Solar photo-Fenton treatment—Process parameters and process control
W. Gernjak a,b,*, M. Fuerhacker b, P. Fernandez-Ibanez a, J. Blanco a, S. Malato a
a Plataforma Solar de Almerıa-CIEMAT, Carretera Senes km 4, 04200 Tabernas (Almerıa), Spainb University of Applied Life Sciences, Department of Water, Air and Environment, Muthgasse 18, 1190 Vienna, Austria
Received 3 August 2005; received in revised form 29 November 2005; accepted 1 December 2005
Available online 6 January 2006
Abstract
Photo-Fenton experiments were performed using alachlor as a model compound (initial concentration 100 mg/L) in a compound parabolic
collector solar pilot-plant. Three process parameters were varied following a central composite design without star points (temperature 20–50 8C,
iron concentration 2–20 mg/L, illuminated volume 11.9–59.5% of total).
Under all experimental conditions, complete alachlor degradation, mineralisation of chloride and 85–95% mineralisation of dissolved organic
carbon (DOC) was achieved. An increase in temperature, iron concentration and illuminated volume from minimum to maximum value reduced
the time required for 80% degradation of initial DOC by approximate factors of 5, 6 and 2, respectively. When process parameter changes were
made simultaneously, these factors multiplied each other, resulting in degradation times between 20 and 1250 min.
Models were designed to predict the time necessary to degrade 50 or 80% of the initial DOC applying response surface methodology (RSM).
Another model based on the logistic dose response curve was also designed, which predicted the whole DOC degradation curve over time very well.
The three varied process parameters (temperature, iron concentration and illuminated volume) were independent variables in all the models.
Mass balances of hydrogen peroxide consumption showed that the same amount of hydrogen peroxide was always needed to degrade a certain
amount of DOC regardless of variations in the process parameters within the range applied.
Possible applications of the models developed for automatic process control are discussed.
# 2005 Elsevier B.V. All rights reserved.
Keywords: Photo-Fenton; Advanced oxidation processes; Wastewater treatment; Solar energy; Response surface methodology
www.elsevier.com/locate/apcatb
Applied Catalysis B: Environmental 64 (2006) 121–130
1. Introduction
Although adopted as the best available technology, bio-
logical treatment can only be partially employed to treat waste
water. In the case of non-biodegradable or toxic wastewater
sources, alternative treatments have to be employed. Among
the chemical oxidative treatments, advanced oxidation pro-
cesses (AOP) are well known for their capacity for oxidising
and mineralising almost any organic contaminant. Compen-
dium reviews of these technologies are available [1–4], but
most of the research reported has been performed at laboratory
scale, not very much at pilot-plant scale and practically none at
full scale.
What keeps AOPs from going commercial are their
comparatively high costs, although, generally, valid cost
figures are difficult to provide, as they are subject to case-
by-case differences depending on the particular waste problem,
* Corresponding author. Tel.: +34 950387957; fax: +34 950365015.
E-mail address: [email protected] (W. Gernjak).
0926-3373/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.apcatb.2005.12.002
possible process integration and uncertainties inherent in such
estimations for not fully developed processes. Attempts have
been made to assess and compare AOPs on the basis of defined
figures-of-merit, chemical parameters such as reaction con-
stants [5,6], electrical energy per order [7] and financial
parameters [6,8].
Although quantitative process assessment is difficult, several
promising cost-cutting approaches have been proposed:
(1) U
sing renewable energy instead electrical energy to drivethe process [9–12]. Heterogeneous semiconductor photo-
catalysis and homogeneous photo-Fenton are the only
AOPs which can use sunlight to produce hydroxyl radicals.
Of the two, photo-Fenton is known to have higher reaction
rates [13]. Furthermore, it uses non-toxic, easy-to-handle
reagents.
(2) I
ntegration of the AOP as part of a sequence of varioustreatments, in which the AOP would typically be a pre-
treatment of non-biodegradable or toxic waste water. Once
biodegradability has been achieved, the effluent can be
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130122
transferred to a cheaper biological treatment. The key is to
minimise residence time and reagent consumption in the
more expensive AOP stage by applying an optimised
coupling strategy [14,15].
(3) M
aximising AOP reaction rates, which leads to higherthroughput and lower capital costs because of the smaller
plant size needed [10,14,16].
(4) I
mprovement of plant components, e.g., the solar collector.The use of non-concentrating compound parabolic collec-
tors (CPC) for this purpose was proposed in the early
nineties [17], and this set-up has been tested successfully
for solar photo-Fenton [12,18] as well as for heterogeneous
photocatalysis [19–21]. Requirements for the solar collec-
tor have also been discussed [11,21,22].
(5) I
mprovement of plant operation and control strategiesleading to more automation and lower operating costs.
