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

Fuel 89 (2010) 2217–2229

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Fuel

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Response surface methodology optimization applied to rubber tyre and plasticwastes thermal conversion

Miguel Miranda a,*, Filomena Pinto a, I. Gulyurtlu a, I. Cabrita a, C.A. Nogueira a, Arlindo Matos b

a LNEG, Estrada Paço do Lumiar, 22, 1649-038 Lisboa, Portugalb 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 2009Received in revised form 2 March 2010Accepted 3 March 2010Available online 19 March 2010

Keywords:Tyre wastesPlastic wastesPyrolysisRecyclingRSM

0016-2361/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.fuel.2010.03.009

* Corresponding author. Tel.: +351 21 092 4417; faE-mail address: [email protected] (M. Miran

a b s t r a c t

Thermal degradation was studied as a method to decompose mixtures of rubber tyre (RT) and differentplastic wastes (PE, PP and PS) with the aim of producing a liquid fuel [1], as well as valuable chemical rawmaterials. An experimental set of runs was performed to establish the operational conditions that max-imize liquid fraction production in a 1 litre batch reactor. Waste blends used were composed of 30% w/wRT and 70% w/w plastics (20% PE, 30% PP and 20% w/w PS). The complex hydrocarbon liquid mixtureobtained during pyrolysis of these residues was highly dependent on experimental parameters, namelytemperature, initial pressure and reaction time, which are the three most important factors affectingliquid yields. Regression analyses of experimental data were performed according to response surfacemethodology (RSM). As a result, experimental conditions optimized based on Factorial Design Method-ology were 370 �C, 0.48 MPa for initial pressure and 15 min for reaction time. In order to validate theresults obtained by the RSM model, three extra runs were conducted sequentially and average valueswere calculated and found to be: gas yield of 4.9% w/w, liquid yield of 81.3% w/w and solid yield of12.7% w/w with an experimental deviation of 0.95%.

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1. Introduction

Environmental pollution as well as efficient use of resourcesrepresents two important issues that concern all modern societies.Tyres and plastics are widely used all over the world, resulting inan increasing amount of residues, which have become a problemto human kind, due to their highly negative environmental impact.Some publications [1,2] have reported the negative implications ofthis growing problem, which has been developing for many years.

Landfilling and incineration processes used so far to deal withthese wastes present several problems. Landfilling, the most com-mon disposal route, does not allow the recovery of the organic con-tent of these wastes. Though incineration has the advantage ofrecovering some of wastes energetic content, pollutants are pro-duced like light hydrocarbons, nitrous and sulphur oxides, dustsand dioxins, which have highly negative bearing on the environ-ment [3]. Moreover, rubber tyres sulphur content may reach valuesaround 2%, due to vulcanisation processes, which means that thecost of tyres incineration process increases, due to the need of fluegas desulphurisation.

The natural raw material for rubber tyre and plastics productionis petroleum [3], whose reserves have a limited lifetime [4,5].

ll rights reserved.

x: +351 21 716 65 69.da).

Therefore, it is advisable to have a better management of theremaining petroleum resources. This goal can be achieved throughthe application of pyrolysis technology to deal with these wastes.As pyrolysis process takes place in an enclosed environment, prob-lems related with direct emissions to atmosphere can be con-trolled. The implementation of this technology allows theconversion of any organic waste, namely non-biodegradable, intonew organic products used either as raw materials for severalindustries, or for energy production as substitutes of conventionalfuels. During pyrolysis, thermal decomposition of wastes occurs inpresence of inert atmosphere under moderate conditions of tem-perature and pressure [4,5]. Polymeric structure is broken down,producing smaller intermediate species, which can further reactand produce a complex mixture of smaller hydrocarbon molecules,being liquid or gaseous in nature. The material or energy recoveryof tyres and plastic wastes can be a good way of achieving thoseobjectives.

In general, tyres wastes pyrolysis produce 33% w/w of solid res-idue, 35% w/w of liquid fraction, 12% w/w of scrap and 20% w/w ofgases. The liquid fraction analysis indicates the presence of mono-mers, dimers and oligomers [6] of the original polymeric structure.When natural rubber was pyrolysed the monomer was isopreneand the dimer was dipentene [7–10]. In a catalytic pyrolysis study[11] of scrap tyres, the maximization of single ring aromatic com-pounds was reported, being found the influence of catalyst in

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Nomenclature

N degree of freedomE(zi) main factor zi

Fexp (zi) Fisher–Snedecor distributionMS(zi) mean of squaresMSE mean of squares of experimental deviationRSM response surface methodologySS(zi) sum of squaresSSE sum of squares of experimental deviationxþi higher level for the natural variable ix�i lower level for the natural variable izi independent variable or factor (coded)

z1 factor temperaturez2 factor initial pressurez3 factor reaction timez0i new research pointY dependent variable or system responseYm system response for m conditionsY0 response in the central point for the factorial designk increment

2218 M. Miranda et al. / Fuel 89 (2010) 2217–2229

decreasing liquid yield and increasing gas yield. Liquid yields com-position were 7.7% v/v of toluene, 1.4% v/v of benzene, 6.4% v/v ofm/p xylenes and 2.2% v/v of o-xylene. In another study by the sameteam [12] it was found that oil yield decreased and gas yield in-creased with higher temperatures. Limonene and light aromaticssuch as benzene, toluene, xylene and styrene were identified asmajor oil components.

When only plastics wastes were pyrolysed, higher liquids yieldswere obtained than those achieved with tyre waste pyrolysis alone[13]. Although several studies have shown that products yields de-pended on the nature of plastic waste blend, for PE (polyethylene)pyrolysis, total conversion was usually higher than 90% w/w andthe main product was liquid, gas yields were always lower than10% w/w and almost no solid was obtained. Liquid analysis showedthe presence of a complex mixture of hydrocarbons, some of whichcould be dehydrogenated, supplying hydrogen to the reactionmedium [4,5].

