Response surface methodology optimization applied to rubber tyre and plastic wastes thermal conversion

Fuel 89 (2010) 2217–2229

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Response surface methodology optimization applied to rubber tyre and plastic wastes thermal conversion Miguel Miranda a,*, Filomena Pinto a, I. Gulyurtlu a, I. Cabrita a, C.A. Nogueira a, Arlindo Matos b a b

LNEG, Estrada Paço do Lumiar, 22, 1649-038 Lisboa, Portugal Departamento de Ambiente e Ordenamento, Universidade de Aveiro, 3810 Aveiro, Portugal

a r t i c l e

i n f o

Article history: Received 6 October 2009 Received in revised form 2 March 2010 Accepted 3 March 2010 Available online 19 March 2010 Keywords: Tyre wastes Plastic wastes Pyrolysis Recycling RSM

a b s t r a c t Thermal degradation was studied as a method to decompose mixtures of rubber tyre (RT) and different plastic wastes (PE, PP and PS) with the aim of producing a liquid fuel [1], as well as valuable chemical raw materials. An experimental set of runs was performed to establish the operational conditions that maximize liquid fraction production in a 1 litre batch reactor. Waste blends used were composed of 30% w/w RT and 70% w/w plastics (20% PE, 30% PP and 20% w/w PS). The complex hydrocarbon liquid mixture obtained during pyrolysis of these residues was highly dependent on experimental parameters, namely temperature, initial pressure and reaction time, which are the three most important factors affecting liquid yields. Regression analyses of experimental data were performed according to response surface methodology (RSM). As a result, experimental conditions optimized based on Factorial Design Methodology were 370 °C, 0.48 MPa for initial pressure and 15 min for reaction time. In order to validate the results obtained by the RSM model, three extra runs were conducted sequentially and average values were calculated and found to be: gas yield of 4.9% w/w, liquid yield of 81.3% w/w and solid yield of 12.7% w/w with an experimental deviation of 0.95%. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Environmental pollution as well as efficient use of resources represents two important issues that concern all modern societies. Tyres and plastics are widely used all over the world, resulting in an increasing amount of residues, which have become a problem to human kind, due to their highly negative environmental impact. Some publications [1,2] have reported the negative implications of this growing problem, which has been developing for many years. Landfilling and incineration processes used so far to deal with these wastes present several problems. Landfilling, the most common disposal route, does not allow the recovery of the organic content of these wastes. Though incineration has the advantage of recovering some of wastes energetic content, pollutants are produced like light hydrocarbons, nitrous and sulphur oxides, dusts and dioxins, which have highly negative bearing on the environment [3]. Moreover, rubber tyres sulphur content may reach values around 2%, due to vulcanisation processes, which means that the cost of tyres incineration process increases, due to the need of flue gas desulphurisation. The natural raw material for rubber tyre and plastics production is petroleum [3], whose reserves have a limited lifetime [4,5]. * Corresponding author. Tel.: +351 21 092 4417; fax: +351 21 716 65 69. E-mail address: [email protected] (M. Miranda). 0016-2361/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2010.03.009

Therefore, it is advisable to have a better management of the remaining petroleum resources. This goal can be achieved through the application of pyrolysis technology to deal with these wastes. As pyrolysis process takes place in an enclosed environment, problems related with direct emissions to atmosphere can be controlled. The implementation of this technology allows the conversion of any organic waste, namely non-biodegradable, into new organic products used either as raw materials for several industries, or for energy production as substitutes of conventional fuels. During pyrolysis, thermal decomposition of wastes occurs in presence of inert atmosphere under moderate conditions of temperature and pressure [4,5]. Polymeric structure is broken down, producing smaller intermediate species, which can further react and produce a complex mixture of smaller hydrocarbon molecules, being liquid or gaseous in nature. The material or energy recovery of tyres and plastic wastes can be a good way of achieving those objectives. In general, tyres wastes pyrolysis produce 33% w/w of solid residue, 35% w/w of liquid fraction, 12% w/w of scrap and 20% w/w of gases. The liquid fraction analysis indicates the presence of monomers, dimers and oligomers [6] of the original polymeric structure. When natural rubber was pyrolysed the monomer was isoprene and the dimer was dipentene [7–10]. In a catalytic pyrolysis study [11] of scrap tyres, the maximization of single ring aromatic compounds was reported, being found the influence of catalyst in

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

Nomenclature N E(zi) Fexp (zi) MS(zi) MSE RSM SS(zi) SSE xþ i x i zi

degree of freedom main factor zi Fisher–Snedecor distribution mean of squares mean of squares of experimental deviation response surface methodology sum of squares sum of squares of experimental deviation higher level for the natural variable i lower level for the natural variable i independent variable or factor (coded)

decreasing liquid yield and increasing gas yield. Liquid yields composition were 7.7% v/v of toluene, 1.4% v/v of benzene, 6.4% v/v of m/p xylenes and 2.2% v/v of o-xylene. In another study by the same team [12] it was found that oil yield decreased and gas yield increased with higher temperatures. Limonene and light aromatics such as benzene, toluene, xylene and styrene were identified as major oil components. When only plastics wastes were pyrolysed, higher liquids yields were obtained than those achieved with tyre waste pyrolysis alone [13]. Although several studies have shown that products yields depended on the nature of plastic waste blend, for PE (polyethylene) pyrolysis, total conversion was usually higher than 90% w/w and the main product was liquid, gas yields were always lower than 10% w/w and almost no solid was obtained. Liquid analysis showed the presence of a complex mixture of hydrocarbons, some of which could be dehydrogenated, supplying hydrogen to the reaction medium [4,5]. This paper reports the results obtained when mixtures of rubber tyre and plastic wastes with different relative contents were pyrolysed. The main objective of using rubber tyre and plastic wastes mixtures was to improve liquid yield of rubber tyre waste pyrolysis and to study possible synergisms between these two types of wastes. A mixture of the three more used plastics, namely PE (polyethylene), PP (polypropylene) and PS (polystyrene), with a composition similar to that found in municipal solid waste (MSW) was chosen. The present work employed a factorial experiment design to investigate thermal decomposition of mixtures of rubber tyre and different types of plastics wastes with the aim of optimising experimental conditions to maximise liquid production. 2. Experimental 2.1. Factorial experimental design The response surface methodology (RSM) is a collection of statistical and mathematical techniques very useful to optimize stochastic functions, in which the objective function is accomplished by an approximation of a low order polynomial, in a sub-region of the domain of a set of independent variables [14,15]. The statistical method used can be applied to maximize (or minimize) a dependent variable which is a function of independent variables. The main advantage of this methodology is an effective reduction of the experimental effort, as well as the quantification of the real effect that independent variables has over the optimization function (liquid yield). The results obtained are specific to the analysed domain (type of reactor used, independent variables chosen, domain of experimental conditions and maximization of dependent variable). Although the information obtained is strictly applied to the analysed domain, it can be used as a first approach in a larger scale reactor.

