Natacha Forey
email: natacha.forey@free.fr

Olivier Atteia

Abdelaziz Omari

Henri Bertin

Saponin foam for soil remediation: On the use of polymer or solid particles to enhance resistance to pollutant destruction

Keywords: Foam, Porous media, Xanthan, Colloidal particle, Light Non-Aqueous Phase Liquid, Soil remediation Highlights

 Addition of xanthan addition increases overall resistance factor but decreases foam strength  Solid colloidal particles increase the foam's resistance to oil-induced destruction  There is an optimal colloidal particle concentration for foam reinforcement

improving the selectivity of hydrocarbon pumping. The present research focuses on this strategy.

When foam comes in contact with organic compounds, its stability is largely affected, regardless of the surfactants used. This has been observed in both bulk and porous media [START_REF] Almajid | Pore-level mechanics of foam generation and coalescence in the presence of oil[END_REF], and mainly depends on the interfacial tensions between oil, gas and water. This de-foaming process takes place via complex mechanisms, and recent theoretical efforts have been carried out to model it [START_REF] Denkov | Mechanisms of Foam Destruction by Oil-Based Antifoams[END_REF]. [START_REF] Manlowe | A Pore-Level Investigation of Foam/Oil Interactions in Porous Media[END_REF] pointed out that the stability of foam in porous media is dependent on the stability of the pseudoemulsion film separating oil and gas bubbles. This pseudo-emulsion film ruptures whenever its drainage time is shorter than the contact time with oil.

Since oil destabilises foam, foam reinforcement is necessary in applications of this technique. Several additives have been used for this purpose, including polymers and colloids [START_REF] Binks | Food-grade Pickering stabilisation of foams by in situ hydrophobisation of calcium carbonate particles[END_REF][START_REF] Hernando | Transport of polymer stabilized foams in porous media: Associative polymer versus PAM[END_REF]. The aim of polymer addition is to increase the bulk solution viscosity of the aqueous phase, inducing long-term stability of the foam, while the aim of adding colloidal particles is to create a mechanical barrier with the subsequent adsorption of particles onto the surface of the bubble [START_REF] Sun | Utilization of Surfactant-Stabilized Foam for Enhanced Oil Recovery by Adding Nanoparticles[END_REF]. However, given the aim of soil remediation, any negative impact of the surfactants or additives on the environment needs to be minimised, and a choice of biodegradable chemical species with low toxicity is fundamental.

Most surfactants that have already been proven to stabilise foams in the presence of oil are not environmentally friendly [START_REF] Farajzadeh | Foam-oil interaction in porous media: Implications for foam assisted enhanced oil recovery[END_REF][START_REF] Osei-Bonsu | Foam stability in the presence and absence of hydrocarbons: From bubble-to bulk-scale[END_REF]. However, due to their low cost, such surfactants are usually favoured in Enhanced Oil Recovery (EOR), as it requires large quantities of surfactant. Thus, an investigation of other types of surfactants is required, and those from other industry fields can offer interesting alternatives.

Among the potential biodegradable surfactants, we focus here on a plant-based surfactant called saponin, as it has promising properties [START_REF] Pradhan | Quest for an eco-friendly alternative surfactant: Surface and foam characteristics of natural surfactants[END_REF]. It has been used in solution for the removal of heavy metal and mixed contaminants via solubilisation and desorption of contaminants [START_REF] Chen | Metal desorption from copper(II)/nickel(II)-spiked kaolin as a soil component using plant-derived saponin biosurfactant[END_REF][START_REF] Song | Simultaneous removal of phenanthrene and cadmium from contaminated soils by saponin, a plant-derived biosurfactant[END_REF]. It is one of the few biodegradable surfactants that has also been used in foam reinforced with colloidal solid particles for the stabilisation of food-grade products [START_REF] Binks | Food-grade Pickering stabilisation of foams by in situ hydrophobisation of calcium carbonate particles[END_REF].

Of the environmentally friendly polymers that can be used as additives, xanthan is a microbial biodegradable polysaccharide with no environmental or health hazards, and is commonly used in the food industry, and medical and environmental applications [START_REF] Bilanovic | Preparation of biodegradable xanthan-glycerol hydrogel, foam, film, aerogel and xerogel at room temperature[END_REF][START_REF] Zhong | Enhanced remedial amendment delivery through fluid viscosity modifications: Experiments and numerical simulations[END_REF]. This product is well known for improving emulsion and the stability of colloidal dispersions, as it increases the continuous phase viscosity and is thixotropic. It presents a very high viscosity at low shear rates (or even a yield stress limit) and highly shear-thinning behaviour at higher shear rates [START_REF] Martínez-Padilla | Effects of xanthan gum rheology on the foaming properties of whey protein concentrate[END_REF][START_REF] Zhong | Rheological behavior of xanthan gum solution related to shear thinning fluid delivery for subsurface remediation[END_REF]. Although xanthan may slightly decrease foamability [START_REF] Martínez-Padilla | Effects of xanthan gum rheology on the foaming properties of whey protein concentrate[END_REF], it has been used by many authors to improve the bulk stability of foams [START_REF] Pu | Experimental investigation of viscoelastic polymers for stabilizing foam[END_REF], both with and without oil [START_REF] Sun | High temperature and oil tolerance of surfactant foam/polymer-surfactant foam[END_REF]. It has also been shown that xanthan helps foam to improve oil recovery in core experiments [START_REF] Sun | High temperature and oil tolerance of surfactant foam/polymer-surfactant foam[END_REF][START_REF] Wei | Role of water-soluble polymer on foam-injection process for enhancing oil recovery[END_REF]. Hence, xanthan was chosen as the reference polymer in the present study.

Another way to reinforce the resistance of foam to oil is through the use of solid colloidal particles. Many particles of various types, shapes and physico-chemical properties are currently used, including alumina particles [START_REF] Yang | Foams Stabilized by In Situ-Modified Nanoparticles and Anionic Surfactants for Enhanced Oil Recovery[END_REF], fly ash nanoparticles [START_REF] Eftekhari | Foam Stabilized by Fly Ash Nanoparticles for Enhancing Oil Recovery[END_REF] and silica particles [START_REF] Nguyen | Pore-Scale Assessment of Nanoparticle-Stabilized CO2 Foam for Enhanced Oil Recovery[END_REF][START_REF] Singh | Synergy between Nanoparticles and Surfactants in Stabilizing Foams for Oil Recovery[END_REF], all of which have been tested in the presence of oil. However, their environmental impact varies with their chemical nature. Silica particles are natural products that have been used in food grade applications [START_REF] Dickinson | Food emulsions and foams: Stabilization by particles[END_REF]. In order to include them in solution, they can be made partially hydrophobic through an appropriate surface treatment, and they are used with surfactants to increase foam stability, although they may decrease foamability in comparison with the surfactant alone [START_REF] Sun | Utilization of Surfactant-Stabilized Foam for Enhanced Oil Recovery by Adding Nanoparticles[END_REF][START_REF] Tang | The effect of SiO2 particles upon stabilization of foam[END_REF][START_REF] Zhang | Aqueous foams stabilized with particles and nonionic surfactants[END_REF]. This justifies the choice of SiO2 particles to test foam reinforcement in this work.