This paper reports the results obtained when mixtures of rubbertyre and plastic wastes with different relative contents were pyrol-ysed. The main objective of using rubber tyre and plastic wastesmixtures was to improve liquid yield of rubber tyre waste pyrolysisand to study possible synergisms between these two types ofwastes. A mixture of the three more used plastics, namely PE (poly-ethylene), PP (polypropylene) and PS (polystyrene), with a compo-sition similar to that found in municipal solid waste (MSW) waschosen. The present work employed a factorial experiment designto investigate thermal decomposition of mixtures of rubber tyreand different types of plastics wastes with the aim of optimisingexperimental conditions to maximise liquid production.

2. Experimental

2.1. Factorial experimental design

The response surface methodology (RSM) is a collection of sta-tistical and mathematical techniques very useful to optimize sto-chastic functions, in which the objective function is accomplishedby an approximation of a low order polynomial, in a sub-regionof the domain of a set of independent variables [14,15]. The statis-tical method used can be applied to maximize (or minimize) adependent variable which is a function of independent variables.The main advantage of this methodology is an effective reductionof the experimental effort, as well as the quantification of the realeffect that independent variables has over the optimization func-tion (liquid yield). The results obtained are specific to the analyseddomain (type of reactor used, independent variables chosen, do-main of experimental conditions and maximization of dependentvariable). Although the information obtained is strictly applied tothe analysed domain, it can be used as a first approach in a largerscale reactor.

The objective function is based on data obtained by statisticaland mathematical techniques, which allows fitting a linear ornon-linear polynomial regression to experimental data. In thismethodology, the parameters of the simulation model are calledfactors and the algorithm used comprises normally two phases.In the first phase, the response surface function is approximatedto a first-order polynomial, until it is proven to fit (or presentlack of fitting) to experimental data. In the second phase, thefunction is approximated by a second-order polynomial, whichindicates the existence of curvature in the system. Factorial exper-iments are conducted in a systematic and efficient way, in accor-dance to a previously established plan, where all variables arechanged simultaneously rather than one at a time, which reducesubstantially the number of experimental runs needed. Due tofactorial design orthogonal property, statistical tests tend todiscriminate in a effective way the effects resulting by naturalvariations of raw materials, replicated unit operations (e.g. equip-ment in parallel), different operators and batch reactors, as wellas other environmental factors [16]. The results obtained are spe-cific to the analysed domain (type of reactor used, independentvariables chosen, domain of experimental conditions and maxi-mization of dependent variable). Although the information ob-tained is strictly applied to the analysed domain, it can be usedas a first approach in a larger scale reactor. Several experimentaldesign 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 thermaldecomposition of low-density polyethylene waste and summa-rised important information regarding response surface method-ology (RSM) in simulation optimization and other optimizationmodels [16] and construction of central composite design for bal-anced orthogonal blocks [15]. A different group of investigatorsproposed two variations of the iterated steepest ascent algorithm[19], using the response surface methodology in the optimizationof folic acid determination in enriched milk, with the applicationof a factorial plan with four factors and two levels [20] and mod-els which allow the combination of both quantitative and quali-tative variables [21].

Least-squares were used in the optimization of a dependentvariable, which for the present study is liquid yield. The use of thismethodology allows a response evaluation with the same precisionas the one obtained if only a single variable was studied in thesame experimental range [18,19]. In the present case, all k factors(xi, i=1 to k) represent quantitative variables of the unknown sys-tem response Y and the values of the function (mass fraction of liq-uids) for different factors are denominated system response Ym.The response surface applied to liquid yield optimization can bepresented by Eq. (1).

g ¼ f ðx1; x2; . . . ; xi; . . . ; xkÞ ¼ Y ð1Þ

M. Miranda et al. / Fuel 89 (2010) 2217–2229 2219

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

Y ¼ b0 þ b1x1 þ b2x2 þ . . .þ bkxk þ e ð2Þ

The deviation involved in the experiments was assumed topresent a normal distribution with a constant variance of r2

[17]. For each factor is associated a level, which can assumetwo values, a positive (+1, as upper level) and a negative (�1,as lower level). The chosen levels represent the experimentalrange. Since each x-variable takes only two different values,the calculations can be simplified by coding each x-scale so thatupper level of x is +1 and the lower level is �1. Extra runs done(central point) were conducted to evaluate the deviation in-volved in the experiment. Previous work done with these wastesshowed that the three more important factors that influenced li-quid yields were temperature, initial pressure and reaction time,which after coding were called as factors z1; z2 and z3 respec-tively. The central point can be represented by the code coordi-nates z�1; z

�2; z�3

� �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 re-sponse, Eq. (3).

@Ymi

@z1¼ @Ymi

@z2¼ @Ymi

@z3¼ . . . ¼ @Ymi

@zk¼ 0 ð3Þ

The regression models obtained were achieved by using a twolevel factorial design ðp ¼ 2kÞ and three factors (k = 3) plus the cen-tral point. This design is orthogonal, which means that the pre-dicted variance response for the region of interest is minimal andthe regression coefficients can be assessed independently. Also,to assume numerical accuracy in the estimation of regression coef-ficients, factors were coded resulting in code variables zi, given byexpression (4),

zi ¼xi �

xþiþx�

i2

xþi�x�

i2

ð4Þ

in which xþi and x�i correspond respectively to the upper and lowerlevel of natural variables. As variables in models are often highlycorrelated, it is possible to determine the relative importance ofeach factor, as well as the simultaneous interaction of the three fac-tors by using this methodology. The main effect can be obtained bythe difference of the average responses Ym, from the highest and thelowest levels of zi, which is given by Eq. (5).