z1 z2 z3 z0i Y Ym Y0 k

factor temperature factor initial pressure factor reaction time new research point dependent variable or system response system response for m conditions response in the central point for the factorial design increment

The objective function is based on data obtained by statistical and mathematical techniques, which allows fitting a linear or non-linear polynomial regression to experimental data. In this methodology, the parameters of the simulation model are called factors and the algorithm used comprises normally two phases. In the first phase, the response surface function is approximated to a first-order polynomial, until it is proven to fit (or present lack of fitting) to experimental data. In the second phase, the function is approximated by a second-order polynomial, which indicates the existence of curvature in the system. Factorial experiments are conducted in a systematic and efficient way, in accordance to a previously established plan, where all variables are changed simultaneously rather than one at a time, which reduce substantially the number of experimental runs needed. Due to factorial design orthogonal property, statistical tests tend to discriminate in a effective way the effects resulting by natural variations of raw materials, replicated unit operations (e.g. equipment in parallel), different operators and batch reactors, as well as other environmental factors [16]. The results obtained are specific to the analysed domain (type of reactor used, independent variables chosen, domain of experimental conditions and maximization of dependent variable). Although the information obtained is strictly applied to the analysed domain, it can be used as a first approach in a larger scale reactor. Several experimental design techniques are described in some textbooks [13,14,17], although not many articles have been published. Koç et al. [18] investigated factorial experiment design to oxidative thermal decomposition of low-density polyethylene waste and summarised important information regarding response surface methodology (RSM) in simulation optimization and other optimization models [16] and construction of central composite design for balanced orthogonal blocks [15]. A different group of investigators proposed two variations of the iterated steepest ascent algorithm [19], using the response surface methodology in the optimization of folic acid determination in enriched milk, with the application of a factorial plan with four factors and two levels [20] and models which allow the combination of both quantitative and qualitative variables [21]. Least-squares were used in the optimization of a dependent variable, which for the present study is liquid yield. The use of this methodology allows a response evaluation with the same precision as the one obtained if only a single variable was studied in the same experimental range [18,19]. In the present case, all k factors (xi, i=1 to k) represent quantitative variables of the unknown system response Y and the values of the function (mass fraction of liquids) for different factors are denominated system response Y m . The response surface applied to liquid yield optimization can be presented by Eq. (1).

g ¼ f ðx1 ; x2 ; . . . ; xi ; . . . ; xk Þ ¼ Y

ð1Þ

M. Miranda et al. / Fuel 89 (2010) 2217–2229

In particular, the system response could be presented by a linear regression model of independent variables, like the first-order function, which can be given by Eq. (2) [17].

Y ¼ b0 þ b1 x1 þ b2 x2 þ . . . þ bk xk þ e

zi ¼

xþ þx i i 2

xþ x i i

ð4Þ

2  in which xþ i and xi correspond respectively to the upper and lower level of natural variables. As variables in models are often highly correlated, it is possible to determine the relative importance of each factor, as well as the simultaneous interaction of the three factors by using this methodology. The main effect can be obtained by the difference of the average responses Y m , from the highest and the lowest levels of zi , which is given by Eq. (5).

Eðzi Þ ¼

n n 2X 2X Y mðzi¼þ1Þ  Y mðzi¼1Þ p m¼1 p m¼1

ð5Þ

This parameter corresponds to the relative influence that independent variables had on the dependent variable. A positive effect means that the average responses achieved in the highest level is higher than the average responses obtained in the lower level. The multi-regression model, which included the main effects and their interactions can be given by Eq. (6).

Y e ¼ b0 þ b1 z1 þ b2 z2 þ b3 z3 þ b12 z1 z2 þ b13 z1 z3 þ b23 z2 z3 þ b123 z1 z2 z3

SSðzi Þ ¼

ð7Þ

ð6Þ

Although, this model is linear, non linear interactions could be considered; however, this should not be a reason of concern, since ‘‘z” represents only numbers when they are inserted in Eq. (6). The polynomial independent term b0 can be calculated by dividing the system responses Y m for the total observations, including the ones done in central point. Relative influence evaluation of dependent variable factors can be calculated using the sum of squares SSðziÞ , Eq. (7), which can be simplified to Eq. (8).

1  ½4Eðzi Þ2 ¼ 2Eðzi Þ2 8

ð8Þ

The previous equation is valid for all factors (main and interactions) and reflects the relation between the factors relative influence over the dependent variable (liquid yield) and the sum of squares. Mean square MS(zi) can be calculated by dividing the sum of squares by the degree of freedom N, which for the factorial program used (two levels) results in a unitary value (N=1) given by expression (9).

MSðzi Þ ¼ SSðzi Þ

ð9Þ

Experimental deviations associated to all experiments lay down in both experimental deviation sum of squares SSE and experimental deviation mean square MSE using expressions (10)–(12).

SSE ¼

n X ðY 0;j  Y 0 Þ2

ð10Þ

j¼1

Y0 ¼

ð3Þ

The regression models obtained were achieved by using a two level factorial design ðp ¼ 2k Þ and three factors (k = 3) plus the central point. This design is orthogonal, which means that the predicted variance response for the region of interest is minimal and the regression coefficients can be assessed independently. Also, to assume numerical accuracy in the estimation of regression coefficients, factors were coded resulting in code variables zi , given by expression (4),

xi 

2219

ð2Þ

The deviation involved in the experiments was assumed to present a normal distribution with a constant variance of r2 [17]. For each factor is associated a level, which can assume two values, a positive (+1, as upper level) and a negative (1, as lower level). The chosen levels represent the experimental range. Since each x-variable takes only two different values, the calculations can be simplified by coding each x-scale so that upper level of x is +1 and the lower level is 1. Extra runs done (central point) were conducted to evaluate the deviation involved in the experiment. Previous work done with these wastes showed that the three more important factors that influenced liquid yields were temperature, initial pressure and reaction time, which after coding were called as factors z1 ; z2 and z3 respectively. The central point can be represented by the code coordi   nates z1 ; z2 ; z3 ¼ ð0; 0; 0Þ. Stationary point can be achieved when partial derivates of each individual response are zero, which in this case, corresponds to the system highest value response, Eq. (3).

