Wet air oxidation of table olive processing wastewater: Determination of key operating parameters by factorial design

water research 42 (2008) 3591–3600

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Wet air oxidation of table olive processing wastewater: Determination of key operating parameters by factorial design Athanasia Katsoni, Zaharias Frontistis, Nikolaos P. Xekoukoulotakis, Evan Diamadopoulos, Dionissios Mantzavinos* Department of Environmental Engineering, Technical University of Crete, Polytechneioupolis, GR-73100 Chania, Greece

article info

abstract

Article history:

The wet air oxidation of an effluent from edible olive processing was investigated.

Received 9 January 2008

Semibatch experiments were conducted with 0.3 L of effluent loaded into an autoclave

Received in revised form

and pure oxygen fed continuously to maintain an oxygen partial pressure of 2.5 MPa.

13 May 2008

The effect of operating conditions, such as initial organic loading (from 1240 to 5150 mg/L

Accepted 15 May 2008

COD), reaction time (from 30 to 120 min), temperature (from 140 to 180  C), initial pH (from

Available online 21 June 2008

3 to 7) and the use of 500 mg/L H2O2 as an additional oxidant, on treatment efficiency was assessed implementing a factorial experimental design. All five parameters had a statistically

Keywords:

considerable effect on COD removal, alongside second order interactions of COD with reac-

Factorial design

tion temperature, contact time and effluent pH. In most cases, high levels of phenols degra-

Olive processing

dation (up to 100%) and decolorization (up to 90%) were achieved followed by low to

Wastewater

moderate mineralization (up to 70%). The oxidation of phenols was affected to a considerable

Wet air oxidation

level by the initial COD, reaction temperature and contact time, as well as the second order interaction between COD and temperature, while all other effects were insignificant. ª 2008 Elsevier Ltd. All rights reserved.

1.

Introduction

The manufacturing of table olives constitutes a major economic activity to several Mediterranean countries like Spain, Italy, Turkey, Tunisia and Greece with an annual world production that exceeds 1.5 million tons (Segovia Bravo et al., 2007); this is nearly equally shared between two varieties, namely the green (commonly referred to as Spanish-style) and black olives (Segovia Bravo et al., 2007; Parinos et al., 2007). Table olive processing occurs through a series of steps, namely: initial olive cleaning, debittering, washing, fermentation and packing; all these steps generate waste streams which, alongside the wash-waters from tanks, machinery,

etc., result in effluent quantities of about 3.9–7.5 and 0.9– 1.9 m3/ton of green and black olives, respectively (Kopsidas, 1992). The organic fraction of table olive processing wastewaters (TOPW) consists of phenols, polyphenols, sugars, acids, tannins, pectins and oil residues, thus leading to an effluent with chemical oxygen demand (COD) values of several grams per liter depending on the olive variety and manufacturing process employed. The inorganic fraction consists of high concentrations of sodium chloride and sodium hydroxide which are used for olive debittering and fermentation, as well as trace amounts of various metals (Parinos et al., 2007; Kopsidas, 1992). TOPW constitute a serious environmental concern since they are either dumped untreated to natural

* Corresponding author. Tel.: þ30 28210 37797; fax: þ30 28210 37852. E-mail address: [email protected] (D. Mantzavinos). 0043-1354/$ – see front matter ª 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2008.05.007

