Effective Method To Determine Supersaturation of Tar Balls Deposited

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An Effective Method to Determine Supersaturation of Tar Balls Deposited along the Caspian Sea Javad Sayyad Amin, Somayye Nikkhah, Sohrab Zendehboudi, and Lesley A. James Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502534w • Publication Date (Web): 16 Apr 2015 Downloaded from http://pubs.acs.org on May 4, 2015

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An Effective Method to Determine Supersaturation of Tar Balls Deposited along the Caspian Sea

Javad Sayyad Amina *, Somayye Nikkhaha, and Sohrab Zendehboudib, Lesley A. Jamesb a

b

Department of Chemical Engineering, University of Guilan, Rasht 41996-13769 Iran

Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's , NL , Canada

Abstract The south coast of the Caspian Sea is being faced with oil pollution because of intensive petroleum activities in the region. Stranded tar balls on the beaches are considered as one of the evidences for oil pollution. In this study, the supersaturation of tar balls, which are collected from Caspian Sea beaches, is investigated by using anti-solvent precipitation technique. The scope of this research is the metastable zone width limit and its influence on supersaturation. Supersaturation is measured for precipitated tar ball particles within an n-hexane/methanol mixture. In general, supersaturation acts as a driving force for tar balls precipitation when the anti-solvent is added. Response surface methodology is utilized to evaluate the influences of vital parameters such as anti-solvent addition rate, mixing regime, initial solute concentration, and the metastable zone on the supersaturation phenomenon, leading to obtain a statistical model to forecast supersaturation. Comparing the response surface model predictions with the experimental data, a very good accuracy is noticed. Moreover, the analysis of variance (ANOVA) technique is employed to evaluate validity of the proposed model, implying that the metastability zone width has the most important effect on the supersaturation. *

Corresponding author .P.O. Box: 41635-3756, Fax: +98 131 669 0271, Rasht-Iran Email: [email protected], [email protected] 1 ACS Paragon Plus Environment

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1. Introduction The Caspian Sea is the largest inland sea in the world so that the coasts of the sea are bordered by several countries including Iran, Azerbaijan, Russia, Kazakhstan and Turkmenistan. The sea is well-known for fishery, tourism, marine activities and petroleum industry. The Caspian Sea is subjected to different pollutants. Marine pollutions come from land-based and coastal area. One of the major issues threatening the environment is oil contamination as the sea is rich in oil and natural gas resources. Outdated technology and activities like exploitation of oil fields, pipeline construction, and traffic of large oil tankers are the main causes, contributing to the pollution in the coastal environment. Stranded tar balls seen on the beaches can be considered as a rational evidence for oil pollution 1-3. Anzali port where is situated along the south shore of the Caspian Sea is one of the most tourist destinations in Iran.

Anzali beaches in Sangachin zone were being attacked by tar balls

particularly during 2010 and 2012. Tar balls are a product of weathering crude oil which is very prevalent in marine environments. In fact, tar balls are pieces of tiny and dark-colored oil which usually appear to be sticky 1-7. After oil spills (or/and natural oil) leak in the sea, the oil floats on the surface and then it spreads into a slick. Sea currents, winds and waves break the oil slick into smaller pieces. The appearance of the oil is normally changed by various physical, chemical, and biological processes, referring to weathering phenomenon

6, 7

. It can threaten the life of coastal

residents, coastal tourism, and marine resources. To clean up beaches, tar balls can be picked up by hand. The scraped which are stuck to tar balls can be removed by dissolving them in solvent. The remaining material is filtered. Then, the anti-solvent is added to extract the dissolved tar ball in solution. Precipitation of the dissolved tar balls consists of basic steps, including the creation of supersaturation, and nucleation, followed by growth of these nuclei 8, 9.