This study focuses on solar photo-Fenton degradation of
alachlor as a model contaminant. Several previous studies have
discussed the influence of iron concentration and its catalytic
behaviour [10,14,16] and temperature [10,23]. However, to our
knowledge, only one study examines the result of alternating
time intervals with and without illumination [24]. To determine
the effect of these process parameters on the degradation of
alachlor, a central composite design without star points varying
three factors (temperature, iron concentration and collector
area) was performed, and the results were analysed by the
response surface methodology [14,16,25].
Alachlor is soluble in water (240 mg/L, 25 8C) and
moderately toxic to aquatic organisms: EC50 (48 h) water flea
(Daphnia magna) 10 mg/L; TL50 (72 h) algae (Selenastrum
capricornutum) 0.012 mg/L. Furthermore, it is a persistent
herbicide with a half-life in soil and water of over 70 and 30
days, respectively. Its main application is grass and weed
control for corn, cabbage, cotton and several other crops [26].
Alachlor is classified by the US Environmental Protection
Agency (USEPA) as Type III, that is, toxic and slightly
hazardous, and a priority substance (PS) by the European
Commission (EC) within the scope of the Water Framework
Directive (WFD, Directive 2000/60/EC). Furthermore, ala-
chlor’s molecular structure can be regarded as that of a rather
typical non-biodegradable contaminant, having an aromatic
ring structure, aliphatic carbon and organically bound chlorine
and nitrogen. Therefore, in addition to its importance as a
contaminant, its use as a model compound in a generic study on
the influence of process parameters is further justified.
Finally, several parameters, which can be easily measured
on-line, are correlated to process progress. Suggestions for the
use of such data in improved process operation strategies are
provided.
2. Experimental
2.1. Analysis
All measurements were performed from samples filtered
through 0.2 mm syringe-driven filters (Millipore Millex-GN).
Dissolved organic carbon (DOC) and inorganic carbon (IC)
were measured by a Shimadzu model 5050A TOC analyser.
Ammonium concentrations were determined with a Dionex
DX-120 ion chromatograph equipped with a Dionex Ionpac
CS12A 4 mm � 250 mm column. Chloride and nitrate con-
centrations were measured with a Dionex DX-600 ion chro-
matograph using a Dionex Ionpac AS11-HC 4 mm � 250 mm
column. Alachlor concentration was analysed using reverse-
phase liquid chromatography with UV detector in an Agilent
Technologies, series 1100 HPLC-UV with C-18 column
(Phenomenex LUNA 5 mm, 3 mm � 150 mm). Complete
alachlor degradation means degradation below the detection
limit of 0.1 mg/L. All other parameters are measured much
above the quantification limit.
For photometric measurements and recording of UV spectra,
a Unicam-2 spectrophotometer was used. Iron determination was
done by colorimetry with 1,10-phenantroline [27]. Hydrogen
peroxide concentrations were analysed by iodometric titration.
pH, oxidation reduction potential (ORP), temperature (T)
and dissolved oxygen (DO) were measured on-line in the pilot-
plant by the corresponding WTW Sensolyt system electrodes.
Global UV (300–400 nm) irradiation in the solar plant was
recorded by a Kipp&Zonen CUV3 detector at the same 378inclination as the reactor modules. That way incident UV-
radiation could be evaluated as a function of time of day taking
into account cloudiness and other environmental variations.
Experiments could thus be compared using a corrected
illumination time t30W (Eq. (1)) [18].
t30W;n ¼ t30W;n�1 þ DtnUV
30; Dtn ¼ tn � tn�1 (1)
where tn is the experimental time for each sample and UV is the
average solar ultraviolet radiation measured during Dtn. In this
case, t30W refers to a constant solar UV power of 30 W/m2
(typical solar UV power on a perfectly sunny day around noon).
All measured parameters have an estimated relative error of
about 2% (on-line analysis) to 5% (laboratory measurements).
However, standard deviation of experiments repeated under the
same experimental conditions (compare Table 1 and Fig. 2a) is
10–11% of the corresponding value with regard to t30W. This
error can be attributed to the fact that the experiments are pilot-
plant experiments under solar conditions. While experimental
conditions such as iron concentration can be replicated well, the
intensity of solar radiation cannot, as it varies with time of day.
So, the use of the corrected illumination time t30W partly
compensates the effect of changing radiation, but it cannot
completely avoid its influence.
