Optimization of a liquid-phase plasma discharge process for biodiesel synthesis from pure oleic acid

Fuel Processing Technology 202 (2020) 106368

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Optimization of a liquid-phase plasma discharge process for biodiesel synthesis from pure oleic acid

T

⁎

Sarah Wua, , Muhammad Aamir Bashira, Jun Zhub a b

Department of Biological Engineering, University of Idaho, Moscow, ID 83844, USA Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Free fatty acids Biodiesel feedstocks Liquid phase plasma discharge Central composite design Esterification Transesterification

A novel liquid phase plasma discharge (LPPD) process was developed and optimized for converting pure oleic acid to biodiesel. Independent variables including weight ratio of H2SO4 to oleic acid (CAT, %) at 0, 1, 2, 3, and 4% and the methanol to oleic acid molar ratio (MOMR) at 4, 6, 8, 10, and 12 were chosen and examined. Central Composite Design (CCD) coupled with Response Surface Methodology (RSM) was used for optimizing the conversion rate, Rconv. Results showed that in 4 min treatment, the LPPD process could achieve an optimal Rconv of 80.78% at CAT 2.38% and MOMR 8.02. Also, Rconv was found to be affected significantly by the CAT (p = 0.0039), but not by MOMR (p = 0.9027). A quadratic regression model for adequately describing the LPPD process performance was established with a p value of 0.0022. The uncertainty analysis further confirmed the model accuracy within a low error range of from 1.2% to 0.66% of the modeled value within examined CAT and MOMR ranges. The data suggested that the novel LPPD process could break the current status quo of lacking effective techniques to convert substrates containing high levels of FFAs to biodiesel.

1. Introduction Biodiesel, which is environmentally friendly and renewable, has increasingly been considered an alternative transportation fuel to petrobased diesel at a global level [1]. Biodiesel is characteristic of non-toxic, biodegradable, and non-flammable, and has significantly less impact on climate change as compared to petro-based diesel when combusted [2]. It was further reported that compared to ethanol production, biodiesel production yielded more net energy gain from the production process than ethanol production (93% net energy gain for the former vs. 25% for latter), and the production process produced only 1.0%, 8.3%, and 13% of the agricultural nitrogen, phosphorus, and pesticide pollutants for every net energy gain, respectively [3]. In addition, the amount of petro-based diesel replaced by biodiesel could lead to reduction of greenhouse gas emissions by up to 41%, which is phenomenal [3]. Therefore, biodiesel is gaining popularity in not only the transportation sector but also the public that is concerned about the environmental consequences resulting from the excessive use of fossil fuels. Currently, the major volume of biodiesel is produced from foodgrade substrates such as edible vegetable and/or soybean oils via a chemical process called “transesterification”. Using these substrates is known to create a competition between food supply for human consumption and biodiesel production [4]. Besides, the high costs of these ⁎

food products have led to the higher production cost of biodiesel than that of petro-based diesel, making it difficult to become a mainstream fuel in the transportation market. To overcome these barriers, researchers have strengthened their efforts in seeking low-cost, alternative feedstocks for biodiesel production including animal fats, recycled greases, used vegetable and cooking oils, etc. [5–7]. However, these substrates are not without problems, and one of the major issues encountered is the increased content of free fatty acids (FFA) in the substrates. Past research showed that the yield of biodiesel could be reduced from around 91% to 59% due to soap formation during transesterification when the FFA content in the feedstock increased from 5% to 33% [8–10]. Therefore, it was suggested that substrates with more than 3% of FFA content must be pretreated prior to use in the transesterification process for biodiesel production [11]. The problem of high FFA content in used oils (e.g., acidic soybean oil, waste cooking oil, etc.) or animal fats has been actively investigated by many researchers using a two-step process, in which acid-based catalysis was used in the first step to pre-esterify the FFA in the used oils, followed by the alkaline-based transesterification to produce biodiesel [12,13]. Identified drawbacks of using acid catalyst (such as H2SO4) to convert FFAs to biodiesel in the pre-esterification process included its low reaction rate, low biodiesel yield, and long product separation [14,15]. Recently, alternative production methods to

Corresponding author at: Department of Biological Engineering, University of Idaho, 875 Perimeter Drive MS 0904, Moscow, ID 83844-0904, USA. E-mail address: [email protected] (S. Wu).

https://doi.org/10.1016/j.fuproc.2020.106368 Received 17 December 2019; Received in revised form 9 February 2020; Accepted 9 February 2020 0378-3820/ © 2020 Elsevier B.V. All rights reserved.

