Kinetic Study of the Pyrolytic Treatment of Petroleum-Contaminated

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Cite This: Ind. Eng. Chem. Res. 2019, 58, 10829−10843

Kinetic Study of the Pyrolytic Treatment of PetroleumContaminated Soils Ye Gao and Kyriacos Zygourakis* Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States

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S Supporting Information *

ABSTRACT: This study presents a systematic approach to determine the kinetics of pyrolytic remediation of petroleumcontaminated soils. Two clean soils with different compositions were spiked with oil crudes to prepare petroleumcontaminated soils. Thermogravimetry with evolved gas analysis (TG−IR and TG−MS) was used first to identify the mineral transformations taking place as the clean soils were heated in an anoxic atmosphere. The same analytical techniques were applied to study the pyrolysis of contaminated soils and identify the temperature ranges over which soil mineral transformations overlap with hydrocarbon desorption and pyrolysis. We then employed distributed activation energy models with multiple pseudocomponents to quantify the kinetics of thermal processes involving only components of the clean soils. Finally, the same approach was used to obtain the full kinetics for the pyrolytic treatment of soil−petroleum mixtures and to accurately characterize the temperature dependence of hydrocarbon desorption and pyrolysis rates. The presented results emphasize the importance of soil composition, provide new insights into the pyrolysis of oil−soil mixtures, and elucidate the trade-off between hydrocarbon removal and soil fertility observed in an earlier study.

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INTRODUCTION While marine oil spills from offshore platforms or tankers attract maximum worldwide public attention,1 the vast majority of oil spills (about 98%) are of moderate scale (100−1000 m3) and occur on land.2,3 Every year about 10−25 million gallons of petroleum products are spilled mostly from pipelines and fixed facilities.4 Because biodegradation is extremely slow in the anaerobic zone of soils, land spills pose long-term threats to groundwater quality.2 Spills of petroleum crudes are particularly harmful. Even when the lighter fractions have evaporated, the remaining heavy ones can prevent revegetation for many decades.3 Petroleum hydrocarbons (even at low concentrations) inhibit the germination and root elongation rates of plants5,6 and are highly toxic to earthworms and soil microbes.6 The polycyclic aromatic hydrocarbon (PAH) components of petroleum are particularly toxic and are known to damage the immune systems of aquatic organisms and wildlife.7 Many of the current soil remediation technologies are either marginally cost-effective or have unintended consequences such as increased residual toxicity or soil damage.8−11 Some treatment processes, such as aerobic bioremediation, may convert harmful pollutants (e.g., PAHs) to even more toxic derivatives,12 thus failing to achieve detoxification even though they may be able to meet regulatory requirements by lowering the total petroleum hydrocarbon (TPH) content of contaminated soils. Thermal technologies are generally effective for © 2019 American Chemical Society

remediating petroleum-contaminated soils, often achieving TPH removal levels over 99%.8,10,13−15 However, these technologies frequently employ high temperatures and oxidative conditions that may damage soil integrity and, ultimately, impair its fertility.9,15−19 As a result, contaminated soils that have been treated with combustion-based thermal techniques often cannot sustain vegetation and can only be used as backfill in construction projects or sent to landfills.20,21 To facilitate ecosystem recovery of contaminated sites by restoring the agricultural value of soils, we recently proposed the use of pyrolysis as an alternative to combustion-based methods.15,22 Working at first with small-scale batch reactors, we showed that pyrolysis at temperatures lower than 500 °C can reduce TPH levels to well below regulatory levels with potentially lower energy requirements and superior posttreatment soil fertility than other ex-situ thermal technologies.15 A subsequent study focused on the fundamental mechanisms of pyrolytic treatment. Hydrocarbon desorption was found to be the main process at temperatures below 350 °C, while thermal cracking and pyrolysis reactions dominate in the 350−500 °C range producing hydrogen, light hydrocarbons, and a solid char that coats the particles of treated Received: Revised: Accepted: Published: 10829

March 17, 2019 May 23, 2019 May 31, 2019 May 31, 2019 DOI: 10.1021/acs.iecr.9b01494 Ind. Eng. Chem. Res. 2019, 58, 10829−10843

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Industrial & Engineering Chemistry Research soils.22 Li and co-workers23 also used batch pyrolysis reactors to show almost complete TPH removal after 30 min at 500 °C and 50% recovery of the carbon present in the contaminated soil. Partial oil recovery is an additional potential benefit of pyrolysis that can lower its energy requirements. In a recently published article, we reported results from the first pilot-scale study of pyrolytic soil treatment performed in a continuous rotating kiln reactor.24 Pyrolysis at 420 °C with 15 min residence time achieved almost complete TPH removal (99.9%), removed almost 95% of the PAHs present in the contaminated soil, and restored the fertility of treated soil to almost clean soil levels. Cell viability assays revealed that pyrolytic treatment effectively detoxified the contaminated soil, indicating that pyrolysis does not have the residual toxicity drawback of some aerobic bioremediation methods.12 At the same time, this study24 revealed an interesting tradeoff. While TPH and PAH removal levels improved with increasing treatment intensity25 (i.e., higher temperatures and longer reactor residence times), the fertility of treated soil first improved and then declined when the treatment intensity exceeded a soil-specific threshold. Pyrolytic treatment at 420 °C with 30 min reactor residence time restored soil fertility (as measured by the 21-day dry weight of Lactuca sativa plants) to 98% of the clean soil level. However, treatment at 470 °C for 15 or 30 min reduced soil fertility to 51% and 39% of the clean soil level, which was only marginally higher that the fertility of the contaminated soil (33% of clean soil fertility). However, a fertility “maximum” was not observed in our earlier studies, which used different background soils than the one used for our recent work.24 This raises an obvious question: Which system properties or interactions are responsible for the observed trade-off between detoxification effectiveness and fertility restoration? At the very minimum, the existence of an optimal treatment intensity for some soils emphasizes the importance of accounting for differences in the composition of soils when designing a pyrolytic remediation process. Soil composition will dictate which mineral transformations take place when contaminated soils are pyrolyzed and how these processes will interact to determine TPH removal levels and soil fertility. This optimization problem may ultimately determine the effectiveness, the economics, and the overall sustainability of pyrolytic soil remediation. To address this problem, however, we must have detailed kinetic information for all the processes taking place when contaminated soils are pyrolytically treated. And since petroleum hydrocarbons typically account for only a few percent of the mass of contaminated soils, the kinetics of soil mineral transformations are of paramount importance. Taking a first step toward the rational design of pyrolysis reactors, this study will combine experimental measurements and mathematical techniques to develop a full kinetic model for the pyrolytic treatment of soils contaminated with petroleum hydrocarbons. Following a two-step approach, we will first identify which soil mineral transformations take place when clean soils (i.e., the original, uncontaminated soils) are heated in an anoxic atmosphere. Experiments with the same soils contaminated with petroleum hydrocarbons will follow to identify the temperatures ranges for hydrocarbon desorption and pyrolysis and to characterize the overlap of soil and hydrocarbon transformations. Finally, we will use a distributed activation energy (DAE) approach to derive kinetic models that quantify the rates of soil and hydrocarbon transformations. Two contaminated soils prepared from clean soils with widely

different properties will be considered to demonstrate how this experimental and theoretical framework can be applied to any soil/petroleum mixture.

