# Continuous-Flow Aqueous System for Heterogeneous Photocatalytic Disinfection of Gram-Negative Escherichia coli

Mejdi Kacem, Gael Plantard, Monica Brienza, Vincent Goetz

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Mejdi Kacem, Gael Plantard, Monica Brienza, Vincent Goetz. Continuous-Flow Aqueous System for Heterogeneous Photocatalytic Disinfection of Gram-Negative Escherichia coli. Industrial and engineering chemistry research, 2017, 56 (51), pp.15001-15007. 10.1021/acs.iecr.7b03644. hal-01879027

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Distributed under a Creative Commons Attribution 4.0 International License
1 Continuous-Flow Aqueous System for Heterogeneous Photocatalytic

2 Disinfection of Gram-Negative *Escherichia coli*

3 Mejdi Kacem,†,‡ Gael Plantard,†,‡ Monica Brienza,† and Vincent Goetz*,†,‡

4 †PROMES CNRS, UPR 8521, Rambla de la thermodynamique, 66100 Perpignan, France

5 ‡University of Perpignan Via Domitia, 52 Paul Alduy, 66100 Perpignan, France

**ABSTRACT:** Within the global objective to develop a sustainable oxidative process based on heterogeneous photocatalysis, the possibility of combining solar UV irradiation and a supported semiconductor all while working in an open reactor running in continuous flow is a pertinent option. Here we investigated a heterogeneous photocatalytic disinfection process performed with a 2D-photocatalytic material implemented in an open-flow flat-plate reactor irradiated with a LED panel. Inactivation of Gram-negative *Escherichia coli* was attempted using different UV light flux densities and feed flow rates. Treatment capacities were calculated under steady-state conditions using a simple mass balance between inlet and outlet. For an irradiated surface of $1.5 \times 10^{-2} \text{ m}^2$, values ranged from 10 to $30 \times 10^5 \text{ MPN} \text{ h}^{-1} \text{ L}^{-1}$ as a function of working conditions. With just a few adjustments, the model based on coupling mass transfer with photocatalysis-driven bacterial inactivation developed previously in the case of a closed-batch cylindrical reactor was tested and extended with success.

<figure><img src="image_2.png" /></figure>

1. INTRODUCTION

20 Water-related problems are increasingly recognized as one of 21 the most serious and immediate environmental threats to 22 humankind. Numerous approaches exist throughout the world 23 to promote wastewater reuse, especially in agriculture.¹ Urban 24 wastewater is forwarded to treatment plants (WWTP), which 25 are a set of devices (primary treatment devices followed by a 26 secondary biological treatment) designed to progressively treat 27 different pollutants. Recent station yields can reach up to 95% 28 pollution abatement. The rate achieved by this simple 29 organization may be sufficient to meet standards on discharge 30 into nature, but to make water reuse viable, a tertiary 31 disinfection treatment is needed. Microbial abatement targets 32 up to 99.9% (3 log₁₀) and obviously depends on future use of 33 the disinfected water (vegetable/tree cultivation, greenspace 34 irrigation, and aquifer recharge). More dramatically, today, 35 almost 700 million people lack improved drinking water 36 sources.² This is because rural areas of developing countries not 37 equipped with sanitation and running water public network are 38 the most affected with proliferation of many waterborne 39 deseases.² In sub-Saharan areas, unsafe drinking water alone 40 accounted for 50% of deaths.³ Providing a simple, robust and 41 efficient disinfection process adapted to a developing country 42 today represents a major health issue.

43 Most of the time, disinfection is carried out using UVC 44 irradiation and/or strong oxidizers such as ozone and chlorine. 45 UVC and ozonation are efficient against most infectious agents. 46 UVC processes involve installation, electricity, and maintenance 47 costs. If effluent is heavily charged with organic matter, then 48 chlorine can lead to organohalides, especially highly undesirable 49 trihalomethanes (THMs).⁴⁻⁶ Research is thus turning to

alternative processes, and among those currently in develop- 50
photocatalysis is an interesting option. Photocatalysis is 51
an environmentally friendly water disinfection technology, 52
especially as the process can be solar-driven.⁷⁻¹⁰ The literature 53
has shown that photocatalysis processes can effectively 54
inactivate a wide range of bacteria,¹¹,¹² including *Escherichia* 55
*coli*¹³ which is by far the most studied organism in the 56
world.¹⁰,¹⁴ Nevertheless, the vast majority of such studies have 57
been conducted using catalysts in slurries, e.g., TiO₂,¹⁵,¹⁶ which 58
need to be post-treated and are almost always integrated into 59
experimental batch-type systems. These systems do not permit 60
easy translation of results to more practical engineered 61
treatment operations, e.g., real-world WWTPs, which inevitably 62
work in a pseudocontinuous way.¹⁷⁻²⁰ Hence, catalysts will 63
likely need to be immobilized on fixed support materials, and 64
contaminated water treatment capacity should be explored 65
and/or estimated in a continuous-flow configuration. 66