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operating costs for large-scale applications as well. A timer (Fisherbrand Traceable Countdown Controller, Catalog#: 14-648-36, Fisher Scientific, Pittsburgh, PA) was used to control the running time of the peristaltic pump, which was 4 min. The entire LPPD system was composed of a high-voltage AC transformer (Plasma Technics Inc., Racine, WI 53404, USA), connected to the stainless-steel electrodes on the reactor, to provide high-voltage discharge to the liquid passing the reactor, and a peristaltic pump (not shown in Fig. 1) for continuous movement of the substrate through the reactor. A transformer regulator was used to control/adjust the applied power during the experiment.

convert high FFA substrates to biodiesel have made their way to the scientific literature, such as the microwave-assisted [16] and ultrasonic assisted methods [17]. It was reported that these two treatments resulting from the collapse of cavitation bubbles could generate emulsions from immiscible liquids such as oil and alcohol in biodiesel conversion and promote contacts of reactants, thus improving the reaction rates [18]. Based on this mechanism, a novel liquid-phase plasma discharge reactor was successfully applied to conversion of soybean oil into biodiesel with very high conversion efficiency achieved [19] because the non-thermal plasma technology could produce the combined effects of both microwave and ultrasound techniques [20]. Despite the reported success in converting virgin soybean oil to biodiesel, using the liquid-phase plasma discharge technology to convert FFA to biodiesel in a one-step process has not been reported in the available literature. The significance of this technical breakthrough rests with the possibility of eliminating the pre-treatment step for feedstocks with high contents of FFAs in biodiesel production as they can be esterified during transesterification. As such, the feedstock sources for biodiesel production can be greatly expanded with the production costs substantially reduced. In this project, a newly designed liquid-phase plasma discharge reactor (LPPD) was evaluated for its performance in converting pure oleic acid to biodiesel in a one-step reaction using sulfuric acid as the catalyst and methanol as the co-substrate. Operating parameters including the catalyst to oleic acid weight ratio (CAT, %, w/w) and methanol to oleic acid molar ratio (MOMR). The two parameters were optimized using Central Composite Design (CCD, please note that this acronym is such defined throughout the text) coupled with Response Surface Methodology (RSM), with the conversion rate of oleic acid to biodiesel as the response variable. The relationships of the applied voltage and power consumption with CAT and MOMR were discussed in detail. Brief comparisons between the designs of the plasma reactor studied in this project and others reported in literature were also presented.

2.3. Experimental design Numerous studies showed that catalyst (CAT) concentration (%, w/ w) and co-substrate to main substrate molar ratio were major influencing factors in transesterification and esterification processes [22,23]. In this study, since sulfuric acid, methanol, and oleic acid were used as the catalyst, co-substrate, and main substrate, respectively, the H2SO4 concentration (CAT) and the molar ratio of methanol to oleic acid (MOMR) were chosen as independent variables to examine their effects on the conversion rate of oleic acid to biodiesel. These two independent variables were all tested at five levels, i.e., 0, 1, 2, 3, and 4 (%, w/w) for CAT and 4, 6, 8, 10, and 12 for MOMR. These levels were chosen based on preliminary trials. To find the optimal combination of these two independent variables for best acid conversion, a Central Composite Design (CCD) combined with Response Surface Methodology (RSM) was adopted with the central point values (zero level) for CCD being CAT = 2 (%, w/w) and MOMR = 8. The Design-Expert software (version 11, Stat-Ease, Inc., Minneapolis, MN) was used to run the CCD/ RSM design, based on which a total of 13 experiments were conducted (Table 1), each lasting 4 min. This reaction time was also determined from preliminary data because longer reaction times were found to have little improvement in conversion efficiency. A second order quadratic model (Eq. 1) was generated by the Design-Expert software to fit the obtained responses to the experimental variables. The substrate (100 mL each) for each run was prepared according to the CAT and MOMR information determined by the Design-Expert software, which was presented in Table 1. The feeding pump speed for liquid transfer for all experiments was set at 100 rpm (the corresponding flowrate was approximately 2.7 mL s−1). All experiments were run under room temperature (20–22 °C).