Although some experiments have previously been performed in bulk to study the resistance of foam to oil [START_REF] Aveyard | Aspects of aqueous foam stability in the presence of hydrocarbon oils and solid particles[END_REF], only a few have successfully been applied in porous media [START_REF] Bai | Experimental Investigation of Nanolaponite Stabilized Nitrogen Foam for Enhanced Oil Recovery[END_REF][START_REF] Singh | Synergy between Nanoparticles and Surfactants in Stabilizing Foams for Oil Recovery[END_REF][START_REF] Sun | Utilization of Surfactant-Stabilized Foam for Enhanced Oil Recovery by Adding Nanoparticles[END_REF]. Moreover, these were used only for EOR applications, rather than for environmental purposes. The main goal of this study is therefore to investigate the influence of polymer or solid particles on foam stability and flow resistance in porous media containing residual oil. Since field applications involve the blocking effects of foam on water, this study focuses on both the sweeping efficiency and the foam water content. Only environmentally friendly products were used, and the different foams were tested both in the absence and the presence of oil in columns filled with a porous medium. The injection parameters were first optimised to obtain the strongest foam, and these foams were then reinforced with xanthan polymer or silica solid particles and tested in oil-free and oilcontaining porous media.

Materials and Methods

Chemicals

In this study, all selected surfactants were biodegradable and presented no environmental hazard, as specified by their safety data sheet. The saponin (non-ionic, Fisher-Scientific, France) had a measured critical micelle concentration (CMC) of 0.062%wt. The two other surfactants, cocamidopropyl hydroxysultaine "CH" (zwitterionic, Cola®Teric CBS, Colonial Chemical, Inc., measured CMC = 0.01%vol) and sodium decylglucosides hydroxypropyl phosphate "SDHP" (anionic, Suga®Fax D10, Colonial Chemical, Inc., measured CMC = 0.074%vol) were provided in solution state.

In terms of additives, commercial FNCS xanthan was supplied by Jungbunzlauer (Austria AG). The colloidal silica particles (LUDOX® TM-50, Sigma-Aldrich, France) had a nominal size of 30 nm, as measured by [START_REF] Orts-Gil | Characterisation of silica nanoparticles prior to in vitro studies: from primary particles to agglomerates[END_REF]. The pollutant oil was MACRON 1821 F-4 from Houghton (US) with a density of 0.818 at T = 20°C. Compressed nitrogen N2 (purity > 99.999%) was supplied by Air Liquide (France) and was used to avoid potential oxidation of the chemicals by O2 and to control the level of purity of the gas.

Experimental facilities

In order to measure the viscosity of the aqueous solutions with xanthan, five solutions with xanthan concentrations of 100 to 500 ppm were prepared, and their shear viscosity was first measured at various shear rates by means of a Kinexus controlled stress rheometer (Malvern Instruments, UK) with a Peltier cylinder cartridge. All the samples were prepared with tap water. The shear viscosity was measured at 20°C ± 0.1°C, with a measurement range of between 1 and 1000 s -1 . Values at a shear rate of 12.5 s -1 , corresponding to the Newtonian plateau were used to plot the shear rate versus the concentration of xanthan.

As the native solid particle suspensions were in an aggregated state, they were systematically submitted to ultrasonic solicitation using ultrasonic bath equipment for 10 minutes (Fisherbrand TM Elmasonic, Fisher Scientific France).

The experimental setup used for the coreflood experiments is presented in Fig. . A PHD ULTRA TM piston pump (Harvard Apparatus, US) was used to inject the aqueous solutions. The gas flow rate was controlled by a mass flow meter (EL-FLOW ® , Bronkhorst ® France) under nominal conditions. Gas and aqueous solutions were co-injected into the column. The column was a transparent PVC tube of length 40 cm and inner diameter 4.4 cm, which was sealed with nylon caps at the inlet and outlet. A length of 40 cm was long enough to minimise the entrance and capillary end effects, as suggested in the literature [START_REF] Eftekhari | Effect of Foam on Liquid Phase Mobility in Porous Media[END_REF].The column was filled with sand from Cantillana (France), containing 98.4% SiO2 grains with a D50 of 279 µm, and a particle size distribution as shown in Fig. 2 ("Cantillana SAS," n.d.).

The pressure drop between the inlet and the outlet of the column was measured with a differential transducer (Rosemount 2051 Coplanar TM , Emerson, France) and values were recorded every 10 s.

2.3.

Experimental procedure Various experimental tests were performed in this study, and these are presented in Table 1 with the experimental approaches used.

. Surfactant solutions

All experiments were performed at room temperature. All foaming solutions were prepared using tap water with an ionic strength of below 10 -4 M. Before use, all aqueous solutions were stirred at 400 rpm for at least 10 minutes, except for the xanthan solutions, which required more than 12 h to ensure the complete dissolution of the polymer in water.

All dispersions of solid particles were prepared by mechanical dispersion in a given amount of water, before sonication for 10 minutes in an ultrasonic bath. In parallel, the surfactant solutions were prepared by dissolution or dilution in the same amount of water. The two were then blended in convenient volume proportions.

Coreflood tests

For the coreflood experiments, the column was placed vertically, and water and sand were gradually introduced by adding 2 cm of water and 2 cm of sand alternately. Gentle manual shaking was applied throughout the procedure to uniformly pack the sand grains. The sand packing porosity was calculated based on the exact amount of water and sand used. The column was then placed horizontally, and the absolute permeability was determined using Darcy's law by imposing various flow rates and measuring the pressure drop along the column.

To saturate the medium and to cause surfactant adsorption on the sand grains, 3.3 pore volumes (PV) of solution were injected prior to co-injection. Similar procedures have previously been followed by other authors [START_REF] Del Campo Estrada | Experimental Study of Foam Flow in Sand Columns: Surfactant Choice and Resistance Factor Measurement[END_REF][START_REF] Eftekhari | Effect of Foam on Liquid Phase Mobility in Porous Media[END_REF][START_REF] Jones | Effect of surfactant concentration on foam: From coreflood experiments to implicit-texture foam-model parameters[END_REF], to ensure that any surfactant injected during co-injection only serves for foaming process.

For each surfactant, the gas and foaming solution were co-injected into the column at a variable total flow rate (Qtot). The liquid and gas (N2) flow rates were varied to reach a target foam quality (FQ=Qgas/Qtot) while recording the induced pressure drop every 10 s.

During co-injection, two main parameters were observed to determine the foam properties: the resistance factor (RF, Eq. ( 1)) and the water saturation (Sw, Eq. ( 2)). These are defined as follows:

Qtot w f P P RF          (1)
where Pf and Pw are the pressure drops measured along the core when the foam and water are flowing, respectively, and

PV V S w w  (2)
where Vw is the volume occupied by the aqueous phase in the porous medium. In the presence of the polymer, a modified resistance factor RF * is calculated (Eq. ( 3)) to subtract the pure polymer viscosifying effect of the aqueous phase. This represents the ratio of the pressure drop of the foam Pf to that of the continuous phase (CP) PCP at the same total flow rate, rather than water.