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EXPERIMENTAL SECTION Clean and Contaminated Soil Samples. Two contaminated soils A and B and their background (clean) soils were used for this study. Clean soil A is a kaolinitic, sandy, clay loam with approximately 25% clay content. After the clean soil was dried, homogenized, and sieved to remove large particles, it was blended with a heavy crude oil (21° API, Chevron ETC, Houston, TX) to prepare contaminated soil A with 3 wt % oil concentration.24 Contaminated soil B was prepared by spiking an Arizona topsoil with oil sludge from a crude oil well site.15 On the basis of X-ray diffraction, the composition of background soil B was 12% clays, 10% carbonates, 31% quartz, 20% K-spar, 26% plagioclase, and 1% pyrite. Both soils had wide particle size distributions. Roughly 40% of their volume consisted of particles with diameters in the 0.3−1 mm range, 10% consisted of particles larger than 1 mm, and the remaining particles were finer than 0.3 mm. The total petroleum hydrocarbon (TPH) content of the two contaminated soils was determined by measuring the solvent-extractable hydrocarbons via GC-FID based on EPA method 8015 M (Eurofins Lancaster Laboratories, Lancaster, PA, USA). The TPH contents of contaminated soils A and B were 14 000 mg/kg and 19 000 mg/kg, respectively. Data from laboratory analyses of the clean and treated soils A and B were presented in our earlier publications.15,22,24 We have reproduced the most important of this data in Tables S1 and S2 of the Supporting Information. Evolved Gas Analysis (EGA) with TG−IR. Thermogravimetric analysis experiments with online infrared gas analysis (TG−IR) were performed using a thermogravimetric analyzer (TGA) (Q500, TA Instruments, New Castle, DE) connected to a non- dispersive infrared (NDIR) gas analyzer (LI-840A, LI-COR, Lincoln, NE). Since all the gas exiting the TGA flows through its optical path, the online NDIR instrument allows for continuous quantitative measurement of the CO2 and H2O concentrations in the gaseous stream exiting the thermogravimetric analyzer. All TG−IR experiments were carried out under nitrogen flowing at a rate of 100 mL/min, and every run started with a 15 min purge at room temperature to flush the air entering the system when the samples are loaded on the TGA. A drying stage followed to remove moisture. The sample temperature was raised to 105 °C, held there until no weight loss could be detected, and remained at 105 °C for another 30 min after that point to ensure good moisture removal. When the drying stage was complete, the temperature was ramped to a final temperature of 800 °C at a constant heating rate of 1 °C/min. All TGA experiments used for kinetic measurements were carried out at a heating rate of 1 °C/min to minimize the difference between the actual temperature of soil particles and the temperature recorded by the TGA thermocouple. This temperature “lag” can be very large when fast heating rates are used.26 The analysis presented in the Supporting Information section shows that a heating rate of 1 °C/min limits the temperature lag to less than 2 °C even for the largest soil particles, thus ensuring the accuracy of our kinetic measurements. Evolved Gas Analysis (EGA) with TG−MS. To detect the release of hydrocarbons and hydrogen occurring during pyrolysis, we also carried out TG−MS experiments using a 10830

DOI: 10.1021/acs.iecr.9b01494 Ind. Eng. Chem. Res. 2019, 58, 10829−10843

Article

Industrial & Engineering Chemistry Research

Figure 1. Experimental thermogravimetry (top panel) and differential thermogravimetry (bottom panel) curves describing the weight loss and weight loss rate, respectively, for three TGA runs with clean soil A (black lines) and three TGA runs with clean soil B (red lines). All six experiments were performed at a heating rate of 1 °C/min.

clean, empty pans in the thermobalance. EGA with online mass spectrometry is a very sensitive method for detecting pyrolysis products in the TGA off-gas. However, only a fraction of the gaseous stream exiting the TGA enters our mass spectrometer transfer line through its capillary front end. Moreover, high molecular weight hydrocarbons may condense in the coupling connecting the transfer line of our mass spec to the TGA. For these reasons, the collected TG−MS data were only used to identify the temperature ranges in which hydrocarbon desorption and pyrolysis take place. No quantitative analysis was done with the EGA data collected with the TG−MS instrument.

different TGA (Q500, TA Instruments, New Castle, DE) equipped with an evolved gas analysis (EGA) furnace and connected to a quadrupole mass spectrometer (Discovery, TA Instruments, New Castle, DE) with a heated capillary transfer line. The mass spectrometer was operated in electron impact ionization mode with 70 eV electron energy. All TG−MS experiments were carried out using argon purge gas with a flow rate of 100 mL/min and started with a 90 min purge at room temperature to flush the air entering the system when the samples are loaded. After the purge, the samples were heated at 10 °C/min to a final temperature of 800 °C. Argon was used instead of N2 as carrier gas so that we could detect CO (a potential product of SOM pyrolysis) since the dominant fragments of both CO and N2 have the same mass-to-charge ratio (m/z = 28). Also, m/z = 28 is the second most common fragment of CO2 after m/z = 44. The TG−MS experiments utilized a heating rate of 10 °C/min, a rate significantly faster than the 1 °C/min used in all other TGA experiments, in order to enhance the volatile release rates and improve the signal-tonoise ratio. To further improve the signal-to-noise ratio, we used the selected ion monitoring technique by limiting the number of ions scanned during each run to 10 or fewer. Using multiple scans for each sample, the mass spectrometric intensities of ion fragments that are the main fingerprints of carbon dioxide (m/z = 44), water (m/z = 18), hydrogen (m/z = 2), methane (m/z = 15), aliphatic hydrocarbons (m/z = 29, 43, and 57), unsaturated or cyclic hydrocarbons (m/z = 67, 69, 70, and 71) and aromatics (m/z = 78, 77, 92, 91) were recorded together with the sample weight and temperature. Ion intensities were corrected using baselines obtained with

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RESULTS AND DISCUSSION Since 90−95% of the mass of petroleum-contaminated soils consists of soil minerals, the thermal transformations of soil components (like clay dehydration or carbonate decomposition) may result in significant weight losses upon heating clean (uncontaminated) soils. These weight losses will be quantified first so that they can be decoupled from losses due to either hydrocarbon desorption or pyrolysis.22 Soils A and B Exhibit Different Weight Loss Patterns During Heating. Figure 1 presents the thermogravimetry (TG) and differential thermogravimetry (DTG) curves describing the weight losses and the rate of weight losses respectively for three TGA runs with clean soil A and another three runs with clean soil B. While both soils have very similar total weight losses of 2.75 ± 0.08 wt % and 2.61 ± 0.12 wt % for soil A and B, respectively (on a dry basis), their weight loss 10831


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Figure 2. Thermogravimetric and evolved gas analysis (TG−IR) for the same TGA run with clean soil A and a heating rate of 1 °C/min. (Top panel) Thermogravimetry and differential thermogravimetry curves describing the weight loss and weight loss rate for the TGA run with clean soil A. (Bottom panel): Water and carbon dioxide concentrations in the TGA outlet gas stream for the same run with clean soil A.