@Y mi @Y mi @Y mi @Y mi ¼ ¼ ¼ ... ¼ ¼0 @z1 @z2 @z3 @zk

20 12 0 P 12 3 n n P Y mðzi¼þ1Þ Y 6B C Bm¼1 mðzi¼1Þ C 7 7 6 m¼1 SSðzi Þ ¼ 4  6B þB  YC  YC @ A @ A 7 5 4 4 4

n X Y 0;j N j¼1

MSE ¼

SSE SSE ¼ NVE N  1

ð11Þ ð12Þ

N VE corresponds to the degree of freedom associated to MSE . In order to establish the statistic evaluation, it can be used the FisherSnedecor distribution, which establishes a relation between factor mean square and deviation mean square using expression (13).

F exp ðzÞ ¼

MSðzi Þ MSE

ð13Þ

It is necessary to calculate the significance level a as well as the confidence degree 1  a to validate the factors significance. When the first factorial program does not fit, it is necessary to employ a second factorial program and establish new factors range, using Steepest Ascent Method. Experimental range is sequentially transferred to other regions, in which the system response is near the optimum. The objective function is accomplished by using the maximum slopes of the experimental plane previously establish. The referred slope can be calculated using function partial derivates for each factor zi (each factor define a gradient vector) as shown in Eqs. (14)–(16).

  @Ye @Ye @Ye ; ; @z1 @z2 @z3   rYe ðrYeÞu ¼ krYek vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u uX @Ye2 krYek ¼ t @zi i

rYe ¼

ð14Þ ð15Þ

ð16Þ

The polynomial independent parameter ðb0 Þ can be calculated using the expression (17), in which were included all runs plus the ones done in central point (n).

b0 ¼

X

Ym k

m

2 þn

ð17Þ

The gradient vector establishes the experiment direction using the increments ðk), in coded variables and the central point of the first factorial program. Each new point can be calculated by expression (18).

z0i ¼ zoi þ kðrYeÞu

ð18Þ

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

The new factorial program will have as central point the highest value achieved for the system response. The estimated response (Ye) for the second factorial program is similar to the one obtained in Eq. (6), in which were also included the main effects and all interactions.

tyre (RT) and 70% w/w of plastics with 20% of PE, 30% of PP and 20% w/w of PS were used. Waste characterization is presented in Table 1 while in Table 2 is shown the ultimate and proximate analysis of both liquid and solid fractions (method reproducibility is 2% of value). The ash content for PP is different and significantly higher than the ones obtained from the other plastics residues (PE and PS) due to his origin. PP residue was obtained from automobile fender which contained higher concentration of Ca.

2.2. Experimental procedure Pyrolysis experiments were carried out in a constant volume batch reactor of 1 litre autoclave, built of Hastelloy C276, by Parr Instruments (Fig. 1). Reactor was first loaded, closed and pressurised to a pre-set value with nitrogen, then heated till the desired temperature value was reached, at which it was maintained during the experimental reaction time previously established. Afterwards, the reactor was cooled down and gases were measured at room temperature and analysed by gas chromatography (GC), after removing H2S by method 11 of EPA. Liquid and solid samples were first separated and weighted. The solid fraction was submitted to a solvent extraction process first with dichloromethane (CH2Cl2) and then with tetrahydrofuran (THF) to remove the remaining liquid and both fractions were also analysed by GC–MS (gas chromatography associated to mass spectrometry). Liquid hydrocarbons were distilled and separated into three fractions: the lighter one with distillation range lower than 150 °C, the second fraction with a distillation range between 150 °C and 270 °C and the third one with distillation range higher than 270 °C. These liquid fractions were analysed by GC-MS. Chemical and physical properties of pyrolysis products were determined by ASTM standards [22]. Rubber tyre wastes were previous crushed into small pieces (filaments of about 2 cm length and 1–2 mm diameter) [23]. They were provided by a scrap tyre recycling plant and the main components were: natural rubber (NR), styrene butadiene rubber (SBR) and butadiene rubber (BR), after the removal of metal and textile components. Plastic wastes were previously pelletised into 2– 3 mm particles diameter. Waste blends with 30% w/w of rubber

3. Results and discussion In previous experiments the relative influence of several parameters in the formation of liquid compounds was evaluated [24]. Previous studies allowed acquiring important information related to reactor operation, establishing products sampling and analysis procedures and the selection of experimental parameters range. 3.1. Factorial design The implemented factorial design is presented in Table 3, which includes the natural variables studied (temperature, initial pressure and reaction time), the respective coded factors (z1, z2 and z3) and the system response obtained for liquid yields. It is also presented the measured average temperature obtained for all runs tested. It is possible to observe that, for most of the runs done, no great deviations were obtained. Runs 9 to 12 were conducted in central point to estimate experimental deviation, which resulted in a statistic standard deviation of 1.7%. The analysis of Table 3 shows that the runs done in central point presented higher system response than those obtained for all factors tested. This could mean that central point is near the optimal point. Table 4 shows all information related with the main factors and interactions effects, as well as the confidence degree associated to significance of factors and interactions. From all main factors stud-

LEGEND

7 8

11

9

13

17 12 18

21

N2

H2 16 373 C OUT 1100

6

119

14

2096

5

500

19 4

1- Autoclave 2 - Furnace 3 - Sttiring system 4 - Internal cooling coil 5 - Liquid sampling tube 6 - Thermocouple 7 - Gas inlet tube 8 - Tube connected to safety rupture disc 9 - Gas release tube 10- Cooling bath 11- Pressure reduction 12- Pressure gage 13- Gas meter 14- Controller 15- Furnace temperature measurement 16- Autoclave temperature measurement 17- Sttiring speed measurement 18- Cooling coil valve control 19- Furnace temperature control 20- PC for data acquisition 21- Gas sampling valve

20 2

3 1

10 15

Fig. 1. Schematic representation of the waste pyrolysis installation.

