Lumping Strategy in Kinetic Modeling of Vacuum Pyrolysis of Plant Oil

Article pubs.acs.org/EF

Lumping Strategy in Kinetic Modeling of Vacuum Pyrolysis of Plant Oil Asphalt Yanyan Zheng, Qiang Tang,* Tiefeng Wang, and Jinfu Wang* Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China ABSTRACT: Plant oil asphalt (POA) is an underutilized lipid-based biomass residue mainly generated in the biodiesel industry. This work presents application of the lumping strategy in kinetic modeling of vacuum pyrolysis of POA in a pilot-scale semibatch reactor. Pyrolysis experiments were conducted under different reaction temperatures (410, 430, and 450 °C) and reaction times (10, 15, 20, 25, 40, 50, and 60 min). The reaction scheme was divided into five lumps, namely, feedstock lump (POA) and four pyrolytic product lumps (including biogas, biochar, hydrocarbon components in pyrolytic oil, and oxygenated components in pyrolytic oil). In the kinetic model, reactions from the feedstock lump to the four pyrolytic product lumps were assumed to be independent parallel, while the secondary reactions between four pyrolytic product lumps were neglected because of the short residence time of the pyrolytic vapor in the pyrolysis zone. Results showed that four independent parallel reactions all followed first-order kinetics. The kinetic model estimated the Arrhenius parameters and showed high capability to predict the concentration of pyrolytic product lumps, especially the relative distribution of hydrocarbon components and oxygenated components in pyrolytic oil.

1. INTRODUCTION Plant oil asphalt (POA) is an underutilized lipid-based biomass residue mainly generated in the biodiesel industry.1−4 Biodiesel is a green renewable fuel, which has favorable characteristics, such as low toxicity, biodegradability, and low exhaust emissions during combustion.5 Biodiesel has been recognized as an important alternative fuel for solving the fossil resource crisis and environmental pollution.6,7 The capacity of biodiesel reached about 6.5 billion gallons by 2013.8 In the biodiesel industry, co-generation of byproduct POA, a dense and viscous black residue, is a headache problem.1−4 Reusing and recycling of the residue POA is of great importance for improving the economic and environmental benefits in the biodiesel process. POA was characterized as a mixture of oligomers of unsaturated fatty acid methyl esters and fatty acids.4 The above oligomers were formed by a combination of CC addition or association and carboxyl dehydration.4 Generally, the yield of POA reaches 5−25 wt % on the feedstock basis.1−4 POA previously suffered from a low added value when used as boiler oil, casting mold lubricant, or road asphalt. Recently, Tang et al.1,2,4 developed a vacuum pyrolysis process converting POA to three-phase biofuel products, including biogas, biochar, and pyrolytic oil. The yield of pyrolytic oil could exceed 80 wt % at 450 °C, and the pyrolytic oil showed similar energy density and oxygen content with commercial biodiesel. This opens a new era for commercial development of recycling of POA. In the vacuum pyrolysis process of POA, the pyrolysis kinetics is essential for reactor design, scale-up, and process control.3,9,10 However, the study on pyrolysis kinetics faces two main difficulties: the complexity of the reaction scheme and the complexity of the involved species. Lumping kinetics is an effective method for kinetics investigation in a multiple reaction system.11−13 A lump refers to a pseudo-component as a collection of a series of © 2015 American Chemical Society