RF is related to the foam strength: the higher the RF, the higher the lamella density, and thus the stronger the foam. Meanwhile, Sw represents the amount of water remaining in the porous medium. A low value of Sw reflects a high efficiency of the foam in terms of displacing water, and shall reflect a decrease in water mobility through the foam.

The standard error for RF values was measured for three typical experiments, and it was found to be 0.68 with 3.2×CMC surfactant alone with four replicates, and 1.2 for 3.2×CMC + 1%wt SiO2 in triplicate. Meanwhile, the standard error for RF* with 3.2×CMC + 200ppm xanthan was measured to be 0.21. The standard errors for the two other experiments were measured to be < 0.4 (data not shown). This set of standard errors demonstrates the reliability of the measurements.

Experiments in the presence of oil

In these experiments, the column was initially completely saturated with oil rather than water, and the procedure described above was followed. Water was injected until no oil was produced, allowing us to calculate the remaining oil saturation. This procedure led to a system at residual oil saturation, a situation encountered in the field at the interface between the aquifer and the LNAPL. To satisfy the conditions for surfactant adsorption, 3.3 PV of surfactant solution were injected before foam injection.

Experiments with the addition of polymer

Several solutions were prepared at fixed saponin concentration (3.2×CMC) to perform column experiments on the addition of polymer. Based on results in the literature [START_REF] Pu | Experimental investigation of viscoelastic polymers for stabilizing foam[END_REF][START_REF] Zhong | Rheological behavior of xanthan gum solution related to shear thinning fluid delivery for subsurface remediation[END_REF], a broad range of polymer concentrations of between 0 and 500 ppm was chosen to test the influence of polymer concentration on foam behaviour.

Experiments were then performed with oil. Two foaming solutions with different polymer concentrations were prepared, and were co-injected with gas into columns in a state of residual oil saturation. The polymer concentrations were chosen so that the same range of concentrations could be scanned as for the experiments without oil.

Experiments with the addition of particles

For these experiments, the range of particle concentrations was suggested by data in the literature, usually reaching a maximum of 1%wt [START_REF] Binks | Food-grade Pickering stabilisation of foams by in situ hydrophobisation of calcium carbonate particles[END_REF][START_REF] Binks | Effect of pH and Salt Concentration on the Phase Inversion of Particle-Stabilized Foams[END_REF][START_REF] Lv | Silica nanoparticles as a high-performance filtrate reducer for foam fluid in porous media[END_REF] under comparable physico-chemical conditions but with different systems.

Experiments were performed following the same experimental procedure, first without and then with oil.

Qtot CP f P P RF          * (3)

Results

3.1.

Choice of surfactant This first set of column experiments was performed in order to compare the saponin surfactant to two other surfactants previously used in the literature for similar purposes [START_REF] Osei-Bonsu | Foam stability in the presence and absence of hydrocarbons: From bubble-to bulk-scale[END_REF][START_REF] Simjoo | Foam stability in the presence of oil: Effect of surfactant concentration and oil type[END_REF]. Table 2 summarises the values of RF and Sw obtained in the steady state for the three selected surfactants. It is observed that both RF and Sw depend on the surfactant used. Based on its low Sw, SDHP is expected to be a promising surfactant, but its RF is below 10, which may lead to a weak foam. CH has a moderate RF but a high Sw, making it uninteresting for the purposes of application. Meanwhile, saponin shows both a relatively high RF and a low Sw, and was therefore selected at this stage for further experiment.

Optimisation of injection parameters

This surfactant hierarchy is based on injection experiments performed at a single Qtot and a given foam quality. The RF values may change at other flow rates. In order to identify the best flow conditions, analogous experiments were performed for a fixed saponin concentration of 3.2×CMC by varying both the foam quality and Qtot over a wide interval.

The results are presented in terms of RF versus foam quality in Fig. 3. For all flow rates, the curves show a region of increase that corresponds to the low-quality regime, where the pressure gradient is independent of the gas flow rate, and a decreasing region that corresponds to the high-quality regime, where the pressure gradient is independent of the liquid flow rate. This is in accordance with the findings of other authors studying chemically different surfactants [START_REF] Osterloh | Effects of Gas and Liquid Velocity on Steady-State Foam Flow at High Temperature[END_REF]. The RF peak is located between 80% and 92% depending on the value of Qtot, and it appears that the optimum foam quality increases as flow rate increases. Moreover, when the injection rate is high, RF decreases with the injection rate. This is in accordance with studies showing that foams of high quality are yield stress fluids with pronounced shear thinning behaviour [START_REF] Denkov | The role of surfactant type and bubble surface mobility in foam rheology[END_REF][START_REF] Princen | Osmotic pressure of foams and highly concentrated emulsions. 2. Determination from the variation in volume fraction with height in an equilibrated column[END_REF]. For low injection rates, RF increases with the injection rate. Fig. 4 shows the variation in Sw as a function of foam quality by gathering all Sw values available, that correspond to any Qtot values. In the low-quality regime, Sw decreases with the foam quality. This indicates that as the foam quality increases, the sweep efficiency becomes more effective, and the amount of water remaining is consequently low. For the strongest foams, for which the quality is above 87%, Sw becomes almost constant. As the amount of gas in the system increases, the number of bubbles increases and the lamella thickness decreases. At a critical level of gas, the lamellae become too thin to be stable and collapse. An equilibrium is then reached between the formation and destruction of lamella, and the water saturation remains stable. Foam quality (%)

When plotted against the total flow rate (Qtot) (see Fig. 5), Sw decreases with Qtot in the low flow rate regime before reaching a limiting saturation at higher values of Qtot. This graph shows that unlike RF, the values of Sw directly depend on Qtot and to a lesser extent on the foam quality. For the remainder of the study, and to maximise sweep efficiency, the experimental conditions were chosen as a foam quality of 87.5% and Qtot of 6.3 mL.min -1 .

In summary, the optimum foam quality and flow rate are defined to give a strong saponin foam in a porous medium, with high RF and low Sw. This foam can then be reinforced with polymer in order to observe the resistance of the foam to oil, and how it behaves when polymer is added.

3.3.

Effects of the addition of polymer 3.3.1. Oil-free conditions An example of the RF and Sw curves obtained as a function of the injected PV is presented in Fig. 6. Similar curves were obtained for other xanthan concentrations. In the beginning, RF increases steadily while Sw decreases linearly, indicating a piston-like foam displacement, until 3. The first analysis concerns the onset of gas breakthrough (BT), i.e. the time of outgassing from the sand column. It can be observed that BT × Sw is almost constant regardless of Cx, meaning that the time to breakthrough is mainly a consequence of the water content reached in presence of foam. Early breakthrough is a consequence of low gas saturation.

