleum Authority (ANP). Stochastic model Biodiesel descriptorsb parametersa Ester content Acidity index Density a X X X b c X Sensor 17 k X X p 𝑎 + 𝑏𝑘 X X 1/k X a b X X c X Sensor 18 k X X p X 𝑎 + 𝑏𝑘 X X 1/k a X X X b X X c Sensor 28 k X p 𝑎 + 𝑏𝑘 X 1/k X Origin X X X (domestic/commercial) E-nose sensors

a Stochastic

(

differential equation: 𝑑𝑋𝑡 = 𝑎 +

421

b

422

quadratic discriminant analysis.

𝑏𝑘 𝑒

)𝑑𝑡 +

𝑘𝑡

𝑐

Viscosity X

All descriptors X

X X

X

X X

X X X X

X X X X

X X X X

X X X

X

X

𝑑𝑊𝑡

(𝑡 + 1)𝑝

X denotes the parameters of the stochastic model that were considered as predictors in the


Page 25 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 3 Confusion matrices obtained by quadratic discriminant analyses of 317 e-nose profiles for ester content, acidity index, density and viscosity showing the efficiency of the stochastic model in predicting the compliance of synthesized biodiesels with the specifications of the Brazilian Petroleum Authority (ANP).

Predicted class Ester contenta Actual class

0 1

Totals in predicted classes

1 19 195

103

214

Predicted class

Acidity indexa Actual class

0 1


0 140 17

1 47 113

157

160

Predicted class

Densitya Actual class

0 1


0 24 19

1 6 268

43

274

Predicted class

Viscositya Actual class Totals in predicted classes All descriptors

0 60 43

0 1

0 48 24

1 12 233

72

245

Predicted class

Totals in actual classes 79 238




Totals in actual classes

0 1 0 169 27 196 Actual class 1 25 96 121 Totals in predicted 194 123 classes a Class 1 - oils compliant with ANP specifications regarding the descriptor indicated, (i.e. ester content ≥ 96.5%, acidity index ≤ 0.5 mg KOH g-1, density 850 – 900 kg m-3 or viscosity 3 – 6 mm2 s-1). Class 0 - oils non-compliant with ANP specifications regarding the descriptor indicated.

ACS Paragon Plus Environment

Assessing Waste Cooking Oils for the Production of Quality Biodiesel

Recommend Documents