Within the objective to promote the direct use of solar 67
energy to activate the photocatalytic oxidative principle, it is 68
obviously necessary to take into account the discontinuous 69
characteristic of the natural sunlight irradiation. This is a major 70
constraint to manage for efficient design and running of the 71
operating process. The large scale-up of a continuous-flow solar 72
photocatalytic disinfection technology therefore needs a 73
simulation tool capable of predicting how the process works 74

Received: September 1, 2017

Revised: November 28, 2017

Accepted: November 29, 2017

Published: November 29, 2017
75 when submitted to not just variable light flux density but also
76 variable effluent feed flow rates.

77 To begin addressing some of these critical issues, the work
78 reported here focused on the photocatalytic disinfection of
79 Gram-negative *E. coli* in a continuous-flow operating system
80 and using a supported catalyst implemented in a flat reactor. As
81 far as we know, few studies have addressed continuous-flow
82 processes,²¹⁻²³ particularly for solar disinfection purposes. The
83 laboratory setup developed here is far from intended to
84 replicate the complexity of actual field-scale engineered
85 processes. However, it does enable early investigations of key
86 parameters that will be critical to the success of larger
87 continuous-flow solar treatment processes. As a first step,
88 experiments were carried out to study the effect of two key
89 parameters that govern the inactivation process: light flux
90 density and feed flow rate. An *E. coli* inactivation simulation
91 model was previously developed in the case of a treatment
92 performed in batch working mode in a closed fluid loop
93 including a cylindrical reactor.²⁴ As a second step, the ability of
94 this model to represent the performances of the open reactor
95 working in continuous flow was tested and evaluated.

## 2. EXPERIMENTAL SECTION

96 **2.1. Catalyst.** TiO₂ catalyst, the material most widely
97 employed for photocatalysis, was used in an immobilized form.
98 The photocatalytic medium (Paper Grad 1048) manufactured
99 by Ahlstrom consisted of TiO₂ (Millenium PC-500) as a
100 coating on nonwoven fiber. Its specific surface area, calculated
101 with reference to the mass of the total photocatalytic medium,
102 was 98 m² g⁻¹. In more detail, the photocatalytic material
103 consisted of cellulosic fibers (38 g m⁻²), TiO₂ (16.7 g m⁻²),
104 and SiO₂ (13.3 g m⁻²), where SiO₂ served as an inorganic
105 binder for the titanium deposited on the paper fibers. This
106 photocatalytic material has already been characterized,²⁵ and
107 the major concern when using the 2D material was leaching,
108 which may lead to a reactivity modification due to the change in
109 TiO₂ recovery. Because of this, the media was systematically
110 prewashed (using Milli-Q water). This prewashed media was
111 reasonably photostable and has a TiO₂ powder recovery rate of
112 about 20%. It is widely used and cited in the literature, and was
113 tested, for example, for photocatalytic treatment of several
114 organic pollutants²⁶ under artificial UV irradiation as well as
115 natural sunlight.²⁷,²⁸

116 **2.2. Bacterial Strain and Growth.** *E. coli* strain DSM
117 30083 was used for the full bacterial inactivation study. This lab
118 strain is widely studied, has a sequenced genome, and is a
119 nonpathogenic primary model organism for lab research.¹⁰,¹⁴ *E. coli* cells were grown under sterile conditions in 100 mL of
120 Luria-Bertani (Miller's LB Broth) medium at 37 °C.²⁹
121 Bacterial growth was monitored by optical density (wavelength
122 = 600 nm) in a spectrophotometer (UVmini-1240, Shimadzu).
123 Cultures in stationary phase were appropriately diluted in a
124 buffer solution (buffered sodium chloride peptone water, pH
125 7.0, Oxoid) in order to obtain targeted initial bacterial
126 concentrations. For all studies, bacterial counts were monitored
127 via MUG/E. coli fluorescent microplates in accordance with the
128 requirements of standard NF EN 9308-3. Bacterial count was
129 then evaluated in MPN L⁻¹ (i.e., Most Probable Number per
130 unit of volume).