These results show that RF rises progressively with xanthan concentration (Cx). However, in contrast to the results in Section 3.2, Sw also increases, indicating the effects of the addition of xanthan. An opposite trend is seen for RF and RF* (Table 3). As Cx increases, RF increases and RF* decreases. An in-depth description of mechanism underlying the observed relationship between RF, RF* and Sw is given in Section 4.1.

. Experiments in the presence of oil

Similar experiments were performed with oil, to observe the resistance of the foam against oil-induced destruction. In these experiments, S i o corresponds to the oil saturation immediately before foam injection, which varies from one experiment to another. Measurements taken after foam injection showed that the oil saturation remained almost constant during the experiment. The results for the presence of oil are shown in Table 4, together with the results for xanthanfree solutions. When RF* is considered, a significant loss is observed compared to experiments in the absence of oil. Indeed, RF* decreases rapidly with xanthan concentration, and it is likely that the foam bubbles are collapsing in contact with oil.

Compared to the use of polymers, it is well known that solid colloidal particles can efficiently stabilise both foams and emulsions against coalescence [START_REF] Singh | Synergy between Nanoparticles and Surfactants in Stabilizing Foams for Oil Recovery[END_REF]. These Pickering-like foams are of increasing interest to researchers, and most studies concern the determination of the optimum physico-chemical conditions of a given system. In the following, a preliminary evaluation of the ability of colloidal SiO2 particles to efficiently stabilise foams is presented for porous media in the absence or presence of oil.

Addition of Solid Colloidal Particles 3.4.1. Optimisation of foam/particle mixture Effects of surfactant concentration in oil-free conditions

Since the addition of xanthan did not lead to foam reinforcement against oil (i.e. RF* did not increase), colloidal particles were considered for use in formulating an oil-resistant foam. Native SiO2 colloidal particles were chosen to perform coreflood experiments. Before reinforcement with colloidal particles, the influence of the surfactant concentration (Cs) on sweep efficiency was investigated. For a saponin surfactant, Cs was varied between 3.2×CMC and 15×CMC for a foam quality of 87.5% and Qtot = 6.3 mL.min -1 . The obtained values of RF and Sw are shown in Fig. 7.

As can be seen from Fig. 7, RF and Sw are well correlated. As Cs is increased to 10×CMC, RF increases and Sw decreases; this behaviour is characteristic of foams that become stronger with more efficient sweeping. Conversely, when the concentration is high enough (e.g. 15×CMC), RF drops and Sw rises, indicating less efficient sweeping. Further explanations of the mechanisms involved are given in Section 4.2.

Effects of colloidal particle concentration in oil-free systems

For a given 3.2×CMC saponin foam, the effect of silica particle concentration on the properties of the foam was studied. In these experiments, two concentrations of SiO2 were used. Table 5 shows the obtained values of RF and Sw. As can be seen from Table 5, the addition of particles at 1%wt does not change the values of Sw or RF. An increase in RF is observed when silica is added at 0.5%wt, without modifying the Sw value.

Optimal addition of particles in an oil-free system

Given the previous results on the importance of surfactant concentration, the saponin concentration was then changed to 10×CMC, while maintaining a SiO2 concentration of 0.5%wt. When solid particles are added at 10×CMC, RF increases from 15.9 to 20.4, but Sw remains unchanged at 0.32 in comparison with saponin alone (Table 6). This indicates that the particles do not change the macroscopic foam structure and that the sweep efficiency remains the same. As the ultimate goal was to develop foam formulations that resist oil-induced destruction within porous media, core experiments were performed in the presence of oil.

Experiments in the presence of oil

In the same way as the oil-free system, the effect of surfactant concentration was investigated in experiments with oil. When the surfactant concentration is increased from 3.2×CMC to 10×CMC, RF increases from 3.6 to 6.3 (with oil) (see Table 6). In the oil-free system, the addition of 0.5%wt SiO2 substantially increases RF, especially for 10×CMC saponin solutions. Thus, 10×CMC + 0.5%wt SiO2 was used in experiments in the presence of oil. The results show an increase in RF from 6.3 (without particles) to 7.7 when silica is added at 0.5%wt (see Table 6). In these experiments, both S i o and Sw remain the same, and are similar to those for the oil-free experiments in which Sw remains unchanged.

To obtain a comprehensive overview of these experiments, the main results for the addition of SiO2 particles are presented in Fig. 8. The effects of surfactant concentration with and without oil can be observed. The increase in RF with increasing surfactant concentration is linked to a decrease in Sw in the absence of oil. The importance of particle addition is also shown in the figure: it causes an increase in RF in the presence of oil, while Sw remains unchanged. surfactant-polymer interaction, a surfactant-polymer complex is formed that slows down adsorption at the surface, decreases the amount of free monomers in solution and thus decreases foamability. This strong interaction can be found for surfactants and polymers of opposite charge [START_REF] Petkova | Foaming and Foam Stability for Mixed Polymer-Surfactant Solutions: Effects of Surfactant Type and Polymer Charge[END_REF].

Conversely, in the case of a weakly interacting system, surfactant monomers can quickly adsorb at the surface during foaming, and the polymer stabilises the new lamella via steric repulsion, thus improving foamability. A system of this type can be found with a non-ionic polymer and a charged surfactant [START_REF] Petkova | Foaming and Foam Stability for Mixed Polymer-Surfactant Solutions: Effects of Surfactant Type and Polymer Charge[END_REF].

Meanwhile, for non-interacting systems, the foamability is thwarted by mitigation of the interface creation rate and a decrease in the diffusion coefficient of the surfactant molecules. In this case, no polymer co-adsorption on the surfactant, or change of gas/water interfacial tension is expected [START_REF] Hernando | Polymer-Enhanced Foams for Water Profile Control[END_REF][START_REF] Petkova | Foaming and Foam Stability for Mixed Polymer-Surfactant Solutions: Effects of Surfactant Type and Polymer Charge[END_REF]. A loss of foamability, even of limited magnitude, is accompanied by a gain in stability. The obtained films are thicker than when the surfactant is used alone and have a low drainage velocity, and lamellae that contain polymers are stabilised by long-range steric interactions [START_REF] Gochev | Thin liquid films stabilized by polymers and polymer/surfactant mixtures[END_REF].

In this situation, it is likely that a weaker foam is created with thicker lamellae, leading to higher Sw, early breakthrough and lower RF*.

The RF*, Sw and breakthrough variations observed in the present study also reveal difficulties in foam formation, a phenomenon that could be due to the formation of a surfactant-polymer complex via strong interactions.

However, in the present study, xanthan and saponin are both non-ionic chemicals whose interactions can take place via hydrogen bonding, a weaker interaction than electrostatic bonding. Formation of a surfactant-polymer complex may not explain the observed behaviour, and it appears that the decrease in foam strength depends mainly on the continuous phase viscosity increase.

