Application of Low-Cost Sorbent for Oil Spill Sorption Using Response Surface Methodological...

A.M. Bahadir and G. Duca (eds.), The Role of Ecological Chemistry 109 in Pollution Research and Sustainable Development, © Springer Science + Business Media B.V. 2009

APPLICATION OF LOW-COST SORBENT FOR OIL SPILL SORPTION USING RESPONSE SURFACE METHODOLOGICAL APPROACH

CORNELIU COJOCARU, MATEI MACOVEANU, IGOR CRETESCU

Faculty of Industrial Chemistry and Environmental Protection, Department of Environmental Engineering, Technical University of Iasi, 71 A Bd. D. Mangeron, 700050 Iasi, Romania, [email protected]

Abstract. The paper deals with oil spill clean-up using peat as low-cost sorbent material. Light fuel oil of regional production has been used in all experiments as the representative petroleum product. The design of experiments and response sur-face methodological approach has been used for the investigation of sorption process. A regression equation (response surface model) has been proposed for the prediction of removal efficiency as a function of design variables (factors), i.e., sorbent dosage, drainage time and initial thickness of oil slick on water. A very good agreement between the experimental data and response surface model has been found. The linkage between the removal efficiency and factors has been illustrated via response surface plots and contour lines maps. Based on the regres-sion equation the maximal removal efficiency has been found by optimization.

Keywords: oil spill, low-cost sorbent, response surface methodology.

Abbreviations:

CCD – central composite design DoE – design of experiment LFO – light fuel oil RSM – response surface methodology RS-model – response surface model

1. Introduction

Spills of oil on land and waters (marine, rivers, lakes and groundwater) as a result of oil transportation and storage cause serious environmental pollution and

110 C. COJOCARU, M. MACOVEANU AND I. CRETESCU

consequently call for prompt action to be taken. The spilled oil conducts to an undesirable taste and odor of drinking water and causes serious environmental damage. Contaminated oily waters cannot be used for municipal water supply, for industry, nor for irrigation. Increasing environmental concerns, especially after several hazardous accidental oil spills in the past decades renewed the interest for investigation of cleanup methods. Physical, chemical and biological processes can be used to remove oil spill from water surface or to decompose it in situ.

In case of marine oil spills pollution, dispersants [5], booms [10] and skimmers [6] can be used as clean-up methods to remove oil slicks from water surface. Likewise, the application of sorbent materials is an attractive method for combat-ing of oil spill pollution mainly due to the lower costs and high effectiveness. In this respect, the use of low-cost materials is very important. The low-cost sorbents are defined as materials, which are abundant in nature or as by-products or waste materials from another industry that require usually little processing [2]. Different types of sorbents such as organic, inorganic and synthetic materials have been successfully used for oil spill sorption [4, 9]. However, only natural organic sorbents such as: kapok, peat moss, straw, hay, sawdust, ground corncobs, feathers, and other readily available carbon-based products are considered as efficient low-cost materials for oil spill clean-up. The major factors promoting the use of organic products are: biodegradability, use of renewable resources, waste recycling of life cycle extension, lower cost per unit, lower impact on ecosystem if released or lost during clean-up operations, and public perception that the products are environ-mental friendly.

To address different local environmental issues, the regional available peat was pre-pared correspondingly as a low-cost sorbent for oil spill and heavy metals sorption.

In our previous works [3, 7] the application of peat-based sorbents for the removal of heavy metals ions from wastewater were investigated. Thus, the ability of local available peat for sorption of lead and copper ions from aqueous solution has been determined using batch experimental techniques. The sorption equilibriums have been studied at different pH values. Different types of mathematical model isotherms have described the equilibrium adsorption capacity of peat sorbent, namely: Freundlich, Langmuir, Redlich-Peterson, Sips, Temkin, Toth and Dubin-Radushkevich isotherms. The thermodynamic parameters like free energy changes for adsorption of heavy metal ions by peat have been ascertained and discussed.

In the present work the ability of peat-based sorbent was tested for oil sorption using response surface methodological approach. The optimization of sorption conditions has been figured out and discussed in this paper.

APPLICATION OF LOW-COST SORBENT FOR OIL SPILL SORPTION 111

2. Experimental

2.1. Materials

The peat employed in the sorption tests is the abundant material of natural origin that was collected from Romanian peat-land Poiana Stampei. The wet raw mate-rial was previously dried at the room temperature for 14 days in open area. After that, the raw material was cut up using the cutting mil GRINGOMIX GM 200 (Retsch, Germany). The obtained disperse material was sieved and the fraction with particle sizes of 1–7 mm had been dried in a hot-air oven at 105°C for 6 h and stored for further use.