131 **2.3. Processing Loop.** The lab setup (Figure 1) consisted
132 of an open processing loop regarded as a perfectly mixed
133 reactor. Its total volume of 0.25 L consisted of two parts: a
134 photoreactor and a recirculation system linked to effluent inlet

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Processing loop: peristaltic pump (1), centrifugal pump (2), photoreactor (3), UV panel (4), withdrawal valve (5), feed valve (6), effluent to be treated (7), and treated effluent (8). Picture of the reactor with the 2D media positioned at the back face.</figcaption></figure>

and outlet. The photoreactor was similar to a flat reactor with a 136
parallel-piped shape, a width of 10 cm, a length of 15 cm, and a 137
thickness of 1 cm. It had a stainless-steel base and was covered 138
in front by a plate of UV-transparent PMMA (UV transmission 139
equal to 90%). The photocatalytic 2D material was placed on 140
the irradiated back face of the reactor. To isolate it from any 141
external light source, the photoreactor was mounted within a 142
closed chamber and exposed to a panel of UVA LED diodes 143
(280 LEDs distributed in 10 rows, λ = 365 nm). The surface 144
and useful volume irradiated thus obtained were 0.015 m² and 145
0.15 L, corresponding to an irradiated area per unit of volume 146
of 10⁻⁴ m² m⁻³. Light intensity calibration curve was 147
established with a hemispherical UV sensor (UVA 365 Lutron 148
Electronic Enterprise) positioned at the same place as the flat 149
reactor surface. The PMMA reactor surface was irradiated with 150
flux density between 3 and 50 W m⁻², with a small pitch of 151
variation equal approximately to 0.2 W m⁻² thanks to the 152
control of the power supplied to the LED. This range of UV 153
flux density is similar to that emitted by the solar radiation. 154

Fluid flow in the closed fluid loop was performed with a 155
centrifugal pump (Cole Parmer), which ensured homogeneous 156
mixing of the solution in the entire system that can be thus be 157
considered a perfectly mixed reactor. In order to establish the 158
continuous flow regime, the processing loop was fed in with the 159
influent (bacterial suspension to be treated) at a constant flow 160
rate. Simultaneously, the same quantity of solution was 161
withdrawn at a same flow rate in order to keep a fixed volume 162
of fluid into the system. The supply/withdrawal flow rates were 163
modulated by a multichannel peristaltic pump (Watson Marlow 164
205 CA). Flow range was monitored between 0.01 and 0.3 L 165
h⁻¹. 166

## 3. MODELING

A model of bacterial inactivation was built and tested²⁴ with 167
success in the case of a reactor operating in a closed 168
recirculating circuit working in batch mode. It is based on 169
two main assumptions (that have been largely discussed 170
elsewhere):²⁴ (i) A reversible adhesion of the bacteria occurs 171
at the surface of the catalyst media; (ii) a photocatalytic 172
reaction, consecutive to the production of hydroxyl radicals by 173
the TiO₂ semiconductor under irradiation, likely degrades the 174
bacteria bonded to or in the very near vicinity of the catalyst. 175
Consequently, the couplings between bacteria mass transfer 176
177 from the liquid bulk to the catalyst surface, the “intrinsic”
178 photocatalytic degradation rates, have to be taken into account
179 in the global mass balances applied to the bacteria.

180 The concentration C (MPN L⁻¹) of bacteria in the liquid
181 bulk is the result of the general mass balance including the fluid
182 inlet and outlet as

$$
V_T \frac{dC}{dt} = \dot{m}(C_{\text{in}} - C_{\text{out}}) - V_r(K_s S_{\text{cat}}(q_e - q) + \alpha I_r^f C) \quad (1)
$$