EðziÞ ¼2p

Xn

m¼1

Ymðzi¼þ1Þ �2p

Xn

m¼1

Ymðzi¼�1Þ ð5Þ

This parameter corresponds to the relative influence that inde-pendent variables had on the dependent variable. A positive effectmeans that the average responses achieved in the highest level ishigher than the average responses obtained in the lower level.The multi-regression model, which included the main effects andtheir interactions can be given by Eq. (6).

Ye ¼ b0 þ b1z1 þ b2z2 þ b3z3 þ b12z1z2 þ b13z1z3 þ b23z2z3

þ b123z1z2z3 ð6Þ

Although, this model is linear, non linear interactions could beconsidered; however, this should not be a reason of concern, since‘‘z” represents only numbers when they are inserted in Eq. (6). Thepolynomial independent term b0 can be calculated by dividing thesystem responses Ym for the total observations, including the onesdone in central point. Relative influence evaluation of dependentvariable factors can be calculated using the sum of squares SSðziÞ,Eq. (7), which can be simplified to Eq. (8).

SSðziÞ ¼ 4�

Pnm¼1

Ymðzi¼þ1Þ

4� Y

0BB@

1CCA

2

þ

Pnm¼1

Ymðzi¼�1Þ

4� Y

0BB@

1CCA

226664

37775 ð7Þ

SSðziÞ ¼18� ½4EðziÞ�2 ¼ 2EðziÞ2 ð8Þ

The previous equation is valid for all factors (main and interac-tions) and reflects the relation between the factors relative influ-ence over the dependent variable (liquid yield) and the sum ofsquares. Mean square MS(zi) can be calculated by dividing thesum of squares by the degree of freedom N, which for the factorialprogram used (two levels) results in a unitary value (N=1) given byexpression (9).

MSðziÞ ¼ SSðziÞ ð9Þ

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

SSE ¼Xn

j¼1

ðY0;j � Y0Þ2 ð10Þ

Y0 ¼Xn

j¼1

Y0;j

Nð11Þ

MSE ¼SSE

NVE¼ SSE

N � 1ð12Þ

NVE corresponds to the degree of freedom associated to MSE. Inorder to establish the statistic evaluation, it can be used the Fisher-Snedecor distribution, which establishes a relation between factormean square and deviation mean square using expression (13).

FexpðzÞ ¼MSðziÞ

MSEð13Þ

It is necessary to calculate the significance level a as well as theconfidence degree 1� a to validate the factors significance. Whenthe first factorial program does not fit, it is necessary to employ asecond factorial program and establish new factors range, usingSteepest Ascent Method. Experimental range is sequentially trans-ferred to other regions, in which the system response is near theoptimum. The objective function is accomplished by using themaximum slopes of the experimental plane previously establish.The referred slope can be calculated using function partial deri-vates for each factor zi (each factor define a gradient vector) asshown in Eqs. (14)–(16).

rYe ¼ @Ye@z1

;@Ye@z2

;@Ye@z3

� �ð14Þ

ðrYeÞu ¼rYekrYek

� �ð15Þ

krYek ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXi

@Ye@zi

� �2vuut ð16Þ

The polynomial independent parameter ðb0Þ can be calculatedusing the expression (17), in which were included all runs plusthe ones done in central point (n).

b0 ¼X

m

Ym

2k þ nð17Þ

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

z0i ¼ zoi þ kðrYeÞu ð18Þ


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

2.2. Experimental procedure

Pyrolysis experiments were carried out in a constant volumebatch reactor of 1 litre autoclave, built of Hastelloy C276, by ParrInstruments (Fig. 1). Reactor was first loaded, closed and pressur-ised to a pre-set value with nitrogen, then heated till the desiredtemperature value was reached, at which it was maintained duringthe experimental reaction time previously established. Afterwards,the reactor was cooled down and gases were measured at roomtemperature and analysed by gas chromatography (GC), afterremoving H2S by method 11 of EPA. Liquid and solid samples werefirst separated and weighted. The solid fraction was submitted to asolvent extraction process first with dichloromethane (CH2Cl2) andthen with tetrahydrofuran (THF) to remove the remaining liquidand both fractions were also analysed by GC–MS (gas chromatog-raphy associated to mass spectrometry). Liquid hydrocarbons weredistilled and separated into three fractions: the lighter one withdistillation range lower than 150 �C, the second fraction with a dis-tillation range between 150 �C and 270 �C and the third one withdistillation range higher than 270 �C. These liquid fractions wereanalysed by GC-MS. Chemical and physical properties of pyrolysisproducts were determined by ASTM standards [22].

Rubber tyre wastes were previous crushed into small pieces (fil-aments of about 2 cm length and 1–2 mm diameter) [23]. Theywere provided by a scrap tyre recycling plant and the main compo-nents were: natural rubber (NR), styrene butadiene rubber (SBR)and butadiene rubber (BR), after the removal of metal and textilecomponents. Plastic wastes were previously pelletised into 2–3 mm particles diameter. Waste blends with 30% w/w of rubber

12

7

N 2 H 2

9

8

19

101 3

4

2

6 5

18

17

16

11

Fig. 1. Schematic representation of t

tyre (RT) and 70% w/w of plastics with 20% of PE, 30% of PP and20% w/w of PS were used. Waste characterization is presented inTable 1 while in Table 2 is shown the ultimate and proximateanalysis of both liquid and solid fractions (method reproducibilityis 2% of value). The ash content for PP is different and significantlyhigher than the ones obtained from the other plastics residues (PEand PS) due to his origin. PP residue was obtained from automobilefender which contained higher concentration of Ca.