2.2. Pilot-plant
The pilot-plant, consisting of compound parabolic collectors
exposed to sunlight, a reservoir tank, a recirculation pump and
connecting tubing, was operated in batch mode. The collector
consists of 20 Pyrex absorber tubes with an inner diameter of
46.4 mm. The reflectors are made of aluminium with a
concentration factor of one. Collector area is 4.16 m2,
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130 123
Table 1
Central composite design without star points
Experiment Process variables Measured Model 1
cFe (mg/L) T (8C) A (m2) tDOC50%30W (min) tDOC80%
30W (min) tDOC50%30W (min) tDOC80%
30W (min)
Centre 1 11 35 2.50 45 75 38 62
Centre 2 11 35 2.50 54 92 38 62
Centre 3 11 35 2.50 46 77 38 62
Cube 1 2 50 0.83 141 252 140 254
Cube 2 2 20 0.83 703 1060 703 1058
Cube 3 2 50 4.16 50 77 61 91
Cube 4 2 20 4.16 308 375 307 378
Cube 5 20 50 0.83 12 35 22 46
Cube 6 20 20 0.83 110 181 111 192
Cube 7 20 50 4.16 5.9 18 10 17
Cube 8 20 20 4.16 39 69 49 69
Set-up, main DOC and regression results for tDOC50%30W and tDOC80%
30W calculated with Model 1. Initial alachlor concentration was always 100 mg/L. A = 0.83, 2.50 and
4.16 m2 means that 11.9, 35.7 and 59.5% of the total volume were illuminated.
illuminated volume (Vi) when the collector is completely
exposed to sunlight is 44.6 L and total volume (VT) 70–82 L,
depending on how full the tank is. Total volume was 75 L for all
experiments in this study. The pilot-plant is equipped with on-
line measurement sensors for T, pH, ORP and DO. The plant
also incorporates heating and cooling devices to control
reaction solution temperatures during an experiment. A flow
diagram of the plant is depicted in Fig. 1.
2.3. Experimental set-up and statistical evaluation
Photo-Fenton experiments were performed as follows:
alachlor was homogeneously dissolved in the pilot-plant with
the collectors covered, the pH was adjusted to 2.7 with
Fig. 1. Flow diagram
sulphuric acid, pre-dissolved ferrous sulphate was added and
the first sample was taken after 15 min of homogenisation.
Then hydrogen peroxide was added, and after 30 min of dark
Fenton reaction, a zero-illumination-time sample was taken,
and illumination began. After the start of illumination, periodic
samples were taken and hydrogen peroxide concentration was
kept between 200 and 400 mg/L by addition simultaneously
compensating consumption.
The experimental set-up of the experiments performed
within a central composite design without star points and main
DOC results are given in Table 1. Apart from the central
composite design, a dark Fenton control experiment was
performed in a magnetically stirred, temperature-controlled, 5-
L flask with 20 mg/L iron at 50 8C.
of pilot-plant.
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130124
All statistical evaluation and calculations were done by
multivariate linear regression and Levenberg–Marquardt non-
linear curve fitting using Origin v7.03 software. tDOC50%30W and
tDOC80%30W values given in Table 1 were obtained by linear
interpolation between the two points adjacent to the limit value.
For Cube 2 and Cube 4 experiments, tDOC80%30W was obtained by
linear extrapolation from the last three measured points. This
method provides sufficient accuracy (relative error 3–5%)
compared to the overall repeatability of the experiments as
detected by the repetition of the centre points of the factorial
design (relative error 10–11%, compare Fig. 2a and Table 1).
3. Results and discussion
3.1. Degradation of alachlor
Fig. 2a–c show the DOC degradation of alachlor versus t30W
for the experiments performed (for experiment details refer to
Table 1). Several qualitative points can be deduced from these
graphics.
First of all, DOC degradation was confirmed under all the
experimental conditions tested in the photo-Fenton experi-
ments, even at the rather low iron concentration of 2 mg/L (see
Fig. 2b). Repeatability of the results is confirmed (see Fig. 2a).
Fig. 2. (a–c) DOC degradation curves measured and predicted with Model 2;
cube points.
Referring to the values for 50 and 80% degradation given in
Table 1 for the centre experiments, the standard deviations are
4.9 and 9.3 min, respectively, which corresponds to 10 and 11%
of the mean values. The beneficial effect of increased
temperature and iron concentration is confirmed as well (see
Fig. 2b and c).
Furthermore, Fig. 2b and c show that by reducing the
illuminated area from 4.16 to 0.83 m2 (uncovering only part of
the CPC) the reaction rate decreases with respect to t30W. But
while the illuminated area is reduced five times, the real
treatment time increases only by a factor of 2.5 instead of 5, as
might be expected if all the reactions were to be induced by
photochemical processes (at least as a rate-limiting step
involved in the recycling of ferrous iron). Observing Eq. (1),
this means that only about half the photons are necessary with
less illuminated area. This suggests that an important part of the
reactions are thermally induced in the dark. Several possibi-
lities could explain the difference in the number of photons
needed for degradation depending on the relationship between
dark and illuminated reactor volume. Either intermediates are
formed under illumination, which boost the reaction further
after leaving the illuminated reactor zones (e.g., hydroqui-
nones/quinones maintaining the catalytic iron cycle [28]), or
intermediates are formed in the dark, which then react quickly
under illumination (e.g., organic acids forming photo-active
complexes with ferric iron). A combination of both explana-
tions is also possible. A dark Fenton control experiment
performed at 20 mg/L iron and 50 8C yielded the highest
reaction rates in the experimental region investigated. Fig. 2b
shows that although degradation was confirmed, the reaction
was considerably slower than the corresponding experiments
under illumination. Furthermore, it seems that DOC degrada-
tion cannot be achieved to the same extent as under illumination
and intermediates produced in the degradation process slowed
down the reaction further. Therefore, it may be concluded that
illumination is necessary for high DOC degradation to be
achieved.