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Tar balls like asphaltenes are extremely complex mixtures containing various components of petroleum 5. These amorphous solids (non-crystalline) are one of the heaviest constituents of crude oils that contain aromatic rings, nitrogen, sulfur, and oxygen atoms

10, 11

. Precipitation of an

amorphous solid is a complex phenomenon involving nucleation and growth mechanisms 8, 9. A widespread methodology to induce precipitation is adding an anti-solvent to the solution to create precipitant via reduction of the solvent solubility. Nucleation may occur spontaneously or artificially. It is worth noting that the nucleation takes place through both primary and secondary processes 12. The supersaturation, the difference between the solution and saturation concentrations, is the main driving force for both nucleation and growth 13-15. It can be made by several methods such as evaporation, cooling, and anti- solvent addition 12. When the solution becomes supersaturated, nucleation does occur. Metastable Zone Width (MSZW) is the difference between the saturation concentration of a solution and the concentration at which the particles are first detected 17, 18. The MSZW is known as a very vital feature in precipitation processes. In general, it is noticed at the first stages of dissolution and nucleation phenomena that happen to the solute particles. Supersaturation should be kept in the metastable zone throughout the process in order to avoid nucleation 16. Numerous studies have been done about tar balls 2-6. Most of these studies have been conducted for identifying the source of tar balls deposited along the coasts. Various techniques such as molecular markers and trace metal have been used for tracing the sources of the pollutants 2, 3, 6. However, no adequate information about determination of supersaturation of tar balls is found in the literature. This paper introduces a new insight into tar balls field by determining the supersaturation of tar balls which is formed due to anti-solvent addition. Prior to this study, Khoshandam and Alamdari


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investigated the enlargement of asphaltene particles in heptane - toluene mixture, considering the supersaturation occurance10. In this study, the precipitation approach was utilized to explore the tar ball dissolution mechanism. This research was intended to determine the supersaturation and the width of metastable zone for tar balls precipitation in an n-hexane - methanol (as a mixture of solvent and anti-solvent) through an experimental approach. A series of experimental trials were carried out to study the effects of anti-solvent addition rate, initial amount of tar balls, shear rate, and metastable zone width on the output parameter (e.g., supersaturation). In general, the simplicity of the experimental procedure is its prominent advantage to use. Furthermore, the response surface methodology (RSM) was employed to find out the impacts of various input parameters on supersaturation through introducing a mathematical equation.

In fact, RSM is a combination of mathematical and

statistical techniques which can be effective for the purposes of process development, optimization and parametric sensitivity analysis

19-23

. To the best of our knowledge, no adequate research

studies/data on the metastable zone width and supersaturation of tar balls exist in the open sources. Also, there are no published documents in the literature to report application of the RSM tool to model the tar ball precipitation while adding the anti-solvent. In what follows, first experimental work was conducted to measure the width of metastable zone. Then, the supersaturation was identified. In the last stage, RSM was used to develop a model to predict the supersaturation of tar balls.

2. Theory Precipitation of an amorphous material, for instance tar balls, is a complex phenomenon involving nucleation and growth mechanisms which can be explored by the crystallization kinetics

24, 25

.

Crystallization is a well-known technique used in chemical engineering processes in which a 4 ACS Paragon Plus Environment

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stable phase precipitates from a solution 12. Precipitation like crystallization can be achieved using several methods, namely; evaporating, cooling, reaction, and anti-solvent addition 12. During antisolvent precipitation, a solution of a compound is mixed with an anti-solvent to deposit. Solid particles are precipitated at a constant temperature by adding an anti-solvent (at a constant rate) to a solution prepared in a solvent where the solute is fairly soluble in it

26-27

. The main advantage

and disadvantage of anti-solvent precipitation are the negligible effect of temperature and high dependency on mixing process, respectively

26

. The disadvantage is mainly due to poor mixing

regimes contributing to high supersaturation and excess primary nucleation

27

. The size

distribution of particles is influenced by various factors (e.g., solubility, supersaturation, nucleation, agitation, growth kinetics, etc) 26, 27. Supersaturation is the driving force in growth and nucleation mechanisms of precipitation. This characteristic is defined as the difference between the solution concentration and the equilibrium concentration. The dimensionless supersaturation is expressed by the following relationship:

S=(C-C*)/C*

(1)

in which, C represents the specific concentration of particles of mixture and C* refers to the saturation concentration, showing the capacity of a mixture to keep particles at equilibrium conditions 10, 15. The relation between the supersaturation and anti-solvent precipitation is explained by a solubilitysupersolubility diagram, as demonstrated in Figure 1

14, 27

. The diagram is made of three different

areas for each compound. Solutions with concentrations situated under the solubility curve are in the undersaturated region where nucleation and growth do not happen. The second area is considered as the metastable zone in which the spontaneous nucleation is not experienced. The last


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region is the supersaturated or labile zone, in which the concentration of the solution is more than the supersolubility curve, and it is expected to observe the spontaneous nucleation throughout the solution 14, 27.