184 The mass balance applied to bacteria bonded to the catalyst
185 surface (q in MPN m⁻²) leads to

$$
\frac{dq}{dt} = K_s(q_e - q) - \alpha I_r^{f'} q \quad (2)
$$

187 where $q_e$ (MPN m⁻²) is bonded bacteria in equilibrium with
188 the liquid determined experimentally²,⁴ and approached with a
189 Freundlich formalism:

$$
q_e = 31C^{(1/0.78)} \quad (3)
$$

191 In the balances, ṁ is feed flow rate (L s⁻¹). Vᵣ and Vₜ are the
192 reactor and total loop volumes (L). The first term of the
193 “disappearance” rate of the bacteria in the liquid phase in eq 1 is
194 described according to the linear driving force (LDF) model is
195 the flux density of bacteria transferred to the catalyst surface. It
196 involves Kₛ (s⁻¹), the mass transfer coefficient expressed with
197 the bounded phase taken as reference, and the equivalent
198 surface of the catalyst per unit of reactor volume, Sₐt (m² L⁻¹).
199 The second part describes the photocatalytic disinfection
200 kinetics expressed as a power function of the irradiation
201 intensity Iᵣ (W m⁻³) per unit of reactor volume and involved an
202 energy constant α ((m³ J⁻¹)⁻¹), which is a function of
203 semiconductor activity, and a coefficient f (dimensionless),
204 which modulates the reaction rate with respect to light
205 intensity. Similarly, the mass balances applied to the bacteria
206 in adhesion (eq 2) involved the flux coming from the liquid
207 phase (first term) and photodisinfection kinetics expressed
208 according to a similar formulation with parameters α'
209 ((m³J⁻¹)⁻¹') and f' (dimensionless).

4. RESULTS AND DISCUSSION

210 **4.1. Preliminary Assays.** The first step was to validate that
211 the processing loop (photoreactor and circuit) may be regarded
212 as a perfectly mixed open reactor. Loop residence time t₁ was
213 determined under dark conditions throughout the system
214 response to a step-like input signal. This type of signal was
215 obtained by feeding the processing loop with a tracer solution
216 with a constant concentration and flowing at a fixed rate. In this
217 work, E. coli was naturally selected as the tracer. Bacterial
218 concentrations at the exit stream of the process over time were
219 monitored. Prior to the start of the experiment, the processing
220 loop was fully filled with an initial E. coli bacterial suspension
221 charged at a concentration C₀ of 10⁵ MPN L⁻¹. For better
222 accuracy, two step-like input signals were successively applied: a
223 first “high” input signal, and a second “low” one. After an initial
224 lag phase (10 h), the loop was first supplied with a bacterial
225 suspension of a concentration Cₐn,₁ of 1.510⁷ MPN L⁻¹ (150
226 times more concentrated than C₀) at a flow rate ṁ of 0.06 L h⁻¹
227 (Figure 2). The exit-stream bacterial concentration Cₐut was
228 monitored over time until reaching steady state, i.e., until
229 reaching a stable concentration identical to that of the inlet
230 stream (Cₐn,₁ = Cₐut). A second concentration gap was applied.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Experimental (○) and simulated (continuous line) bacterial concentrations in the exit stream of the processing loop for an input step-like signal. Experimental measures of bacterial concentrations in the feed stream (●).</figcaption></figure>

The processing loop at an initial concentration C₀ = Cₐn,₁ was 231
supplied with a second bacterial suspension flowing at a same 232
flow rate (0.06 L h⁻¹) but of a concentration Cₐn,₂ of 10⁵ MPN 233
L⁻¹ (150 times more diluted than Cₐn,₁). 234

The response of a perfectly mixed open reactor to such a signal may be expressed following the general formulation of eq 1, and obviously corresponds to the special case where no photocatalyst is loaded and when irradiation is off, i.e., with no reaction taking place. The integration of eq 1 for each step leads to

$$
C_{\text{out}}(t) = C_{\text{in}} - (C_{\text{in}} - C_{0}) \exp(-t/t_{1}) \quad (4) \quad \text{241}
$$

where t₁ = V<sub>T</sub>/ṁ (s) is loop residence time and C₀ is initial
concentration of the step considered.
242
243

Time-course profiles of the calculated and experimentally
measured bacterial concentrations in the exit stream of the
processing loop were plotted in Figure 2. The best agreement
between calculated and experimental concentration profiles was
for a residence time of t₁ = 4 h, to be compared against 4.16 h in
the case of a perfect mixed open reactor. This corresponded to
a dead volume of 9 mL (while the loop volume is of 250 mL),
which was considered negligible for further study.
251