Similar experiments were performed with oil to determine the potential oil resistance, and to identify whether the foam creation mechanism observed previously was reproduced. It appears that the expected stabilization of foam against destruction by oil is not induced by xanthan addition. The same degradation of the foam was observed by [START_REF] Hernando | Polymer-Enhanced Foams for Water Profile Control[END_REF] in foams reinforced with non-ionic polyacrylamide, where it was shown that oil acted as a defoaming agent. In an equivalent way to the oil-free experiments, the observed increase in RF is due only to the increase in the continuous phase viscosity, and is hindered by structural loss of the foam. As a corollary, BT decreases and Sw becomes significant in the presence of polymer.

Overall, it appears that the addition of xanthan to a saponin foaming solution results in an increase in the continuous phase viscosity that mitigates liquid drainage and thickens lamellae. It also increases RF, thanks to the viscous contribution, but at the same time hinders the generation of foam, leading to weaker foam in oil-free sandpacks. Moreover, no positive stabilisation effect was observed in the presence of oil, as reported by [START_REF] Shen | Does Polymer Stabilize Foam in Porous Media? Presented at the SPE/DOE Symposium on Improved Oil Recovery[END_REF]. It is therefore not a suitable way to increase foam strength in the presence of oil, but can still be useful if the aim is only to increase the overall RF.

Surfactant concentration

To interpret data from the variations in surfactant concentration, the dynamic displacement of foam in porous media is considered. In fact, during foam flow, lamellae are continuously stretched at the pore body and compressed at the pore throat, where the distance between the walls is smaller, as illustrated in Fig. 9. As surfactant unimers on gas/liquid interface are in dynamic equilibrium with surfactant micelles, compensation for the lack of surfactant molecules during the stretching period is more nearly instantaneous as Cs increases. This leads to lamellae that are more stable against breakage, and stronger foam is transported along the core. However, above a certain value of Cs, the aqueous film may be depleted in the surfactant micelles compared to the nodes due to the important amount of surfactant present. More space is available in the nodes for micelles to be packed in, a situation that is not encountered at standard surfactant concentrations. Thus, an additional attractive force of osmotic origin contributes to the disjoining pressure, leading to the thinning of lamellae and coalescence of less resistant foams. Indeed, according to [START_REF] Jones | Effect of surfactant concentration on foam: From coreflood experiments to implicit-texture foam-model parameters[END_REF], relatively low surfactant concentrations are needed for stabilisation in bulk foam, where large bubbles are formed with low gas/water interface area. Conversely, higher surfactant concentrations are necessary to obtain a similar surface coverage in porous media, as the bubbles are of smaller size and of the same order of magnitude as pore bodies.

Particle addition

The results for 3.2×CMC in the absence of oil show similar values of RF and Sw, without particles and with 1%wt SiO2 (Table 5), suggesting that particles do not have any impact on how the foam rearranges itself inside the porous media. It therefore appears as if the surfactant alone is responsible for foamability and foam stability.

However, an increase in RF is noted when silica is introduced at only 0.5%wt, with no significant increase in the aqueous phase viscosity. The apparent aqueous phase viscosity was determined by measuring RF*, which was equal to RF, implying that the pressure gradients during the flow of water or surfactant solution are the same. Thus, both solutions have the same apparent viscosity. The increase in RF suggests an improvement in gas/water interface rheology when SiO2 is added at this concentration. For other systems, it is usually reported that a loss of foam strength, or equivalently RF, is observed when the particle concentration exceeds a certain threshold value due to particle aggregation [START_REF] Alyousef | The effect of nanoparticle aggregation on surfactant foam stability[END_REF][START_REF] Veyskarami | Synergistic effect of like and opposite charged nanoparticle and surfactant on foam stability and mobility in the absence and presence of hydrocarbon: A comparative study[END_REF] or surfactant adsorption on the surface of the particles, thereby limiting the amount of available surfactant [START_REF] Yang | Foams Stabilized by In Situ-Modified Nanoparticles and Anionic Surfactants for Enhanced Oil Recovery[END_REF]. Thus, the limiting solid concentration should be below 1%wt.

The increase in RF that is observed when SiO2 is added to a 3.2 or 10×CMC solution indicates a microscopic foam change. This probably arises from particles gathering at the gas/water interface, as this would be promoted by a large number of surfactant molecules. This in turn Fig. 9: Sketch illustrating foam displacement at pore scale would strengthen the interface rigidity and induce a higher RF. To interpret this finding, it should be borne in mind that the best adsorption of solid colloids has been reported for partially hydrophobic particles, with a ratio of hydrophobic to hydrophilic sites ratio that depends on the system of interest [START_REF] Binks | Aqueous Foams Stabilized Solely by Silica Nanoparticles[END_REF][START_REF] Tang | The effect of SiO2 particles upon stabilization of foam[END_REF]. It is therefore possible that when free surfactant molecules are available to change the hydrophobicity of colloids, this makes the adsorption of particles at gas/water interface more energetically favourable, thus increasing the interface rigidity. As [START_REF] Yang | Foams Stabilized by In Situ-Modified Nanoparticles and Anionic Surfactants for Enhanced Oil Recovery[END_REF] reported for a different system, with the appropriate surface charges and right surfactant/particle ratio, surfactants could adsorb at the particle surface and improve the stability of the foam. Both the particle network created in the film and the adsorption of the modified particles at the interface contribute to the increase in the bulk stability of the foam and its blocking ability in porous media.

In the oil-free system, the correlation between surfactant concentration and RF increase is explained by the need for additional surfactant due to dynamic foam displacements. In the presence of oil, surfactant concentration and the increase in RF also seem to be correlated. Indeed, since the surfactant is involved in the oil/water emulsion, a higher surfactant concentration is needed for the formation of foam.

The increase in RF when 0.5%wt SiO2 is added to the solution in the presence of oil is similar to the results of oil recovery experiments by [START_REF] Sun | Utilization of Surfactant-Stabilized Foam for Enhanced Oil Recovery by Adding Nanoparticles[END_REF], in which they added 0.5% SiO2 to an SDS foam. Although they reported a slightly higher increase, the surfactant was different, which could lead to different particle/surfactant interactions and potentially stronger adsorption at the interface.

The subsequent increase in RF observed with the addition of particles suggests that colloidal particles do reach the gas/water interface and create a barrier against the oil-induced coalescence of bubbles. This phenomenon is illustrated in Fig. 10, where the presence of oil leads to the foam collapsing in the absence of particles. As particles are added to the formulation, they adsorb at the interface, rigidify it and stabilise the foam against the oil. The thermodynamics of systems with solid particles constituting a rigid barrier at the gas/water interface depends on the contact angle, which itself relies on the degree of hydrophobicity of the particles. When the gas/water interface is covered by solid colloids, they effectively mitigate the entry of oil droplets at the interface, but this is in competition with the opposing capillary effect that takes place between adjacent adsorbed particles [START_REF] Denkov | Mechanisms of Foam Destruction by Oil-Based Antifoams[END_REF].