3. Results and discussion

In previous experiments the relative influence of several param-eters in the formation of liquid compounds was evaluated [24].Previous studies allowed acquiring important information relatedto reactor operation, establishing products sampling and analysisprocedures and the selection of experimental parameters range.

3.1. Factorial design

The implemented factorial design is presented in Table 3, whichincludes the natural variables studied (temperature, initial pres-sure and reaction time), the respective coded factors (z1, z2 andz3) and the system response obtained for liquid yields. It is alsopresented the measured average temperature obtained for all runstested. It is possible to observe that, for most of the runs done, nogreat deviations were obtained. Runs 9 to 12 were conducted incentral point to estimate experimental deviation, which resultedin a statistic standard deviation of 1.7%. The analysis of Table 3shows that the runs done in central point presented higher systemresponse than those obtained for all factors tested. This could meanthat central point is near the optimal point.

Table 4 shows all information related with the main factors andinteractions effects, as well as the confidence degree associated tosignificance of factors and interactions. From all main factors stud-

373 C OUT 1100

119

2096

500

LEGEND

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

15

14

20

13

21

he waste pyrolysis installation.

Table 1Wastes ultimate and proximate analysis (done as received).

RT PE PP PS

Rubber tyre (RT) and plastics (PE, PP and PS)PCS (MJ kg�1) (ASTM D 2015 e D 5865) 38.5 46.4 37.6 39.0C (%) 86.1 84.8 70.5 86.1H (%) 7.2 14.5 11.6 7.4N (%) 0.2 0.3 0.5 6.1S (%) 1.5 0.3 <QLb = 0.1 <QL = 0.1Oa (%) 0.1 – – –Moisture (% w/w) (105 �C) 2.0 0.0 0.1 0.3Ash (% w/w) (800 �C and 750 �C) 2.9 0.1 17.2 0.0Volatile matter (% w/w) (950 �C/NP 3423c) 61.6 99.8 82.6 99.5Fixed carbona (% w/w) 33.5 0.1 0.1 0.2

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

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

Liquids Solids

1 Fraction 2 Fraction 3 Fraction Solid before extraction Solid after extractionT < 150 �C 150 < T < 270 �C T > 270 �C

PCS (MJ kg�1) 44.5 43.7 41.4 34.2 29.9Density (kg m�3) 765.5 864.5 1014.1 – –C (%) 81.9 85.7 88.4 81.6 80.8H (%) 11.8 12.0 9.7 7.3 1.5N (%) 1.6 1.7 2.1 1.8 1.1S (%) <0.1 <0.1 0.3 0.9 1.9Moisture (% m/m) – – – 0.50 1.1Ahs (% m/m) – – – – 13.2Volatile mater (% m/m) – – – – 5.1Fixed carbona (% m/m) – – – – 80.6

a Calculated from difference.

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

Run Natural variables Measure temperature (�C) Coded factors Liquid yields – g (% w/w)

T (�C) P (MPa) t (min) z1 z2 z3

1 350 0.21 10 356 �1 �1 �1 50.02 450 0.21 10 451 +1 �1 �1 72.43 350 1.03 10 353 �1 +1 �1 50.04 450 1.03 10 449 +1 +1 �1 70.05 350 0.21 30 355 �1 �1 +1 73.76 450 0.21 30 421 +1 �1 +1 59.37 350 1.03 30 351 �1 +1 +1 68.28 450 1.03 30 426 +1 +1 +1 59.39 400 0.62 20 396 0 0 0 74.410 400 0.62 20 401 0 0 0 71.711 400 0.62 20 398 0 0 0 73.712 400 0.62 20 403 0 0 0 72.5


ied, factor z1 (temperature) presented the highest effect, followedby factors z3 (reaction time) and z2 (initial pressure). Both temper-ature and reaction time presented positive effects, which indicatedthat the function maximization was accomplished by the simulta-neous increase of these factors. Interactions, z1z3 (temperature -reaction time) presented the highest value, although negativevalues were obtained, which indicated a complex cross effect. Thisambiguous situation could mean that factors behaviour was non-linear or that factors range was extremely high. Confidence degreeof both temperature and reaction time, as well as their interactionwere highly significant based on Fisher-Snedecor distribution.After applying minimum squares method to experimental data,

the adjusted regression model for a confidence degree higher than98% was given by Eq. (19).

g ¼ 66:3þ 2:39z1 þ 2:25z3 � 8:22z1z3 ð19Þ

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

Table 4First factorial design – effects of the factors and interactions. Analysis of variance.

Source of variation Effects Sum of squares SS Degrees of freedom N Mean square MS Fexp Significance level a Confidence degree ð1� aÞ 100

Principal factorsz1 (T) 4.77 45.6 1 45.6 31.3 0.01129 98.87***

z2 (P) �1.99 7.9 1 7.9 5.4 0.10219 89.78**

z3 (t) 4.50 40.5 1 40.5 27.9 0.01327 98.67***

Interactionsz1 z2 0.81 1.3 1 1.3 0.9 0.41374 58.63*

z1 z3 �16.44 540.7 1 540.7 371.4 0.00031 99.97***

z2 z3 �0.80 1.3 1 1.3 0.9 0.41630 58.37*

z1 z2 z3 1.99 7.9 1 7.9 5.5 0.10165 89.84**

Experimental deviation – 4.4 3 1.5 – – –Total – 649.6 10 – – – –

* Not Significant.** Significant.

*** Highly Significant.

Table 5Model global significance – first factorial design.