2. Materials and methods 2.1. Source of substrate Since oleic acid was reported to be a major component FFA in waste oils [21], it was chosen as the substrate in this research. The oleic acid used was purchased from Sierra Chemical Co. (West Sacramento, CA). Before use, the oleic acid was analyzed for acid value using the potassium hydroxide (KOH) titration method, which was 246 mg KOH/g. And the molar mass of the oleic acid (C18H34O2) was 282.47 g/mol. Sulfuric acid (catalog#: A300C-212) was used as the catalyst and methanol (catalog#: A412-1) was used as the co-substrate. They both were purchased from Fisher Scientific (Pittsburgh, PA). All the chemicals were used as received.

2.4. Sample collection and analysis The prepared substrate (100 mL each) was stored in a flask connected to the LPPD reactor via a peristaltic pump. Once the pumping started, the power of the high-voltage AC transformer was tuned on, and the applied voltage and power were recorded when stable plasma discharge occurred. The applied voltage was recorded by an oscilloscope and the power was measured using a Watts meter connected to the LPPD reactor. Liquid samples were taken at the end of the 4 min discharging period and were immediately mixed with water to stop further esterification reaction. Each sample was then centrifuged for 10 min and allowed to settle to remove any acid or methanol left in the upper layer. The settled sample was then analyzed by titration to determine the free fatty acid (FFA) content according to Standard EN 14104 [24] using the following formula.

2.2. Plasma reactor design The design of the liquid-phase plasma discharge reactor (LPPD) was shown in Fig. 1, which was built on the first generation of the reactor design reported elsewhere [19]. The reactor body (fabricated from polycarbonate material) was evenly divided into three sections separated by two dielectric plates, each having a 1 mm opening (ϕ) in the center. This small opening was aimed at concentrating the electrons generated by the electrical discharge in the reactor to facilitate electron movement, mass transfer, and breakdown of the substrate molecules. Stainless steel bars were connected to all sections of the reactor body with the top and bottom sections functioning as ground voltage electrodes, while the middle section the high voltage electrode. This design allowed the liquid to be exposed to the plasma discharge twice as it moved through the reactor to improve conversion efficiency. It also allowed continuous operation with liquid coming into the reactor from the bottom and leaving at the top via a peristaltic pump (rev. 4, 4), which could greatly reduce the size of reactor, and potentially the

Conversion rate (%) =

(FFAB − FFAA ) ∗ 100 FFAB

FFAB, FFAA - free fatty acid before and after treatment of the sample.

2

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Fig. 1. Schematic of the liquid-phase plasma discharge reactor system. Table 1 Experimental runs using the Response Surface Methodology in Design-Expert Software. Run

1 2 3 4 5 6 7 8 9 10 11 12 13

Catalyst concentration (%, w/w)

Methanol to oil molar ratio (MOMR)

Conversion rate of oil to biodiesel (%)

3 2 1 2 2 3 2 0.59 2 1 2 3.41 2

10 8 6 5.17 8 6 8 8 8 10 8 8 10.83

71.80 76.31 56.68 64.52 79.65 65.18 83.74 45.63 77.69 60.18 79.86 71.52 56.16

Table 2 ANOVA analysis for fitting models for conversion rate under 4 min treatment. Model ANOVA Analysis

Model A – Catalyst (%, w/w) B – MOMR AB A2 B2 Residual Lack of fit

Sum of squares

df

Mean square

F-value

p-value

1401.21 402.34 0.3625 2.43 619.51 509.06 157.80 126.23

5 1 1 1 1 1 7 3

280.84 402.34 0.3625 2.43 619.51 509.06 22.54 42.08

12.46 17.85 0.0161 0.1080 27.48 22.58

0.0022 0.0039 0.9027 0.7521 0.0012 0.0021

5.33

0.0698

presented in Table 2. As seen from the p-value for the model, it could be concluded that the model was able to fit the response variable, i.e., Rconv, quite well with a p value of 0.0022 and an F value of 12.46 (much greater than 1.0), which was considered significant. Besides, it was interesting to note that Rconv was significantly affected by CAT (p value = 0.0039) but not by MOMR (p value = 0.9027). The interacting effect of CAT and MOMR was also not significant in terms of affecting Rconv (p = 0.7521). However, the quadratic entries of both CAT and MOMR showed a significant impact on Rconv with p values being 0.0012 and 0.0021, respectively. The goodness-of-fit of the model in describing the relationship between the independent and response variables could also be evaluated by the “lack-of-fit” results shown in Table 2. Obviously, the p value for the lack-of-fit test was 0.0698, which was greater than 0.05, indicating that the lack-of-fit was insignificant. To verify this observation, more experiments were conducted to correlate the modeled results with the observed responses and the data were presented in Fig. 2. The coefficient of determination for the response variable was 0.8979, meaning that the correlation coefficient was 0.9476. This level of correlation indicated that the regression model could explain 89.79% of the response variability if it was employed to simulate the acid conversion process to biodiesel.