The petroleum-based liquid employed in the sorption tests was Light Fuel Oil (LFO) of regional production provided by PETROM Company (Romania). The viscosity of LFO was measured using an Ubbelohde viscometer and its density was determined using a common pycnometer. Three runs of the measurements were made at room temperature of 22 ± 2°C. Taking into consideration the aver-age values of measurements, the following results obtained for LFO in terms of viscosity and density: 6.13 ± 0.03 cSt and 0.920 ± 0.005 g/cm2, respectively.

2.2. Methods and analysis

Two tests regarding oil spill sorption were carried out in this paper. The test no. 1 deals with determination of initial sorption capacity of sorbent as the pick-up ratio. It is expressed as the ratio between amount of oil (g) retained per unit mass of sor-bent (g). The procedure for determining oil sorption capacity generally followed the known standard methods for testing of oil spill sorbents, i.e., ASTM F 726-99 and CAN/CGSB-183.2-94. Thus, a fresh sorbent sample has been placed on a 7.5 mm layer of LFO. The sample is left for a period of 15 min for complete satura-tion with oil product. The sorbent is then removed from liquid phase using a mesh basket and the weight of sorbent is measured as oil drips after 30 s. The initial sorption capacity S (g oil/g sorbent) has been determined by gravimetric method of analysis using a digital balance with resolution of 0.001 g and may be written as follows:

S

O

S

SSO

WW

WWW

S =−

= (2.1)


where WS means the weight of fresh sorbent sample (g), WSO denotes the weight of sorbent saturated with oil product (g) and WO is the weight of oil product retained into sorbent matrix (g). Note that, under normal conditions, a sorbent will not be exposed to sufficient oil layer thickness to become completely saturated. There-fore, this test gives the maximum sorption capacity data without the competing presence of water.

The second test deals with removal of oil slick by sorption in the presence of water.

The application of peat as floating sorbent to remove LFO slick from water sur-face involves the performing of following experiment. A test cell with 74 mm (diameter) × 100 mm (depth) was filed with 300 cm3 of distilled water, obtaining a column of water with approximately 80 mm height. Then V0 volume of LFO product was added into the test cell. The added volume of LFO was selected in that way to form a 3/8 mm floating layer on water surface. A fresh sample of the sorbent (previously weighed) was lowered into the test cell using a mesh basket with 1 mm openings. The test cell was covered and left for 15 min for complete saturation of sorbent with LFO. After saturation the sorbent was removed allowing the liquid to drain for 12/48 s (drainage time). The performance of oil slick removal from water surface was measured in terms of removal efficiency Y (%) that can be written as:

1000

0 ×−

=V

VVY R (2.2)

where V0 is the initial (added) volume of LFO in water (cm3) and VR is the remained volume of LFO in water after sorption test (cm3).

Taking into consideration the different experimental methods for determination of remained volume fractions of LFO after sorption the following relationship is valid:

( ) ( )21RRR VVV += (2.3)

where the volume fraction VR(1) refers to remained visible layer of LFO on water

surface while VR(2) fraction includes LFO shines on water and eventually dissolved

components of oil product in the aqueous phase. The volume VR(1) was determined

by collecting the remained oil layer from water surface using an oleophilic skimmer and graduated cylinder. After removing of visible oil layer by skimmer, the vol-ume fraction VR

(2) of LFO in water was determined by means of UV-VIS spectro-scopic method. Of course, in the most experiments the condition of VR

(1) >> VR(2)

was obviously. Note, that all experiments were conducted at room temperature of 22 ± 2°C.


3. Results and discussion

3.1. Sorption capacity

The sorption capacity of peat for LFO was determined in triplicate according to test no. 1 and the results are given in Table 3.1.1 together with average value and standard deviation data.

Run no. 1 Run no. 2 Run no. 3 Average Standard deviation S (g/g) 7.387 7.472 7.878 7.579 0.214

The initial sorption capacity of 7.579 ± 0.214 g/g is given by the properties of

sorbent such as porosity and capillaries as well as by the properties of oil product as viscosity, density and surface tension. The sorption capacity of 7.579 g/g is relatively high suggesting that the retention of liquid phase into sorbent matrix occurs due to adsorption and capillary phenomena.

3.2. Experimental design and response surface modeling for oil slick removal by sorption

The removal of oil slick from water surface by sorption (test no. 2) was investi-gated using the design of experiments (DoE) and response surface methodological (RSM) approach.