Source of Variation Sum of squares SS Degrees of freedom N Mean square MS Significance

Experimental deviation Total residuals

Fexp ð1� aÞ 100 Fexp ð1� aÞ 100

Regression1st order (terms) 94.0 3 31.3 21.5 98.43 – –Interactions 551.2 4 137.8 94.7 99.83 – –Total 645.2 7 92.2 63.3 99.70 1.3 58.01

ResidualsQuadratic terms 277.8 1 277.8 190.8 99.92 – –Experimental deviation 4.4 3 1.5 – – – –Total 282.1 4 70.5 – – – –

Total 927.4 11 – – – – –

56

60

64

68

72

76

0.00 0.25 0.50 0.75 1.00 1.25Increment

Liq

uid

yiel

d (%

w/w

)

Increment - T (ºC); P (MPa); t (min) 0 - T 400.0; P 0.62; t 20.00.25 - T 408.7; P 0.59; t 21.60.50 - T 417.4; P 0.56; t 23.30.75 - T 426.1; P 0.53; t 24.91.00 - T 434.8; P 0.50; t 26.61.25 - T 443.5; P 0.47; t 28.2

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


coefficient (0.83). The values obtained indicate that the model pre-viously proposed was inadequate and a second factorial programshould be done. The new factors range application, for the secondfactorial program, could be accomplish by using the Steepest As-cent Method. At this point, the region of experimentation can beshifted to another region with a new set of factors range. Thismethod allowed the definition of the research direction whichwould be used, in order to find the path to system optimizationusing increments k. These increments are usually determined sub-jectively, taking into account factors relative importance and theexperimental system knowledge and they should be calculatedby using the expressions (14), (15), (16) and (18).

In Table 6 is presented the values for coded factors calculatedfor all different increments chosen. The experimental procedurewas based on experimental runs with the new data until a maxi-mum response was achieved. The conditions associated to themaximum achieved, corresponded to the definition of the new cen-tral point, in which the second factorial program would be done. In

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

Increment k Coded factors Natural variables Real temperature (�C)

z01 z02 z03 T (�C) P (MPa) t (min)

0 0 0 0 400 0.62 20 –0.25 0.174 �0.072 0.164 408.7 0.59 21.6 4020.50 0.348 �0.145 0.328 417.4 0.56 23.3 4110.75 0.522 �0.217 0.493 426.1 0.53 24.9 4181.00 0.696 �0.290 0.657 434.8 0.50 26.6 4241.25 0.870 �0.362 0.821 443.5 0.47 28.2 4321.50 1.044 �0.435 0.985 452.2 0.44 29.9 –


Table 6 may be observed that the research direction led to an in-crease of both temperature and reaction time and to a decreaseof initial pressure, which was in accordance with the previous re-sults of the first factorial program. The run proposed with theincrement of 1.50 was not possible to accomplish, as the new tem-perature was higher than batch reactor temperature limit. Experi-mental results of Fig. 2 show that the increase of temperature andof reaction time, for values higher than central point ðk ¼ 0Þ, re-sulted in a decrease in liquid yield. This result indicated that thenew central point coordenates should be the same used in the firstfactorial program, as the highest liquid yield was achieved. For thepresent case, the second factorial program was not shift for otherspace region, but more restrictive factors range were used.

The results obtained for the analysed domain used in the firstfactorial program (run temperature 350–450 �C, initial pressure

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

Run Natural variables Measure temperatur

T (�C) P (MPa) t (min)

13 370 0.48 15 37114 430 0.48 15 43115 370 0.76 15 37316 430 0.76 15 42317 370 0.48 25 37218 430 0.48 25 42719 370 0.76 25 37120 430 0.76 25 4209 400 0.62 20 39610 400 0.62 20 40111 400 0.62 20 39812 400 0.62 20 403

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

Source of Variation Effects Sum of squares SS Degrees of freedom N M

Principal factorsz1 (T) �7.56 114.3 1 1z2 (P) �0.42 0.4 1z3 (t) �2.07 8.6 1

Interactionsz1 z2 �1.29 3.4 1z1 z3 �1.00 2.0 1z2 z3 0.68 0.9 1z1 z2 z3 1.31 3.4 1Experimental deviation – 4.4 3Total – 137.3 10 –

* Not Significant.** Significant.

*** Highly Significant.

Table 9Model global significance – Second factorial design.

Source of Variation Sum of squares SS Degrees of freedom N Me

Regression1st order (terms) 123.2 3 41Interactions 9.7 4 2.4Total 132.9 7 19

ResidualsQuadratic terms 0.0054 1 0.0Experimental deviation 4.4 3 1.5Total 4.4 4 1.1

Total 137.3 11 –

0.21–1.03 MPa and reaction time 10–30 min) showed that centralpoint presented the highest liquid yields. These results suggest thatthe optimum point or region could be near the central point. Thus,a second factorial program with the same central point, but withshorter range factors (run temperature 370–430 �C, initial pressure0.48–0.76 MPa and reaction time 15-25 min) was tested. The aimof the second factorial program was a better definition of experi-mental conditions. In most studies found in literature, a secondfactorial program with a factors range completely different fromthe first factorial program is quite common, that is to say thatthe analysed domain is shift for other space region.