3. Results and discussion 3.1. A quadratic model established using the CCD and RSM analysis Table 1 presents the conversion rate results from the experimental runs generated by the Design-Expert software. The central composite design produced a quadratic model (Eq. 1) to describe the relationship between the two independent variables, i.e., catalyst concentration (CAT, %, w/w) and methanol to oleic acid molar ratio (MOMR), and the response variable, i.e., the conversion rate (%) of oleic acid to biodiesel, which was listed in the far right column in Table 1. The coefficients (βis) in Eq. 1 were determined by the built-in regression analysis of the Design-Expert software based on the experimental data.

R conv (%) = −102.26 + 41.72 ∗ CAT + 33.33 ∗ MOMR + 0.39 ∗ CAT ∗ MOMR–9.44 ∗ CAT 2–2.14 ∗ MOMR2

ANOVA Parameters

(1)

To check the model fitting, the goodness-of-fit analysis for the model was examined using ANOVA analysis and the results were 3

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Fig. 2. The predicted vs. actual conversion rates based on the quadratic regression equation (at 4 min treatment time).

chemicals added to the reaction, i.e., CAT and MOMR. Since the analytical balance (Mettler Toledo NewClassic ME Precision Balance, Catalog#: 01-912-403, Fisher Scientific, Pittsburgh, PA) used to measure the chemicals had an accuracy limit of ± 0.3%, the range of Δxi could be assumed to be within ± 0.3% * xi (In this study, the worst case scenario was assumed, i.e., all measurements had a deviation of ± 0.3% of the true values). Expanding Eq. 2 gave the following Eq. 3

To further examine the goodness-of-fit of the model obtained, additional parameters related to the model fitting quality were also evaluated, including adjusted R2, coefficient of variation (CV), and adequate precision, which were 0.8268, 6.94%, and 8.9605, respectively. For the adjusted R2, although lower than 0.8979 from the linear regression equation shown in Fig. 2, it could still explain 82.68% of the variation in the response of the prediction model caused by the variation in independent variables, i.e., CAT and MOMR. This information showed the robustness of the model in describing the electrochemical process converting oleic acid to biodiesel. In addition, the CV for the model was 6.94%, suggesting that the model could precisely determine the response variables (i.e., Rconv) based on the independent variables in simulation [25]. On top of that, the obtained “adequate precision” value of 8.9605 further confirmed the above observation because it was much greater than 4.0 (indicating adequate signals). All these data have corroborated that the quadratic model generated by the Design Expert (version 11) software based on the experimental data collected could sufficiently predict the values of responses within the design space in the experimental design in this study [26]. In addition, Fig. 3 showed the residual analysis of model fitting regression calculated by the ANOVA operation. The random pattern of the residual distribution was typical of the pattern observed for good regression results [27]. As can be seen, the residual data were basically symmetrically distributed, with a tendency to cluster towards the middle of the plots but without clear patterns in general. Therefore, the data presented in Fig. 3 provided further evidence that the regression model developed could generate reliable observations if used in simulation of oleic acid conversion to biodiesel using the liquid plasma discharge process operated under the experimental conditions defined in this study. Finally, it is worth to conduct an uncertainty analysis (or systematic error) for the regression equation derived from the CCD/RSM methodology to check how well Eq. 1 performs in predicting the conversion rate. According to Coleman and Steele [28], the systematic error of a model can be linearly approximated using Eq. 2.