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M. Miranda et al. / Fuel 89 (2010) 2217–2229 Table 1 Wastes ultimate and proximate analysis (done as received). RT Rubber tyre (RT) and plastics (PE, PP and PS) PCS (MJ kg1) (ASTM D 2015 e D 5865) C (%) H (%) N (%) S (%) Oa (%) Moisture (% w/w) (105 °C) Ash (% w/w) (800 °C and 750 °C) Volatile matter (% w/w) (950 °C/NP 3423c) Fixed carbona (% w/w) a b c

86.1 7.2 0.2 1.5 0.1

PE

PP

PS

38.5 84.8 14.5 0.3 0.3 – 2.0 2.9 61.6 33.5

46.4 70.5 11.6 0.5
37.6 86.1 7.4 6.1
39.0

0.3 0.0 99.5 0.2

Calculated from difference. QL (quantification limit). Portuguese Standard for the determination of volatile matter.

Table 2 Ultimate and proximate analysis for different liquids fractions and solids. Liquids

PCS (MJ kg1) Density (kg m3) C (%) H (%) N (%) S (%) Moisture (% m/m) Ahs (% m/m) Volatile mater (% m/m) Fixed carbona (% m/m) a

Solids

1 Fraction T < 150 °C

2 Fraction 150 < T < 270 °C

3 Fraction T > 270 °C

Solid before extraction

Solid after extraction

44.5 765.5 81.9 11.8 1.6 <0.1 – – – –

43.7 864.5 85.7 12.0 1.7 <0.1 – – – –

41.4 1014.1 88.4 9.7 2.1 0.3 – – – –

34.2 – 81.6 7.3 1.8 0.9 0.50 – – –

29.9 – 80.8 1.5 1.1 1.9 1.1 13.2 5.1 80.6

Calculated from difference.

Table 3 First factorial design – natural variables, coded factors and responses. Run

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

Natural variables

Measure temperature (°C)

T (°C)

P (MPa)

t (min)

350 450 350 450 350 450 350 450 400 400 400 400

0.21 0.21 1.03 1.03 0.21 0.21 1.03 1.03 0.62 0.62 0.62 0.62

10 10 10 10 30 30 30 30 20 20 20 20

356 451 353 449 355 421 351 426 396 401 398 403

ied, factor z1 (temperature) presented the highest effect, followed by factors z3 (reaction time) and z2 (initial pressure). Both temperature and reaction time presented positive effects, which indicated that the function maximization was accomplished by the simultaneous increase of these factors. Interactions, z1z3 (temperature reaction time) presented the highest value, although negative values were obtained, which indicated a complex cross effect. This ambiguous situation could mean that factors behaviour was nonlinear or that factors range was extremely high. Confidence degree of both temperature and reaction time, as well as their interaction were highly significant based on Fisher-Snedecor distribution. After applying minimum squares method to experimental data,

Liquid yields – g (% w/w)

Coded factors z1

z2

z3

1 +1 1 +1 1 +1 1 +1 0 0 0 0

1 1 +1 +1 1 1 +1 +1 0 0 0 0

1 1 1 1 +1 +1 +1 +1 0 0 0 0

50.0 72.4 50.0 70.0 73.7 59.3 68.2 59.3 74.4 71.7 73.7 72.5

the adjusted regression model for a confidence degree higher than 98% was given by Eq. (19).

g ¼ 66:3 þ 2:39z1 þ 2:25z3  8:22z1 z3

ð19Þ

The global significance of the proposed model is presented in Table 5, which also shows the experimental deviation and the sum of squares associated to quadratic terms, as components of the residual terms (deviations that are not explained by the model). Confidence degree for the most important factors and interactions were significantly high, the quadratic terms value was high (99.9%), which was in accordance with the lower value of the global model adjustment (58.0%) and with the low correlation

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

Table 4 First factorial design – effects of the factors and interactions. Analysis of variance. Source of variation Principal factors z1 (T) z2 (P) z3 (t) Interactions z1 z2 z1 z3 z2 z3 z1 z2 z3 Experimental deviation Total * ** ***

Effects

Sum of squares SS

Degrees of freedom N

Mean square MS

F exp

Significance level a

Confidence degree ð1  aÞ 100

4.77 1.99 4.50

45.6 7.9 40.5

1 1 1

45.6 7.9 40.5

31.3 5.4 27.9

0.01129 0.10219 0.01327

98.87*** 89.78** 98.67***

0.81 16.44 0.80 1.99 – –

1.3 540.7 1.3 7.9 4.4 649.6

1 1 1 1 3 10

1.3 540.7 1.3 7.9 1.5 –

0.9 371.4 0.9 5.5 – –

0.41374 0.00031 0.41630 0.10165 – –

58.63* 99.97*** 58.37* 89.84** – –

Not Significant. Significant. Highly Significant.

Table 5 Model global significance – first factorial design. Source of Variation

Sum of squares SS

Degrees of freedom N

Mean square MS

Significance Experimental deviation ð1  aÞ 100

F exp

ð1  aÞ 100

21.5 94.7 63.3

98.43 99.83 99.70

– – 1.3

– – 58.01

277.8 1.5 70.5

190.8 – –

99.92 – –

– – –

– – –

–

–

–

–

–

F exp Regression 1st order (terms) Interactions Total

94.0 551.2 645.2

3 4 7

31.3 137.8 92.2

Residuals Quadratic terms Experimental deviation Total

277.8 4.4 282.1

1 3 4

Total

927.4

11

76 Liquid yield (% w/w )

coefficient (0.83). The values obtained indicate that the model previously proposed was inadequate and a second factorial program should be done. The new factors range application, for the second factorial program, could be accomplish by using the Steepest Ascent Method. At this point, the region of experimentation can be shifted to another region with a new set of factors range. This method allowed the definition of the research direction which would be used, in order to find the path to system optimization using increments k. These increments are usually determined subjectively, taking into account factors relative importance and the experimental system knowledge and they should be calculated by using the expressions (14), (15), (16) and (18). In Table 6 is presented the values for coded factors calculated for all different increments chosen. The experimental procedure was based on experimental runs with the new data until a maximum response was achieved. The conditions associated to the maximum achieved, corresponded to the definition of the new central point, in which the second factorial program would be done. In

Total residuals

72 68 64 60

Increment - T (ºC); P (MPa); t (min) 0 - T 400.0; P 0.62; t 20.0 0.25 - T 408.7; P 0.59; t 21.6 0.50 - T 417.4; P 0.56; t 23.3 0.75 - T 426.1; P 0.53; t 24.9 1.00 - T 434.8; P 0.50; t 26.6 1.25 - T 443.5; P 0.47; t 28.2

56 0.00

0.25

0.50 0.75 Increment

1.00

1.25

Fig. 2. Liquid yield – increments ðkÞ for second factorial design for the steepest ascent method.