components with similar kinetics characteristics. Application of the lumping strategy in kinetic modeling highly simplifies the reaction scheme. In the 1960s, Aris14,15 and Wei and Kuo16,17 pioneeringly reported the theoretical analysis of lumping methodology. The lumping methodology has been successfully used in kinetics investigations in processes such as catalytic cracking18,19 and catalytic reforming,20,21 thus contributing to the rapid development of reactor design and process optimization. Weekman et al.22 developed a three-lump kinetic model (heavy oil, gasoline, and gas + char) for catalytic cracking of heavy oil. This model showed good predictions of conversion of heavy oil, yield of gasoline, and selectivity to gasoline but failed to be extrapolated to feedstocks with different compositions. More complicated models, such as the 10-lump kinetic model,23,24 were developed by considering the composition of feedstock. In the 10-lump kinetic model, the feedstock was divided into eight lumps. The dividing of aromatics into two lumps (aromatic ring and side chain) is the key to the success of the 10-lump model. Therefore, a right reaction mechanism is foundation for lumping kinetic modeling. Besides, the computational amount of the 10-lump model is considerably huger than that of the three-lump model. Thus, the number of lumps should be determined by actual process demand and computational amount. Although lumping strategy has been widely used in kinetic modeling of various processes,25−28 this is the first report on lumping kinetics of vacuum pyrolysis of POA. In this work, a five-lump kinetic model for vacuum pyrolysis of POA is developed on the basis of the characteristics of the semi-batch reactor and the pyrolysis reaction pathway. The prediction Received: November 11, 2014 Revised: February 25, 2015 Published: February 26, 2015 1729

DOI: 10.1021/ef502530q Energy Fuels 2015, 29, 1729−1734

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Figure 1. Scheme of the bench-scale vacuum pyrolysis experimental setup.

ability of the concentration of pyrolytic product lumps, especially the relative distribution of hydrocarbon components (HCs) and oxygenated components (OCs) in pyrolytic oil, was evaluated.

2. EXPERIMENTAL SECTION 2.1. Material. The POA sample was a byproduct of the biodiesel plant using waste cooking oil and acidic oil as raw materials, provided by Shandong Bio-Energy Products and Technology Co., Ltd. 2.2. Experimental Setup. The aim of the present work was to investigate the vacuum pyrolysis kinetics of POA. A scheme of the bench-scale vacuum pyrolysis experimental setup is shown in Figure 1. A total of 20 g of POA feedstock was injected into the pyrolysis reactor with a volume of 100 mL. The pyrolysis experiments of POA were carried out at different temperatures (410, 430, and 450 °C) and a series of reaction times (10, 15, 20, 25, 40, 50, and 60 min). The pyrolysis reactor was heated from room temperature to a set point at 20 K min−1. Vacuum pressure was set at 40 kPa considering the yield of pyrolytic oil and energy cost.1,2 The reaction temperature and pressure were controlled by an electric control panel and a vacuum pump, respectively. Pyrolysis of POA could yield three-phase biofuel products, including biogas, biochar, and pyrolytic oil. The concentration of product i is defined as the mass of product i relative to the initial mass of POA feedstock. During the pyrolysis, the mass of pyrolytic oil was measured using an electronic balance, while the composition of pyrolytic oil was quantitatively characterized by gas chromatography−mass spectrometry (GC−MS). Details of the GC−MS analysis procedure were reported elsewhere.2 The volume of non-condensable gas was measured by the time integral of the gas flow rate. Then, the mass of gas was calculated from the gas volume, and the composition was tested by GC−MS. The solid char was the ethanol-insoluble material in the reaction residue, while the ethanol-soluble material was the unconverted POA. Using the above quantification methods, the mass balance of the system was within the deviation allowed (±3%). In this work, the pyrolysis experiments were replicated 3 times. Statistical analysis showed that the mean deviations of the experimental data at different conditions were all within 5%. Involved data for kinetics calculation in Table 2 were the average experimental data.

Figure 2. Five-lump vacuum pyrolysis kinetics model (OC, oxygenated compounds in pyrolytic oil; HC, hydrocarbon compounds in pyrolytic oil).

in pyrolytic oil, and biochar, respectively. Transient concentrations of POA and the lumped products at different reaction times were tested for model validation in section 4. In the semi-batch vacuum pyrolysis reactor, the pyrolytic products vaporized and immediately left the pyrolysis zone with a short residence time. The diversity between the residence time of lump 1 and that of lumps 2−5 was the main characteristic of the semi-batch vacuum pyrolysis reactor. The secondary reactions between the four product lumps immediately stopped after the pyrolytic vapor was quenched. In the pyrolysis zone, the residence time of the pyrolytic vapor was far shorter than that of POA pyrolysis. In the kinetic model, reactions from the feedstock lump to the four pyrolytic product lumps were assumed to be independent parallel, while the secondary reactions between four pyrolytic product lumps were neglected because of the short residence time of the pyrolytic vapor in the pyrolysis zone. Assuming that the four parallel pyrolysis reactions all followed first-order kinetics, the reaction rate matrix for the five-lump scheme could be expressed as eq 1