∆r ≈

n

∑i =1

∂z ∆x i ∂x i

∆r ≈

n

∑i =1

∂z ∆x i = (41.72 + 0.39 ∗ MOMR–18.88 ∗ CAT) ∂x i

∗ ∆CAT + (33.33 + 0.39 ∗ CAT–4.28∗MOMR) ∗ ∆MOMR

(3)

With Eq. 3, Δr could be calculated for each operating condition experimented in Table 1 to estimate the deviations of Rconvs obtained from the regression equation from the true Rconvs (when the regression equation was calculated using the controlling values without biases). The calculated results were presented in Table 3. The uncertainty analysis for the model developed using CCD/RSM showed that it was able to predict the conversion rate of the novel LPPD reactor within an error range of from −1.2% to 0.66% of the modeled value for the two contributing parameters, i.e., CAT (ranging from 0.59 to 3%) and MOMR (from 5.17 to 10.83), which should be considered relatively accurate within the experimented ranges. In fact, the optimal ranges (see next section) for these two parameters to achieve the highest conversion rate were narrower than their ranges tested in Table 3, which meant that the systematic error could be much smaller. Therefore, it could be concluded that the quadratic model developed in this study would be minimally affected by the measurement errors associated with the instrument used in determining the optimal conversion rate of FFA to biodiesel within the ranges of the two parameters examined in the experiments. 3.2. Responses of Rconv to the test variables Fig. 4 presented the surface response plots of conversion rate in relation to the two independent variables (CAT and MOMR). It was evident that the ranges for these two variables selected for evaluation in this study were appropriate because the optimal value of the response variable (the acid conversion rate, Rconv) was captured effectively from the experimental design. There are a few additional comments that can be made based on the information presented in Fig. 4. First, the optimal

(2)

where Δr is the total error of Rconv (%) caused by the experimental errors of the two variables in Eq. 1, i.e., CAT and MOMR (n = 2). The contributors to the systematic error of the model could be considered in our study resulting from the measurement errors of 4

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Fig. 3. The predicted vs. studentized residuals for the regression model describing the biodiesel conversion rate with CAT and MOMR.

these parameters without losing its efficiency in converting oleic acid to biodiesel. The insensitivity of selecting the control parameters in terms of CAT and MOMR was actually beneficial to operating the conversion process in a practical setting because precisely controlling the operating condition was in general difficult to achieve in field operations. The tolerance of the fluctuation in the control condition of the LPPD process should be instrumental in lowering the operating cost, while still achieving a good conversion efficiency. Finally, it was noted from Fig. 4 that the maximum Rconv achieved for the liquid plasma discharge reactor in this study was around 80%, which should be considered acceptable given the fact that the substrate used (oleic acid) was at 100% concentration. A recent study using immobilized enzymes as a treatment to convert free fatty acids (FFA) to biodiesel achieved conversion rates ranging from 73.5 to 93% [29], depending upon the species of enzymes used. In parallel, many other researchers studied the conversion of acid-containing oily substrates to biodiesel. Ahmed et al. [30] used waste shark liver oil as substrate to produce biodiesel and reported a poor conversion rate of only 40% when basic catalyst was employed, and the reaction was operated at 60 °C for 15 min. Ma et al. [13] reported that a conversion rate of 60% was achieved in an experiment in which immobilized lipase (Novozym425) was adopted for catalyzing alcoholysis of soybean acidic oils at 45 °C for 2 h for biodiesel production. Using a combination of both catalyst pre-treatment and microwave technology, Idowu et al. [31] studied biodiesel production from animal waste fats containing 18–25% FFA by weight and reported the conversion rates ranging from 47 to 88% with treatment times ranging from 30 to 60 min. Compared to these data and considering the experimental conditions used by the above authors such as elevated temperatures, enzyme uses, long reaction times, and microwave treatment, the performance of the liquid plasma discharge reactor operated under room temperature to convert oleic acid to biodiesel in just 4 min was relatively good.

Table 3 Numerical results for calculating systematic errors (or biases) for the regression model. Run

CAT ± ΔCAT concentration (%, w/w)

MOMR ± ΔMOMRa

Rconv (%) Model results (w/o biases)

Δr (%)

Δr/Rconv

1 2 3 4 5 6 7 8 9 10 11 12 13

3 ± 2 ± 1 ± 2 ± 2 ± 3 ± 2 ± 0.59 2 ± 1 ± 2 ± 3.41 2 ±

10 ± 0.06 8 ± 0.048 6 ± 0.036 5.17 ± 0.031 8 ± 0.048 6 ± 0.036 8 ± 0.048 8 ± 0.048 8 ± 0.048 10 ± 0.06 8 ± 0.048 8 ± 0.048 10.83 ± 0.06498