From a practical point of view, this means that lowering the
ratio of illuminated to total volume is beneficial because fewer
photons are required, meaning smaller collector area and lower
capital costs. The limitations of this approach to optimise the
effect of promoting incident photons would depend on the
residence time in dark and illuminated zones in the reactor
system. Quantification of the effects of temperature, iron
concentration and variation of collector area will be dealt with
later in this paper.
The stoichiometric demand for hydrogen peroxide necessary
to completely oxidise 100 mg/L of alachlor can be calculated
with Eq. (2) as 12.6 mM. As can be seen in Fig. 7, hydrogen
peroxide consumption had to be two to three times higher if
80% DOC degradation was to be achieved. The dark Fenton
experiment consumed even more hydrogen peroxide in
accordance with Eqs. (3)–(9), where reactions involved in
the catalytic iron cycle are described. Sychev and Isaak [29]
have reported the reaction rates given. The dark reactions
reducing ferric iron consume hydrogen peroxide molecules and
produce only a less active peroxyl radical instead of a hydroxyl
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130 125
Fig. 4. Typical degradation experiment (Cube 6). HPLC chromatograms at
225 nm wavelength of detection showing the formation of intermediates.
Alachlor is the chromatogram before H2O2 addition and 0 min refers to the
chromatogram after the Fenton reaction in the dark.
radical (Eq. (4)). This peroxyl radical can reduce another ferric
iron ion forming oxygen (Eq. (6)). So all together, two ferrous
iron ions would be recycled at the expense of one hydrogen
peroxide molecule, but without generating any oxidising
species.
C14H20ClNO2þ 34H2O2 ! 14CO2þNH4Cl þ 42H2O (2)
Fe2þ þH2O2 ! Fe3þ þ �OH þ OH� ð53�76 M�1 s�1Þ(3)
Fe3þ þH2O2 ! Fe2þ þ �HO2þHþ ð1�2 � 10�2 M�1 s�1Þ(4)
Fe2þ þ �HO2 ! Fe3þ þHO2� ð0:72�1:5 � 106 M�1 s�1Þ
(5)
Fe3þ þ �HO2! Fe2þ þO2þHþ ð0:33�2:1 � 106 M�1 s�1Þ(6)
Fe2þ þ �OH ! Fe3þ þOH� ð2:6�5 � 108 M�1 s�1Þ (7)
½Fe3þðOH�ÞxðH2OÞy� þ hn
! Fe2þ þ ðx� 1ÞOH� þ yH2O þ �OH (8)
½Fe3þðRCO2�Þ�þ hn ! Fe2þ þ �R þ CO2 " (9)
Fig. 3 shows other parameters measured in a typical
degradation experiment. As can be observed, mineralisation of
the compound is not only confirmed by the decrease in DOC,
but also by the complete transformation of organic chlorine into
chloride and organic nitrogen into nitrate and ammonium. Both
heteroatoms are attached in the aliphatic side chains of the
aromatic ring. Typically, attack on the aromatic ring is faster
compared to attack on aliphatic side chains. This explains the
gradual mineralisation of the nitrogen. Chloride release begins
immediately, indicating that chloride substitution can take
place at any stage of the degradation process. Nevertheless,
complete release is only achieved at the end of the experiment.
It can be clearly seen that alachlor conversion into oxidised
intermediates does not mean direct mineralisation of DOC, as
almost all alachlor is converted before DOC begins to decrease.
Fig. 3. Typical degradation experiment (Cube 6). Alachlor conversion and
formation of inorganic ions.
Fig. 4 shows HPLC chromatograms recorded at 225 nm
wavelength of the same experiment. At least 12–15 peaks can
be distinguished as intermediates, however neither identifying
these compounds nor investigating the degradation pathway
was within the scope of this study as this was done before by
Penuela and Barcelo [30]. The disappearance of the peaks in the
chromatograms at 225 nm during degradation also confirms the
disappearance of aromatic compounds, which are at least
slightly absorbing at this wavelength.
Fig. 5b shows UV absorbance by the samples at 254 and
300 nm. Although absorption at 254 is commonly used as an
Fig. 5. Typical degradation experiment (Cube 6). (a) DO, H2O2 consumption,
ORP; (b) UV absorption (1 cm pathlength), pH.