Table 6 Steepest Ascent method direction – Increments, coded factors and natural variables. Increment k

0 0.25 0.50 0.75 1.00 1.25 1.50

Coded factors

Natural variables

Real temperature (°C)

z01

z02

z03

T (°C)

P (MPa)

t (min)

0 0.174 0.348 0.522 0.696 0.870 1.044

0 0.072 0.145 0.217 0.290 0.362 0.435

0 0.164 0.328 0.493 0.657 0.821 0.985

400 408.7 417.4 426.1 434.8 443.5 452.2

0.62 0.59 0.56 0.53 0.50 0.47 0.44

20 21.6 23.3 24.9 26.6 28.2 29.9

– 402 411 418 424 432 –

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

Table 6 may be observed that the research direction led to an increase of both temperature and reaction time and to a decrease of initial pressure, which was in accordance with the previous results of the first factorial program. The run proposed with the increment of 1.50 was not possible to accomplish, as the new temperature was higher than batch reactor temperature limit. Experimental results of Fig. 2 show that the increase of temperature and of reaction time, for values higher than central point ðk ¼ 0Þ, resulted in a decrease in liquid yield. This result indicated that the new central point coordenates should be the same used in the first factorial program, as the highest liquid yield was achieved. For the present case, the second factorial program was not shift for other space region, but more restrictive factors range were used. The results obtained for the analysed domain used in the first factorial program (run temperature 350–450 °C, initial pressure

0.21–1.03 MPa and reaction time 10–30 min) showed that central point presented the highest liquid yields. These results suggest that the optimum point or region could be near the central point. Thus, a second factorial program with the same central point, but with shorter range factors (run temperature 370–430 °C, initial pressure 0.48–0.76 MPa and reaction time 15-25 min) was tested. The aim of the second factorial program was a better definition of experimental conditions. In most studies found in literature, a second factorial program with a factors range completely different from the first factorial program is quite common, that is to say that the analysed domain is shift for other space region. In Table 7 is presented all information related with second factorial program: natural variables range, coded factors and system response. Liquids yields increased for most of the runs and were higher than the ones obtained in central point for runs 13, 15, 17

Table 7 Second factorial design – natural variables, coded factors and responses. Run

13 14 15 16 17 18 19 20 9 10 11 12

Natural variables

Measure temperature (°C)

T (°C)

P (MPa)

t (min)

370 430 370 430 370 430 370 430 400 400 400 400

0.48 0.48 0.76 0.76 0.48 0.48 0.76 0.76 0.62 0.62 0.62 0.62

15 15 15 15 25 25 25 25 20 20 20 20

371 431 373 423 372 427 371 420 396 401 398 403

Liquid yields – g (% w/w)

Coded factors z1

z2

z3

1 +1 1 +1 1 +1 1 +1 0 0 0 0

1 1 +1 +1 1 1 +1 +1 0 0 0 0

1 1 1 1 +1 +1 +1 +1 0 0 0 0

76.7 72.7 78.2 69.0 76.2 67.7 76.5 67.9 74.4 71.7 73.7 72.5

Table 8 Second factorial design – effects of the factors and interactions. Analysis of variance. Source of Variation

Effects

Sum of squares SS

Principal factors z1 (T) z2 (P) z3 (t)

7.56 0.42 2.07

114.3 0.4 8.6

Interactions z1 z2 z1 z3 z2 z3 z1 z2 z3 Experimental deviation Total

1.29 1.00 0.68 1.31 – –

3.4 2.0 0.9 3.4 4.4 137.3

* ** ***

Mean square MS

F exp

Significance level a

Confidence degree ð1  aÞ 100

1 1 1

114.3 0.4 8.6

78.5 0.2 5.9

0.00303 0.65262 0.09365

99.70*** 34.74* 90.64**

1 1 1 1 3 10

3.4 2.0 0.9 3.4 1.5

2.3 1.4 0.6 2.3

0.22634 0.32577 0.48076 0.22360 – –

77.37* 67.42* 51.92* 77.64* – –

Degrees of freedom N

–

– –

Not Significant. Significant. Highly Significant.

Table 9 Model global significance – Second factorial design. Source of Variation

Sum of squares SS

Degrees of freedom N

Mean square MS

Significance Experimental deviation

Experimental deviation

F exp

ð1  aÞ 100

F exp

ð1  aÞ 100

Regression 1st order (terms) Interactions Total

123.2 9.7 132.9

3 4 7

41.1 2.4 19.0

28.2 1.7 13.0

98.94 64.80 97.06

– – 17.4

– – 99.24

Residuals Quadratic terms Experimental deviation Total

0.0054 4.4 4.4

1 3 4

0.0054 1.5 1.1

0.0037 – –

4.47 – –

– – –

– – –

Total

137.3

–

–

–

–

–

11

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

and 19. In Table 8 is presented information related with the analysis of variance. All factors have negative effects which meant that, for the new range of factors, liquid yield optimization was achieved with the decrease of experimental conditions, as average responses for the higher level were lower than the average responses obtained for the lower level. Factor temperature presented the highest significance value, followed by reaction time, while initial pressure was not statistically significant. This temperature behaviour (confidence degree of 99.7%), which was different from that observed in the first factorial program, could be explained by the new factors range adopted. No significant effects were found for interactions and confidence degree was also lower. The regression model applied to experimental data could be explained by expression (20), which could be simplified to Eq. (21), if factors with significance highly than 90% were used.

g ¼ 73:1  3:78z1  0:21z2  1:04z3  0:65z1 z2  0:50z1 z3 þ þ 0:34z2 z3 þ 0:65z1 z2 z3

ð20Þ

g ¼ 73:1  3:78z1  1:04z3

ð21Þ

Table 9 shows the global model significance for the second factorial program. Global significance was extremely good (99.24%) associated with the multiple coefficient correlation of 0.984 and a quadratic term of 0.054, which indicated an adequate fitting of the proposed model to experimental data. The adjustment obtained from the proposed linear model allows predicting, with

extremely accuracy, the production of liquid compounds for the analysed domain associated with a very small experimental deviation (1.5%). Both average system response Y 0 (73.11) and average central point response Y 0 ; (73.08), presented the same numeric value, which suggest that a maximum response area was achieved. The comparison of the results obtained in the two factorial desings carried out showed that factor temperature clearly presented a non-linear behaviour if a wide range of this factor is considered, although the system can be globally represented by a linear model. When factors range were shortened (second factorial program), the linear model became adequate for representing the liquid yields prediction. The generated model can be used to predict the system response as a function of the variables in a study based on expression (20). Due to the fact that the 3D graphic presentation of three factors is very complicated, Figs. 3–5 show the variation of two factors whilst the third one was kept constant. The use of RSM results in a lot of information and in a large amount of plots. As it is not possible to present all the graphics associated to the factors and interactions effects, the graphics chosen were only those associated to the experimental conditions optimized (Table 10). In Figs. 3–5a are present, respectively, information obtained for liquid yield regarding the influence of temperature factor for different initial pressures, reaction time for different initial pressures and reaction time for different temperatures. Figs. 3–5b present the liquid yields obtained from the analysis of initial pressure for different temperatures, initial pressure for different reaction times and temperature