For the second preliminary assays, the experiments were 252
performed both in presence and absence of the catalyst. In both 253
cases, concentration profiles were expressed as a function of 254
reactor residence time (t<sub>r</sub> = V<sub>r</sub>/ṁ), time of irradiation, and the 255
representative time of bacteria-to-catalyst contact (when 256
present). The starting of the assays was first carried out 257
under dark conditions (Figure 3) for t<sub>r</sub> between 0 and 30 h. 258
The photocatalytic media appeared to initially slow the increase 259
of the bacterial concentration in the exit stream. This was 260
probably a consequence of transfer then adhesion of the 261
bacteria from the liquid phase to the catalyst surface. Once the 262
steady state was reached, bacterial concentration in the exit 263
stream was identical to that of the inlet stream, which suggests a 264
balanced state of bacteria between the catalyst surface being 265
fully loaded of cells and the liquid bulk. This made it possible to 266
experimentally illustrate the “adhesion effect” of the material on 267
the bacteria. Under light conditions, bacterial disinfection 268
obtained by photolysis and photocatalysis were clearly 269
distinguished. Both profiles decreased exponentially and then 270
stabilized within 8 and 11 h, respectively, of irradiation. While 271
the share attributable to the direct bactericidal action of light 272
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Experimental bacterial concentration profiles at the outlet of the process for a feed flow rate of 0.025 L h⁻¹ and at inlet concentration Cₗ = 1 × 10⁷ MNP L⁻¹ without (□) and with (Δ) photocatalytic media. Dark conditions between 0–30 h followed by photolysis (□) or photocatalysis (Δ) with a light flux density at the reactor surface equal to 35 W m⁻².</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Experimental and simulated time-course of bacterial concentrations in the exit stream of the process for a fixed feed flow rate of 0.025 L h⁻¹ and at inlet concentration Cₗ = 1 × 10⁷ MNP L⁻¹. Dark conditions followed by photocatalysis with light flux densities of 10 W m⁻² (◇), 20 W m⁻² (○), and 35 W m⁻² (Δ).</figcaption></figure>

273 was not fully negligible, it nevertheless remained far lower than 274 that of photocatalysis, which is consistent with the literature.

275 **4.2. Light Flux Density Effect.** Irradiation level is a key 276 parameter in characterizing the photocatalytic disinfection 277 process. The influence of flux density in the heterogeneous 278 photocatalysis disinfection process has been widely re- 279 ported,³⁰⁻³² but most of these studies were conducted in 280 batch systems where residence time in the reactor was 281 ultimately not a limiting factor for effective inactivation of the 282 bacteria. In contrast to the batch system, the disinfection of 283 bacteria in an open system takes place in a single pass. In this 284 case, the control of the irradiation conditions in the reactor 285 becomes critical to effective abatement of the cells. Our aim was 286 therefore to evaluate the irradiation effect on the bacteria in the 287 open reactor and assess the capacities of the photocatalytic 288 treatment for population reduction, particularly in steady state.

289 Under dark conditions, the processing loop without any 290 bacteria (At t<sub>r</sub> = 0, C₀ = 0 MPN L⁻¹) was filled with a bacterial 291 suspension charged at 10⁷ MPN L⁻¹. Feed flow rate was fixed at 292 0.025 L h⁻¹ and held constant throughout the experiment. After 293 at least 25 h, when the outlet concentration profile met the 294 value of the inlet concentration (equilibrium was reached 295 between bacteria in adhesion and in liquid phase), the 296 experiments were next led under controlled light flux density. 297 The photocatalytic tests were investigated under irradiation flux 298 densities at the surface of the reactor of 10, 20, and 35 W m⁻². 299 Time-course of bacterial concentration was monitored periodi- 300 cally in the exit stream of the processing loop. The results 301 obtained are shown in Figure 4.

302 As expected, during the first phase, under dark conditions 303 when the bacterial concentrations in the loop increase, the 304 curves corresponding to the three assays overlap. The bacteria 305 concentration profiles during this step only depend on the loop 306 residence time and the initial condition, both of which were 307 identical for the three assays. The good agreement between 308 experimental measures substantiated a decidedly good 309 reproducibility of the assays.