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130126
indicator for the content of aromatic compounds (DIN 38404-
C3), in the case of photo-Fenton, this is not the only cause of
absorption at 254 nm, which is also due to dissolved iron and
hydrogen peroxide. So to avoid at least the interference of
hydrogen peroxide, it is advisable to observe absorption at
300 nm. The drop in the aromatic compound content correlates
to the absorption of the solution as depicted in Fig. 5. Conversion
of aromatic compounds to oxidised molecule fragments, which
then lose their aromaticity, can usually lead to detoxification
without need of complete mineralisation. Typical early inter-
mediate degradation products, such as phenols, hydroquinones
and quinones, are also known to be considerably toxic and light
absorbing in the ultraviolet region. Consequently, the increasing
absorption at the beginning of the reaction can most probably be
attributed to such intermediates, while subsequent decreasing
absorption reflects the dearomatisation of these compounds. In
another previous study by our group, we showed that acute
toxicity determined with the Vibrio Fischeri bioluminescence
test showed a profile similar to absorption at 300 nm. First,
toxicity rose higher than the toxicity of alachlor itself, and then
decreased as DOC began to decrease [31].
3.2. Influence of process parameters—Model 1
To ensure effective detoxification, wastewater detoxification
must intrinsically involve process assessment. Measurements
of biodegradability enhancement, decrease in toxicity, chemi-
cal oxygen demand or dissolved organic carbon are among the
most frequently applied figures-of-merit. In this work, DOC
degradation was chosen for process evaluation, because other
figures-of-merit can be estimated based upon this measure, if
empirically determined values are available for the given waste
water and oxidation process [12,31].
DOC degradation curves are usually sigmoidal because in
the initial degradation stages, the pollutant is transformed into
oxygenised intermediates but without a loss of carbon dioxide
resulting in initially stable DOC. When degradation proceeds,
DOC decrease accelerates until it slows down again in the final
stages. This particular behaviour impedes calculating rate
constants based on simple rate equations. Alternatively, process
efficiency can be compared based on a given DOC decrease
[9,12]. According to a previous study [31], detoxification of an
alachlor solution can be ensured once DOC degradation reaches
50–80% of the initial value. It was therefore attempted to
develop a model that could predict the time (tDOC50%30W ; tDOC80%
30W )
required for these levels of degradation.
Response surface methodology (RSM) [25] has recently been
applied by several authors to modelling tasks related to photo-
Fenton [14,16]. We attempted the same mathematical approach
in our work, but fitting degradation to second order polynomial
equations was unsuccessful. If all parameters were included, the
resulting models were over-fitted and the response surfaces
folded, and gave local minima and maxima where their
occurrence was not logical from a physical–chemical perspec-
tive. If the number of parameters were limited by forward and
backward selection of parameters, the models simply were not
able to predict the target variables satisfactorily.
One disadvantage was that the model target values have very
high relative errors for fast experiments. This is due to least
square minimisation, which also takes into account absolute
differences. To counter this effect, the model calculation was
directly weighted with the target value (tDOC50%30W ; tDOC80%
30W ) to put
additional weight on the fast experiments, but the approach
yielded poor results nevertheless.
The main reason for the failure of this methodological
approach is probably the wide range of results that the model
must cover. For the fastest experiment tDOC50%30W and tDOC80%
30W
were 6 and 18 min, respectively, for the slowest one 703 and
1060 min. Therefore, the polynomial function approach to the
problem proved invalid.
We then tried a search for functions which seemed
appropriate to describe the problem in a mechanistic approach,
given the knowledge existing about the photo-Fenton process
and the expected influence of T, Fe and the relationship of
collector surface (or illuminated volume) to total volume.
After carefully examining the data and attempting several
types of functions, we decided that the target function should be
a product of functions of the process parameter. To be able to
model the curvature in the n-dimensional space we selected the
potential function. The resulting equation was Eq. (10), where
C, pFe, pT and pA are the four parameters that have to be
optimised, while cFe, T and A are the iron concentration, the
temperature and the collector surface. This equation was then
used to model tDOC50%30W and tDOC80%
30W .
tDOC50%30W or tDOC80%
30W ¼ C � cPFe
Fe � T pT � A pA (10)
A second degree polynomial for three factors, including
linear, quadratic, cross-product terms and offset, has 10
parameters that have to be adjusted. The advantage in this
aspect of Eq. (10) is obvious, as for a given data set for n
observations it leaves more degrees of freedom, which for a
given Pearson’s coefficient results in a higher Fisher’s value in
ANOVA analyses, i.e., a more plausible model. Furthermore,
polynomial functions tend to have poor extrapolation qualities,
which is another reason for searching for alternative functions
more closely related to the physical–chemical behaviour of the
system.
To distinguish this approach from another described later,
we refer to these results as Model 1. The results of parameter
optimisation are given in Eqs. (11) and (12). The results of the
model applied to the experimental results are given in Table 1.
The model results are accurate, except that the relative error is
considerable in very fast experiments. Note that the effect of
changing a process factor can be estimated directly when the
value of the exponent is known.
tDOC50%30W ¼ 220; 200 � c�0:800
Fe � T�1:765 � A�0:515 (11)
tDOC80%30W ¼ 167; 000 � c�0:740
Fe � T�1:558 � A�0:638 (12)
As described, the results of these models are valid for
constant process variables, i.e., those which do not change,
such as temperature, iron concentration and collector area.