80

75

75

70

70 Liquid yield (%)

Liquid yield (%)

80

65 60 55

P = 0.48 MPa

65 60 55

50

P = 0.55 MPa P = 0.62 MPa

50

45

P = 0.69 MPa P = 0.76 MPa

45

380

390

T = 388 ºC T = 400 ºC T = 412 ºC T = 430 ºC

40

40 370

T = 370 ºC

400

410

420

0.48

430

0.55

0.62

0.69

0.76

Initial pressure (MPa)

Temperature (ºC)

a) Temperature effect on liquid yield for different initial pressures

b) Initial pressure effect on liquid yield for different temperatures 430

418

74-77

77

Liquid yield (%)

424

77-80

80

412 71-74

74-77 71-74

406

74 68-71 71

77-80

400 394

65-68

68-71 65-68

388 68

382

412 65 0.48

391 0.55

0.62

0.69

Temperature (ºC)

376

370 0.76

Initial pressure (MPa)

c) 3-D view of initial pressure and temperature effect on liquid yield

0.48

0.55

0.62

0.69

Temperature (ºC)

370 0.76

Pressure (MPa)

c1) Isolines view of initial pressure and temperature effect on liquid yield

Fig. 3. Response surface for second factorial program. Constant reaction time – 15 min.

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

for different reaction times. Figs. 3,4,5c and 3c1 to 5c1 show, respectively the 3D view and isolines obtained from the analysis of Figs. 3–5a and b. 3.2. Effect of temperature on liquid yields

3.3. Effect of reaction time on liquid yields Figs. 4 and 5 present the response surface for the second factorial program for both constant temperature of 370 °C and initial pressure of 0.48 MPa, respectively, as well as 3D view and isolines associated to both factors in c and c1 plots for the same figures. Figs. 4a and 5a present the information related with the effect of reaction time on liquid yields for different initial pressures and for different temperatures, respectively. For the analysed domain (Fig. 4a, c and c1), the function tended to be constant with the

80

80

75

75

70

70 Liquid yield (%)

Liquid yield (%)

In Fig. 3a may be observed the effect of temperature for different initial pressures on liquid yields, keeping the reaction time constant at 15 min. Globally, it was observed that the increase of temperature tended to decrease liquid yields, even with a cross effect found at 390 °C of temperature. For temperatures lower than 390 °C higher liquids yields were obtained when higher values of initial pressure were used. For temperatures higher than 390 °C, lower values of initial pressure resulted in higher liquids yields. Liquid yields decrease is probably associated with both the decrease in the amount of second distillation fraction and the increase in solid yield. A similar cross effect was observed by Koç et al. [18] when low-density polyethylene waste was pyrolysed. For the temperature of 370 °C, although higher liquid yields were obtained at 0.76 MPa, the initial pressure of 0.48 MPa was selected, due to the very small difference in liquid yields (around 2% w/w), which could represent lower investment costs. In Fig. 5b may be analysed the effect of temperature on liquid yield for different reaction times, when initial pressure was kept constant at 0.48 MPa. Small interactions were observed, as the increase of initial pressure

tended to decrease liquid yields except for the run done at 370 °C, whose behaviour was almost constant. The increase in both temperature and reaction time probably favoured the conversion of liquid compounds into higher molecular weight structures, which tended to be solid at room temperature, as higher solid yields were obtained at the maximum temperature tested (450 °C). Response surfaces, 3D view and isolines, associated to both factors may be observed in c and c1 plots of Figs. 3 and 5. In both figures, as average temperature increased, the function tended to decrease both with the increase of initial pressure (Fig. 3a, c and c1) and reaction time (Fig. 5b, c and c1). In both situations, maximum liquid yields were achieved for the temperature of 370 °C. Also, the analysis of Fig. 2 indicates that liquid yield maximization was not accomplished with the increase of run temperature.

65 60 55

P = 0.48 MPa P = 0.55 MPa

50

60 55

t = 15 min t = 17.5 min

50

P = 0.62 MPa

t = 20 min

P = 0.69 MPa

45

65

t = 22.5 min

45

P = 0.76 MPa

t = 25 min

40

40 15

17.5

20

22.5

25

0.48

0.55

0.62

0.69

0.76

Initial pressure (MPa)

Reaction time (min)

a) Reaction time effect on liquid yield for different initial pressures

b) Initial pressure effect on liquid yield for different reaction time 25

77-80

Liquid yield (%)

80

77-80

74-77

77

23

71-74

74-77 71-74

74 68-71 71

20

68-71 65-68

65-68 18

68

Reaction time (min)

22 65

19 0.48

0.55

0.62

0.69

Reaction time (min)

15 0.76

Initial pressure (MPa)

c) 3-D view of initial pressure and reaction time effect on liquid yield

0.48

0.55

0.62

0.69

15 0.76

Initial pressure (MPa)

c1) Isolines view of initial pressure and reaction time effect on liquid yield

Fig. 4. Response surface for second factorial program. Constant temperature – 370 °C.

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

3.4. Effect of initial pressure on liquid yields The effect of initial pressure on liquid yields can be analyzed in Fig. 3b, keeping the reaction time constant at 15 min and Fig. 4b, for a constant temperature of 370 °C, associated to 3D view and isolines of c and c1 plots, respectively. Regarding Fig. 3b, c and c1, when initial pressure increased liquid yields tended to be constant, although when temperature decreased it was found an increase in liquid yields. This information is in accordance with the second factorial program (Table 8), in which was concluded that factor initial pressure (z2) present a low effect in liquid yield maximization. In Fig. 4b, c and c1 it may be observed that the increase of initial pressure, for different reaction times, led to no significant changes. Liquids yields reached values higher than 75% w/w.