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receivers or sent to evaporation ponds where natural attenuation processes may result in bad odors or pollution may diffuse in surface and ground waters (Parinos et al., 2007; Beltran-Heredia et al., 2000a,b). Due to their relatively high organic content and the presence of some classes of compounds such as polyphenols, TOPW exhibit antimicrobial, ecotoxic and phytotoxic properties, thus rendering them unsuitable for complete treatment by aerobic (Kyriacou et al., 2005; Aggelis et al., 2001; Brenes et al., 2000; Beltran-Heredia et al., 2000a,b) or anaerobic (Aggelis et al., 2001) processes. Depending on the operating conditions in question, aerobic treatment was capable of reducing the COD content by as much as about 70–90% and this was accompanied by low to moderate degradation of phenols and other aromatics. Conversely, anaerobic treatment led to partial (i.e. about 50%) COD removal accompanied by a 12% phenolic reduction. In recent years, emphasis has been given on the use of advanced oxidation processes (AOPs) to treat TOPW. Oxidation by ozone received particular attention as ozonation, occurring through direct and secondary, hydroxyl radical-induced reactions, was found capable of removing most of the phenolic compounds present in TOPW (Benitez et al., 2002, 2001a; Beltran-Heredia et al., 2000c; Rivas et al., 2000; Beltran et al., 1999). Process efficiency could be further enhanced combining ozonation with ultraviolet irradiation and hydrogen peroxide and this was attributed to the increased production of hydroxyl radicals and other reactive moieties (Benitez et al., 2002, 2001a; Beltran et al., 1999). Besides ozonation, Fenton (Rivas et al., 2003) and photo-Fenton (Benitez et al., 2001b) oxidation, as well as electrochemical oxidation over boron-doped diamond (BDD) anode (Deligiorgis et al., 2008) and TiO2 photocatalysis (Chatzisymeon et al., 2008) have also been employed to treat TOPW. Wet air oxidation (WAO) belongs to the family of AOPs and is a thermochemical process where hydroxyl radicals and other active oxygen species are formed at elevated temperatures (i.e. 200–320  C) and pressures (i.e. 2–20 MPa) (Levec and Pintar, 2007). The process is known to have great potential for the treatment of wastewaters with moderate to high organic content (i.e. 10–100 g/L COD) converting dissolved organic pollutants into highly oxidized intermediates and eventually to carbon dioxide and water. Process efficiency can be improved by the presence of suitable homogeneous or heterogeneous oxidation catalysts, as well as of extra oxidants such as hydrogen peroxide (Bhargava et al., 2006). Several studies which are summarized in recent review articles (Levec and Pintar, 2007; Bhargava et al., 2006) have reported the use of WAO to treat various types of effluents including olive oil mill wastewaters (Gomes et al., 2007; Rivas et al., 2001a; Chakchouk et al., 1994); nonetheless, the treatment of TOPW by WAO has only merely been investigated (Rivas et al., 2001b). The aim of this work was to study the wet air oxidation of wastewaters from black table olive processing regarding the effect of various operating conditions such as reaction temperature, initial concentration, effluent pH, contact time and the addition of hydrogen peroxide on the conversion of COD, phenols, aromatics and color. A factorial design methodology was adopted to determine the statistical significance of each parameter on treatment performance.

2.

Materials and methods

2.1.

TOPW

The effluent used in this study was taken from a table olive manufacturing plant located in the region of Chania, Western Crete, Greece. The process through which the effluent was generated comprises mixing 130–140 kg of black olives of the Kalamai variety, 10 kg of sodium chloride, 0.24 kg of calcium chloride and 1 kg of lactic acid in 90–100 kg of water. The original effluent has a COD content of 51,500 mg/L, pH ¼ 4.5 and a dark brown-black color, while it also contains a substantial fraction of aromatic compounds.

2.2.

WAO experiments

A high pressure reactor (Parr Instruments, USA) made of alloy C-276 and capable of operating at pressures up to 21 MPa and temperatures up to 350  C was used. In a typical semibatch run, 0.3 L of diluted TOPW was batch loaded into the reactor which was then heated up to the operating temperature under continuous nitrogen flow. This procedure was used to minimize unwanted conversion during the heating-up period. As soon as the desired temperature was reached, oxygen was continuously sparged into the reactor to start the reaction and the oxygen partial pressure was maintained at 2.5 MPa. The reactor contents were stirred at 800 rpm ensuring good mass transfer from the gas to the liquid phase. To test if the oxidation was limited by the amount of oxygen dissolved at 2.5 MPa partial pressure, experiments were performed at 180  C and various oxygen partial pressures showing that the reaction was not mass-transfer limited for oxygen pressures above 1 MPa. The system was also equipped with a bursting disk to vent the reactor contents in the case of reactor overpressure. In all cases, the original effluent was diluted with water to achieve initial concentrations equal to or less than about 5000 mg/L COD and then fed to the WAO reactor. At these concentrations, the effluent’s inherent pH was neutral. For those experiments where the effluent’s pH was adjusted to acidic conditions, the appropriate amount of 98% w/w H2SO4 was added, while when hydrogen peroxide was used as an extra oxidant, the appropriate amount of a 35% w/w solution was added to achieve a 500 mg/L H2O2 initial concentration. Preliminary experiments were also conducted with the raw, undiluted effluent yielding low oxidation rates. More importantly though, oxidation at about 50,000 mg/L COD led to a rapid temperature increase in the reactor due to the exothermic nature of WAO reactions; it is well documented (Bhargava et al., 2006; Debellefontaine and Foussard, 2000) that WAO becomes thermally self-sustained at COD loadings greater than 12,000–15,000 mg/L, while even higher concentrations may also lead to energy recovery. To comply with safety policies and avoid the consequences of likely runaway reactions, all runs were performed with diluted TOPW. Liquid samples of approximately 5 mL were periodically withdrawn from the reactor through a tube located inside the reactor vessel and analyzed as follows.