310 During the irradiation phase, the curves obtained also 311 depicted two regimes, i.e., a first regime where bacterial 312 concentrations decreased, and a second regime representing the 313 steady state. This latter scheme corresponded, as for any 314 perfectly mixed open reactor, to the balance between the inlet 315 and outlet steam of bacteria in the system and the

“photocatalytic reaction”, i.e., the disinfection effect, taking 316 place in the reactor under irradiation. Relatively to the 317 respective light flux densities studied, when the steady states 318 were reached, the corresponding bacterial abatements achieved 319 were 80, 89, and 96%. A decisive advantage of working to a 320 permanent regime is to have direct access to the disinfection- 321 rate values thanks to eq 1, i.e., 13 × 10⁵, 14.7 × 10⁵, and 16 × 322 10⁵ MPN h⁻¹ L⁻¹ for 10, 20, and 35 W m⁻², respectively. Even 323 if the effect is moderated, as expected, then results showed that 324 the disinfection rates increased with light flux density. This 325 confirms the dependency of bacterial disinfection on amount of 326 irradiation, i.e., a specific link between the amounts of photons 327 received in the reactor and the production of the radical species 328 responsible for the photocatalytic reaction (oxidative ac- 329 tion).¹⁹,³³ The experiments also demonstrate that in the case 330 of a 2D media probably because of the mass transfer limitation 331 and/or the light saturation of the photocatalytic sites the 332 photons efficiency dramatically decreases with increasing 333 intensity. The photons flux is directly proportional to the 334 level of intensity and at a wavelenght of 365 nm (UV LED 335 panel) 1 J s⁻¹ corresponds to 3 µmol s⁻¹ of photons, but the 336 experimental rate of the bacteria inactivation is far from being 337 proportional to the light flux density.

338

**4.3. Feed Flow Rate Effect.** Next, experiments were 339 carried out to assess the influence of the flow rate conditions. 340 As previously, the processing loop was charged initially with a 341 bacterial suspension at 10⁷ MPN L⁻¹. The feed flow rates 342 studied were 0.06, 0.04, and 0.025 L h⁻¹. As previously, the 343 experimental tests were initiated in the dark then conducted 344 under a constant UV light flux density at the reactor surface of 345 35 W m⁻². Figure 5 shows the results of the bacterial 346 concentrations measured in the exit stream for the variable feed 347 flow rates. 348

Under dark conditions, the increase in bacterial concen-
tration was obviously highly dependent on feed flow rate. 349
Residence time decreased as flow rate increased. Relatively to 350
the respective feed flow rates applied, the residence time values 351
obtained were 4.16, 6.25, and 10 h, which are values of 353
comparable amplitude to those of classic treatment plants 354
(biological treatment). 355

As previously, under irradiation, the bacterial profiles 356 depicted two regimes: a first transitory regime, and a second 357 steady state. Both regimes were strongly dependent on feed 358
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Experimental and simulated time-course of bacterial concentrations in the exit stream of the process for a fixed light flux density of 35 W m⁻² and at inlet concentration Cᵢₙ = 1 × 10⁻⁷ MNP L⁻¹. Dark conditions followed by photocatalysis with feed flow rates equal to 0.06 L h⁻¹ (◇), 0.04 L h⁻¹ (Ο), 0.025 L h⁻¹ (Δ).</figcaption></figure>

flow conditions. The relative bacterial concentration reductions achieved for flow rates of 0.06, 0.04, and 0.025 L h⁻¹ were assessed at 37, 76, and 96%, respectively, but corresponded to bacterial disinfection rates of around 30 × 10⁵, 22 × 10⁵, and 16 × 10⁵ MPN h⁻¹ L⁻¹, respectively. These results clearly showed that high concentrations maintained in the loop in high-flow-rate cases were conducive to high rate of treatment.

Residence time and therefore feed flow condition as well as irradiation intensity appeared to be major factors to be taken into account to properly meet the treatment objectives. Therefore, finding the right trade-off between all factors would be the best way to guarantee the optimal treatment yield. This further demonstrates the great need for a mathematical tool that would be able to predict disinfection performance while taking into account the different hydrodynamic characteristics of the reactor, the catalyst surface, and the various key parameters on which the process depends. This is particularly relevant within an objective of a direct use of the solar energy that is naturally discontinuous. In this case, the validation of a simulation tool able to be representative of the treatment capacities under different irradiation levels (and in transient regime) only represents the first step. The second bottleneck to overcome, not taken into consideration in this paper, will be to implement a storage function able to manage the discontinuity of the resource. Among the different options, hybridation between photocatalysis and sorption is probably one of the most promising.³⁴,³⁵

## 5. PHOTOCATALYTIC TREATMENT SIMULATION

Apart from the experimental conclusions obtained, the objective of the study was to obtain the values of the unknown parameters involved in the mass balances that best reproduce the experimental photocatalytic results. The model summarized in the previous section was based on the coupling between two distinct phenomena.