In Table 7 is presented all information related with second fac-torial program: natural variables range, coded factors and systemresponse. Liquids yields increased for most of the runs and werehigher than the ones obtained in central point for runs 13, 15, 17

e (�C) Coded factors Liquid yields – g (% w/w)

z1 z2 z3

�1 �1 �1 76.7+1 �1 �1 72.7�1 +1 �1 78.2+1 +1 �1 69.0�1 �1 +1 76.2+1 �1 +1 67.7�1 +1 +1 76.5+1 +1 +1 67.90 0 0 74.40 0 0 71.70 0 0 73.70 0 0 72.5

ean square MS Fexp Significance level a Confidence degree ð1� aÞ 100

14.3 78.5 0.00303 99.70***

0.4 0.2 0.65262 34.74*

8.6 5.9 0.09365 90.64**

3.4 2.3 0.22634 77.37*

2.0 1.4 0.32577 67.42*

0.9 0.6 0.48076 51.92*

3.4 2.3 0.22360 77.64*

1.5 – – –– – –

an square MS Significance

Experimental deviation Experimental deviation

Fexp ð1� aÞ 100 Fexp ð1� aÞ 100

.1 28.2 98.94 – –1.7 64.80 – –

.0 13.0 97.06 17.4 99.24

054 0.0037 4.47 – –– – – –– – – –

– – – –


and 19. In Table 8 is presented information related with the anal-ysis of variance. All factors have negative effects which meant that,for the new range of factors, liquid yield optimization was achievedwith the decrease of experimental conditions, as average responsesfor the higher level were lower than the average responses ob-tained for the lower level. Factor temperature presented the high-est significance value, followed by reaction time, while initialpressure was not statistically significant. This temperature behav-iour (confidence degree of 99.7%), which was different from thatobserved in the first factorial program, could be explained by thenew factors range adopted. No significant effects were found forinteractions and confidence degree was also lower. The regressionmodel applied to experimental data could be explained by expres-sion (20), which could be simplified to Eq. (21), if factors with sig-nificance highly than 90% were used.

g ¼ 73:1� 3:78z1 � 0:21z2 � 1:04z3 � 0:65z1z2 � 0:50z1z3þþ 0:34z2z3 þ 0:65z1z2z3 ð20Þ

g ¼ 73:1� 3:78z1 � 1:04z3 ð21Þ

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

40

45

50

55

60

65

70

75

80

370 380 390 400 410 420 430

Temperature (ºC)

Liq

uid

yie

ld (

%)

P = 0.48 MPa

P = 0.55 MPa

P = 0.62 MPa

P = 0.69 MPa

P = 0.76 MPa

a) Temperature effect on liquid yield for different initial pressures

0.48 0.550.62

0.690.76

370

391

412

65

68

71

74

77

80

Liq

uid

yie

ld (

%)

Initial pressure (MPa)

Temperature (ºC)

77-80

74-77

71-74

68-71

65-68

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

c

Fig. 3. Response surface for second factorial p

extremely accuracy, the production of liquid compounds for theanalysed domain associated with a very small experimental devia-tion (1.5%). Both average system response Y0 (73.11) and averagecentral point response Y0;� (73.08), presented the same numericvalue, which suggest that a maximum response area was achieved.The comparison of the results obtained in the two factorial desingscarried out showed that factor temperature clearly presented anon-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), thelinear model became adequate for representing the liquid yieldsprediction. The generated model can be used to predict the systemresponse as a function of the variables in a study based on expres-sion (20). Due to the fact that the 3D graphic presentation of threefactors is very complicated, Figs. 3–5 show the variation of twofactors whilst the third one was kept constant. The use of RSM re-sults in a lot of information and in a large amount of plots. As it isnot possible to present all the graphics associated to the factorsand interactions effects, the graphics chosen were only those asso-ciated to the experimental conditions optimized (Table 10). In Figs.3–5a are present, respectively, information obtained for liquid yieldregarding the influence of temperature factor for different initialpressures, reaction time for different initial pressures and reactiontime for different temperatures. Figs. 3–5b present the liquid yieldsobtained from the analysis of initial pressure for different temper-atures, initial pressure for different reaction times and temperature

40

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50

55

60

65

70

75

80

0.48 0.55 0.62 0.69 0.76


Liqu

id y

ield

(%)

T = 370 ºC

T = 388 ºC

T = 400 ºC

T = 412 ºC

T = 430 ºC

b) Initial pressure effect on liquid yield for different temperatures

0.48 0.55 0.62 0.69 0.76370

376

382

388

394

400

406

412

418

424

430

Pressure (MPa)

Temperature (ºC)

77-80

74-77

71-74

68-71

65-68

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

rogram. Constant reaction time – 15 min.


for different reaction times. Figs. 3,4,5c and 3c1 to 5c1 show,respectively the 3D view and isolines obtained from the analysisof Figs. 3–5a and b.

3.2. Effect of temperature on liquid yields

In Fig. 3a may be observed the effect of temperature for differ-ent initial pressures on liquid yields, keeping the reaction timeconstant at 15 min. Globally, it was observed that the increase oftemperature tended to decrease liquid yields, even with a cross ef-fect found at 390 �C of temperature. For temperatures lower than390 �C higher liquids yields were obtained when higher values ofinitial pressure were used. For temperatures higher than 390 �C,lower values of initial pressure resulted in higher liquids yields. Li-quid yields decrease is probably associated with both the decreasein the amount of second distillation fraction and the increase in so-lid yield. A similar cross effect was observed by Koç et al. [18] whenlow-density polyethylene waste was pyrolysed. For the tempera-ture of 370 �C, although higher liquid yields were obtained at0.76 MPa, the initial pressure of 0.48 MPa was selected, due tothe very small difference in liquid yields (around 2% w/w), whichcould represent lower investment costs. In Fig. 5b may be analysedthe effect of temperature on liquid yield for different reactiontimes, when initial pressure was kept constant at 0.48 MPa. Smallinteractions were observed, as the increase of initial pressure

40

45

50

55

60

65

70

75

80

15 17.5 20 22.5 25

Reaction time (min)

Liq

uid

yie

ld (

%)

P = 0.48 MPa

P = 0.55 MPa

P = 0.62 MPa

P = 0.69 MPa

P = 0.76 MPa

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

b

0.48 0.55 0.620.69

0.7615

19

22

65

68

71

74

77

80

Liq

uid

yie

ld (

%)


Reaction time (min)

77-80

74-77

71-74

68-71

65-68

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

c1

Fig. 4. Response surface for second factorial p

tended to decrease liquid yields except for the run done at370 �C, whose behaviour was almost constant. The increase in bothtemperature and reaction time probably favoured the conversionof liquid compounds into higher molecular weight structures, whichtended to be solid at room temperature, as higher solid yields wereobtained at the maximum temperature tested (450 �C). Responsesurfaces, 3D view and isolines, associated to both factors may beobserved in c and c1 plots of Figs. 3 and 5. In both figures, as aver-age temperature increased, the function tended to decrease bothwith the increase of initial pressure (Fig. 3a, c and c1) and reactiontime (Fig. 5b, c and c1). In both situations, maximum liquid yieldswere achieved for the temperature of 370 �C. Also, the analysis ofFig. 2 indicates that liquid yield maximization was not accom-plished with the increase of run temperature.