water research 42 (2008) 3591–3600

2.3.

Analytical methods

COD was determined by the dichromate method. The appropriate amount of sample was introduced into commercially available digestion solution containing potassium dichromate, sulfuric acid and mercuric sulfate (Hach Europe, Belgium) and the mixture was then incubated for 120 min at 150  C in a COD reactor (Model 45600-Hach Company, USA). COD concentration was measured colorimetrically using a DR/2010 spectrophotometer (Hach Company, USA). The total phenolic content was determined colorimetrically at 725 nm on a Shimadzu UV 1240 spectrophotometer using the Folin–Ciocalteau reagent according to the procedures described in detail elsewhere (Atanassova et al., 2005). Gallic acid monohydrate was used as standard to quantify the concentration of phenols in TOPW. Sample absorbance was scanned in the 200–800 nm wavelength band on a Shimadzu UV 1240 spectrophotometer. Changes in sample absorbance at two specific wavelengths, i.e. 567 and 275 nm were monitored to assess the extent of decolorization and aromatics removal, respectively, that had occurred during treatment. All spectrophotometric measurements were carried out in triplicate and mean values are quoted as results. The standard deviation was never larger than 5% for the range of concentrations tested.

3.

Results and discussion

There are two ways one can investigate the effect of a large number of variables. The most commonly used method involves the variation of one variable while keeping the other variables constant, until all variables have been studied. This methodology has two disadvantages: first, a large number of experiments are required, and second it is likely that the combined effect of two or more variables may not be identified. In this work, a statistical approach was chosen based on a factorial experimental design that would allow us to infer about the effect of the variables with a relatively small number of experiments. The independent variables of the experimental design are presented in Table 1. Each one of the five variables received two values, a high value (indicated by the plus sign) and a low value (indicated by the minus sign). Regarding the initial pH which took values of 7 (natural pH of the diluted effluent) and 3 (after adding H2SO4), it should be noticed that the solution was not buffered to the aforementioned values. However, pH was monitored constantly

Table 1 – Independent variables of the 25 experimental design Level Initial Temperature pH0 Reaction Oxidant of COD ( C) time (min) concentration value (mg/L) (mg/L)  þ