68.94 79.34 55.33 62.57 79.34 67.90 79.34 50.59 79.34 53.22 79.34 70.56 61.83

−0.597 0.0362 0.365 0.4073 0.0362 0.2043 0.0362 0.027 0.0362 −0.465 0.0362 −0.183 −0.746

−0.009 0.0005 0.0066 0.0065 0.0005 0.003 0.0005 0.0005 0.0005 −0.009 0.0005 −0.003 −0.012

0.009 0.006 0.003 0.006 0.006 0.009 0.006 ± 0.00177 0.006 0.003 0.006 ± 0.0104 0.006

a MOMR is a ratio of methanol and oleic acid. Since the measured values for the two parameters could go upward and downward by 0.3%. The total error for the ratio could thus reach 0.6%.

Rconv (80.78%) was reached when CAT and MOMR were at 2.38 (%, w/ w) and 8.02, which was the location at the center of the smallest contour circle on the figure. However, compared to the optimal conversion rate, the conversion rate at any point on the smallest contour circle shown in Fig. 4 was 80.77%, which was virtually the same as the optimal value of 80.78%. Therefore, it could be suggested that for the two independent variables, any combinations of these two variables within the contour circle could be selected as the control parameters to operate the LPPD reactor to convert oleic acid to biodiesel without affecting the conversion efficiency. In this case, the ranges for the values of CAT and MOMR could be chosen from 2.34 to 2.41 and from 7.95 to 8.08, respectively. Second, these ranges could be greatly expanded if the conversion rate of 80% was acceptable (this only led to a reduction of less than 1% in the conversion rate). The expanded ranges for CAT and MOMR were from 2.08 to 2.66 (%, w/w) and from 7.40 to 8.61, respectively. These expanded ranges of the control parameters implied that the LPPD reactor could be operated in a relatively wide range of

3.3. The relationship between applied voltage, CAT, and MOMR Fig. 5 showed the measured applied voltages when plasma discharge took place for different CAT concentrations under different MOMRs. First, when CAT was absent, regardless of the MOMR level, the applied voltage stayed unchanged at 7.4 kV, indicating that there was no effect of MOMR on the discharge voltage. Second, although information on the effect of catalyst on acid conversion in the literature 5

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plasma discharge. This was understandable because the added catalyst (NaOH) increased the conductivity of the solution, thus reducing the resistance to the flow of electrons through the solution (current), which in turn lowered the applied voltage needed for initiation of plasma discharge [33]. Finally, there is one caveat when discussing the CAT concentration vs. applied voltage used, i.e., the high CAT concentration combined with low applied voltage does not necessarily lead to the optimal conversion rate of the plasma process, as shown in Fig. 4. Obviously, the maximum conversion rate was achieved when the CAT concentration fell in a range between 2 and 3% (with the range of the corresponding applied voltage being approximately between 1.1 and 2.2 kV) for the LPPD reactor studied. In general, it is perceived that a lower applied voltage required to produce a plasma discharge is advantageous because it could reduce the electricity consumption, thus potentially reducing the operating cost when the technology is scaled up for large applications. However, the results from this study showed that pursuing low applied voltages coupled with high CAT concentrations alone to save energy might fail to achieve optimization of the plasma process conversion efficiency. Comparable comments were reported by a study to convert soybean oil to biodiesel using NaOH as catalyst [19]. As MOMR increased from 4% and up, the measured applied voltages at all CAT concentrations (except at 0% CAT) first experienced a large drop and then entered a plateau (Fig. 5). One exception was for CAT concentration of 3%, which increased from roughly 0.45 kV to 1.48 kV when MOMR increased from 6 to 12. This was unexpected and could be caused by experimental errors. The large decline in applied voltage when MOMR increased from 4 to 6 indicated the strong effect of MOMR on the initiation of the plasma discharge for MOMRs within this range. After that, it appeared that MOMR became irrelevant with respect to the applied voltage. Since methanol is a co-substrate in biodiesel production from oily substrates, the quantity of its use has been extensively investigated by many past researchers, with the range of MOMR reported to be from 6 to 15 and higher [34–37]. Excessive use of alcohol in the transesterification reaction to convert oily substrates to biodiesel is certainly not desired because it increases not only the cost of production, but the difficulty in the downstream separation process. The results from this study showed that the newly developed liquid plasma discharge technology was more efficient in terms of lowering MOMR than other techniques because the optimal MOMR was found to be around 8 (Fig. 4). Even at MOMR 6, a 73% conversion rate could still be achieved (compared to 80% at MOMR 8). 3.4. The relationship between power, CAT, and MOMR Although power and voltage are related via current (P = IV), since the discharging current was not measured in the experiment, these two parameters become independent variables and can be examined separately. The relationship of power consumption with CAT concentration and MOMR was presented in Fig. 6. Comparing the data in Fig. 6 with those in Fig. 5 indicated that the top position of the measured applied voltage of 7.4 kV in Fig. 5 took the bottom position of the power consumption of around 20 W in Fig. 6. The ranking of the remaining curves in Fig. 5 remained unchanged in Fig. 6. Since power is the product of current and voltage, low power consumption data mean low current passing through the medium when voltage is held constant. Therefore, it could be seen that for applied voltage 7.4 kV (CAT = 0%), only a very small amount of current passed through the solution (18.5/ 7400 = 0.0025 A). For other treatments with CAT concentration from 1 to 4% and MOMR 8, the passing currents were 0.12, 0.14, 0.18, and 0.37 A, respectively. For other MOMRs, a similar trend of increasing current with increasing CAT concentration existed, too. This observation was not unexpected because, as indicated early, a higher CAT concentration would increase the solution conductivity, leading to increases in passing current. To clearly see the changes in current with MOMR and CAT, the