Applicability in the field

If a foam is used as a blocking agent for water flow, the value of Sw should be as low as possible. For the injection of foam into surficial aquifers, the RF should be significantly higher than 1 for the foam to act as a sweeping agent, but should not be too high due to injectivity constraints. A value slightly lower than or close to 10 is acceptable for practical purposes [START_REF] Maire | Shear-thinning fluids for gravity and anisotropy mitigation during soil remediation in the vadose zone[END_REF][START_REF] Portois | Field demonstration of foam injection to confine a chlorinated solvent source zone[END_REF]. It is shown in Table 6 that for the optimised experiments, these conditions are met, with RF being close to 8 while the Sw is quite low at around 0.36.

Fig. 10: Sketch illustrating the mechanism of foam reinforcement by particles against oil in porous media

In the presence of xanthan, the obtained RF was lower than with particles (6.4 compared to 7.8), and it has been shown that this effect is mainly due to an increase in the viscosity of the fluid in lamellae, while the foam itself seemed to be weakened. This effect, which is not clearly visible in 1D experiments, should be visible in 2D experiments. Indeed, in 2D or 3D situations, a weak foam tends to lead to preferential pathways and thus to a low sweep efficiency. Therefore, in order to reach an homogeneous foam distribution, the addition of particles is preferable over xanthan polymer.

Finally, when considering foam technology for soil remediation, several practical aspects should be taken into account, such as the cost and possible technical issues.

Since foam is mainly composed of gas, the main cost of the procedure lies in the biodegradable surfactant and particles used. Silica particles are easily available and affordable, whereas a biodegradable surfactant can be more expensive.

However, apart from the cost of the surfactant, the procedure is still shown to be less expensive, easier and more feasible than other techniques. In fact, the bare ground needed for excavation is not necessary, little equipment is needed, and the sweeping efficiency is high. Thus, foam is an ideal candidate for a heterogeneous porous medium, particularly when compared to gas sparging or soil flushing. Several challenges still remain, such as the development of oilresistant foams on a larger scale, or the implementation of a push-and-pump technique in which foam would be injected into the same well where oil is pumped. The use of foam is also still contraindicated in scenarios with fast-flowing underground water, as this technique is still under development.

Finally, even with a biodegradable surfactant used in food-grade applications, the addition of this chemical may impact the environment to some extent. Although the health and environmental issues caused by a pollutant are substantially more important than those of a biodegradable surfactant, a risk assessment is always required.

Conclusions

The objective of the present study was to obtain a biodegradable formulation, consisting of surfactant and additives, that can ensure the stability of foam stability in porous media in the presence of organic pollutants. The findings of the present study can be summarised as follows:

-Saponin appears to be the most effective biodegradable surfactant of the three alternatives considered here. -Coreflood experiments confirm the detrimental effects of oil on the stability of foam both in bulk and in porous media for all formulations studied. -The addition of xanthan is less efficient than colloidal solid particles for foam reinforcement, as it increases only the continuous phase viscosity, inducing an increase in flow resistance and a decrease in water saturation. In this respect, environmentally friendly interacting polymer-surfactant systems may be of interest. -Particle addition is shown to inhibit the destruction of foam in the presence of oil by creating a physical barrier at the gas/water interface. In the present study, the optimum concentration of saponin and SiO2 were found to be 10×CMC and 0.5%wt, respectively.

The first comment referred to the addition of quantitative data in the abstract. This was taken into account and the abstract was consequently modified (see lines3-5 p.2).

Then, the reviewer suggested improvement of the manuscript with the help of a native English speaker. The manuscript has therefore been revised by a professional English speaker.

In the first comments Reviewer 1 argued that the solutions were not completely stable and homogeneous during the injection period. We replied that the microscope was not the best way to judge the stability of the injected solution.

To that concern, it can be first replied that although the microscope may shear the solutions and break the possible weak interactions existing between the different particles, it does give a qualitative statement about stability. Another way to judge stability would be to study a possible particles sedimentation. In their experiments with nanoparticles, Binks et al. (2015) used photography to monitor the stability of solution that sediment. However, our solutions showed no visual sedimentation, indicating that any aggregation would be smaller than 200 µm, the limit of visual detection by the human eye. It is also possible that the size of the possible aggregates is not small enough to lead to segregation. Authors used laser diffraction analysis [START_REF] Binks | Food-grade Pickering stabilisation of foams by in situ hydrophobisation of calcium carbonate particles[END_REF]Karakashev et al. 2011) to measure the size of their particles, however, this cannot be used to access the stability of the solution, since stirring is necessary during the measurement, which supply a shearing effect and can break any possible aggregation.

The fourth comments required duplicate or triplicate tests, we have to emphasize the fact that in experiments of foam injection in porous medium, authors usually do not run duplicate or triplicate given the long-time of 2 to 3 days necessary to perform one single coreflood measurement (Aarra et al. 2014;Boeije et al. 2018;[START_REF] Eftekhari | Foam Stabilized by Fly Ash Nanoparticles for Enhancing Oil Recovery[END_REF]Jones et al. 2018;Ma et al. 2013;Pang 2010;[START_REF] Sun | High temperature and oil tolerance of surfactant foam/polymer-surfactant foam[END_REF]Zeng et al. 2016). In our study triplicates were run for five different tests, which all showed a good reproducibility. For all those reasons, and given the necessity to move forward on to the next part of the project, on 2D-tank experiments, it was chosen not to perform duplicates.

Finally, the reviewer commented on the fact that there was a lack of consideration about the mass recovery of the different chemicals.

For each experiment involving oil, the amount of recovered oil is lower than 3.5 mL, which represents less than 2% of the initial amount present in the column. It is difficult to be more precise than that, particularly in the presence of surfactants, because it is very difficult to separate surfactants from oil, and even when achieved, the extraction factor is not up to 100% (Gao et al.

Fig. 1 :

 1 Fig. 1: Schematic representation of the experimental setup for foam injection in the core experiments

Fig. 3 :

 3 Fig. 3: RF for saponin injection at 3.2×CMC versus foam quality for different Qtot

Fig. 5 :

 5 Fig. 5: Final water saturation versus Qtot (mL.min -1 ) for saponin-based foams at 3.2×CMC

Fig. 7 :

 7 Fig. 7: Measured RF and Sw at different saponin concentrations

Table 1 :

 1 Summary of experiments

	Tests	Purpose	Experimental approach
	Set 1: Choice of surfactant	Selection of the surfactant	Try three different
			surfactants
	Set 2: Optimisation of	Choosing the experimental	Vary both Qtot and foam
	injection parameters	conditions	quality to achieve the lowest
			Sw and highest RF

Table 2 :

 2 Measured RF and Sw for different surfactants at 3.2×CMC, Qtot = 6.3mL.min -1 and at a quality of 87.5%

	Surfactant	RF	Sw
	Saponin	11.5	0.39
	Cocamidopropyl hydroxysultaine (CH)	10.0	0.44
	Sodium decylglucoside hydroxypropyl	6.6	0.34
	phosphate (SDHP)

Table 3 :