Figure 1. A qualitative schematic of solubility - labile diagram.

If anti-solvent is added to the solution, the solution composition will move to the left direction, near the solubility curve. Therefore, the solution approaches towards the supersaturation conditions, and the nucleation occurs. Due to the growth of primary particles, the concentration of solution declines. The supersaturation condition can be maintained as long as the anti-solvent is added and the particles proceed growing. When adding anti-solvent is stopped, the concentration of solution attains the saturated concentration. To avoid the spontaneous nucleation, supersaturation must be kept in the metastable zone limit. The metastable zone is defined as the region between the supersolubility and the solubility curves, as shown in Figure 1. In this area, the spontaneous nucleation does not arise

17, 18, 21, 28, 29

. The

width of the metastable zone is known as the difference between the saturation concentration and 6 ACS Paragon Plus Environment

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the solution concentration at which the particles initially appear. Various factors including mixing regime, initial solute concentration, type of anti-solvent, location of addition, presence of impurities, and temperature are considerably affecting the metastable zone width 12.

3. Experimental Section 3.1. Materials Pure Methanol [CH4O; MW: 32.04g/mol; Mass purity >0.99] and n-hexane [C6H14; MW: 8618 g/mol; Mass purity >0.99] are the solvents used in the experimental work. The tar balls are the main materials (or samples) for this investigation.

3.2. Experimental Technique To examine the metastable zone width, the experiments were carried out under both agitated and non agitated conditions. Tar balls were collected from Anzali beaches (see Figure 2). The samples were washed to remove the sands. N-hexane was selected as a solvent due to its rapid dissolution as well as its availability. Methanol was also utilized as an anti-solvent. An unsaturated clear solution of tar balls in methanol was provided, being surrounded by a glass vessel at room temperature and pressure. The schematic diagram of the apparatus employed in this study is depicted in Figure 3. The tests were designed to determine the MSZW by detecting the moment of appearing and disappearing of the turbidity. The main equipment part is a vessel which is made of Pyrex glass to observe the mixture. Images and video films of the samples were taken by a digital microscope, which has a resolution up to 2 megapixels, in the setup. In addition, the transparency and turbidity of precipitation are monitored by a computer connected to the microscope. For more information, the experimental procedure is briefly described as follows:


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A solution containing tar balls and methanol was added to the vessel. After that, a certain amount of n-hexane was gradually added to the vessel till the precipitation of tar balls disappeared. When tar balls were entirely dissolved, the solution became clear and transparent. The transparency point was considered as the solubility or saturation point. This state was detected by the aid of microscope images. Then, the anti-solvent was steadily added to the saturated solution of tar balls in solvent until the solute was precipitated. The first stage of precipitation phenomenon was considered as the commencement of nucleation. Since nuclei are very fine or invisible, direct detection of nucleation events is difficult, somehow. Therefore, the nucleation of tar balls was monitored by the digital microscope linked to a computer. The microscope was installed on the top of the system to capture the high resolution images of turbidity appearance. The experiments were performed at a variety of concentrations of tar balls. For the sake of reproducibility and accuracy, each experiment was repeated three times and the precipitation appearance and disappearance were detected carefully for all trials.

Figure 2. A sample of tar ball collected for the current experimental and theoretical study.


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Figure 3. A simple drawing of the apparatus and instrument setup used for the experiments [1): Vessel; 2): Magnetic stirrer; 3): Digital microscope; 4): Computer].