Fig. 4. Response surface plots of FFA conversion rate to biodiesel under different MOMRs and CATs using 4 min treatment time; (a) contour plot, (b) 3D plot.

was scarce, its effect on soybean oil conversion to biodiesel was found to be critical, and both high and low catalyst concentrations were found to result in low conversion efficiencies [19]. Similar observations were also reported by another study [32] that the catalyst usage affected the reaction efficiency in vegetable oil conversion to biodiesel because as the catalyst concentration increased, the conversion rate and the biodiesel yield all increased. These findings were supported by the results from this study. Third, the value of the applied voltage of 7.4 kV was the highest among all other treatments with CAT added, and it appeared that as the CAT increased from 0 to 4%, the applied voltage decreased drastically from 7.4 to 0.4 kV, which implied that adding catalyst could lower the applied voltage needed to induce a plasma discharge. This finding appeared to indicate that the higher the CAT concentration, the lower the applied voltage was needed to launch a 6

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Fig. 5. The relationships of recorded applied voltage with CATs and MOMRs.

3.5. Brief comparison of the LPPD reactor in this study with other liquid plasma reactor designs in terms of applied voltage and power consumption

current data were calculated and plotted in Fig. 7 (excluding the data with 0% CAT). The variations of current showed an interesting observation. The high CAT concentration certainly led to high current due to high conductivity as seen from the 4% CAT data. Also, the current was relatively stable (around 0.35 A on average) when MOMR increased to 6 and higher. The currents for CAT 1 and 2% were also fairly stable over the tested MOMR range (4 to 12) and varied between 0.07 and 0.13 A for the former and 0.11 and 0.14 A for the latter. Unlike the above observations, the current with CAT 3% showed a large swing up when MOMR increased from 4 to 6 (from 0.11 to 0.38 A, a 245% increase), followed by a drastic decline when MOMR increased from 6 to 8 (from 0.38 to 0.18 A, a 52.6% decrease), before entering a relatively stable stage. Although the reason for this large fluctuation was unclear, this phenomenon could suggest that there existed an unstable (or transition) region for CAT in the MOMR range tested. In other words, when CAT concentration increased from 2 to 3%, a large change of current could occur, which might upset the plasma discharge process. Therefore, it may suggest that under the experimental conditions used in this study, the CAT concentration of 3% should be avoided, based on which the optimal range of CAT concentration from 2.08 to 2.66% obtained early may need to be narrowed down to close to 2.08% to ensure a steady operation of the plasma discharge reactor.