 3 Shear viscosity at 12.5 s -1 , RF, pressure drop ratio ΔPpolymer/ΔPw, RF*, Sw, BT and BT×Sw at 3.2×CMC of saponin and

	various xanthan concentrations
	Cx (ppm) µ.10 -3	RF	ΔPpolymer/ΔPw	RF*	Sw	BT (PV)	BT × Sw
		(Pa.s)
	0	1.1	11.5	1.0	11.5	0.39	0.67	0.261
	100	2.3	10.0	1.6	6.5	0.38	0.64	0.243
	200	3.7	18.4	2.4	7.8	0.43	0.64	0.275
	300	5.7	22.4	4.3	5.6	0.45	0.65	0.292
	400	8.1	23.4	5.6	4.2	0.45	0.62	0.279
	500	11.5	24.3	7.8	3.1	0.52	0.52	0.270
	3.3.2

Table 4 :

 4 RF, pressure drop ratio ΔPpolymer/ΔPw, RF*, Sw, S i o and foam breakthrough onset BT at 3.2×CMC of saponin in the presence of oil for various xanthan concentrations

	Cx (ppm)	RF	ΔPpolymer/ΔPw	RF*	Sw	S i o	BT (PV)
	0	3.5	1.0	3.5	0.31	0.18	0.51
	200	4.0	2.8	1.4	0.48	0.12	0.38
	500	6.4	6.0	1.1	0.44	0.21	0.31

Table 5 :

 5 Measured RF and Sw at 3.2×CMC of saponin in oil-free experiments, with and without colloidal solid particles

		RF	Sw
	Saponin alone	11.5	0.39
	Saponin + SiO2 (0.5%wt)	14.2	0.41
	Saponin + SiO2 (1%wt)	11.5	0.38

Table 6 :

 6 Measured RF, Sw and S i o at 10×CMC of saponin with and without oil, and with and without colloidal particles

	RF	Sw	S i o

Acknowledgments

This research was funded by Innovasol Consortium. We thank Marian Montbrun for the help provided in conducting the experiments. We are grateful to Dr I. Delsarte and Dr. G. Cohen for their comments on the manuscript.

COT (Carbon Organic Total) could be used to measure the amount of carbon in a sample, and then lead to the sum of components. However, another mean would still be necessary to differentiate surfactants from polymer or surfactants from oil.


R Zocatelli

C Le Milbeau

F Leroy

J Jacob

S Gogo

F Laggoun-Défarge

SEASONAL DISTRIBUTION OF 5-N-ALKYLRESORCINOLS FROM MOLINIA CAERULEA: POTENTIAL IMPLICATIONS FOR VEGETATION DYNAMICS IN PEATLANDS

Sphagnum-dominated peatlands constitute an important stock of carbon (C) in soil at the global [START_REF] Gorham | Northern peatlands: role in the carbon cycle and probable responses to climatic warming[END_REF][START_REF] Yu | Global peatland dynamics since the Last Glacial Maximum[END_REF]. Sphagnum mosses have a key role in peat accumulation by creating acidic, nutrient poor, wet and anoxic conditions and generating recalcitrant litters [START_REF] Van Breemen | How Sphagnum bogs down other plants[END_REF]. However, in response to global change, many peat bogs are colonized by vascular plants (such as M. caerulea), that could disrupt their carbon storing capacities. In this context, the effect of the interaction between Sphagnum and colonizing plants on carbon storage needs to be elucidated. Specifically, chemical interactions between plant species can have a significant effect on the structure and functioning of the ecosystem.

La Guette peatland (Neuvy-sur-Barangeon, France) is a Sphagnum fen invaded by vascular plants since the 1970s. M. caerulea (Poaceae) is the most abundant vascular plant in this peatland. Here, we report for the first time the occurrence of a series of 5-n-alkylresorcinols (AR) homologues in M. caerulea leaves and in underlying peat samples in the peatland. These compounds were previously reported in bog-forming plant species classified as sedges [START_REF] Avsejs | 5-n-Alkylresorcinols as biomarkers of sedges in an ombotrophic peat section[END_REF]. They are reputed to play a key role in plant chemical interactions by stimulating seed germination and shoot elongation via enzymes activity [START_REF] Scognamiglio | Allelopathic potential of alkylphenols from Dactylis glomerata subsp. Hispanica (Roth) Nyman[END_REF]. This could be the key to the success of M. caerulea in colonizing peatlands. In order to untangle the role of these molecules in peatland functioning, we first explore abiotic factors that determine their amount and distribution in M. caerulea leaves. Then we study AR accumulation in peats in order to discuss their potential use in paleoenvironmental studies.

M. caerulea leaves were collected in the La Guette peatland between April and December 2014 during seven field campaigns. During each campaign, leaves were collected near 4 plots where a specific monitoring of environmental parameters was done (air and soil temperature, water level, photosynthetically active radiation and number of M. caerulea leaves). Peat profiles were sampled in two zones: (i) invaded and (ii) not invaded by M. caerulea. Lyophilized and crushed samples were ultrasonically extracted and separated according to [START_REF] Zocatelli | Spatial variability of soil lipids reflects vegetation cover in a French peatland[END_REF] into neutral, acidic and polar compounds on aminopropyl bonded silica. The alcohol fraction was silylated and 5α-cholestane was added as internal standard prior to analysis using gas chromatography-mass spectrometry.

In M. caerulea leaves, homologues series of 5-n-alkylresorcinols ranged from 5n-C15 to 5n-C25 with a strong odd-over-even predominance. AR dominant homologues vary through the season. Total ARs contents found in M. caerulea leaves varied between 3.6 ± 0.5 µg g -1 in April to 28.3 ± 2.4 µg g -1 in November. Dominant 5n-C17, 5n-C19 and 5n-C21 homologues in April samples gradually shift towards 5n-C19, 5n-C21 and 5n-C23 in December samples. Average chain length (ACL) of ARs varied from 19.7 ± 0.7 to 21.4 ± 0.11 during M. caerulea season life cycle. Total AR contents in leaves are negatively correlated with photosynthetically active radiation (r 2 = 0.64; p<0.05). Other measured parameters do not appear to have any significant effect on AR content and distribution. The correlation between photosynthetically active radiation and AR concentration are unlikely to result from photodegradation [START_REF] Magnucka | Various effects of the photosystem IIinhibiting herbicides on 5-nalkylresorcinol accumulation in rye seedlings[END_REF]. Reversely, it could be linked to AR accumulation rates in cuticular waxes of plants (Ji and Jetter, 2009). AR were present in high contents (from 8.0 to 15.1 μg g -1 of total AR) in peat samples invaded by M. caerulea, although contents are lower than those reported by [START_REF] Avsejs | 5-n-Alkylresorcinols as biomarkers of sedges in an ombotrophic peat section[END_REF] in an ombrotrophic peat. AR series varied from 5n-C9 to 5n-C25, with a maximum at 5n-C19 (ACL = 19.6). In samples of peat that were not invaded by M. caerulea, AR contents were low (from 0.06 to 2.41 µg g -1 ), and distributions ranged from 5n-C19 to 5n-C25 with a maximum at 5n-C21 (ACL = 21.8). This study reveals that AR could respond to photosynthetically active radiation in M. caerulea. In addition to sedges, AR could be used as specific biomarkers of M. caerulea in palaeoenvironmental studies.