In the second part, the detection of turbidity appearance and disappearance for tar balls in nhexane-methanol system was examined carefully through slowly mixing by a magnetic stirrer. An appropriate degree of agitation (3.34s-1) was selected in order to achieve suitable mixing. The procedure was the same as the previous part. In all experimental runs, the metastable zone width of the solutions of tar balls in n-hexanemethanol systems under agitating and non agitating condition was obtained. Provided experimental results, we attempted to meet the objectives of the study (e.g., determination of supersaturation and development of a statistical model). It is obvious that the type of anti-solvent plays an important role in the precipitation phenomenon of tar balls, considering various aspects such as the amount of precipitated tar balls, stability of the precipitated materials, aggregation occurrence, particle size, and so on. At the beginning of this research, it was planned to examine various types of anti-solvents such as alcohols and n-paraffinic compounds. Some anti-solvents including methanol were also employed; however our


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experimental outcome revealed that the applications of majority of them in terms of precipitation degree, stability, degree of solvent recovery, availability, and economic prospective are under question. Considering the existing drawbacks, our research group is currently working on a variety of solvents or/and anti-solvents, their characteristics, selection criteria and their technical effectiveness for investigation of tar balls environmental matters. The research results are going to be presented in our next research paper.

4. Response surface methodology and statistical analysis. The Response Surface Methodology (RSM) is recognized as a useful method to find out the contribution of different parameters to the output of the experiments, and to determine the interaction effects between factors and the coefficients of regressive model. RSM is a group of mathematical and statistical techniques to predict (and model) the response of variable (i.e. the measured values using laboratory tests) influencing by a set of independent variables. In other words, RSM is a simple predictive tool to reduce the number of experiments, and decide the contribution of each factor in response. This technique estimates a response function based on the experimental/real data through a polynomial model and evaluates the fitness of the model. Furthermore, it assesses the validity of coefficients with the aid of analysis of variance (ANOVA). ANOVA is an efficient tool to statistically determine the significance of the RSM. 23 The RSM is able to explain the behavior of a system and the mathematical relationship between the response and the independent variables by a second order polynomial. The second- order model is written as Equation (2):

= ° + + +


(2)

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Here, Y is the predicted response, X i and X j are the independent variables, β◦ ,βi ,βii ,βij refer to the constant, linear, quadratic and interaction coefficients, respectively 19-23.

5. Results and discussion 5.1. Metastability zone width and supersaturation. The solubility – labile diagrams showing the concentration of tar balls versus the ratio of solvent to solution are presented in Figures 4 and 5 under non agitation and agitation conditions, respectively. The solubility and labile curves seem to be parallel. These figures illustrate the concentration profile of tar balls at different addition rates. As evident from Figures 4 and 5, the solution changes from its undersaturated state to supersaturated condition by adding anti-solvent (shifting to the left side of the horizontal line). The solubility increases when the concentration of tar balls increases. A decrease in the ratio of solvent to solution leads to lower solubility, and consequently formation of more precipitated substance. In the present study, the limitation of the metastable zone in n-hexane/methanol mixtures using methanol (as anti-solvent) was measured experimentally. The width of metastable zone of tar ball solution for each particular concentration is shown in Figures 4, 5, and 7. MSZW represents the region between the solubility and labile curve. The displacement of the curves is a function of the shear rate and anti-solvent addition rate. The more anti-solvent is added, the less width of the zone is achieved. The precipitation occurs by adding anti-solvent so that it contributes to a high magnitude of supersaturation, resulting in the occurrence of spontaneous nucleation. To prevent spontaneous nucleation, controlling the size distribution of product and knowing the supersaturation mechanisms are important in order to attain comprehensive understanding about the metastable zone as the metastability width has a strong influence on the supersaturation.


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Figure 4. Solubility and labile curves to demonstrate the MSZW for different initial concentration of tar balls under non agitation condition [The dashed lines represent the solubility and the solid lines depict the labile curve (a, b, and c correspond to 0.4, 1.4, and 2 mg.ml-1, respectively)].


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Figure 5. Solubility and labile curves that represent MSZW for different initial concentration of tar balls under agitation condition [The dashed and solid lines refer to the solubility and the labile curves, respectively (panels a, b, and c are plotted for 0.4, 1.4, and 2 mg.ml-1, respectively)].

In the “tar balls/methanol/n-hexane” systems, supersaturation was produced by adding antisolvent. Theoretically, supersaturation can control the nucleation rate noticeably. A decrease in the nucleation was observed by increasing the supersaturation ratio. The corresponding supersaturation ratio, S, was calculated using Equation (1) which is based on the concentration. The effects of anti-solvent addition rate and shear rate on supersaturation were examined through the experimentation.