80

80

75

75

70

70

65

65

Liquid yield (%)

Liquid yield (%)

increase of reaction time for all initial pressures tested. On the other hand, when keeping constant the initial pressure (Fig. 5a, c and c1), the simultaneous increase in both reaction time and temperature tended to decrease liquid yields, (except for the run done at 370 °C). As reaction time increased liquid compounds, which may be chemical unstable molecules, probably they react with one another and were converted into solid compounds. The lowest value was obtained for the temperature of 430 °C and a reaction time of 25 min. The highest liquid yield was obtained for a reaction time of 15 min and for a temperature of 370 °C, which suggest that these values should be used under this experimental domain. This information is in accordance with the results obtained in the second factorial program (Table 8) which suggest that temperature (z1) and reaction time (z3) are the two most important factors as well as their interaction (z1z3) in liquid yield maximization.

60 55

Temperature = 370 ºC Temperature = 388 ºC

50

60 55

Reaction time = 15 min Reaction time = 17.5 min

50

Reaction time = 20 min

Temperature = 400 ºC

45

Reaction time = 22.5 min

45

Temperature = 412 ºC

Reaction time = 25 min

Temperature = 430 ºC

40

40 15

17.5

20

22.5

25

370

380

Reaction time (min)

390

400

410

420

430

Temperature (ºC)

a) Reaction time effect on liquid yield for different temperatures

b) Temperature effect on liquid yield for different reaction times 25 77-80

77-80

80

74-77

Liquid yield (%)

74-77

23

77

71-74

71-74 74 68-71 71

68-71

20

65-68

65-68 18

68 22 65

19 370

385

400

415

Temperature (ºC)

Reaction time (min)

Reaction time (min)

15

370

430

385

400

415

15 430

Temperature (ºC)

c) 3-D view of temperature and reaction time effect on liquid yield

c1) Isolines view of temperature and reaction time effect on liquid yield

Fig. 5. Response surface for second factorial program. Constant initial pressure – 0.48 MPa.

Table 10 Optimized experimental conditions. Run

A B C Proposed by the model

Natural variables T (°C)

P (MPa)

t (min)

370 370 370 370

0.48 0.48 0.48 0.48

15 15 15 15

Measure temperature (°C)

Liquid yield – g (% w/w)

Average

Standard deviation

373 372 372 –

81.5 80.0 82.7 76.7

81.4

1.35

–

–

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

3.5. Optimized experimental conditions and liquid composition

Figs. 6–8 present the information related with liquid composition obtained for rubber tyre and plastic wastes blends pyrolysed at the optimal experimental conditions. These figures also show that liquid composition obtained for the three experiments, A, B and C, done at equal experimental conditions (370 °C, 0.48 MPa and 15 min) present no significant changes. In Table 2 is presented the ultimate and proximate analysis of liquid fractions and of solids before and after solvent extraction. Alkanes from C5 to C20 were identified. Fig. 6 shows that major compounds formed were hexadecane 9% v/v and heptadecane 7% v/v followed by nonane 6% v/v and undecane 5% v/v. In Fig. 7 may be observed that alkenes main compounds were nonene, decene, undecene and dodecene, though C5 to C22 compounds were also identified. Nonene reached 4% v/v and was the alkene that presented the highest concentration. C6 to C16 aromatic compounds were also quantified. Liquid aromatic composition is presented in

The factorial design applied to liquid yields led to the following optimized experimental conditions for obtaining maximum liquid yields: 370 °C, initial pressure of 0.48 MPa and reaction time of 15 min. In order to validate the results, three experiments were conducted sequentially (Table 10) and average calculated liquid yield found to be 81.3% w/w with an experimental deviation around 0.95%. At these conditions, gas yield of 4.9% w/w and solid yield of 12.7% w/w were obtained. For all liquids, distillation curves were found to be between those of standard gasoline and diesel fuel oil. Pyrolysis gases were composed of hydrocarbons from C1 to C5, hydrogen and carbon dioxide. Liquids obtained were found to be a complex mixture of hydrocarbons formed by 47% w/w of alkanes, 14% w/w of alkenes and 39% w/w of aromatic compounds.

Alkanes concentration (% v/v)

20 Run A

18

Run B

Run C

16 14 12 10 8 6 4 2 Decadecane

Nonadecane

Octadecane

Heptadecane

Hexadecane

Pentadecane

Tetradecane

Tridecane

Dodecane

Undecane

Decane

Nonane

Octane

Methylciclohexane

Heptane

Ciclohexane

Hexane

2-Methylpentane

Pentane

0

Fig. 6. Optimize experimental conditions – alkanes composition obtained from runs A, B and C (same experimental conditions: 370 °C, 0.48 MPa, 15 min).

7.0 Run A

Run B

Run C

Alkenes concentration (% v/v)

6.0 5.0 4.0 3.0 2.0 1.0

Hexadecene

Tetradecene

Tridecene

Dodecene

Undecene

Decene

Nonene

Octene

Heptene

Hexene

0.0

Fig. 7. Optimize experimental conditions – alkenes composition obtained from runs A, B and C (same experimental conditions: 370 °C, 0.48 MPa, 15 min).

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M. Miranda et al. / Fuel 89 (2010) 2217–2229

30

5 Run C

25

4

20 3 15 2 10 1

Ethylbenzene

Toluene

Mesithylene

nPropylbenzene

o-Xylene

m/p-Xylene

Benzene

0

nButhylbenzene

5

Aromatics concentration (% v/v)

Run B

p-Cimene

Aromatics concentration (% v/v)

Run A

0

Fig. 8. Optimize experimental conditions – aromatic composition obtained from runs A, B and C (same experimental conditions: 370 °C, 0.48 MPa, 15 min).

Fig. 8, due to the high variability concentrations found for these compounds two scales were drawn. Toluene 16% v/v and ethylbenzene 17% v/v were the aromatic compounds formed with the highest contents. Besides these compounds, p-cimene and m/p-xylenes were those detected with higher contents. In the GC-MS equipment used it was not possible to separate m-xylene from p-xylene. Other authors [18] reported the presence of alcohols, aldehydes, ketones, olefins, saturated paraffins and carboxylic acids in thermal decomposition of low-density polyethylene waste. The increase of aromatic compounds concentration seems to be at the expenses of alkane fraction, probably due to the conversion of pentane into benzene [25]. The high ethylbenzene concentration could be explained by hydrogen transfer from molecular structure of PE to PS via radical mechanism that occurred during thermal degradation [26]. Another possible explanation is presented by Rodriguez et al. [27], which refer that liquid aromaticity result from rubber tyre degradation, since SBR polymer is generally composed by 25% styrene (aromatic) and 75% butadiene (aliphatic), which due to radical aliphatic and aromatic recombination reactions and aliphatic cyclization reactions result in more liquid aromatic compounds. A significant effect of reaction temperature increase was observed in the formation of toluene [28]. It was study the possible synergism between rubber tyre and plastic wastes in order to improve liquid yield by using the systematization of results. To accomplish this study it was analysed the results obtained from the pyrolysis of these residues separated and compared to the pyrolysis of these residues mixed together for a waste blend with 30% rubber tyre and 70% plastics (20% PE, 30% PP and 20% PS) (w/w) . The results obtained suggested that there was no synergism between rubber tyre and plastics wastes pyrolysis regarding the production of liquid compounds as no improvement in liquid yield was obtained.