3.3. Effect of reaction time on liquid yields

Figs. 4 and 5 present the response surface for the second facto-rial program for both constant temperature of 370 �C and initialpressure of 0.48 MPa, respectively, as well as 3D view and isolinesassociated to both factors in c and c1 plots for the same figures.Figs. 4a and 5a present the information related with the effect ofreaction time on liquid yields for different initial pressures andfor different temperatures, respectively. For the analysed domain(Fig. 4a, c and c1), the function tended to be constant with the

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55

60

65

70

75

80

0.48 0.55 0.62 0.69 0.76


Liqu

id y

ield

(%)

t = 15 min

t = 17.5 min

t = 20 min

t = 22.5 min

t = 25 min

) Initial pressure effect on liquid yield for different reaction time

0.48 0.55 0.62 0.69 0.7615

18

20

23

25


Reaction time (min)

77-80

74-77

71-74

68-71

65-68

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

rogram. Constant temperature – 370 �C.


increase of reaction time for all initial pressures tested. On theother hand, when keeping constant the initial pressure (Fig. 5a, cand c1), the simultaneous increase in both reaction time and tem-perature tended to decrease liquid yields, (except for the run doneat 370 �C). As reaction time increased liquid compounds, whichmay be chemical unstable molecules, probably they react withone another and were converted into solid compounds. The lowestvalue was obtained for the temperature of 430 �C and a reactiontime of 25 min. The highest liquid yield was obtained for a reactiontime of 15 min and for a temperature of 370 �C, which suggest thatthese values should be used under this experimental domain. Thisinformation is in accordance with the results obtained in the sec-ond factorial program (Table 8) which suggest that temperature(z1) and reaction time (z3) are the two most important factors aswell as their interaction (z1z3) in liquid yield maximization.

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50

55

60

65

70

75

80

15 17.5 20 22.5 25

Reaction time (min)

Liq

uid

yie

ld (

%)

Temperature = 370 ºC





a) Reaction time effect on liquid yield for different temperatures

370 385400

415430

15

19

22

65

68

71

74

77

80

Liq

uid

yie

ld (

%)

Temperature (ºC)

Reaction time (min)

77-80

74-77

71-74

68-71

65-68

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

Fig. 5. Response surface for second factorial pro

Table 10Optimized experimental conditions.

Run Natural variables Measure temper

T (�C) P (MPa) t (min)

A 370 0.48 15 373B 370 0.48 15 372C 370 0.48 15 372Proposed by the model 370 0.48 15 –

3.4. Effect of initial pressure on liquid yields

The effect of initial pressure on liquid yields can be analyzedin Fig. 3b, keeping the reaction time constant at 15 min andFig. 4b, for a constant temperature of 370 �C, associated to 3Dview and isolines of c and c1 plots, respectively. RegardingFig. 3b, c and c1, when initial pressure increased liquid yieldstended to be constant, although when temperature decreased itwas found an increase in liquid yields. This information is inaccordance with the second factorial program (Table 8), in whichwas concluded that factor initial pressure (z2) present a low effectin liquid yield maximization. In Fig. 4b, c and c1 it may be ob-served that the increase of initial pressure, for different reactiontimes, led to no significant changes. Liquids yields reached valueshigher than 75% w/w.

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60

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70

75

80

370 380 390 400 410 420 430

Temperature (ºC)

Liq

uid

yie

ld (

%)

Reaction time = 15 min

Reaction time = 17.5 min


Reaction time = 22.5 min


b) Temperature effect on liquid yield for different reaction times

370 385 400 415 43015

18

20

23

25

Temperature (ºC)

Reaction time (min)

77-80

74-77

71-74

68-71

65-68

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

gram. Constant initial pressure – 0.48 MPa.

ature (�C) Liquid yield – g (% w/w) Average Standard deviation

81.5 81.4 1.3580.082.776.7 – –


3.5. Optimized experimental conditions and liquid composition

The factorial design applied to liquid yields led to the followingoptimized experimental conditions for obtaining maximum liquidyields: 370 �C, initial pressure of 0.48 MPa and reaction time of15 min. In order to validate the results, three experiments wereconducted sequentially (Table 10) and average calculated liquidyield found to be 81.3% w/w with an experimental deviationaround 0.95%. At these conditions, gas yield of 4.9% w/w and solidyield of 12.7% w/w were obtained. For all liquids, distillation curveswere found to be between those of standard gasoline and dieselfuel oil.

Pyrolysis gases were composed of hydrocarbons from C1 to C5,hydrogen and carbon dioxide. Liquids obtained were found to bea complex mixture of hydrocarbons formed by 47% w/w of alkanes,14% w/w of alkenes and 39% w/w of aromatic compounds.