The experimental design used for the modeling of sorption process was carried out choosing three main factors (design variables), namely: the dosage of sorbent, drainage time and the initial thickness of oil slick on water surface. Since the clean-up process occurs on the top surface of water column it is appropriate to express the sorbent dosage as amount of sorbent per unit of surface area. Thus, in this application the sorbent dosage was expressed as sorbent amount in (g) per unit of polluted area given in (dm2), M (g/dm2). The drainage time is given in seconds, t(s). As regards the third factor, i.e. initial thickness of oil slick h (mm), it is in linear relationship with the initial volume of oil product V0 (cm3).

Since in real environmental issues concerning oil spill pollution the thickness of oil slick is one of the most important factors in selection of the clean-up method, it is more appropriate to work with thickness of oil later rather than with the volume of spilt oil.

A central composite design (CCD) of orthogonal type was employed in this study for planing the experiments as it is shown in Table 3.2.1. Thus, the operating ranges and the levels of the considered variables are given in actual and coded

Table 3.1.1. Initial sorption capacity of peat for LFO, T = 22 ± 2°C.


values (Table 3.2.1). The results of response, i.e., removal efficiency Y %, were determined experimentally according to experimental runs of CCD in order to as-certain the performance of sorption process.

Factors (controllable input variables) Response Sorbent dosage Drainage time Initial thickness

of oil slick Removal Efficiency

Run number (N) and typea

M g/dm2

levelb x1

t (s) levelb x2

h (mm)

levelb x3

Y (%)

1 O1 4.64 1 45 1 7.4 1 60.04 2 O2 2.32 –1 45 1 7.4 1 21.37 3 O3 4.64 1 15 –1 7.4 1 65.51 4 O4 2.32 –1 15 –1 7.4 1 34.12 5 O5 4.64 1 45 1 4.2 –1 90.23 6 O6 2.32 –1 45 1 4.2 –1 57.56 7 O7 4.64 1 15 –1 4.2 –1 95.31 8 O8 2.32 –1 15 –1 4.2 –1 67.16 9 S1 4.89 α 30 0 5.8 0 76.88 10 S2 2.07 –α 30 0 5.8 0 34.72 11 S3 3.48 0 48 α 5.8 0 53.72 12 S4 3.48 0 12 –α 5.8 0 62.82 13 S5 3.48 0 30 0 7.8 α 45.25 14 S6 3.48 0 30 0 3.9 –α 89.14 15 C1 3.48 0 30 0 5.8 0 56.11 16 C2 3.48 0 30 0 5.8 0 55.90

aO = orthogonal design points. C = center points. S = star or axial points. b−1 = low value. 0 = center value. +1 = high value. +/−α = star point value.

Thus, based on experimental design the response surface model has been de-veloped. The least square estimations of the regression coefficients from this mod-el have been calculated by means of Multiple Linear Regression (MLR) method and can be written as follows [1, 8]:

where β is a (L × 1) vector of regression coefficients, X is a (N × L) extended design matrix of the coded levels of input variables, Y is a (N × 1) column vector of response determined experimentally according to the arrangement points into

Table 3.2.1. Central composite orthogonal design and experimental response.

β = (XT X)-1 XTY (3.1)

As can be observed from Table 3.2.1, the maximum value of removal effi-

ciency in the experiments was of 95.31 % while the minimal value of this response was 21.37%. In order to enhance the removal efficiency (Y > 95%) the optimization approach was applied using the response surface methodology.


CCD, N is the number of experimental runs and L is the number of regression coefficients within the extended RS-model.

According to experimental results given by experimental design (Table 3.2.1.) the RS-model has been developed by MLR-method to figure out the functional relationship for approximation and prediction of removal efficiency. Thus, the second-order model with coded terms is as follows:

3121

23

21321

155.1475.1802.5924.1667.16013.4627.16831.57ˆ

xxxxxxxxxY

+++−−−+=

(3.2)

subjected to: -α ≤ xi ≤ +α 3,1=∀ i

Note that, all terms in Eq. (3.2) are the meaningful terms retained according to statistical analysis given by Student’s t-test. The empirical model in terms of real variables is as follows:

hMtMhMhtMY

622.0085.0266.243.1875.38563.0135.18731.156ˆ 22

+++−−−+=

(3.3)

subjected to 2.07 ≤ M ≤ 4.89 (g/dm2); 12 ≤ t ≤ 48 (s); 3.9 ≤ h ≤ 7.8 (mm) The significance of regression model was tested by means of analysis of vari-

ance (ANOVA). In Table 3.2.2 the ANOVA results are presented summarizing the sum of squares of residuals and regressions together with the corresponding degrees of freedom, F-value, P-value and ANOVA coefficients (i.e. coefficients of multiple determination R2 and adjusted statistic Radj

2). The mathematical expressions used for calculation of the ANOVA estimators (i.e., SS, MS, F-value, R2, Radj

2) are given in the literature concerning RSM [1, 8].