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Figure 6 illustrates the supersaturation at different concentrations of tar balls, for both agitated and non agitated conditions. As it is obvious, the supersaturation for non agitated system was lower than that for the agitated systems. The results convey the message that the supersaturation ratio decreases via increasing the ratio of solvent to solution, X. In the latter case, the results show the supersaturation is controlled by altering the initial amount of tar balls, anti-solvent ratio, shear, and the width of the metastability. Supersaturation phenomenon in growth and nucleation should be comprehensively explored (in terms of process mechanism) to develop population balance equations for tar ball particles. Technically and economically, it seems important that the materials left from the collected tar balls need to be dissolved in solvent and then precipitate the dissolved tar balls by adding anti-solvent.


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Figure 6. Comparison of calculated supersaturation with the real data for both agitation and non agitation conditions [X denotes the difference between the ratio of solvent to solution of labile and solubility points; The dashed and solid lines represent the agitated and non agitated conditions, respectively (a, b, and c correspond to 0.4, 1.4, and 2 mg.ml-1, respectively)].

5.2. Response Surface Methodology (RSM) and ANOVA Response surface methodology (RSM) was used to study the influences of anti-solvent addition rate, initial amount of tar balls, shear rate, and the difference between the saturation concentration and the solution concentration (metastable zone width) on the supersaturation for an anti-solvent addition precipitation process. The mathematical relationship between the supersaturation and the independent variables is expressed by the following equation: 15 ACS Paragon Plus Environment

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S =14.0486+0.278044*X1-0.13132*X2-7.5249X3-234.001X4+ 0.088049*X12 +0.002838X22+490.954X42-

(3)

0.0078X1X2-0.68623*X1X3- 5.8361*X1X4+0.126614X2X3+ 0.09X2X4+165.441X3X4

where S is the response, i.e. supersaturation, and X1, X2 , X3 ,and X4 define the initial amount of tar balls [mg], anti-solvent addition rate [ml], shear rate [s-1], and metastable zone width [unitless], respectively. In addition, the variables X12, X22 and X42 stand for the second-order effects. X1X2, X1X3, X1X4, X2X3 and X3X4 represent the interaction effect variables. The squared effect of X3 was neglected due to its low importance. The significance of the quadratic model was evaluated by means of the analysis of variance (ANOVA) 23. The ANOVA analysis was performed at the 95% confidence level. The analysis of variance (ANOVA) results are presented in Table 1.


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Table 1. Analysis of variance (ANOVA) table for supersaturation at 95% confidence level. Variable

F-value

P-value

Model

45.16

0.000

Linear

33.17

0.000

X1

3.83

0.05

X2

0.09

0.764

X3

2.69

0.110

X4

33.64

0.000

Square

3.95

0.016

2

6.37

0.016

X 22

0.03

0.862

X 42

10.8392

0.018

Interaction

7.74

0.000

X 1X 2

0.10

0.784

X 1X 3

10.12

0.003

X 1X 4

2.60

0.116

X 2X 3

0.34

0.564

X 2X 4

0.01

0.943

X 3X 4

24.89

0.000

R2=0.95

RPred2= 0.8984

Radj2= 0.9214

X1

The significance of the fit model was checked by the multiple correlation coefficients (R2). To experience great accuracy of the model, the R2 value should be near (close to) 1. In our case, the high value of R2 (R2 = 0.95) implies that the model is statistically significant. Moreover, the high extent of adjusted multiple correlation coefficient (adj. R2 = 0.9214) and predicted multiple correlation coefficient (pred. R2 = 0.8984) revealed that the constructed model is statistically acceptable. The goodness of the model was evaluated by F-values and p-value. If the p-value is lower than 0.05, the coefficient is highly significant at 95% confidence level. It should be noted that p-value is known as a tool to verify the significance of each coefficient of the model. It is