0

2

4

6

8

10

12

14

16

18

20

Pent

ane

2-M

ethy

lpen

tane

Hex

ane

Cic

lohe

xane

Hep

tane

Met

hylc

iclo

hexa

ne

Oct

ane

Non

ane

Dec

ane

Run A R

Alk

anes

con

cent

rati

on (

% v

/v)

Fig. 6. Optimize experimental conditions – alkanes composition obtained from

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Hex

ene

Hep

tene

Oct

ene

Non

ene

Dec

ene

Run A Ru

Alk

enes

con

cent

rati

on (

% v

/v)

Fig. 7. Optimize experimental conditions – alkenes composition obtained from

Figs. 6–8 present the information related with liquid composi-tion obtained for rubber tyre and plastic wastes blends pyrolysedat the optimal experimental conditions. These figures also showthat liquid composition obtained for the three experiments, A, Band C, done at equal experimental conditions (370 �C, 0.48 MPaand 15 min) present no significant changes. In Table 2 is presentedthe ultimate and proximate analysis of liquid fractions and of sol-ids before and after solvent extraction.

Alkanes from C5 to C20 were identified. Fig. 6 shows that majorcompounds formed were hexadecane 9% v/v and heptadecane 7%v/v followed by nonane 6% v/v and undecane 5% v/v. In Fig. 7may be observed that alkenes main compounds were nonene,decene, undecene and dodecene, though C5 to C22 compounds werealso identified. Nonene reached 4% v/v and was the alkene that pre-sented the highest concentration. C6 to C16 aromatic compoundswere also quantified. Liquid aromatic composition is presented in

Und

ecan

e

Dod

ecan

e

Tri

deca

ne

Tetr

adec

ane

Pent

adec

ane

Hex

adec

ane

Hep

tade

cane

Oct

adec

ane

Non

adec

ane

Dec

adec

ane

un B Run C

runs A, B and C (same experimental conditions: 370 �C, 0.48 MPa, 15 min).

Und

ecen

e

Dod

ecen

e

Tri

dece

ne

Tetr

adec

ene

Hex

adec

ene

n B Run C

runs A, B and C (same experimental conditions: 370 �C, 0.48 MPa, 15 min).

0

1

2

3

4

5

Ben

zene

m/p

-Xyl

ene

o-X

ylen

e

n-Pr

opyl

benz

ene

Mes

ithy

lene

p-C

imen

e

n-B

uthy

lben

zene

Tolu

ene

Eth

ylbe

nzen

e

0

5

10

15

20

25

30

Run A Run B Run C

Aro

mat

ics

conc

entr

atio

n (%

v/v

)

Aro

mat

ics

conc

entr

atio

n (%

v/v

)

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 thesecompounds two scales were drawn. Toluene 16% v/v and ethylben-zene 17% v/v were the aromatic compounds formed with the high-est contents. Besides these compounds, p-cimene and m/p-xyleneswere those detected with higher contents. In the GC-MS equip-ment 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 thermaldecomposition of low-density polyethylene waste. The increase ofaromatic compounds concentration seems to be at the expenses ofalkane fraction, probably due to the conversion of pentane intobenzene [25]. The high ethylbenzene concentration could be ex-plained by hydrogen transfer from molecular structure of PE toPS via radical mechanism that occurred during thermal degrada-tion [26]. Another possible explanation is presented by Rodriguezet al. [27], which refer that liquid aromaticity result from rubbertyre degradation, since SBR polymer is generally composed by25% styrene (aromatic) and 75% butadiene (aliphatic), which dueto radical aliphatic and aromatic recombination reactions and ali-phatic cyclization reactions result in more liquid aromatic com-pounds. A significant effect of reaction temperature increase wasobserved in the formation of toluene [28].

It was study the possible synergism between rubber tyre andplastic wastes in order to improve liquid yield by using the system-atization of results. To accomplish this study it was analysed theresults obtained from the pyrolysis of these residues separatedand compared to the pyrolysis of these residues mixed togetherfor a waste blend with 30% rubber tyre and 70% plastics (20% PE,30% PP and 20% PS) (w/w) . The results obtained suggested thatthere was no synergism between rubber tyre and plastics wastespyrolysis regarding the production of liquid compounds as noimprovement in liquid yield was obtained.

4. Conclusions

Pyrolysis process has shown to be a possible way to convertmixtures of rubber tyre and plastic wastes into economical valu-able products, such as liquids with fuel characteristics or raw

material to chemical and petrochemical industries. Waste blendused was composed by 30% rubber tyre, 20% PE, 30% PP and 20%w/w PS and experimental parameters for liquid maximizationwere accomplish by using response surface methodology (RSM)which allowed establishing the priority of factors effect as wellas theirs relative contribution to the system response. Temperaturewas the most important factor followed by reaction time, being ini-tial pressure the parameter that least affected liquid yields. Also, alinear model with good fit to experimental data was accomplishedin the second factorial program, although temperature factor pre-sents a non-linear behaviour. Experimental conditions optimizedwere 370 �C 0.48 MPa for initial pressure and reaction time of15 min. Though these values are specific to the type of reactorused, they can be used as a first approach for a larger scale reactor.However, a new experimental program should be applied for a lar-ger scale reactor, as different heat and mass transfer conditionscould lead to different optimization values.

In order to validate the results, three experiments were con-ducted sequentially and average values were calculated and foundto be: gas yield of 4.9 % w/w, liquid yield of 81.3 % w/w and solidyield of 12.7 % w/w with an experimental deviation of 0.95%. Thecomplex hydrocarbon liquid mixture obtained was composedmainly by alkanes, 47% w/w. Hexadecane (9% v/v) and heptadecane(7% v/v) were the compounds formed with the highest contents. Thealkene formed in the highest amount was nonene 4% v/v. Toluene(16% v/v) and ethylbenzene (17% v/v) were the aromatic compoundsobtained in highest concentrations. Average values obtained for thecomplex liquid hydrocarbon mixture (w/w) at optimal point werefound to be 47% alkanes, 14% alkenes and 39% aromatics.

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

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