3. LUMPED KINETIC MODEL The kinetic model developed in this work assumes five discrete lumps, as shown in Figure 2. Lump 1 represents the feedstock plant oil asphalt. Lumps 2, 3, 4, and 5 represent the four cracked products, which are biogas, OCs in pyrolytic oil, HCs 1730

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4. RESULTS AND DISCUSSION Vacuum pyrolysis of POA yielded biogas, biochar, and pyrolytic oil. Among the three-phase biofuel products, pyrolytic oil is the most desirable product as a potential transport fuel. Pyrolytic oil is a complex mixture, and its composition determines the combustion performances. The GC−MS analysis showed that pyrolytic oil mainly consisted of OC and HC. OC include fatty acids and fatty acid methyl esters, while HC contain paraffins, olefins, and aromatics. Table 1 presents the dominant

where K is the rate constant matrix, kij is the rate constant for the pyrolysis reaction from lump i to lump j, C is the lump concentration matrix, and Ci is the concentration of lump i, namely, the mass fraction of lump i relative to initial mass of lump 1 (POA). Herein, the initial values for solving the above homogeneous differential matrix (eq 1) become C1,0 = 100 wt%,

Table 1. Distribution of Dominated Compounds in Pyrolytic Oil Obtained from Pyrolysis of POA compound

C2,0 = C3,0 = C4,0 = C5,0 = 0 wt% (2)

The transient concentrations of the five lumps, as analytical solution of the above matrix, could be represented as eqs 3−7, respectively C1 = C1,0 exp( −k pt )

(3)

C2 = (C1,0k12/k p)(1 − exp(−k pt ))

(4)

C3 = (C1,0k13/k p)(1 − exp(−k pt ))

(5)

C4 = (C1,0k14 /k p)(1 − exp(−k pt ))

(6)

C5 = (C1,0k15/k p)(1 − exp(−k pt ))

(7)

where k p = k12 + k13 + k14 + k15

(8)

Given the experimental concentrations of lumps at different reaction times (10, 15, 20, 25, 40, 50, and 60 min), the rate constants were estimated using least-squares regression. The coefficient of determination (R2) defined by eq 9 was used to measure the agreement between the experimental and calculated data. For R2, a value further approaching 1.000 indicates better accuracy of the fit.

R2 =

TSS − ESS TSS

(9)

N

∑ (Ciexp − Ciave)2 i=1

(10)

N

ESS =

∑ (Ciexp − Cical)2 i=1

(11)

where N is the number of the data points, Cave is the average i value of the experimental value of Ci, and Cexp and Ccal i i are the experimental and calculated values of Ci, respectively. Arrhenius parameters of the cracking reaction from lump i to lump j, including pre-exponential factor (Aij) and activation energy (Eij), could be obtained by the Arrhenius equation as eq 12. ln(kij) = ln(Aij ) −

Eij RT

concentration (wt %) 1.6 1.7 2.7 2.2 1.5 1.2 1.2 1.6 5.1 0.3 0.2 1.4 6.3 0.8 3.2 0.2 11.7 3.2 23.8 3.7

components in the pyrolytic oil, among which methyl oleate and 8-heptadecene are the most abundant OC and HC, respectively. More detailed compounds detected in pyrolytic oil had been reported in our previous studies.2−4 Distribution of the carbon number of the components in the pyrolytic oil was demonstrated in Figure 3. The carbon number of the components in the pyrolytic oil distributes in the range of 7− 24. Components with carbon numbers 19 and 17 are the most dominating. The pyrolytic oil was upgraded to pyrolytic biodiesel via esterification.2,4 The pyrolytic biodiesel showed good physicochemical properties as a potential transport fuel.2,4 The pyrolytic biodiesel could be considered as a mixture of diesel and fatty acid methyl esters. The relative selectivity of OC to HC in pyrolytic oil consequentially affects the oxygen content of pyrolytic biodiesel, thus determining its fuel properties, such as heating value and cetane number. On the basis of the principle for lumping, the number of lumps should be determined considering the demand of the process and complexity of the kinetic model. In this system, a clear demonstration of the distribution of three-phase biofuel products, coupling with the relative distribution of OC to HC in the pyrolytic oil, is of concern for process design and monitoring properties of the pyrolytic oil. Therefore, the vacuum pyrolysis of the POA system adopted the classification of the five lumps demonstrated in Figure 2.