1240 5150

140 180

3 7

30 120

0 500

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throughout the reaction showing that only marginal changes had occurred between the initial and final (i.e. after 120 min) solutions. As already mentioned, all runs were performed at a fixed oxygen pressure of 2.5 MPa. WAO usually operates at an excess of oxygen; therefore, pressure is not considered to be a critical parameter for practical applications as long as the process is not limited by the amount of oxygen dissolved in the liquid. The experimental design followed in this work was a full 25 experimental set, which required 32 experiments. The order in which each experiment was performed was selected randomly and is shown in Table 2 along with the values of each independent variable for each run. Table 2 also shows the obtained results in terms of mg/L of COD oxidized (depended variable or response Y1) and mg/L of TP oxidized (depended variable or response Y2). Table 3 shows the extent of aromatics degradation and effluent decolorization for representative experiments of Table 2 alongside the respective extent of TP degradation and COD removal. As clearly seen, TP are easily oxidized even under mild conditions, i.e. low temperatures, short reaction times and in the absence of oxidant (e.g. runs 13, 14, 15 and 25). On the other hand, other compounds that were originally present in TOPW or formed as secondary reaction by-products are more resistant to wet oxidation and this is consistent with the substantially lower COD and aromatics conversion. On the assumption that TP are represented by gallic acid (monohydrate), the stoichiometry of its reaction to carbon dioxide and water dictates that 100 mg of gallic acid would require 102 mg oxygen for the complete oxidation; therefore, the last column of Table 2 practically corresponds to the concentration of COD oxidized due to the phenolic fraction of the effluent. In this view, the discrepancy between the last two columns of Table 2 is a measure of the oxidizability of effluent’s fractions other than TP. Noticeably, increased TP degradation is accompanied by a consistently high effluent decolorization; this is expected since the dark color of agro-industrial effluents like TOPW is associated, to a great degree, with the presence of polyphenolic compounds (Deligiorgis et al., 2008). Estimation of the average effect, the main effects (i.e. the effect of each individual variable on the response) and the two and higher order interactions was made by means of the statistical package Minitab 14. The results are presented in Table 4. To assess the significance of the effects, an estimate of the standard error is required. An estimate of the standard error is usually made by performing repeat runs. Alternatively, three and higher order interactions can be used, since these interactions may be considered negligible and may measure differences arising from experimental error (Box et al., 1978). The variance of each effect would then be: P 2 ðthree and higher order effectÞ Variance of effects ¼ Number of three and higher order effects (1) The standard error is then the square root of the variance (half this amount for the average). If an effect is about or below the standard error, it may be considered insignificant (or in other terms, not different from zero). The contribution of a variable, however, whose effect appears different from zero, is not necessarily very large. One way to identify the

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Table 2 – Design matrix of the 25 factorial experimental design and observed response (Y1: mass of COD removed per liter; Y2: mass of TP removed per liter) Order of running experiments

Level value of each variable in the experimental run COD

Temperature

pH0

Time

Oxidant

Y1 (mg/L) of COD removed

 þ  þ  þ  þ  þ  þ  þ  þ  þ  þ  þ  þ  þ  þ  þ  þ

  þ þ   þ þ   þ þ   þ þ   þ þ   þ þ   þ þ   þ þ

    þ þ þ þ     þ þ þ þ     þ þ þ þ     þ þ þ þ

        þ þ þ þ þ þ þ þ         þ þ þ þ þ þ þ þ

                þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ

318 1617 625 2819 150 919 444 2274 526 2028 784 3204 337 1402 500 2762 570 1484 804 2937 370 1263 621 2485 678 2136 851 3306 556 1920 716 2890

25 14 6 30 15 13 24 23 22 8 3 16 26 2 5 28 10 21 11 1 17 7 20 12 32 27 31 4 29 9 18 19

most important effects is to construct the normal probability plot (Box et al., 1978; Daniel, 1976). All effects that are small can be explained as white noise, following a normal distribution with a mean of zero. In the normal probability

Table 3 – Percentage of COD, TP, aromatics and color removal at various conditions Order of experiments 26 2 5 28 29 9 18 19

COD 27 27 40 54 45 37 58 56

(22) (6) (42) (15) (22) (10) (40) (18)

TP 89 86 100 93 85 88 96 97

(89) (46) (100) (91) (71) (57) (85) (89)

Aromatics

Color

19 18 41 48 39 20 55 57

81 61 86 74 88 72 77 83

Order of experiments as in Table 2. Numbers in brackets show removals for the respective electrochemical experiments (adapted from Deligiorgis et al., 2008).

Y2 (mg/L) of TP removed 61 306 68 357 56 302 69 337 67 328 71 362 64 319 72 343 31 277 64 358 54 288 62 348 63 320 69 364 61 327 69 359

plot these effects will appear on a straight line. Any effects with a significant contribution will lie away from the normal probability line. The normal probability plot for the oxidation of COD appears in Fig. 1a. All five variables studied, namely initial COD, reaction temperature, solution pH, reaction time and the addition of hydrogen peroxide appear to have a significant effect on COD reduction; with the exception of pH, the effects are positive indicating that an increase in their level brings about an increase in the amount of COD oxidized. Second order interactions between COD  temperature, COD  pH and COD  time also appear to influence COD oxidation to a reasonable degree, while other interactions such as pH  H2O2, temperature  time, COD  pH  H2O2 and COD  temperature  pH  H2O2 lie close to the normal probability line and their effect may not be substantial. This can evidently be illustrated in Fig. 1b where the effect of all variables and their interactions is shown in the form of the Pareto chart; the ordinate shows the absolute value of the effect, while the abscissa shows the main effects and the interactions. The vertical line shown in Fig. 1b is a reference line indicating Lenth’s pseudo-standard error: any effect that extends past this line is potentially significant (Mosteo et al.,