Figure 3. Thermogravimetric and evolved gas analysis (TG−IR) for the same TGA run with clean soil B and a heating rate of 1 °C/min. (Top panel) Thermogravimetry and differential thermogravimetry curves describing the weight loss and weight loss rate for the TGA run with clean soil B. (Bottom panel) Water and carbon dioxide concentrations in the TGA outlet gas stream for the same run with clean soil B.

and a much more pronounced peak between 300 and 475 °C (black lines of Figure 1). Soil B, on the other hand, exhibits

patterns are completely different. The rate of weight loss or (devolatilization rate) of soil A has a small peak around 225 °C 10832


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Figure 4. Experimental thermogravimetry (top panel) and differential thermogravimetry (bottom panel) curves describing the weight loss and weight loss rate, respectively, for three TGA runs with contaminated soil A (black lines) and three TGA runs with contaminated soil B (red lines). All six experiments were performed at a heating rate of 1 °C/min.

very little weight loss at temperatures below 300 °C, a slowly increasing rate of weight loss between 300 and 500 °C and a very sharp weight loss peak between 500 and 650 °C. Excellent repeatability was observed for soil A. Runs with clean soil B samples again showed very good repeatability for temperatures below 500 °C. Slightly larger sample-to-sample variations were observed in both the position and the height of the 500−650 °C peak (red lines of Figure 1). We also observed that many (but not all) soil B samples exhibited some very abrupt weight losses that trigger sharp spikes in the rates of weight loss calculated by taking the derivatives of the direct weight measurements. These abrupt weight losses (Figure 3) are caused by sudden release of volatiles such as water held between clay layers27 or carbon dioxide produced from carbonate decomposition.28,29 Different Mineral Transformations Dominate Weight Loss from Soils A and B. Our earlier work22 showed that water and carbon dioxide were the only gases detected by TG−MS when clean (background) soils were heated in inert atmospheres. Here, we take advantage of the quantitative capabilities of TG−IR to quantify the weight losses attributed to H2O and CO2 release and highlight the differences in the composition and, thus, the thermal behavior of the two soils. This is crucial for decoupling the clean soil kinetics from those of the petroleum−soil mixtures. Figures 2 and 3 present the EGA patterns for water and carbon dioxide from a typical TG−IR run as samples of clean soil A and clean soil B are heated at a constant rate of 1 °C/ min under flowing nitrogen. Only measurements obtained after the drying stage are presented to ensure that all the moisture is

removed and, consequently, the total observed weight losses are only due to soil mineral transformations (or SOM pyrolysis). The H2O release curve for soil A has two small peaks at around 130 and 240 °C (Figure 2), which correspond to the small peaks of the weight loss rate (DTG) curve of Figure 1. A much more prominent H2O peak appears between 300 and 450 °C and accounts for most of the weight loss of this sample (see corresponding TG and DTG curves of Figure 1). On the other hand, the H2O release from soil B follows a totally different pattern with a small peak around 140 °C, a slow increase from about 300 to 350 °C, and a constant but fairly low H2O concentration for the remainder of the run. The CO2 release patterns for soils A and B are very different. Figure 2 shows that soil A starts releasing CO2 at about 200 °C, its concentration in the TGA off-gas reaches a small maximum at 300 °C and slowly decreases thereafter. Release of CO2 from soil B also starts at 200 °C and slowly increases with increasing temperature. As the pyrolysis temperature rises above 500 °C, however, a sharp CO2 peak is detected with a corresponding substantial drop in the sample weight. Since the H2O concentration remains constant between 500 and 650 °C where the large CO2 peak is observed, we can conclude that different thermal processes are responsible for the release of water and carbon dioxide from soil B in this temperature range. Very similar release patterns to those shown in Figures 2 and 3 were obtained from the other TG−IR runs with samples from soil A or B, resulting in small standard deviations for the average amounts of water and carbon dioxide release. TG−IR measurements showed that soil A released a total of 1.65 ± 10833


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Figure 5. Evolved gas analysis (EGA) with TG−MS of contaminated soil B showing the release patterns for water, carbon dioxide, hydrogen, and hydrocarbons. This TGA experiment was performed at a heating rate of 10 °C/min. (Top panel) Ion intensities vs temperature of selected fragments: m/z = 44 for CO2, 18 for H2O, 2 for H2, and 29 for C2H5. (Bottom panel) Ion intensities vs temperature of selected fragments: m/z = 2 for H2, 15 for CH3 (indicating release of methane), 29 for C2H5, and 43 for C3H7. The two fragments with m/z = 29 and m/z = 43 indicate release of alkanes.

0.14 wt % water and 0.76 ± 0.12 wt % carbon dioxide. The corresponding amounts for soil B were 0.87 ± 0.14 wt % for water and 1.64 ± 0.18 wt % for carbon dioxide. Note that the CO2/H2O mass ratios were very different for the two soils: 0.46 ± 0.11 for soil A and 1.90 ± 0.08 for soil B, underscoring the significant composition differences of the two soils. We should also note that the sum of the water and carbon dioxide amounts measured by NDIR in the 105−660 °C range (where most of the weight changes were observed) was within 5% of the weight loss measured by thermogravimetry for the same temperature range. Clearly, different mineral transformations dominate the weight loss of the two background soils. Thermal transformations of clays dominate the weight loss of soil A between 350 and 500 °C, while carbonate decomposition reactions account for most of the weight loss of soil B between 500 and 650 °C. Water loss from clay components is also important for soil B. Thermal transformations of clays have been extensively studied in the literature.30−35 In general, clay dehydration takes place at temperatures below 500 °C. Water absorbed on the external surface of clays and interlayer water connected to the clay with hydrogen and ionic bonds are progressively released with increasing temperature.35 Dehydration is usually reversible. But, irreversible dehydroxylation can occur (usually at temperatures above 500 °C) through a two-step proton transfer by two hydroxyl groups.33,35,36 The temperature range for dehydroxylation varies by clay type. Smectite dehydrox-