where TSS (total sum of squares) and ESS (explained sum of squares) are defined as eqs 10 and 11, respectively TSS =

formula

Hydrocarbon Compounds (HC) nonane C9H20 decane C10H22 pentadecane C15H32 heptadecane C17H36 5-undecene C11H22 1-tridecene C13H26 1-tridecene C13H26 2-tetradecene C14H28 8-heptadecene C17H34 pentylbenzene C11H16 hexylbenzene C12H18 Oxygenated Compounds (OC) decanoic acid C10H20O2 palmitic acid C16H32O2 stearic acid C18H36O2 oleic acid C18H34O2 linoleic acid C18H32O2 methyl hexadecanoate C17H34O2 methyl stearate C19H38O2 methyl oleate C19H36O2 methyl linoleate C19H34O2

(12) 1731

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Table 3. Reaction Rate Constants for the Five-Lump Vacuum Pyrolysis Kinetics Model temperature (°C)

Arrhenius parameters

k (min−1)

410

430

450

Ea (kJ mol−1)

kp k12 k13 k14 k15

0.0223 0.0041 0.006 0.0101 0.0022

0.0340 0.0055 0.0123 0.0124 0.0043

0.0621 0.0103 0.0266 0.0196 0.0078

104.96 94.40 152.82 96.77 129.99

A (min−1) 2.29 6.56 2.86 1.92 1.93

× × × × ×

106 104 109 105 107

Figure 3. Carbon number distribution of compounds in pyrolytic oil obtained from pyrolysis of POA.

As previously described, isothermal vacuum pyrolysis experiments of POA were carried out under 410, 430, and 450 °C, respectively. The concentrations of lumps 1−5 at different operating conditions are listed in Table 2. The pyrolysis rate of POA significantly increased with the temperature. On the basis of the parallel reaction kinetic model in Figure 2, the product distribution was determined by the different rate constants of the four parallel reactions from lump 1 to lumps 2−5. The rate constants at different pyrolysis temperatures were obtained by least-squares regression of the experimental concentrations of lumps at different reaction times using eqs 3−7, as shown in Table 3. As shown in Figure 4, the Arrhenius plots of the kij data present good linearity (R2 > 0.995), which validated the high accuracy of the five-lump kinetic model. The Arrhenius parameters, including pre-exponential factor (Aij) and activation energy (Eij), are listed in Table 3. Among the four parallel pyrolysis reactions, conversion of lump 1 (POA) to lump 3 (OC in pyrolytic oil) possessed the largest activation energy E13 at 152.82 kJ mol−1, while conversion of lump 1 (POA) to lump 4 (HC in pyrolytic oil) possessed the second least activation energy E14 at 96.77 kJ mol−1. Therefore, k13 was more temperature-sensitive than k14. As previously discussed, controlling the relative distribution of OC to HC in pyrolytic oil is feasible because of the diversity between E13 and E14. Activation energies E12 and E15 were 94.40 and 129.99 kJ

Figure 4. Arrhenius plots of the vacuum pyrolysis rates (kij).

mol−1, respectively. The concentration of lump 5 (biochar) is more temperature-sensitive than that of lump 2 (biogas). Therefore, the increase of the temperature has a more significant effect on the concentration of biochar than biogas. Among the four parallel pyrolysis reactions, the reaction toward OC in pyrolytic oil from POA is most sensitive to the reaction temperature, responding to its largest activation energy. Preexponential factors A12, A13, A14, and A15 are 6.56 × 104, 2.86 × 109, 1.92 × 105, and 1.93 × 107 min−1, respectively. In another perspective, the activation energy and pre-exponential factor for the weight loss of feedstock POA are 104.96 kJ mol−1 and 6.56 × 104 min−1, respectively.