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Table 4 – Estimated effects of the 25 factorial design for the oxidation of COD and TP Effect

Value of effect Oxidation of COD

Oxidation of TP

Average effect

1384.2  13.5

196.8  1.1

Main effects COD Temperature pH Reaction time Oxidant

1662.2  26.9 734.4  26.9 317.4  26.9 306  26.9 180  26.9

268.3  2.2 28.1  2.2 2.3  2.2 13.7  2.2 4.3  2.2

Two-factor interactions COD  temperature COD  pH COD  time COD  oxidant Temperature  pH Temperature  time Temperature  oxidant pH  time pH  oxidant Time  oxidant

504.1  26.9 134.4  26.9 175.3  26.9 5.4  26.9 12.2  26.9 55.3  26.9 30.1  26.9 13.6  26.9 74.3  26.9 9  26.9

17.2  2.2 3.7  2.2 4.7  2.2 2.7  2.2 4.5  2.2 7.9  2.2 6  2.2 1.5  2.2 5.1  2.2 5  2.2

Three-factor interactions COD  temperature  pH COD  temperature  Time COD  temperature  oxidant COD  pH  time COD  pH  oxidant COD  time  oxidant Temperature  pH  time Temperature  pH  oxidant Temperature  time  oxidant pH  time  oxidant

0.7  26.9 14  26.9 4.6  26.9 13.4  26.9 51.5  26.9 30.8  26.9 3.2  26.9 41.1  26.9 30.3  26.9 7.3  26.9

2.8  2.2 3.5  2.2 3.1  2.2 1  2.2 1.9  2.2 1  2.2 2.3  2.2 2.7  2.2 3.5  2.2 1.2  2.2

Four-factor interactions COD  temperature  pH  time COD  temperature  pH  oxidant COD  temperature  time  oxidant COD  pH  time  oxidant Temperature  pH  time  oxidant

10.4  26.9 54.1  26.9 17.6  26.9 11  26.9 24.3  26.9

0.6  2.2 1.6  2.2 1  2.2 1.7  2.2 2  2.2

31.6  26.9

1.6  2.2

Five-factor interactions COD  temperature  pH  time  oxidant

2006; Lenth, 1989). Based on the variables and interactions which are statistically significant, a model describing the experimental response was constructed as follows: 1662:2 734:4 317:4 306 180 X1 þ X2  X3 þ X4 þ X5 2 2 2 2 2 504:1 175:3 134:4 74:3 X1 X2 þ X1 X4  X1 X3 þ X3 X5 þ 2 2 2 2 55:3 55:2 51:5 X2 X4  X1 X2 X3 X5 þ X1 X3 X5  ð2Þ 2 2 2

Y1 ¼ 1384:2 þ

where Y1 is the mass of COD oxidized (mg/L), Xi are the transformed forms of the independent variables according to: Xi ¼

Zhigh þZlow 2 Zhigh Zlow 2

Zi 

and Zi are the original (untransformed) values of the variables. The coefficients that appear in Eq. (2) are half the calculated effects, since a change of X ¼ 1 to X ¼ 1 is a change of two units along the X axis. The model predicts a linear

dependency of the mass of COD oxidized on the operating variables and the respective interactions. Usually, the three and higher order interactions are not expected to be significant. Their existence in the model indicates that the response surface is actually non-linear. One of the objectives of the experimental design is to provide a simple and reliable model capable of relating directly the response factor to the most significant variables. As seen in Fig. 1b, the last four terms of Eq. (2) (shown as black bars) are definitely less important than the rest (shown as white bars). To assess whether the model represented by Eq. (2) may be simplified omitting these four terms, the values of the residuals (i.e. observed minus predicted values of Y1) were plotted in a normal probability plot for both the initial and the reduced models (Fig. 2). As seen, most of the points from the residual plot for the reduced model lie close to the straight line (confidence interval ¼ 95%) confirming the conjecture that effects associated with the last four