The latter would not change in a real case plant either, of
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130 127
Fig. 6. DOC/DOCi values calculated by logistic dose response curve modelling
of the real data for each experiment against measured DOC/DOCi from all
experiments (see text).
course, while temperature obviously changes in a solar
collector, if no external temperature control is applied. The
same is possible for the iron concentration, if there should be
precipitation due to high pH or the presence of phosphate, for
example.
3.3. Influence of process parameters—Model 2
It would be desirable to have a dynamic model which is
capable of predicting the reaction speed at every moment of the
degradation process. As mentioned, DOC degradation curves
have a sigmoidal form. Of the common curves fitting sigmoidal
tendencies, the Boltzmann function and the logistic dose
response curve are outstanding for their simplicity. Both have
only four parameters to adjust. The problem with the
Boltzmann function is that its curvature is symmetric at both
sides of the inflexion point, which is not necessarily the case in
a DOC degradation curve. So this function must be discarded in
favour of the logistic dose response curve, which is commonly
used to describe dose response curves in pharmacology. The
four parameters of the equation (Eq. (13)) are A1 and A2, which
are the initial and the final DOC values (DOCi, DOCf), t1/2, the
time when degradation is half-way between DOCi and DOCf
and p, an exponent largely determining the curvature and the
slope of the curve. DOC in Eq. (13) refers to the measured DOC
value at any time during degradation.
DOC
DOCi¼ A1 � A2
1þ ðt30W=t1=2Þ p þ A2 (13)
As we used normalised values (DOC/DOCi), A1 was always
one, except for the experiments in which 20 mg/L iron was used
at 50 8C because these were the only ones in which DOC
degradation during the Fenton reaction before illumination was
remarkable. Consequently, A1 was set at 0.82, the average value
of both experiments at zero illumination time (see Fig. 2c). At
the same time, it was assumed that the DOCf is always 5% of
DOCi and A2 is 0.05, thus resulting a non-linear fitting problem
with only two parameters. This assumption was made to
optimise modelling between 20 and 80% DOC degradation,
Table 2
Logistic dose response curve parameters modelled from experimental data
Experiment Fitted values Fitted valu
t1/2 (min) p t1/2 (min)
Centre 1 42 3.02 36
Centre 2 53 3.39 36
Centre 3 45 3.13 36
Cube 1 139 2.93 140
Cube 2 689 4.27 688
Cube 3 48 3.86 55
Cube 4 272 4.91 272
Cube 5 15 1.77 22
Cube 6 107 3.51 110
Cube 7 5.1 1.26 8.9
Cube 8 37 2.82 44
These parameters were modelled from process parameters as described in the text
which is considered the most relevant region because
detoxification takes place somewhere in this phase of
degradation.
We then fitted each experiment, which gave an excellent
coefficient of determination (square of Pearson’s coefficient) of
higher than 0.99 for each experiment and also when all
measured samples were plotted against their calculated value
(see Fig. 6). This confirms the adequacy of the equation for this
problem. The fitted parameters are given in Table 2.
Then we fitted t1/2 the same way as described above for
tDOC50%30W and tDOC80%
30W (see Eq. (10)) and the result was Eq. (14).
No logical reason for any correlation between p and the
influencing process variables could be found. So we started out
again with a second degree polynomial including cross-
products of the variables. This time the results of the regression
were logical and consistent. Forward selection was applied to
find the optimum selection of parameters in the multivariate
linear regression process [32] based on the criteria of
maximising not only coefficient of determination (square of
es with Model 2
p tDOC50%30W (min) tDOC80%
30W (min)
3.17 38 62
3.17 38 62
3.17 38 62
2.95 145 247
4.24 706 1022
3.86 57 85
4.92 278 383
1.76 24 58
3.55 114 177
1.27 9.6 33
2.82 45 79
and regression results for tDOC50%30W and tDOC80%
30W calculated with Model 2.
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130128
Pearson’s coefficient), but also the Fisher’s value. Eq. (15) was
the result (Fisher’s value of 135). Regression results for
Eqs. (14) and (15) are given in Table 2. It also lists the values
calculated with Model 2 for tDOC50%30W and tDOC80%
30W :
t1=2 ¼ 197; 000 � c�0:795Fe � T�1:740 � A�0:576 (14)
p ¼� 0:0431 � T þ 0:203 � A� 0:000929 � cFe � T � 0:0235
� cFe � Aþ 0:00237 � T � Aþ 4:97 (15)
Model 2, the final equation for the DOC degradation curve as
a function of illumination time, temperature, iron concentra-
tion, collector surface and irradiation intensity implicitly
included in the illumination time (see Eq. (1)), results from
inserting Eqs. (14) and (15) into Eq. (13). The results calculated
for all samples measured are plotted against the measured
values in Fig. 2. The fit is very good for most experiments,
except, similar to the above modelling problems, in fitting very
fast experiments, probably due to the extremely wide intervals
of the reaction rates observed.