Table 2. Concentration of Lumps at Various Conditions during Pyrolysis of POA concentration of lumps (wt %) material

T (°C)

t (min)

C1

C2

C3

C4

C5

POA

410

0 10 20 50 60 0 10 15 25 40 0 10 25 40

100.000 76.500 61.998 35.002 26.001 100.000 75.100 61.010 44.022 21.002 100.000 56.010 21.004 8.003

0.000 4.002 7.501 12.402 13.01 0.000 4.004 6.203 8.993 12.602 0.000 7.706 12.007 16.013

0.000 6.351 10.087 17.186 20.174 0.000 9.962 12.719 19.699 28.936 0.000 17.253 33.356 38.532

0.000 9.649 16.913 28.814 33.826 0.000 11.515 13.775 20.786 27.064 0.000 13.747 24.644 28.468

0.000 2.501 3.512 6.603 7.002 0.000 3.410 5.003 7.504 9.507 0.000 6.302 9.008 12.005

430

450

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Energy & Fuels The prediction ability of the five-lump kinetic model is illustrated in the parity plot, as shown in Figure 5. The R2 value

for all involved data points is 0.998. A comparison between experimental and calculated concentrations of the five lumps at different reaction temperatures and different reaction times is illustrated in Figure 6. The five-lump kinetic model presented a high capability to estimate the concentration of both feedstock and product lumps with minor deviations. From Figures 5 and 6, the parallel pyrolysis reactions were verified to be following first-order kinetics. The developed reaction scheme conformed to the vacuum pyrolysis reaction system. The kinetic model can be applied in designing and scaling-up the pyrolysis process in the future work.

5. CONCLUSION Pyrolysis of lipid-based biomass POA to biofuels has good prospect in economic and environmental perspectives. Kinetics of vacuum pyrolysis of POA was investigated under different reaction temperatures (410, 430, and 450 °C) and reaction times (10, 15, 20, 25, 40, 50, and 60 min) in a pilot-scale semibatch reactor.

Figure 5. Comparison between the experimental and calculated values for all involved data points.

Figure 6. Comparison between experimental and calculated concentrations of lumps 1−5 during vacuum pyrolysis of POA. 1733

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Energy & Fuels A five-lump kinetic model was proposed to estimate the Arrhenius parameters. The kinetic model showed high capability to predict the concentration of the lumps, validating the feasibility of the assumption that parallel reactions from the feedstock lump to the four pyrolytic product lumps followed first-order kinetics, while the secondary reactions between four product lumps could be neglected because of the short residence time of the pyrolytic vapor in the pyrolysis zone. The kinetic modeling method in this work may provide a platform for similar semi-batch pyrolysis systems. The kinetic model predicted the relative distribution of HCs and OCs in pyrolytic oil, thus effectively monitoring the properties (such as the heating value and combustion performance) of the upgraded pyrolytic biodiesel. The kinetic model in this work lays a foundation for further studies on pyrolysis reactor design and utilization of POA.

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AUTHOR INFORMATION

Corresponding Authors

*Telephone: +86-10-62796109. E-mail: tangqiang10@tsinghua. org.cn. *Telephone: +86-10-62796109. E-mail: [email protected]. cn. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge Shandong Bio-Energy Products and Technology Co., Ltd. for providing the POA material. NOMENCLATURE K = rate constant matrix kij = rate constant for the cracking reaction from lump i to lump j (min−1) C = lump concentration matrix Ci = weight fraction of lump i (wt %) R2 = coefficient of determination TSS = total sum of squares ESS = explained sum of squares N = number of experimental points Cave i = average value of the experimental value of Ci (wt %) Cexp = experimental values of Ci (wt %) i Ccal i = calculated values of Ci (wt %) Aij = pre-exponential factor of the cracking reaction from lump i to lump j (min−1) Eij = activation energy of the cracking reaction from lump i to lump j (kJ mol−1)

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