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a

99 A 95

B AB

90 80

CE ACE

Percent

70

E AD

D

Factor

60 50 40

Name

A (X1)

COD

B (X2)

Temp

30

C (X3)

pH

20

D (X4)

Time

E (X5)

Oxidant

ABCE BD

10 AC

5 C 1

0

-500

500

1000

1500

Effect

Term

b

A B AB C D E AD AC CE BD ABCE ACE BCE ABDE ADE BDE BE ACDE ABD CD ACD BC ABCD DE CDE AE ABE ABCDE BCDE BCD

Reduced model

Full model

0

200

400

600

800

1000

Factor

Name

A (X1)

COD

B (X2)

Temp

C (X3)

pH

D (X4)

Time

E (X5)

Oxidant

1200

1400

1600

1800

Effect Fig. 1 – Normal probability plot (a) and Pareto chart (b) of the effects for COD removal. White bars: effects of high significance; black bars: effects of low significance; gray bars: non-significant effects.

terms of Eq. (2) may be readily explained by random noise. This is also clearly illustrated in Fig. 3 showing a comparison between the experimental values and those computed by the full and reduced models. Therefore, the experimental response of COD removal can be reduced and described as follows: 1662:2 734:4 317:4 306 180 X1 þ X2  X3 þ X4 þ X5 2 2 2 2 2 504:1 175:3 134:4 X1 X2 þ X1 X4  X1 X3 þ ð3Þ 2 2 2

Y1 ¼ 1384:2 þ

The initial COD concentration is by far the single most significant variable affecting COD oxidation. Although kinetic investigations were outside the scope of this work, an attempt was made to elucidate the order of reaction

with respect to COD concentration based on the experimental data of Table 2. If the reaction were first order, COD conversion would remain constant for runs performed at different initial COD values and all other variables being identical. This indeed seems to occur in most cases (within experimental error). For instance, for the pairs of experiments 3 & 16, 31 & 4 and 18 & 19 (all performed at 180  C), the respective conversions are 63.3 & 62.4, 68.8 & 64.4, and 57.9 & 56.3%. Similarly, for the pairs of experiments 26 & 2, 22 & 8 and 32 & 27 (all performed at 140  C) the respective conversions are 27.2 & 27.3, 42.5 & 39.5 and 54.7 & 41.5%. Based on these conversion values, the mean first order kinetic constant can be estimated. Its values are 0.49 1/h (standard deviation: 0.06) at 180  C and 0.25 1/h (standard

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a

99

95 90 80

Percent

70 60 50 40 30 20 10 5

1 -150

-100

-50

0

50

100

Residuals - Full COD model

b

99

95 90 80

Percent

70 60 50 40 30 20 10 5

1 -200

-100

0

100

200

Residuals - Reduced COD Model Fig. 2 – Normal probability plots of the residuals for COD removal. (a) Full model; (b) reduced model.

deviation: 0.09) at 140  C (Rivas et al., 2001b) who studied the wet oxidation of an alkaline effluent from green olive processing with an initial COD value of about 13,000 mg/L reported that treatment for 360 min at 180  C and about 4 MPa oxygen partial pressure led to 30 and 90% COD and phenols removal, respectively, while addition of hydrogen peroxide at 340 mg/L had little effect on COD removal and no effect on phenols removal. They also found that the reaction was first order regarding COD with the kinetic constant taking values between 0.06 and 0.15 1/h for oxygen pressures varying between 2 and 6 MPa.

Regarding the removal of total phenols, the normal probability plot of the effects (Fig. 4) shows that the single most important factor affecting their removal by wet oxidation is the initial COD, while the reaction time and temperature and the interaction between initial COD and temperature are less but still statistically significant. The presence of oxidant and the initial solution pH, along with all other interactions, are not significant and may be explained as random noise. All significant effects are positive indicating that an increase in their level brings about an increase in the amount of TP oxidized.