The first phenomenon is the transfer of bacteria from the solution to the surface of the catalyst, which involves equilibrium conditions between bacteria in adhesion and bacteria in the bulk phase, and a mass transfer coefficient Kₛ. In a fully developed laminar regime, the regime corresponding to the flow conditions of the experiments performed in this work and in the batch working mode of the previous set of experiments, Kₛ can be considered of the same order of

magnitude,²² its value was set to 2.16 × 10⁻⁶ s⁻¹. Likewise, the equilibrium condition qₑ = 31C⁽¹/⁰.⁷⁸⁾ has to be independent of the processing loop. Under dark conditions, these “assumptions” are validated by the comparison of the experimental bacterial concentrations in the liquid phase and the simulated concentrations obtained by the resolution of the set of differential equations for the three different fluid flow conditions (Figures 4 and 5). For each case with C and q set equal to 0 at the initial time, the model was able to translate the working of the process in the presence of the catalyst, whatever the flow conditions. The model successfully managed to reproduce the progressive accumulation of bacteria in the reactor, all while taking into account the bacterial adhesion phenomenon. Obviously, the model described the subsequent state of balance that took place between the flux of bacteria fed into the reactor and the catalyst surface being in equilibrium with the liquid bulk.

Under irradiation, the second phenomenon that enters into play and happens simultaneously to bacteria transfer is the photocatalytic disinfection reactions. It is expressed as a function of bacteria concentration in the liquid phase (C), bacteria in adhesion (q), and power functions of the irradiation intensity Iᵣ (W m⁻³) involving several kinetic coefficients, i.e., α ((m³ J⁻¹)⁻¹), α' ((m³ J⁻¹)⁻¹'), f, and f' (dimensionless). Ideally, these coefficients have to be fully independent of reactor geometry, size, and closed or open working mode. Nevertheless, under irradiation, the values of α, α', f, and f' were optimized to provide the best fit to the new set of data obtained. The parameters were identified using an optimization method (optimization via Matlab) based on the minimization of the mean relative error (MRE) following

$$ \text{MRE (\%)} = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{(C_{\text{exp}} - C)}{C_{\text{exp}}} \right| \times 100 \quad (5) \quad 431 $$

where C and Cᵢₙₐ₅ are the bacterial concentration values predicted by the model and the experimental data, respectively, and n is number of data points. Table 1 reports the kinetic

<table><caption>**Table 1. Optimized Mass Transfer and Kinetic Parameters Involved in the Models**</caption><thead><tr><th>coefficient mass transfer – kinetic</th><th>continuous mode (this study)</th><th>batch mode<sup>2,4</sup></th></tr></thead><tbody><tr><td>K<sub>s</sub> (10<sup>-6</sup> s<sup>-1</sup>)</td><td>2.16</td><td>2.16</td></tr><tr><td>α (10<sup>-6</sup> m<sup>3</sup>J<sup>-1</sup>)<sup>-f</sup></td><td>4.1</td><td>10.2</td></tr><tr><td>α' (10<sup>-6</sup>m<sup>3</sup>J<sup>-1</sup>)<sup>-f'</sup></td><td>1.6</td><td>19.4</td></tr><tr><td>f</td><td>1.39</td><td>1.2</td></tr><tr><td>f'</td><td>0.093</td><td>0.064</td></tr><tr><td>MRE (%)</td><td>20.2</td><td>9.8</td></tr></tbody></table>

parameters identified. Following this optimization process, calculations were performed with the same values of the kinetic parameters, regardless of the operating conditions. Under irradiation, even though deviations were observed, the simulated results obtained under variable light flux densities Iᵣ (Figure 4) and feed flow rates (Figure 5) fitted with a reasonable degree of accuracy (MRE = 20.2%) to the experimental bacterial concentration measures in both transitory and steady-state regimes. The kinetic model was successfully able to describe the overall approach based on the description of the disinfection process in the liquid as well as in the solid phase. This confirmed, accordingly, that bacterial
reduction in the fluid bulk and especially in the steady state is 447
448 definitely governable by the two main phenomena previously
449 described: bacterial transfer to the catalyst surface and the
450 photocatalytic reaction rate dependent on the quantity of
451 energy absorbed into the photoreactor.