Input power including current and voltage on the cusp of plasma discharge is considered an important factor in evaluating the design of plasma discharge reactors. In addition, for a given reactor, energy efficiency was found to be dependent upon voltage-related parameters [38]. Reviewing literature shows that there is currently no information related to the effect of applied voltage and current on the performance of the liquid plasma discharge process in converting oleic acid to biodiesel. Thus, information of using this technology in treating water/ wastewater was collected on the premise of understanding that such comparison may not be entirely valid. Clements et al. [39] reported experimental results concerning contact glow discharge electrolysis and dielectric barrier discharge reactors to purify water, in which they found that for electrical discharge to take place in water, a high-voltage of 15–100 kV was necessary in a pulsed corona discharge reactor. In another study, a voltage of 25 kV was applied to a multiple-needle electrode type of plasma reactor to produce electrical discharges in air with the formation of ozone in liquid [40]. Sunka et al. [41] experimented both a needle-plate reactor and a coaxial flow-through reactor for generating electrical discharges in

Fig. 6. The relationships of recorded power consumption with MOMRs and CATs. 7

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Fig. 7. The relationships of calculated current with MOMRs and CATs.

the plasma reactor. The results showed that the quadratic model generated by the CCD/RSM analysis could adequately predict the conversion rate of the plasma discharge process with a p value of 0.0022. The uncertainty analysis for the model further confirmed the model accuracy, which was within an error range of from −1.2% to 0.66% of the modeled value when CAT ranged from 0.59 to 3% and MOMR from 5.17 to 10.83. According to the model, the optimal conversion rate was found to be 80.78% at the catalyst (CAT) concentration of 2.38 (%, w/ w) and the methanol to acid molar ratio (MOMR) of 8.02. It appeared that the acid conversion rate was significantly affected by CAT (p = 0.0039), but not by MOMR (p = 0.9027). Based on the data obtained from this study, it can be concluded that the novel LPPD process can be developed into a promising technology to convert substrates containing high concentrations of free fatty acids into biodiesel.

water and found that the plasma discharge inception voltages were 12–16 kV for the former and 15–18 kV for the latter, respectively, with the current measured between 8 and 50 A depending upon water conductivity. Wang et al. [42] studied a pulsed needles-plate reactor system coupled with TiO2 photocatalysis to remove phenol from water and observed a very high voltage of 10,000 kV to be effective. Shin et al. [43] researched submerged capillary point electrode reactors for oxidizing organic compounds in water. The minimum applied voltage was found to be approximately 4–4.5 kV with a relatively low current obtained of around 38 mA. Another study reported data on using submerged glow discharge reactors/point-plate to degrade 2,4-dichlorophenol in water and the applied voltage was set at only 600 V with the current maintained at 120–150 mA [44], both of which were low as compared to the results from other studies. It appeared that the applied voltage and current needed to incur plasma discharge in water were somewhat related to the types of wastewater under treatment. Summarizing the relevant information found in the literature indicated wide ranges of applied voltage (from 1 to 10,000 kV) and the measured current (from 38 mA to 50 A) at the onset of plasma discharge in liquids. Compared to these applied voltages and currents, the values measured from the experiments conducted in this study (ranging from 2.2 to 3.1 kV and from 100 to 140 mA using CAT 2% over MOMR 4 to 12 when plasma discharge occurred) were all located towards the very lower end of the reported range, i.e., 1–10,000 kV for the applied voltage and 38 mA – 50 A for the current, respectively. To further examine the energy efficiency of the current LPPD reactor, it might be instructive to evaluate the energy spent on per volume biodiesel produced. Using the data above, the high-end power consumed can be calculated (3.1 kV and 140 mA, power = 3.1 × 0.14 = 0.434 kW). At 2.7 ml s−1, the total electrical discharging time was recorded to be 0.0032 s (3.2 millisecond) for 80 ml biodiesel produced (80% conversion rate). This translated to a power consumption of 0.386 × 10−6 kWh for 80 ml (or 4.82 × 10−6 kWh for 1 L). Therefore, it may be concluded that the energy efficiency of the newly developed liquid phase plasma discharge reactor in converting oleic acid to biodiesel is relatively good.

CRediT authorship contribution statement Sarah Wu:Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Supervision, Project administration, Funding acquisition.Muhammad Aamir Bashir:Methodology, Validation, Formal analysis, Investigation, Writing - original draft.Jun Zhu:Conceptualization, Formal analysis, Writing - review & editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This work is financially supported by the USDA National Institute of food and agriculture (NIFA) Foundational and Applied Science Program (Grant #: 2019-67021-29942) and USDA NIFA Hatch project IDA01573, United States. References

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