ylation has been reported for temperatures between 350 and 650 °C, while dehydroxylation of montmorillonite, a member of the smectite group, was reported to take place between 550 and 700 °C.34 Clay dehydration and dehydroxylation are the dominant mineral transformations for soil A. Soil B also exhibits significant water release that follows a different pattern. The differences observed in the water release patterns for soils A and B are attributed to different clay types, while the total amounts of released water (1.65 wt % for soil A and 0.95 wt % for soil B) are consistent with the clay contents of the two soils (25 wt % for soil A and 12 wt % for soil B). Carbonate decomposition is the major soil mineral transformation taking place when soil B is heated. Dolomite and calcite are among the most common carbonates found in soils. Natural dolomite is a mixture of calcium and magnesium carbonates and may undergo a two-step decomposition mechanism, first decomposing to magnesite and calcite.28 Both carbonates decompose at similar temperatures between 600 and 800 °C.28,29 However, some carbonates such as magnesite may start decomposing at temperatures as low as 350 °C.29,37 Clearly, the TGA data of Figure 4 showing a sharp CO2 peak between 550 and 650 °C is consistent with literature data and supports a conclusion that this CO2 release is due to carbonate decomposition. The CO2 release from soil A (Figure 2) is most likely not due to a carbonate decomposition reaction, but probably the product of soil organic matter (SOM) pyrolysis. 38,39 10834


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safely conclude that pyrolysis reactions take place in the 350− 500 °C range. Alkanes are also detected in the same temperature range since C2−C4 and possibly larger hydrocarbons are also pyrolysis products.47 Fragments with m/z = 67 (for *C5H7), m/z = 69 (for *C5H9), m/z = 70 (for *C5H10), and m/z = 71 (for *C5H11) were also detected in the 350−500 range indicating release of unsaturated or cyclic hydrocarbons. This observation provides additional support for the hypothesis that pyrolysis reactions (such as β-scission) take place in this temperature range. As expected, the final product of this reaction cascade is a carbonaceous material (char or coke) with low H/C atomic ratio ( 3σi. To simplify the mathematics, we will use Gaussian PDFs here and, as we will see later, the kinetic parameters of all our models will satisfy the condition Ei0 > 3σi. Anthony and Howard54 used a slightly different argument to justify the use of Gaussians, while other investigators64 used the Weibull distribution to overcome this problem. If we assume first-order kinetics with pre-exponential factor ki0 for all species of each pseudocomponent, eq 1 yields Ä É ∞Å t ij yzÑÑÑÑ ÅÅÅ −E / RT ÅÅ1 − expjj− xi(t ) = ki0e dτ zzÑÑ·fi (E) dE 0 Å ÅÇ k 0 {ÑÑÖ i = 1, 2, ..., N + M (4)

∫

N

∑

(6)

∞

fi (E) =

mR (T ) =1− mR (T0)

∞

Since activation energies are positive, the PDFs f i(E) must satisfy the condition:

∫0

∫

∫

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Figure 6. Experimental data and DAE model predictions for clean (background) soil A. (Top panel) Normalized reacting fraction Y defined by eq 6 vs temperature for clean soil A. The thermogravimetric data were obtained from the average of the three TGA runs of Figure 1 for soil A. The solid line is the data fit from a 4-component DAE model. (Bottom panel) Comparison of reaction rates predicted by the DAE model and measured experimentally (open symbols). The total DAE model reaction rate is the sum of the rates of the four pseudocomponents A1−A4 of clean soil A.

(2) The full kinetics for petroleum-contaminated soils were also determined from thermogravimetric experiments with contaminated soil samples performed with a heating rate of 1 °C/min. Assuming that the addition of oil crude did not alter significantly any soil properties, we used (a) the same N pseudocomponents identified from clean soil experiments to describe soil mineral transformations taking place during pyrolytic treatment of contaminated soils, and (b) M additional pseudocomponents to model the hydrocarbon desorption and pyrolysis. The assumption that the addition of oils did not alter the soil properties imposed an additional constraint on the mass fractions of components associated with water and carbon dioxide release. Specifically, we required that the ratio of the mass fraction c4 of the component that releases carbon dioxide over the sum (c1 + c2 + c3) of the waterreleasing mass fractions should be equal to the value estimated for the clean soil. Kinetics of Mineral Transformations in Clean Soils. Table 1 presents the kinetic constants for the four-component DAE models obtained by fitting the thermogravimetric data of the averages computed from the TGA runs presented in Figure 1 for clean soils A and B. Because of the complexity of the water release patterns for both soils, we used three pseudocomponents to accurately model water release. A single pseudocomponent reaction was sufficient for modeling carbon dioxide release. The four-component DAE models fit very well the weight vs temperature data for contaminated soils A and B (top panels of Figures 6 and 7). More importantly, however, the conversion

rates computed for the various components of the DAE models provide very good approximations of the water and carbon dioxide release patterns obtained by TG−IR measurements (Figures 2 and 3). Excellent agreement is observed for soil A (Figure 6). The first two water-releasing pseudocomponents (A1− H2O and A2−H2O) correspond to low temperature clay dehydration processes. Irreversible dehydroxylation is the likely thermal process represented by the A3-H2O pseudocomponent. It has a maximum at 550 °C and an activation energy of 161 kJ/mol, which is in line with the activation energies reported in the literature (typically 140−190 kJ/mol) for clay dehydroxylation.34,66,67 The main characteristic of soil B is a big CO2 peak that starts at 550 °C and reaches its maximum at just over 600 °C (Figure 7). Carbonate decomposition is responsible for this peak and has an activation energy of 181 kJ/mol, which is within the range of 170−210 kJ/mol reported in the literature.28,29 The pseudocomponent corresponding to hightemperature water release (clay dehydroxylation) from soil B has an activation energy of 183 kJ/mol, again within the range of literature values. We should note here that our DAE model does not attempt to describe the thermal behavior of individual soil minerals. Instead, it lumps the numerous soil minerals into four pseudocomponents to approximate the aggregate behavior of these complicated mixtures. The good agreement of the pseudocomponent activation energies estimated by the DAE model to those reported in the literature underscores the usefulness and wide applicability of our approach for optimizing thermal remediation processes. 10837


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Figure 7. Experimental data and DAE model predictions for clean (background) soil B. (Top panel) Normalized reacting fraction Y defined by eq 6 vs temperature for clean soil B. The thermogravimetric data were obtained from the average of the three TGA runs of Figure 1 for soil B. The solid line is the data fit from a four-component DAE model. (Bottom panel) Comparison of reaction rates predicted by the DAE model and measured experimentally (open symbols). The total DAE model reaction rate is the sum of the rates of the four pseudocomponents A1−A4 of clean soil B.