Ideally, the concentration profiles should be simulated with 452
the same set of parameters whatever the working mode of the 453
reactor, i.e., batch mode or continuous mode (Table 1). This is 454
indeed the case for the mass transfer coefficient (Ks), but 455
differences emerge for the other kinetic coefficients. Note 456
however that the set of kinetic parameters remains of very 457
similar order of magnitude. It is also interesting to underline 458
that it is mainly the kinetic constant α', characteristic of the 459
inactivation of bacteria in adhesion to the surface of the catalyst, 460
that differs from one type of experiments to another. The 461
coefficient α' differs by a factor of 10 when switching from 462
batch to permanent operation, whereas the kinetic constant α 463
differs by a factor of around two. Although the operating 464
conditions are radically different in both cases, i.e., transient 465
versus permanent regime, cylindrical versus Cartesian geome- 466
try, the initial conditions (in particular, the initial state of the 467
catalyst surface) are probably the most marked differences 468
between the two types of experiments. In batch experiments, 469
the catalyst surface is initially free of bacteria and is rapidly 470
charged at the start of treatment. However, in the steady-state 471
experiments, when irradiation is delivered, the catalyst surface is 472
in equilibrium with the liquid phase and saturated with bacteria. 473
Under irradiation, the density of bacteria at the catalyst surface 474
decreased dramatically at the beginning of the experiment; this 475
is due to the inactivation of bacteria on the surface or in the 476
very near vicinity of the catalyst. Thus, it is probably the 477
assumption of a perfect reversibility of the phenomenon of 478
adhesion (implicit in the adopted formalism) which is partly at 479
fault. Considering that the inactivated bacteria are systemati- 480
cally or immediately released to the liquid phase certainly leads 481
us to overestimate the role played by the surface of the catalyst 482
in the overall inactivation process. On this basis, which should 483
probably be supported by additional tests, it is a priori the 484
results from the experiments carried out with the process 485
operating in continuous mode and leading to a steady state that 486
makes it possible to best approach the reality of a disinfection 487
process. 488

6. CONCLUSION

In this work, we developed a flat-plate open reactor irradiated 489
with an LED panel that made it possible to study photocatalytic 490
inactivation of the target bacteria E. coli. Depending on the 491
initial conditions, with a continuous feed flow and a constant 492
irradiation, the system designed allow to study phenomena in 493
transient as well as steady-state or permanent working mode. In 494
the case of permanent regime, a decisive advantage is a direct 495
access to the experimental inactivation-rate values via a simple 496
mass balance between inlet and outlet. The small scale-up pilot 497
facilitated preliminary investigation of the key factors governing 498
the continuous flow water disinfection system, i.e., light flux 499
density and feed flow rate. At around 35 W m⁻², a level of 500
irradiation in the range of solar UV, the experimentally 501
determined treatment capacity of the selected 2D media is in 502
the range of 15–30 × 10⁵ MPN h⁻¹ L⁻¹. 503

Beside experimental investigations, the second objective was 504
to build on a study undertaken in a previous work a 505
representative model for open reactors operating in continuous 506
mode for photocatalytic inactivation of E. coli. The model is 507

based on coupling the mass transfer between bacteria in the 508
liquid bulk phase and bacteria in “adhesion” at the surface of 509
the photocatalytic media and the inactivation of E. coli at 510
different rates in both phases. After an optimization procedure, 511
the model was successfully compared to the experimental data. 512
It is able to reproduce the evolution in concentrations of viable 513
bacteria in the range of UV flux densities corresponding to solar 514
irradiation and at variable feed flow rates of comparable 515
amplitude to those applied in real-world wastewater treatment 516
plants. Finally, we anticipate this model to be a starting point 517
for the development of a numerical tool for scaling up efficient 518
photocatalytic open reactors using immobilized photocatalytic 519
media and operating under sunlight. This would then offer the 520
possibility to predict process capacities under a dynamic flow 521
regime, as a function, for example, of irradiation availability 522
(weather, daily cycles, and geographic location) and to meet, 523
for example, given operational constraints or objectives, e.g., 524
percentage of abatement, treatment of a given water volume, 525
and so on. 526

■ AUTHOR INFORMATION

Corresponding Author

*E-mail: vincent.goetz@promes.cnrs.fr.

ORCID

Vincent Goetz: 0000-0001-5700-6487

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

We thank G. Hernandez, R. Garcia, and J. J. Huc for their help 535
in designing and building the demonstrator. This work was 536
supported by the Programme “Investissements d’avenir” 537
(Investment for the Future) of the Agence Nationale de la 538
Recherche (National Agency for Research) of the French State 539
under award number ANR-10-LABX-22-01-SOLSTICE. It was 540
also supported by the regional project “Chercheur d’Avenir”. 541

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