4. Conclusions Pyrolysis process has shown to be a possible way to convert mixtures of rubber tyre and plastic wastes into economical valuable products, such as liquids with fuel characteristics or raw

material to chemical and petrochemical industries. Waste blend used was composed by 30% rubber tyre, 20% PE, 30% PP and 20% w/w PS and experimental parameters for liquid maximization were accomplish by using response surface methodology (RSM) which allowed establishing the priority of factors effect as well as theirs relative contribution to the system response. Temperature was the most important factor followed by reaction time, being initial pressure the parameter that least affected liquid yields. Also, a linear model with good fit to experimental data was accomplished in the second factorial program, although temperature factor presents a non-linear behaviour. Experimental conditions optimized were 370 °C 0.48 MPa for initial pressure and reaction time of 15 min. Though these values are specific to the type of reactor used, they can be used as a first approach for a larger scale reactor. However, a new experimental program should be applied for a larger scale reactor, as different heat and mass transfer conditions could lead to different optimization values. In order to validate the results, three experiments were conducted sequentially and average values were calculated and found to be: gas yield of 4.9 % w/w, liquid yield of 81.3 % w/w and solid yield of 12.7 % w/w with an experimental deviation of 0.95%. The complex hydrocarbon liquid mixture obtained was composed mainly by alkanes, 47% w/w. Hexadecane (9% v/v) and heptadecane (7% v/v) were the compounds formed with the highest contents. The alkene formed in the highest amount was nonene 4% v/v. Toluene (16% v/v) and ethylbenzene (17% v/v) were the aromatic compounds obtained in highest concentrations. Average values obtained for the complex liquid hydrocarbon mixture (w/w) at optimal point were found to be 47% alkanes, 14% alkenes and 39% aromatics. References [1] Larsen-Morten Boberg, Schultz Lars, Glarborg Peter, Skaarup-Jensen Lars, DamJohansen Kim, Frandsen Flemming, et al. Fuel 2006;85:1335–45. [2] Plastics Europe – Association of Plastics Manufactures; 2003. [3] Plastics a material choice for the 21st century-Insights into plastic consumption and recovery in Western Europe; 1997. [4] Pinto Filomena, Gulyurtlu I, Costa Paula, Cabrita I. J Anal Appl Pyrolysis 1999;51:39–55. [5] Pinto Filomena, Gulyurtlu I, Costa Paula, Cabrita I. J Anal Appl Pyrolysis 1999;51:57–71.

M. Miranda et al. / Fuel 89 (2010) 2217–2229 [6] Fortuna F, Cornacchia G, Mincarini M, Sharma VK. J Anal Appl Pyrolysis 1997;40–41:403–17. [7] Groves SA, Lehrle RS, Blazso M, Szekely T. J Anal Appl Pyrolysis 1991;19:301. [8] Tamura S, Murakami K, Kuwazoe H. Appl Polym Sci 1987;33:1122. [9] Chien JCW, Kiang JKY. Eur Polym J 1979;15:1059. [10] Williams PT, Besler S. Fuel 1995;74(9):1277–83. [11] Williams Paul T, Brindle Alexander J. J Anal Appl Pyrolysis 2003;67:143–64. [12] Cunliffe Adrian M, Williams Paul T. J Anal Appl Pyrolysis 1998;44:131–52. [13] Miller JC, Miller JN. Statistics for analytical chemistry. 3rd ed. Ellis Horwood; 1993. [14] Montgomery DC. Design and analysis of experiments. 4th ed. New York: John Wiley & Sons; 1997. [15] Park Sung H, Kim Kiho. J Appl Stat 2002;29(6):885–93. [16] Neddermeijer H. Gonda, van Oortmarssen Gerrit J, Piersma Nanda, Dekker Rommert. In: Proceedings of the 2000 winter simulation conference. [17] Myers RH, Montgomery DC. Response Surface methodology: process and product optimization using designed experiments. New York: John Wiley & Sons; 1995. [18] Koç Adil, Bilgesü Ali Y, Alibeyli Rafig, Çetin Koçak M. J Anal Appl Pyrolysis 2004;72:309–15.

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[19] Palubeckis Gintaras. Inform Technol Control 2003(3):7–13. [20] Catharino Rodrigo Ramos, Godoy Helena Teixeira. Ciênc Tecnol Aliment, Campinas 2001;21(3):326–9. [21] Chantarat Navara, Zheng Ning, Allen Theodore T, Huang Deng. In: Proceedings of the 2003 Winter Simulation Conference; 2003. [22] Annual Book of ASTM Standards, 1994, Volumes 05.01 and 05.02, ASTM D 8693, ASTM D 240-92, ASTM D 396-92, ASTM D 910-93, D 975-93, ASTM D 165593, ASTM D 2069-91, ASTM D 2699-89, ASTM D 2880-92, ASTM D 3699-92, ASTM D 4485-92b. [23] Ucar Suat, Karagoz Selhan, Ozkan Ahmet R, Yanik Jale. Fuel 2005;84:1884–92. [24] Pinto Filomena, Miranda Miguel, Gulyurtlu I, Cabrita I. In: International symposium – recycling and reuse of tyres, Dundee; 2001. p. 27–38. [25] Cunliffe Adrian M, Williams Paul T. J Anal Appl Pyrolysis 1998;44:131–52. [26] Arandes José M, Ereña Javier, Azkoiti Miren J, Olazar Martin, Bilao Javier. J Anal Appl Pyrolysis 2003;70:747–60. [27] Rodriguez Isabel de Marco, Laresgoiti MF, Cabrero MA, Torres A, Chomón MJ, Caballero B. Fuel Process Technol 2001;72:9–22. [28] Kaminsky Walter, Carsten Mennerich. J Anal Appl Pyrolysis 2001;58– 59:803–11.