The partial derivative with respect to illumination time of
the resulting equation represents the DOC degradation rate
when the other parameters are constant (except changes in
irradiation intensity, which are taken into account). If the
other parameters are not constant, the degradation curve can
be calculated by parts. A complete degradation curve can thus
be reconstructed for varying process parameters. This would
present no problem should online prediction of the degrada-
tion curve be necessary, as long as temperature and radiation
intensity are measured on-line and information about changes
in iron concentration is made available to the control system.
So, this is a possibility for on-line prediction of process
progress and for making decisions about when to end the
process and whether to transfer or discharge the treated
effluent.
3.4. Determination of process progress by easy-to-measure
on-line parameters
As mentioned above, detoxification semantically implies
that at one point of the degradation process the waste water is no
longer toxic and can be disposed of to a subsequent treatment or
the environment. Measuring chemical oxygen demand, DOC or
toxicity is expensive, time-consuming and slow. This justifies
the search for alternative assessment criteria that are cheaper
and faster.
One approach is to model the process based on process
variables describing the system as above. Another approach is
to measure alternative parameters directly. We measured the
absorbance at various wavelengths and hydrogen peroxide
consumption off-line, and DO, ORP and pH on-line. Fig. 5
shows the same parameters for the experiment shown in Fig. 3,
which may be considered paradigmatic.
ORP measurements gave little information in this first
screening because of their many influencing parameters. The
most interesting effect was that the ORP reflects whether iron
ions present are ferric or ferrous very well (see Fig. 5 for the
step between �150 and 00, where Fenton reaction takes place
converting ferrous in ferric iron). This could be an indirect
indicator of a lack of hydrogen peroxide as in this case the
ferric/ferrous iron relationship is changed by Eqs. (8) and (9)
from equilibrium in the presence of hydrogen peroxide, while
Eq. (3) cannot take place.
pH measurements are mainly useful because depending
upon initial pollutant concentrations and the mineralisation
processes taking place (acids or bases produced), the pH can
be modified during treatment. So on-line pH measurement
can avoid precipitation of iron, if pH tends to rise in a
particular system. In our experiments, pH changed only
slightly during the treatment reflecting the formation of
chloride, inorganic nitrogen species and carboxylic acids as
intermediates.
DO measurements were of interest, as several different
phenomena were present in the DO profile. First, DO decreased,
indicating the incorporation of DO in the reaction mechanism
by Eqs. (16) and (17) (Dorfman mechanism) [33]. The resulting
peroxyl radical can then further participate in the reaction
mechanism, e.g., by Eq. (5) generating an additional hydrogen
peroxide molecule. When degradation proceeded, the ratio of
reactants (particularly hydrogen peroxide) to pollutant changed
and the reaction of the radicals generated (by Eq. (3)) with
hydrogen peroxide (Eq. (18)) was favoured, leading to the mere
decomposition of hydrogen peroxide into water and oxygen
(Eq. (19)). Consequently, the DO profile is a reaction progress
indicator. Furthermore, at the stage at which massive oxygen
production took place, a lack of hydrogen peroxide can again be
perceived in the DO profile, because DO concentration
decreases from its supersaturation range by degassing to the
atmosphere, if no new oxygen is supplied into the solution
simultaneously by the reaction:
�R þ O2 ! �RO2 (16)
�RO2þH2O ! ROH þ �HO2 (17)
�OH þ H2O2 ! H2O þ �HO2 (18)
2H2O2 ! 2H2O þ O2 (19)
Light absorbance by the solution at 254 nm can be used in
wastewater treatment to estimate the content of aromatic
substances (DIN 38404-C3). However, this approximation is
applicable to waste water with a rather constant composition
such as urban waste water, as it depends largely on the
molecular extinction coefficient of the present organic
substances at the chosen wavelength. In the particular case
of photo-Fenton, it is advisable to measure at somewhat higher
wavelengths for two reasons. First, if hydrogen peroxide is
present, its absorption influences measurement below 300 nm,
and second, if the treated waste water contains aromatics many
of the typical quinone/hydroquinone reaction intermediates
formed can be detected by measurement at higher wavelengths
as that is where they typically absorb. Indication of these
intermediates is especially desirable, as they are known to be
highly toxic. Fig. 5 clearly shows that the value again has a
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130 129
Fig. 7. H2O2 consumption vs. the measured DOC/DOCi values of all experi-
ments performed, including the Fenton experiment. The curve fits show the
H2O2 consumption for photo-Fenton and Fenton (a) points are marked accord-
ing to temperature; (b) points are marked according to iron concentration.
typical profile that could be used to estimate process progress.
The remnant absorption is due to iron complexes. Depending
on pH, ferric iron forms different aquo complexes involving
more or fewer hydroxyl radicals. These iron complexes differ in
their light absorption properties [4], which interferes with this
parameter, making it a qualitative measure.