Table 2. Kinetic Parameters of Six-Component DAE Models for Contaminated Soils A and Ba soil

pseudocomponent

log10 k0, min−1

E0, kJ/mol

σ, kJ/mol

cj

RMSE

soil A (contaminated)

A1-H2O A2-H2O A3-H2O A4-CO2 A5-LH A6-HH A1-H2O A2-H2O A3-H2O A4-CO2 A5-LH A6-HH

6.1 6.8 8.8 11 5.8 12.7 6.8 8.1 9.4 9.3 5.8 12.7

70.4 108 169 156 69 167 82.3 130 186 185 71.4 170

5.8 2.2 13 30 7.9 8.7 17.5 2.5 1.6 0.3 7.9 6.6

0.07 0.29 0.09 0.22 0.23 0.10 0.12 0.11 0.01 0.48 0.20 0.08

0.0020

soil B (contaminated)

0.0058

Notes: Kinetic parameters were estimated by fitting the experimental data from the average of the three runs for soils A and B shown in Figure 1. Ai-CO2 and Aj-H2O denote pseudocomponents that release CO2 and H2O, respectively. RMSE (or root-mean-square error) is the square root of the sum of the squares of the differences between model predictions and experimentally measured values for sample weights (see eq 7). a

To investigate how the sample-to-sample variability may affect the kinetics, we also estimated the parameters of 4component DAE models by fitting the data obtained from each of three individual runs with clean soil B of Figure 1. The activation energies and the other kinetic parameters computed from the individual runs for the four pseudocomponents of soil B were within 2−5% of the values computed from the average of the three runs. Sample-to-sample differences were even smaller for experiments with soil A.

While four-component DAE models were accurate enough for the two soils considered here, additional pseudocomponents may have to be considered if the soil contains larger amounts of SOM, or if we know that it contains mixtures of carbonate minerals that decompose at different temperatures. Full Kinetics for Pyrolytic Remediation of Contaminated Soils. Table 2 presents the kinetic constants for 6component DAE models obtained by fitting the average thermogravimetric data computed from the three runs for contaminated soil A and the other three runs for contaminated 10838


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Figure 8. Experimental data and DAE model predictions for contaminated soil A. (Top panel) Normalized reacting fraction Y defined by eq 6 vs temperature for contaminated soil A. The thermogravimetric data were obtained from the average of the three TGA runs of Figure 4 for soil A. The solid line is the data fit from a 6-component DAE model. (Bottom panel) Comparison of reaction rates predicted by the DAE model and measured experimentally (open symbols) for contaminated soil A. The soil component rate (blue solid line) is the sum of the rates of the four pseudocomponents A1−A4 modeling soil mineral transformations, while the total reaction rate (black solid line) is the sum of the rates of all six pseudocomponents A1−A6 of contaminated soil A.

conversion of the A6-HH fraction of soil A starts well before 300 °C and it has a maximum at about 350 °C. While this temperature shift might not be what one expects, it is independently supported by our recent pilot-scale study24 that achieved very high levels of TPH removal (93−99%) by treating contaminated soil A at 370 °C with 30 or 60 min residence times in a rotary kiln reactor. A possible explanation for the different reactivity pattern we observed for contaminated soil A is that in our case the oil is dispersed mostly as a layer coating the particles of the contaminated soil. As is wellknown, mass transfer in films of heavy hydrocarbons can significantly affect the pyrolysis kinetics and the rate of coke formation.68 Another possible explanation is that soil minerals, like clays, may catalyze the pyrolysis reactions and facilitate coke deposition.69,70 Other studies, however, do not support this assertion.71 Clearly, more work is needed before we can conclusively say whether or how the dispersion of oil or the soil composition will affect the pyrolysis of a specific oil−soil mixture. Figure 9 shows that the six-component DAE model also fits very well the weight vs temperature data for soil B. The conversion rates for both the A5-LH and A6-HH pseudocomponents of soil B show similar temperature dependence to that observed for soil A, with the maximum of the A6-HH peak now shifted to a slightly higher temperature of 380 °C. These findings suggest that drastic reductions of the TPH content of soil B can be achieved at temperatures in the 380−420 °C

soil B shown in Figure 4. We used four pseudocomponents to model water and carbon dioxide release, and added two pseudocomponents to model hydrocarbon desorption and pyrolysis. The six-component DAE model fits very well the weight vs temperature data for contaminated soil A (top panel of Figure 8). The conversion rate of the A5-LH pseudocomponent, which models the desorption of hydrocarbons, has a maximum at about 200 °C and falls off gradually to zero as the temperature rises above 350 °C (bottom panel of Figure 8). This pattern agrees very well with the observations of Shin and co-workers51 who separated Atabasca sand bitumen into its maltene and asphaltene fractions and pyrolyzed both the separated fractions and the original complete bitumen in a TGA with 1 °C/min heating rate. Their estimated maltene devolatilization rate had a broad distribution extending beyond 400 °C with a maximum at about 230 °C. The same investigators found that the rates of maltene and asphaltene cracking were very similar, but with a narrower distribution than the maltene devolatilization rate. Cracking took place between 325 and 500 °C, with a sharp peak at about 400 °C. This is, of course, the expected behavior according to the extensive literature of petroleum hydrocarbon pyrolysis.44−48 Figure 8, however, shows a shift toward lower temperatures for the conversion rate for the A6-HH pseudocomponent that represents the heavy hydrocarbon fraction of soil A. The 10839


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Figure 9. Experimental data and DAE model predictions for contaminated soil B. (Top panel) Normalized reacting fraction Y defined by eq 6 vs temperature for contaminated soil B. The thermogravimetric data were obtained from the average of the three TGA runs of Figure 4 for soil B. The solid line is the data fit from a six-component DAE model. (Bottom panel) Comparison of reaction rates predicted by the DAE model and measured experimentally (open symbols) for contaminated soil B. The soil component rate (red solid line) is the sum of the rates of the four pseudocomponents A1−A4 modeling soil mineral transformations, while the total reaction rate (black solid line) is the sum of the rates of all six pseudocomponents A1−A6 of contaminated soil B.

are obtained from nonisothermal experiments that heat samples at a constant rate of 1 °C/min. Most of the water lost from soil A is released between 350 and 450 °C, a range that overlaps with that of hydrocarbon pyrolysis reactions (Figure 6). Figure 8 shows that conversion of the heavy hydrocarbon fraction A6-HH is almost complete when the sample temperature reaches 420 °C. But, water loss is far from complete at 420 °C and a significant fraction of the total water lost from soil A is released at temperatures above 420 °C. This partial overlap of the temperature ranges for hydrocarbon pyrolysis and water loss accounts for the existence of “optimal” treatment conditions for soil fertility. As contaminated soil moves through a kiln reactor kept at 420 °C, the reaction rate for the heavy hydrocarbon fraction is fast enough to achieve almost complete conversion in 15 or 30 min. At the same time, these operating conditions are not severe enough to completely remove the water from the clay components of soil A. Treatment at 470 °C, however, results in almost complete water loss from the clays of soil A, causing soil damage and loss of fertility. The data in Figures 6 and 8 lead us to the conclusion that loss of fertility at high treatment intensities24 is caused by irreversible damage to the clay components of the soil due to their dehydration and/or dehydroxylation. Since the fertility of soils treated at 420 °C was restored to almost the clean soil level,24 we can safely assume that water losses from soil A at