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observed from Table 1 that the model has the p-value (= 0.0000) and F-value (45.16), showing the model is significant. It is also important to mention that the linear, square, and interaction coefficients are all highly significant (p-value = 0.000). As seen in Table 1, the initial amount of tar balls and the metastable zone width have the most significant influences on the response as they have high F-values and low p-values. Although anti-solvent addition rate has insignificant impact on the output of the model, it cannot be disregarded. Some of the coefficients with the pvalues more than 0.05 are not statistically significant; however they remain in the correlation to keep hierarchy of the model (see Table 1). Surface plot is a graphical description of the RSM model, demonstrating the relationship between the factors and their effects on the response output, while the other factors are kept constant at the medium level. The dependency of supersaturation to the initial amount of tar balls, anti-solvent addition rate, shear rate, and metastable zone width is exhibited in Figure 7. Panel a of Figure 7 illustrates the effects of the width of metastability zone (X4) and the initial amount of tar ball (X1) on the supersaturation ratio. It can be also concluded that by diminishing X4 and boosting up X1 to 10 (mg), the amount of supersaturation shows a considerable increase. In other words, the supersaturation is increased to a maximum level through increasing the initial amount of tar balls. The effect of anti-solvent addition rate (X2) and width of metastability zone (X4) are visualized in Figure 7b. It indicates that supersaturation decreases as long as the metastability zone becomes wider. Then, further addition of anti-solvent has no effect on supersaturation. Figure 7c relates the supersaturation to the shear rate (X3) and width of metastability zone (X4). As demonstrated in Figure 7c, supersaturation lowers with increasing the metastability zone width and also the shear causes a drastic decrease in the magnitude of supersaturation. It is found that MSZW ≤ 0.02 at non-agitated conditions results in the supersaturation of above 10. Based on Figure 7, the highest supersaturation can be achieved where 18 ACS Paragon Plus Environment

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there is a large amount of tar balls under non agitated conditions, but in a narrower metastable zone.

Figure 7. Surface plot of supersaturation; a) combinatory effect of width of metastability zone and initial amount of tar ball; b) combinatory effect of anti-solvent addition rate and width of metastability zone; c) combinatory effect of shear rate and width of metastability zone.

The accuracy of the predicted RSM model was also evaluated on the basis of the statistical parameters including mean absolute error (MEAE), mean squared error (MSE), minimum absolute percentage error (MIPE), and maximum absolute percentage error (MAPE). The definitions of the errors are provided in the Appendix. The parameters MSE = 1.25, MEAE = 0.104, MIPE = 0.055%, and MAPE = 31.59% were determined for the RSM model while a comparison is made between real and estimated outputs. The low value of the errors means that the model is reliable.


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This finding is also in agreement with the value of R2. It implies that there is a good match between the experimental data and the predicted RSM results. As the last stage of statistical assessment, the normal probability plot of the actual and predicted supersaturation is depicted in Figure 8. Since the points are scattered around the 45° line, a very good agreement between the model predictions and the actual data is expected.

Figure 8. RSM outputs versus the experimental data of supersaturation.

Last but not least, it might be desirable to know about the amount of solvent and anti-solvent which is used in both precipitation and dissolution processes in “tar balls/methanol/n-hexane” systems to find the width of metastable zone. The information on the width of metastable zone and supersaturation could be helpful in a precipitation process. The supersaturation is a key parameter to develop the population balance equations to analyze experimental data on particle size distribution of tar balls. It should be noted that the extracted tar balls from remaining materials could be used in asphalt industry which is an asset environmentally and economically. 20 ACS Paragon Plus Environment

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This study provides a simple but reliable and economical strategy/guideline to obtain the amount of precipitated tar balls and then find an effective solution for removal/treatment of these unwanted materials from the shores of Caspian Sea and other waters with similar concerns in terms of technical and economic prospects.

6. Conclusions The solubility and precipitation of tar balls through anti-solvent (e.g., methanol) addition were investigated in this experimental and modeling study to obtain the metastable zone width (MSZW) and supersaturation. A proper parametric sensitivity analysis was also performed based on the tests outcomes to evaluate the effects of various parameters, such as anti-solvent addition rate, initial amount of tar balls, shear rate, and metastable zone width on the supersaturation processes. The experiments clearly showed that the supersaturation is strongly dependent on the width of the metastability zone of the system. Having the experimental results, the response surface methodology was applied to develop a simple-to-use predictive tool for precise and quick estimation of supersaturation for anti-solvent addition precipitation of tar balls. The proposed model was examined via the analysis of variance (ANOVA) methodology. It was found that MSZW and anti-solvent flow rate have greater impacts on the supersaturation, compared to other input variables. The mean absolute error less than 0.104 and the high correlation coefficient (e.g., 0.95) demonstrate that the proposed model exhibits great capability to forecast the supersaturation with reasonable accuracy. NOMENCLATURE Acronyms ANOVA = Analysis of variance