Hydrogen peroxide was measured off-line but could
theoretically be measured on-line. Such sensors exist (Alldos
Eichler GmbH, Prominent Dosiertechnik GmbH), although
they are not very commonly employed. Fig. 7 shows DOC
degradation as a function of hydrogen peroxide consumption.
It can clearly be seen that the amount of degradation is strongly
correlated to the amount of hydrogen peroxide consumed. It
should be remarked that within the range of the parameters
investigated, no influence of any of the selected process
variables (iron concentration, temperature and collector area)
on the amount of hydrogen peroxide consumption needed for
degradation could be detected. On the contrary, Fig. 7 shows
that Fenton degradation needed more hydrogen peroxide to
reach the same degradation level. This is in accordance with the
fact that, contrary to the dark Fenton reaction (Eq. (4)), in
photo-Fenton, transformation of ferric to ferrous iron takes
place mainly without hydrogen peroxide consumption (Eqs. (8)
and (9)). The consumption of hydrogen peroxide (mmol/L) as a
function of DOC degradation (between 0 and 1) can be
estimated with a polynomial function (Eq. (20), coefficient of
determination (square of Pearson’s coefficient) of 0.94,
standard deviation of error 5.1 mmol/L), where H2O2cons
represents the hydrogen peroxide consumption and %DOC the
share of initial DOC degraded (between 0 and 1).
H2Ocons2 ¼ 1110 �%DOC5 � 2100 �%DOC4 þ 1430 �%DOC3
� 400 �%DOC2 þ 58:9 �%DOC� 0:0325 (20)
The theoretical hydrogen peroxide consumption for com-
plete mineralisation of 100 mg/L Alachlor is 12.6 mmol/L
(Eq. (2)). Calculated with Eq. (20), 55% of DOC mineralisation
takes place before this. This correlation could be used for
process control. It should be noted that the data and correlation
shown are only valid for the case in hand, because hydrogen
peroxide consumption depends on many parameters, mainly the
type and amount of wastewater contamination. So similar
empirical data will have to be obtained for different cases
before such a correlation can be established.
Fig. 7 shows furthermore, that extensive DOC degradation
needs considerably higher amounts of hydrogen peroxide than
moderate DOC degradation (e.g., 11.3, 25.2, 46.5 and
66.2 mmol/L for 50, 80, 90 and 95% DOC degradation,
calculated with Eq. (20)). So apart from merely extending
treatment time (and associated costs) increased reagent
consumption has to be included in the economic considerations
for making a decision as to when to stop treatment and/or with a
view to possible combination of AOPs with subsequent
biological treatment.
4. Conclusions
Hundred milligrams per litre of alachlor solutions were
mineralised with solar photo-Fenton treatment over a wide
range of varying process variables for iron concentration,
temperature and collector area per volume.
Response surface methodology was applied to assess the
influence of process parameters on the reaction rate. The use of
second degree polynomials, including cross-product terms,
yielded invalid results. Consequently, mechanistic modelling
was attempted and the equation employed for modelling DOC
degradation became a potential function. Prediction was further
improved by modelling the DOC degradation curve as a
function of illumination time with the ‘‘logistic dose response’’
curve. This produced an analytical expression as a function of
time, irradiation intensity, iron concentration, temperature and
solar collector area per volume. Given the analytical
expression, the DOC degradation rate (first derivative) can
be calculated for any moment of a real process with changing
process variables, as long as feed waste water and process
variable values are available. If on-line process parameter
information is available, an on-line control system can be
automated based on this information.
Further parameters measurable on-line were also investi-
gated. ORP, DO, pH and UV absorption of the solution
produced only qualitative information about the degradation
process progress. On the contrary, hydrogen peroxide
measurements revealed that DOC degradation could be
predicted by a polynomial relationship with hydrogen peroxide
consumption as the only independent variable. That means that
W. Gernjak et al. / Applied Catalysis B: Environmental 64 (2006) 121–130130
none of the process variables varied changed the amount of
oxidising reagent necessary to reach a certain level of DOC
decrease. Furthermore, it was shown that a multiple of the
amount of reagent necessary for high DOC degradation levels
must be added compared to what is required for moderate
levels. This is important to process economic assessment and
the possibility of a subsequent biological treatment.
It should be noted that the models and correlations employed
here were calculated for these experimental data and serve only
as a model case. Due to the complexity of the system and
depending on the waste water, the correlations are likely to be
somewhat different in other cases. Nevertheless, the modelling
approach is proposed as a methodology for obtaining models
also useful for other cases.
Acknowledgements
The authors wish to thank the European Commission for
financial support through the CADOX Project (contract no.
EVK1-CT-2002-00122). Mr. Gernjak wants to thank the
Austrian Academy of Sciences for a grant under the DOC
Programme. The authors wish to express their gratitude to Mrs.
Eva Augsten for the laboratory analysis and Mrs. Deborah
Fuldauer for the correction of the English style.
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