range. Unfortunately, we do not have enough information to confirm this. While we were able22 to reduce the TPH content of contaminated soil B from 19 000 mg/kg to below detection level by pyrolytic treatment at 420 °C, the long and impractical 3 h treatment times used for those experiments do not allow us to draw definite conclusions. The activation energies for the A5-LH components of soils A and B were nearly identical at 69 and 71 kJ/mol, respectively. The activation energies for the A6-HH hydrocarbon fraction were higher, but again almost identical at 167 and 170 kJ/mol for soils A and B, respectively. All these activation energies are in general agreement with the values reported by Shin and coworkers.51 In general, the DAE models provide accurate descriptions of the experimental data (Figures 6−9 and Tables 1 and 2). More accurate fits could have been obtained by adding, for example, a third hydrocarbon component to model the high-temperature effects detected by Figure 5. Without additional information about the oil composition, however, adding more hydrocarbon pseudocomponents would be conjectural. How Soil Composition Differences Influence the Fertility of Treated Soils. A closer analysis of the reactivity patterns of Figures 6 and 8 provides the explanation for the trade-off between TPH removal and soil fertility observed in our earlier study.24 To properly interpret these results, however, we must keep in mind that all TGA measurements 10840



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temperatures below 420 °C are reversible, a conjecture supported by several literature studies.30−35

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AUTHOR INFORMATION

Corresponding Author

*Tel.: (713) 348-5208. E-mail: [email protected].

CONCLUSIONS This study presented a systematic approach that combines experiments and mathematical modeling to determine the kinetics of pyrolytic remediation of soils contaminated with petroleum hydrocarbons. We first collected and analyzed TG− IR and TG−MS data to identify the most important transformations occurring when two sets of clean and contaminated soils are pyrolyzed. A two-step DAE methodology was then developed to model the clean and contaminated soils with small sets of pseudocomponents that accurately captured the overall conversion patterns of these complicated mixtures. The DAE models provided very good fits to experimental data obtained for soils with widely different compositions, suggesting that the framework of analytical and theoretical techniques presented here can be employed by practitioners in the thermal remediation field to obtain the pyrolysis kinetics of a broad range of petroleum/soil systems. Our results also show that soil composition can dramatically affect both the efficacy of pyrolytic treatment for TPH removal and the fertility of pyrolytically treated soil. For example, the drastic increase of soil pH due to carbonate decomposition is the main reason for the loss of fertility of soil B, while the fertility of soil A is primarily affected by the extent of irreversible dehydration of its clay components upon heating. When there is an overlap of the temperature ranges over which clays release water and hydrocarbons undergo pyrolysis, an “optimal” set of pyrolysis conditions may exist that gives sufficiently high TPH removal and maximum restoration of soil fertility. This is the case with soil A. In general, however, the optimal set of conditions, or even its existence, will strongly depend on the soil composition and some soils (e.g., soil B) may not have such an optimum.15 This study also raised questions about whether the dispersion of oil or the catalytic properties of some soil components (e.g., clays) will affect the pyrolysis kinetics, possibly lowering the temperatures required for complete TPH removal and detoxification. Additional work is required to conclusively answer these questions. By emphasizing the importance of soil composition and by raising the possibility of trade-offs between pollutant removal and restoration of soil fertility, this kinetic study advanced our fundamental understanding of the multiple and concurrent processes taking place during the pyrolytic treatment of petroleum-contaminated soils. Kinetic models will inform the design of pyrolysis reactors optimized for specific petroleum− soil systems, enable robust operation, and facilitate acceptance of this novel technology by regulatory agencies and stakeholders.

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Article

ORCID

Kyriacos Zygourakis: 0000-0002-1044-1139 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the Energy Technology Company of Chevron Corporation for providing the samples of clean and contaminated soils used for this study. We also thank Dr. Dilip Asthagiri for many valuable suggestions.

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REFERENCES

(1) Lim, M. W.; Von Lau, E.; Poh, P. E. A comprehensive guide of remediation technologies for oil contaminated soil - Present works and future directions. Mar. Pollut. Bull. 2016, 109 (1), 14−45. (2) Duffy, J. J.; Peake, E.; Mohtadi, A. F. Oil Spills on Land as Potential Sources of Groundwater Contamination. Environ. Int. 1980, 3, 107−120. (3) Jemelov, A. Environmental Effects of Terrestrial Oil Spills. Encyclopedia of the Anthropocene 2018, 1, 323−335. (4) Etkin, D. S. Analysis of oil spill trends in the United States and worldwide. In 2001 International Oil Spill Conference; American Petroleum Institute: Washington, DC, 2001; pp 1291−1300. (5) Tang, J. C.; Lu, X. Q.; Sun, Q.; Zhu, W. Y. Aging effect of petroleum hydrocarbons in soil under different attenuation conditions. Agric., Ecosyst. Environ. 2012, 149, 109−117. (6) Tang, J. C.; Wang, M.; Wang, F.; Sun, Q.; Zhou, Q. X. Ecotoxicity of petroleum hydrocarbon contaminated soil. J. Environ. Sci. 2011, 23 (5), 845−851. (7) Barron, M. G. Ecological Impacts of the Deepwater Horizon Oil Spill: Implications for Immunotoxicity. Toxicol. Pathol. 2012, 40 (2), 315−320. (8) Hinchee, R. E., Smith, Lawrence A. In situ thermal technologies for site remediation; CRC Press, 1992. (9) O’Brien, P. L.; DeSutter, T. M.; Casey, F. X. M.; Khan, E.; Wick, A. F. Thermal remediation alters soil properties - a review. J. Environ. Manage. 2018, 206, 826−835. (10) Stegemeier, G. L., Vinegar, Harold J., Thermal conduction heating for in situ thermal desorption of soils. In Hazardous and radioactive waste treatment technologies handbook; Oh, C. H., Ed.; CRC Press: Boca Raton, FL, 2001. (11) Vidonish, J. E.; Zygourakis, K.; Masiello, C. A.; Sabadell, G.; Alvarez, P. J. Thermal Treatment of Hydrocarbon-Impacted Soils: A Review of Technology Innovation for Sustainable Remediation. Engineering 2016, 2 (4), 426−437. (12) Chibwe, L.; Geier, M. C.; Nakamura, J.; Tanguay, R. L.; Aitken, M. D.; Simonich, S. L. M. Aerobic Bioremediation of PAH Contaminated Soil Results in Increased Genotoxicity and Developmental Toxicity. Environ. Sci. Technol. 2015, 49 (23), 13889− 13898. (13) Pellerin, C. Alternatives to incineration: there’s more than one way to remediate. Environ. Health Perspect. 1994, 102 (10), 840. (14) Valenti, M. Cleaning soil without incineration. Mech. Eng. 1994, 50. (15) Vidonish, J. E.; Zygourakis, K.; Masiello, C. A.; Gao, X.; Mathieu, J.; Alvarez, P. J. Pyrolytic treatment and fertility enhancement of soils contaminated with heavy hydrocarbons. Environ. Sci. Technol. 2016, 50a (5), 2498−2506. (16) Biache, C.; Mansuy-Huault, L.; Faure, P.; Munier-Lamy, C.; Leyval, C. Effects of thermal desorption on the composition of two coking plant soils: Impact on solvent extractable organic compounds and metal bioavailability. Environ. Pollut. 2008, 156 (3), 671−677.

ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.9b01494. Selected agronomic parameters and fertility metric for uncontaminated, contaminated, and treated soils for soil A and soil B; TGA temperature lag and optimal heating rate for kinetic measurements; using Gaussians as PDFs for DAEM pseudocomponents (PDF) 10841


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Industrial & Engineering Chemistry Research (17) O’Brien, P. L.; DeSutter, T. M.; Casey, F. X. M.; Wick, A. F.; Khan, E. Evaluation of Soil Function Following Remediation of Petroleum Hydrocarbons-a Review of Current Remediation Techniques. Curr. Pollut. Rep. 2017, 3 (3), 192−205. (18) Pape, A.; Switzer, C.; McCosh, N.; Knapp, C. W. Impacts of thermal and smouldering remediation on plant growth and soil ecology. Geoderma 2015, 243, 1−9. (19) Terefe, T.; Mariscal-Sancho, I.; Peregrina, F.; Espejo, R. Influence of heating on various properties of six Mediterranean soils. A laboratory study. Geoderma 2008, 143 (3−4), 273−280. (20) Scullion, J. Remediating polluted soils. Naturwissenschaften 2006, 93, 51−65. (21) Exner, J. H. Alternatives to incineration in remediation of soil and sediments assessed. Rem. J. 1995, 5 (3), 1−18. (22) Vidonish, J. E.; Alvarez, P. J. J.; Zygourakis, K. Pyrolytic Remediation of Oil-Contaminated Soils: Reaction Mechanisms, Soil Changes, and Implications for Treated Soil Fertility. Ind. Eng. Chem. Res. 2018, 57 (10), 3489−3500. (23) Li, D. C.; Xu, W. F.; Mu, Y.; Yu, H. Q.; Jiang, H.; Crittenden, J. C. Remediation of Petroleum-Contaminated Soil and Simultaneous Recovery of Oil by Fast Pyrolysis. Environ. Sci. Technol. 2018, 52 (9), 5330−5338. (24) Song, W.; Vidonish, J. E.; Kamath, R.; Yu, P. F.; Chu, C.; Moorthy, B.; Gao, B. Y.; Zygourakis, K.; Alvarez, P. J. J. Pilot-Scale Pyrolytic Remediation of Crude-Oil-Contaminated Soil in a Continuously-Fed Reactor: Treatment Intensity Trade-Offs. Environ. Sci. Technol. 2019, 53 (4), 2045−2053. (25) Abatzoglou, N.; Chornet, E.; Belkacemi, K.; Overend, R. P. Phenomenological Kinetics of Complex-Systems - the Development of a Generalized Severity Parameter and Its Application to Lignocellulosics Fractionation. Chem. Eng. Sci. 1992, 47 (5), 1109− 1122. (26) Antal, M. J.; Varhegyi, G.; Jakab, E. Cellulose pyrolysis kinetics: Revisited. Ind. Eng. Chem. Res. 1998, 37 (4), 1267−1275. (27) Vidal, O.; Dubacq, B. Thermodynamic modelling of clay dehydration, stability and compositional evolution with temperature, pressure and H2O activity. Geochim. Cosmochim. Acta 2009, 73 (21), 6544−6564. (28) Samtani, M.; Dollimore, D.; Alexander, K. S. Comparison of dolomite decomposition kinetics with related carbonates and the effect of procedural variables on its kinetic parameters. Thermochim. Acta 2002, 392, 135−145. (29) Su, L.; Zhang, G.; Dong, Y.; Feng, J.; Liu, D. Comparison of Thermal Decomposition Kinetics of Magnesite and Limestone. Adv. Mater. Res. 2013, 652−654, 2580−2583. (30) Bray, H. J.; Redfern, S. A. T. Kinetics of dehydration of Camontmorillonite. Phys. Chem. Miner. 1999, 26 (7), 591−600. (31) Bray, H. J.; Redfern, S. A. T. Influence of counterion species on the dehydroxylation of Ca2+-, Mg2+-, Na+- and K+-exchanged Wyoming montmorillonite. Mineral. Mag. 2000, 64 (2), 337−346. (32) Frost, R. L.; Ruan, H.; Kloprogge, J. T.; Gates, W. P. Dehydration and dehydroxylation of nontronites and ferruginous smectite. Thermochim. Acta 2000, 346 (1−2), 63−72. (33) Frost, R. L.; Vassallo, A. M. The dehydroxylation of the kaolinite clay minerals using infrared emission spectroscopy. Clays Clay Miner. 1996, 44 (5), 635−651. (34) Levy, J. H. Effect of Water-Vapor Pressure on the Dehydration and Dehydroxylation of Kaolinite and Smectite Isolated from Australian Tertiary Oil Shales. Energy Fuels 1990, 4 (2), 146−151. (35) Stackhouse, S.; Coveney, P. V.; Benoit, D. M. Densityfunctional-theory-based study of the dehydroxylation behavior of aluminous dioctahedral 2:1 layer-type clay minerals. J. Phys. Chem. B 2004, 108 (28), 9685−9694. (36) Frost, R. L.; Fredericks, P. M.; Shurvell, H. F. Raman microscopy of some kaolinite clay minerals. Can. J. Appl. Spectrosc. 1996, 41 (1), 10−14. (37) Olszak-Humienik, M.; Jablonski, M. Thermal behavior of natural dolomite. J. Therm. Anal. Calorim. 2015, 119 (3), 2239−2248.