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MAPE = maximum absolute percentage error MEAE = mean absolute error MIPE = minimum absolute percentage error MSE = mean squared error MSZW= Metastable zone width RSM = Response Surface Methodology Variables C= concentration C*= saturation concentration n = number of experimental data S= Supersaturation R2 = determination coefficient R2adj = adjusted multiple correlation coefficient R2pred = the predicted determination coefficient Sexp = experimental supersaturation Spred = predicted supersaturation Y= the predicted response in RSM equation Greek Symbols β◦= the constant coefficient of RSM equation βi = the linear coefficient of RSM equation βii = the quadratic coefficient of RSM equation βij = the interaction coefficient of RSM equation


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APPENDIX The statistical parameters including MSE, MEAE, MIPE and MAPE are utilized to test the accuracy of the RSM model compared with other existing models. The equations to compute the above parameters are as follows: Mean squared error:

= ∑ ( − )

(A1)

Mean absolute error: (!"#$ %!$&"' )

= ∑

!"#$

(A2)

Maximum absolute percentage error: ( % =

*+,!"#$ %!$&"' ,

∗ 100

(A3)

0( % =

*,!"#$ %!$&"' ,

∗ 100

(A4)

!"#$

Minimum absolute percentage error: !"#$

In the above equations, Sexp, Spred, and n refer to the supersaturation ratio obtained from the experiments and from the RSM model, and the number of data, respectively.

AUTHOR INFORMATION Corresponding Author


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*E-mail: [email protected]; [email protected].

Acknowledgements We would like to thank the Iranian Department of Environment, Guilan Province (Iran) for providing required facilities and financial supports. Constructive comments given by Amir Akbarzadeh, from the Research Institute, Guilan Environment (Rasht-Iran), are also greatly appreciated.

References [1] Chandru, K.; Zakaria, M. P.; Anita, S.; Shahbazi, A.; Sakari, M.; Shahpoury, B. P.; Rahim Mohamed, C. A. Characterization of Alkanes, Hopanes, and Polycyclic Aromatic Hydrocarbons (PAHs) in Tar-balls Collected from the East Coast of Peninsular Malaysia, Mar. Pollut. Bull. 2008, 56, 950 – 962. [2] Hegazi, A. H. Utilization of Trace Metals in Source Diagnosing of Tar Balls From the Mediterranean City of Alexandria, Egypt. Pet. Sci. Technol. 2009, 27, 386–395. [3] El-Gayar, M. Sh. Distribution of Biomarkers in Highly Weathered Petroleum’s Residue from Mediterranean Waters. Pet. Sci. Technol. 2000, 18(5-6), 725-741. [4] Tao, Z.; Bullard, S.; Arias C. High Numbers of Vibrio Vulnificus in Tar Balls Collected from Oiled Areas of the North-Central Gulf of Mexico Following the 2010 BP Deepwater Horizon Oil Spill, Eco Health. 2011, 8, 507–511. [5] Emtiyazjoo, M.; Shafeian, E. E.; Farkhani, D.; Mazhar, F. SURVEYING THE POTENTIAL BIODEGRADATION of Asphaltene and Tarball in Khark Oil Zone by Brevibacterium Iodinum Bacterium. Ind. J. Fund. Appl Life Sci. 2014, 4 (2), 402-411. [6] Suneel,V.; Vethamony, P.; Kumar V.; Babu M. T.; Prasad K. V. S. R. Simulation of Trajectories of Tar Ball Transport to the Goa Coast, Water Air Soil Pollut. 2013, 224, 1538. [7] Xie, H.; Yapa, P. D.; Nakata. K. Modeling Emulsification after an Oil Spill in the Sea. J Marine. Syst. 2007, 68, 489–506.