Source DFa SSb MSc F-value P-value R2 Radj2

Model 7 6455.05 922.15 318.716 <0.0001 0.996 0.993 Residual 8 23.147 2.893 Total 15 6478.197

aDegree of freedom; bsum of squares; cmean square. All ANOVA statistical estimators exhibit that the RS-model developed for the

prediction of removal efficiency is statistically validated for the approximation of the response over the range of experimentation.

Also, the model fitness can be easily estimated by the examination of the residuals that may be defined for any observation j as [8]:

jjj YYe ˆ−= Nj ,1=∀ (3.4)

Table 3.2.2. Analysis of variance (ANOVA) of RS-model (response: removal efficiency).

where jY is a model prediction of the corresponding observation Yj. The plot of predicted and experimental responses, as well as the plot of the residuals versus the observation order (N) is shown in Fig. 3.2.1. A goodness-of-fit between RS-model and the corresponding experimental data can be evidently observed.


Fig. 3.2.1. Experimental data plotted against the predicted ones for removal efficiency (a) and residual analysis (b).

The response surfaces plots and contour-lines maps have been drawn and ana-lyzed. The most representative response surface plot is shown in Fig. 3.2.2. reveal-ing the influence of factors (design variables) upon the removal efficiency. Thus, the response surface indicates that the increasing of sorbent dosage M conducts to enhancing of removal efficiency. While the increment of both drainage time t and initial thickness of oil slick h leads to decreasing of removal efficiency. As the magnitude of influence upon response, the factors M and h have similar impor-tance. By contrast, the drainage time has the most reduced main effect being 4-fold lower than the main effects of other factors. Small interaction effects appear between sorbent dosage and other two factors.

Fig. 3.2.2. Response surface plot and contour-lines map depending on M and h factors, holding the other factor at its center level t = 30 s.

In addition, based on RS-model (regression equation) the optimization problem has been solved. The optimization problem consists in searching by simulation, the input combination of design variables (factors) that maximize the investigated response (removal efficiency). In this respect, the response function in terms of coded variables, i.e., Eq. (3.2), was used for simulation and optimization over the valid region of experimentation. Thus, the formulation of objective function for optimization may be written in this case as:

RS-Model

100 4

3

2

1

0

−1

−2

−3

−4

80

60

40

20

00 1 2 3 4 5 6 7 8 9

N

Rem

oval

effic

ienc

y (%

)

10 11 12 13 14 15 16 0 1 2 3 4 5 6 7 8 9N

10 11 12 13 14 15 16

Experiment

e=Y

-Y

⁄


( ){ }321 ,,ˆmax xxxYx

(3.5)

subjected to: -α ≤ xi ≤ +α 3,1=∀ i

The optimization problem was solved by means of Monte Carlo method based on stochastic simulations. The computed optimal point in terms of actual factors is as follows: M* = 4.89 t* = 12 s and h* = 3.9 mm. For these optimum con-ditions the predicted response is 105% while the experimental value is about 98.74%.

The experimental value of Y* = 98.74% given by optimal conditions is the highest removal efficiency obtained in all experiments conducted in this work. The optimal solution found suggests that the oil sorbents in high dosage are more efficient to remove thinner oil slicks while for thicker layers the other methods should be applied like skimming assisted by booms. All computations were per-formed by means of Mathcad and Matlab software.

4. Conclusions

The application of peat as floating sorbent for oil slick (LFO) removal from water surface was investigated using the design of experiments and response surface methodology. In this respect, the central compositional design has been used to develop the response surface model that predicts the dependence between the removal efficiency and design variables (factors).

The response surface plots indicate that the removal efficiency is influenced considerably by the sorbent dosage and the initial thickness of oil slick on water. The optimal solution determined for sorption process over the region of experi-mentation is as follows: sorbent dosage of 4.89 g/dm2, drainage time of 12 s and the initial thickness of oil slick of 3.9 mm. In such conditions a maximum removal efficiency of 98.74% was obtained experimentally.

Acknowledgments. The authors appreciatively acknowledge the financial support from Romanian Ministry of Education and Research (Project CEEX-PD no. 5933/06).

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