(38) Plante, A. F.; Fernández, J. M.; Leifeld, J. Application of thermal analysis techniques in soil science. Geoderma 2009, 153 (1), 1−10. (39) Williams, E. K.; Fogel, M. L.; Berhe, A. A.; Plante, A. F. Distinct bioenergetic signatures in particulate versus mineral-associated soil organic matter. Geoderma 2018, 330, 107−116. (40) Yang, H. P.; Yan, R.; Chen, H. P.; Lee, D. H.; Zheng, C. G. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel 2007, 86 (12−13), 1781−1788. (41) Lighty, J. S.; Silcox, G. D.; Pershing, D. W.; Cundy, V. A.; Linz, D. G. Fundamental Experiments on Thermal-Desorption of Contaminants from Soils. Environ. Prog. 1989, 8 (1), 57−61. (42) Lighty, J. S.; Silcox, G. D.; Pershing, D. W.; Cundy, V. A.; Linz, D. G. Fundamentals for the Thermal Remediation of Contaminated Soils - Particle and Bed Desorption Models. Environ. Sci. Technol. 1990, 24 (5), 750−757. (43) LaMarca, C.; Libanati, C.; Klein, M. T.; Cronauer, D. C. Enhancing chain transfer during coal liquefaction: A model system analysis. Energy Fuels 1993, 7 (4), 473−478. (44) Savage, P. E. Mechanisms and kinetics models for hydrocarbon pyrolysis. J. Anal. Appl. Pyrolysis 2000, 54 (1), 109−126. (45) Gray, M. R.; McCaffrey, W. C. Role of chain reactions and olefin formation in cracking, hydroconversion, and coking of petroleum and bitumen fractions. Energy Fuels 2002, 16 (3), 756− 766. (46) Banerjee, D. K.; Laidler, K. J.; Nandi, B. N.; Patmore, D. J. Kinetic studies of coke formation in hydrocarbon fractions of heavy crudes. Fuel 1986, 65, 480. (47) Savage, P. E.; Klein, M. T.; Kukes, S. G. Asphaltene reaction pathways. 1. Thermolysis. Ind. Eng. Chem. Process Des. Dev. 1985, 24 (4), 1169−1174. (48) Yasar, M.; Trauth, D. M.; Klein, M. T. Asphaltene and resid pyrolysis. 2. The effect of reaction environment on pathways and selectivities. Energy Fuels 2001, 15, 504−509. (49) Savage, P. E. Mechanisms and kinetics models for hydrocarbon pyrolysis. J. Anal. Appl. Pyrolysis 2000, 54 (1−2), 109−126. (50) Friedman, H. L. Kinetics of Thermal Degradation of CharForming Plastics from Thermogravimetry. Application to Phenolic Plastic. J. Polym. Sci., Part C: Polym. Symp. 1964, 6 (6pc), 183. (51) Shin, S.; Im, S. I.; Kwon, E. H.; Na, J.-G.; Nho, N. S.; Lee, K. B. Kinetic study on the nonisothermal pyrolysis of oil sand bitumen and its maltene and asphaltene fractions. J. Anal. Appl. Pyrolysis 2017, 124, 658−665. (52) Vand, V. A theory of the irreversible electrical resistance changes of metallic films evaporated in vacuum. P. Phys. Soc. 1943, 55, 0222−0246. (53) Pitt, G. J. The Kinetics of the Evolution of Volatile Products from Coal. Fuel 1962, 41 (3), 267−274. (54) Anthony, D. B.; Howard, J. B. Coal Devolatilization and Hydrogasification. AIChE J. 1976, 22 (4), 625−656. (55) de Caprariis, B.; De Filippis, P.; Herce, C.; Verdone, N. Double-Gaussian Distributed Activation Energy Model for Coal Devolatilization. Energy Fuels 2012, 26 (10), 6153−6159. (56) Aboyade, A. O.; Carrier, M.; Meyer, E. L.; Knoetze, J. H.; Gorgens, J. F. Model fitting kinetic analysis and characterisation of the devolatilization of coal blends with corn and sugarcane residues. Thermochim. Acta 2012, 530, 95−106. (57) Cai, J. M.; Wu, W. X.; Liu, R. H. An overview of distributed activation energy model and its application in the pyrolysis of lignocellulosic biomass. Renewable Sustainable Energy Rev. 2014, 36, 236−246. (58) Cai, J. M.; Wu, W. X.; Liu, R. H.; Huber, G. W. A distributed activation energy model for the pyrolysis of lignocellulosic biomass. Green Chem. 2013, 15 (5), 1331−1340. (59) Cai, J. M.; Yang, S. Y.; Li, T. Logistic distributed activation energy model - Part 2: Application to cellulose pyrolysis. Bioresour. Technol. 2011, 102 (3), 3642−3644. (60) Mani, T.; Murugan, P.; Mahinpey, N. Determination of Distributed Activation Energy Model Kinetic Parameters Using 10842


Article

Industrial & Engineering Chemistry Research Simulated Annealing Optimization Method for Nonisothermal Pyrolysis of Lignin. Ind. Eng. Chem. Res. 2009, 48 (3), 1464−1467. (61) Zhang, J. Z.; Chen, T. J.; Wu, J. L.; Wu, J. H. Multi-GaussianDAEM-reaction model for thermal decompositions of cellulose, hemicellulose and lignin: Comparison of N-2 and CO2 atm. Bioresour. Technol. 2014, 166, 87−95. (62) Cai, J. M.; Jin, C. A.; Yang, S. Y.; Chen, Y. Logistic distributed activation energy model - Part 1: Derivation and numerical parametric study. Bioresour. Technol. 2011, 102 (2), 1556−1561. (63) Cai, J. M.; Liu, R. H. New distributed activation energy model: Numerical solution and application to pyrolysis kinetics of some types of biomass. Bioresour. Technol. 2008, 99 (8), 2795−2799. (64) Lakshmanan, C. C.; White, N. A New Distributed ActivationEnergy Model Using Weibull Distribution for the Representation of Complex Kinetics. Energy Fuels 1994, 8 (6), 1158−1167. (65) Byrd, R. H.; Gilbert, J. C.; Nocedal, J. A trust region method based on interior point techniques for nonlinear programming. Math. Program. 2000, 89 (1), 149−185. (66) Killingley, J. S.; Day, S. J. Dehydroxylation Kinetics of Kaolinite and Montmorillonite from Queensland Tertiary Oil-Shale Deposits. Fuel 1990, 69 (9), 1145−1149. (67) Patterson, J. H.; Hurst, H. J.; Levy, J. H.; Killingley, J. S. Mineral Reactions in the Processing of Australian Tertiary Oil Shales. Fuel 1990, 69 (9), 1119−1123. (68) Radmanesh, R.; Chan, E.; Gray, M. R. Modeling of mass transfer and thermal cracking during the coking of Athabasca residues. Chem. Eng. Sci. 2008, 63, 1683−1691. (69) Ranjbar, M. Influence of reservoir rock composition on crude oil pyrolysis and combustion. J. Anal. Appl. Pyrolysis 1993, 27, 87−95. (70) Verkoczy, B. Factors affecting coking in heavy oil cores, oils and SARA fractions under thermal stress. J. Can. Petrol. Technol. 1993, 32 (7), 25−33. (71) Liu, P.; Zhu, M.; Zhang, Z.; Zhang, D. Pyrolysis of an Indonesian oil sand in a thermogravimetric analyser and a fixed-bed reactor. J. Anal. Appl. Pyrolysis 2016, 117, 191−198.

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Kinetic Study of the Pyrolytic Treatment of Petroleum-Contaminated

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