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4000

Y1, mg/L COD removed

3000

2000

1000

0 0

5

10

15

20

25

30

Order of experiments Fig. 3 – Comparison between experimental and predicted values for COD removal. experimental values.

The mathematical model that describes the oxidation of TP as a function of the reduced variables is: Y2 ¼ 196:8 þ

268:3 28:1 13:7 17:2 X1 þ X2 þ X4 þ X1 X2 2 2 2 2

(4)

where Y2 is the mass of TP oxidized (mg/L). Adequacy of the model was also checked by means of constructing the normal plot of the residuals (Fig. 5). Once again, all points from this residual plot lie close to the straight line confirming the conjecture that effects other than those

B,

Full model; ,, reduced model; 6,

considered in the model may be readily explained by random noise.

3.1. Comparison between WAO and BDD electrochemical oxidation of TOPW We recently studied (Deligiorgis et al., 2008) the electrochemical oxidation of TOPW over BDD electrodes to assess the significance of initial COD, reaction time, initial pH, H2O2 concentration and current intensity on COD and

99 A 95

B AB D

90 80

Percent

70 60 50 40

Factor

Name

A (X1)

COD

30

B (X2)

Temp

20

D (X4)

Time

10 5

1 0

50

100

150

200

250

Effect Fig. 4 – Normal probability plot of the effects for TP removal.

300

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99

95 90 80

Percent

70 60 50 40 30 20 10 5

1 -3

-2

-1

0

1

2

3

Residuals - TP model Fig. 5 – Normal probability plot of the residuals for TP removal.

phenols degradation implementing a factorial design approach. With the exception of current intensity for electrochemical experiments and temperature for WAO experiments, the other four variables were common and took identical values, thus allowing a fair comparison of the two treatments. COD degradation during WAO was affected by a greater number of variables than during electrolysis. In particular, during WAO several two-factor interactions appeared statistically important, indicating that the system under study was fairly non-linear. On the contrary, during electrochemical treatment basically only main effects were significant, indicating a highly linear system. These observations imply that different reaction pathways, mechanisms and kinetics are involved in the two processes. Of the two processes, WAO was more effective as evidenced by the higher values of the average effects indicating mean COD degradation (1384.2 mg/L for WAO as compared to 432.7 mg/L for electrochemical oxidation). This is also evident in Table 3 where COD removal values from WAO runs are compared to the respective electrochemical runs; WAO always yields greater COD degradation than electrolysis and this is also the case with total phenols. However, phenols are readily oxidizable with either process and this is consistent with the fact that far fewer variables affect their degradation compared to COD.

4.

Conclusions

The conclusions drawn from this study can be summarized as follows: (1) A strong agro-industrial effluent from edible olive processing was treated by wet air oxidation with emphasis

given on the effect of various operating conditions, such as initial COD loading, contact time, starting effluent pH, reaction temperature and the use of H2O2 as an extra oxidant on treatment efficiency. In general, the process was capable of achieving satisfactory levels of phenols degradation and decolorization followed by moderate mineralization at mild treatment conditions. (2) To evaluate the importance of the various parameters on treatment efficiency, a factorial design approach was followed. All five parameters in question affected COD removal at a statistically significant level with the initial COD concentration being by far the most influential variable. Moreover, second order interactions of COD with temperature, contact time and effluent pH were also significant. On the other hand, the phenolic content was readily susceptible to wet oxidation and this is consistent with the fact that fewer variables, namely initial COD, reaction temperature, contact time and the interaction between COD and temperature, affected significantly phenols conversion. (3) Simple empirical mathematical models were developed which adequately simulated quantitatively the amount (rather than the conversion) of COD and total phenols removed as a function of the most significant main effects and two-factor interactions. From an engineering point of view, treatment efficiency should be assessed in terms of the mass of COD (or any other pollution index indeed) removed rather than the respective reaction conversion; the latter may be misleading as it typically decreases with increasing COD loading. (4) The reaction rate of the wet oxidation of table olive wastewater was found to follow first order kinetics with respect to COD removal. The first order kinetic constant was estimated 0.49 1/h at 180  C.

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