Page 25 of 26

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[8] Sarode, A. L.; Wang, P.; Obara, S.; Worthen, D. R.; Supersaturation, nucleation, and crystal growth during single- and biphasic dissolution of amorphous solid dispersions: Polymer effects and implications for oral bioavailability enhancement of poorly water soluble drugs, EUR J PHARM BIOPHARM 2014, 86, 351–360. [9] Solaimany-Nazar, A.; Rahimi, H. Dynamic Determination of Asphaltene Aggregate Size Distribution in Shear Induced Organic Solvents, Energy Fuels 2008, 22, 3435–3442. [10] koshandam, A.; Alamdari, A. Kinetics of Asphaltene Precipitation in a Heptane-Toluene Mixture, Energy Fuels 2010, 24, 1917–1924. [11] Mujica, V.; Nieto, P.; Puerta L.; and Acevedo S. Caging of Molecules by Asphaltenes. A Model for Free Radical Preservation in Crude Oils, Energy Fuels 2000, 14, 632-639 [12] Mullin, J.W. Crystallization. (Butterworth-Heinemann), 123, 2001. [13] Takiyama, H.; Supersaturation operation for quality control of crystalline particles in solution crystallization, Adv. Powder Technol. 2012, 23, 273–278. [14] Kind, M.; Mersmann, A. On Supersaturation during Mass Crystallization from Solution. Chem. Eng. Technol. 1990, 13, 50-62. [15] Roelands, C. P. M.; Jiang Sh.; Kitamura M.; Horst J.H.; Kramer, H. J. M.; Jansens, P.J. Antisolvent Crystallization of the Polymorphs of L-Histidine as a Function of Supersaturation Ratio and of Solvent Composition, Cryst. Growth Des. 2006, 6(4) 955-963. [16] Nonoyama, N.; Hanaki, K.; Yabuki, Y., Constant Supersaturation Control of AntisolventAddition Batch Crystallization, Org. Process Res. Dev. 2006, 10, 727-732 [17] O’Grady, D.; Barrett, M.; Casey, E.; Glennon, B., The Effect of Mixing on the Metastable Zone Width and Nucleation Kinetics in the Anti-Solvent Crystallization of Benzoic Acid. Chem. Eng. Res. Des. 2007, 85 (A7), 945–952. [18] Mersmann, A. Bartosch, K.; How to predict the metastable zone width. J. Cryst. Growth 1998 183, 1998, 240-250. [19] Bezerra, M. A.; Santelli, R.; Oliveira, E.P.; Villar, L.S.; Escaleira, L.A., Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 2008,76, 965-977. [20] Mante, O. D.; Agblevor, F. A.; McClung, R. A study on catalytic pyrolysis of biomass with Y-zeolite based FCC catalyst using response surface methodology. Fuel 2013,108, 451–464. [21] Trifkovic, M.; Sheikhzadeh, M.; Rohani, S. Determination of metastable zone width for combined anti-solvent/cooling crystallization. J. Cryst. Growth 2009, 311, 3640–3650.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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[22] Lai, Y.; Huang, H.; Huang, Q.; Zhang, H.; Guo, Zh. Optimization of the experimental conditions for the synthesis of micro-size mono disperse spherical silver powders using Box– Behnken design. Powder Technol. 2014, 263, 7–13. [23] Li, H.; Song, Y.; Li, Q.; He, J.; Song, Y. Effective microbial calcite precipitation by a new mutant and precipitating regulation of extracellular urease. Bioresour. Technol. 2014, 167, 269 – 275. [24] Carter, C.B.; Norton, M.G. Ceramic Materials Science and Engineering. Springer, New York, 2007. [25] Kashchiev, D. Nucleation. Butterworth Heinemann, Burlington, 2000. [26] Nowee, S. A.; Abbas, A.; Romagnoli, J.A. Antisolvent Crystallization: Model Identification, Experimental Validation and Dynamic Simulation, Chem. Eng. Sci. 2008, 63, 5457 – 5467. [27] Nagy, Z. K.; Fujiwara, M.; Braatz, R.D. Modelling and control of combined cooling and antisolvent crystallization processes. J Process Contr 2008, 18 (9), 856 – 864. [28] Alamdari, A.; Shariat-Nasab, S.; Jahanmiri, A. Kinetic study of adductive crystallization of bisphenol-A. Chem. Eng. Res. Des. 2010, 88, 1615 – 1623. [29] Sangwal, K. On the interpretation of metastable zone width in anti - solvent crystallization. Cryst. Res. Technol. 2010, 45, 909 – 919.


Effective Method To Determine Supersaturation of Tar Balls Deposited

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