th International Meeting on Organic Geochemistry (IMOG) 1-6 September 2019, Gothenburg, Sweden


Hai Huang
email: hai.huang@inria.fr

Fabien Gandon
email: fabien.gandon@inria.fr

Learning URI Selection Criteria to Improve the Crawling of Linked Open Data (Extended Abstract) *

A Linked Data crawler performs a selection to focus on collecting linked RDF (including RDFa) data on the Web. From the perspectives of throughput and coverage, given a newly discovered and targeted URI, the key issue of Linked Data crawlers is to decide whether this URI is likely to dereference into an RDF data source and therefore it is worth downloading the representation it points to. Current solutions adopt heuristic rules to filter irrelevant URIs. But when the heuristics are too restrictive this hampers the coverage of crawling. In this paper, we propose and compare approaches to learn strategies for crawling Linked Data on the Web by predicting whether a newly discovered URI will lead to an RDF data source or not. We detail the features used in predicting the relevance and the methods we evaluated including a promising adaptation of FTRL-proximal online learning algorithm. We compare several options through extensive experiments including existing crawlers as baseline methods to evaluate their efficiency.

Introduction

Linked Data extends the principles of the World Wide Web from linking documents to that of linking pieces of data to weave a Web of Data. This relies on the well-known linked data principles [Heath and Bizer, 2011;Berners-Lee, 2006] including the use of HTTP URIs that can be dereferenced and the provision of useful and linked descriptions upon access so that we can discover more things.

A large amount of data are now being made available as linked data in various domains such as health, publication, agriculture, music, etc., and the Web of Linked Data is growing exponentially [START_REF] Ermilov | [END_REF]. In order to harvest this enormous data repository, crawling techniques for Linked Data are becoming increasingly important. Different from conventional crawlers, crawling for Linked Data is performed selectively to collect structured data connected by RDF links. The design objective of Linked Data crawlers is to fetch linked RDF data in different formats -RDF/XML, N3, RDFa, JSON-LD, etc. -as much as possible within a reasonable time while minimizing the download of irrelevant URIs -i.e. leading to resources without RDF content. Therefore our challenge is to identify as soon as possible the URIs referencing RDF sources without downloading them first. Our research question here is: can we learn efficient URI selection criteria to identify sources of Linked Open Data?

To solve this problem, we propose and compare methods on real data to predict whether a newly discovered URI will lead to RDF data source or not. We extract information from the targeting and referring URI and from the context (RDF data graph) where URIs and their links were discovered in order to produce features fed to learning algorithms and in particular to an FTRL-proximal online method employed to build the prediction model.

The contributions of this work include: 1) we identify the features to predict whether a target URI will lead to some RDF data or not; 2) we adapt the FTRL-proximal algorithm to our task and build an online prediction model; and 3) we implement a Linked Data crawler with the online prediction model and evaluate its performance.

Related Work

Semantic web crawlers differ from traditional web crawlers in only two aspects: the format of the source (RDF format) it is traversing, and the means to link RDF across data sources. There exist some work [Dodds, 2006;[START_REF] Isele | [END_REF]Hogan et al., 2011] in the field of Semantic Web/Linked Data crawling. The two main representative crawlers for Linked Data are LDSpider [START_REF] Isele | [END_REF] and SWSE crawler [Hogan et al., 2011]. They crawl the Web of Linked Data by traversing RDF links between data sources and follow the Linked Data principles [Heath and Bizer, 2011;Berners-Lee, 2006].

In order to reduce the amount of HTTP lookups and downloading wasted on URIs referencing non-RDF resources, these previous works apply heuristic rules to identify relevant URIs [START_REF] Isele | [END_REF]Hogan et al., 2011]. The URIs with common file extensions (e.g., html/htm, jpg, pdf, etc.) and those without appropriate HTTP Content-Type Header (such as application/rdf+xml) are classified as non-RDF content URIs. The content of these URIs would not be retrieved by these crawlers. Although this heuristic-based method is efficient, it impairs the recall of the crawling. This method makes the assumption that data publishers have provided correct HTTP Content-Type Header but this is not always the case. It can happen that the server does not provide the desired content type. Moreover, it may happen that the server returns an incorrect content type. For example, Freebase did not provide any content negotiation or HTML resource representation [Färber et al., 2018], and only text/plain was returned as content type. In [START_REF] Hogan | [END_REF], it is reported that 17% of RDF/XML documents are returned with a content-type other than application/rdf+xml. As a result, a huge volume of RDF data is missed by these methods.

Traditional focused crawlers on the Web aim to crawl a subset of Web pages based on their relevance to a specific topic [START_REF] Chakrabarti | [END_REF]Diligenti et al., 2000]. These methods build a relevancy model typically encoded as a classifier to evaluate the web documents for topical relevancy. Our work here is different in the sense that we do not filter on the topics but on the type of content. Meusel et al. [START_REF] Meusel | [END_REF] proposed a focused crawling approach to fetch microdata embedded in HTML pages. [START_REF] Umbrich | [END_REF] built a crawler focused on gathering files of a particular media type and based on heuristics.

Prediction Model for Crawling Criteria

In this section, we present the prediction task, the feature sets extracted for the task of prediction and then describe the prediction model based on FTRL-proximal online learning algorithm.

Task Description

Since the task of Linked Data crawlers is to fetch RDF data on the Web, we are interested in RDF-relevant URIs: Definition 1. (RDF-Relevant) Given a URI u, we consider that u is RDF-relevant if the representation obtained by dereferencing u contains RDF data. Otherwise, u is called non RDF-relevant. We note U R the set of RDF relevant URIs and U I the set of non RDF-relevant URIs with U = U R ∪U I .

For the URIs that have certain file extensions such as *.rdf/owl or HTTP Content Types Headers such as application/rdf+xml, text/turtle, etc., it is trivial to know the RDFrelevance of them. In this work, we focus on a knid of URIs called hard URIs whose RDF-relevance cannot be known by these heuristics. Definition 2. (Hard URI) We call u a hard URI if the RDF relevance of u cannot be known straightforwardly by its file extension or HTTP Content-Type Header.

For example, URI u with HTTP Content-Type header text/html is a hard URI since RDFa data could be embedded in u. As reported in [Färber et al., 2018], the URIs with HTTP Content-Type Header text/plain may contain RDF data so they are hard URIs too.

Then, for our prediction task, we consider four types of URIs involved in the prediction: the target URI, the referring URI, direct property URIs and sibling URIs. Definition 3. (Target URI) We call target URI and note u t the URI for which we want to predict if the deref (u t ) will lead to a representation that contains RDF data i.e. if u t is RDF-Relevant. Definition 4. (Context RDF Graph) Given an RDF relevant URI u r containing the RDF graph G r t , we define G r t = (V, E) as the context RDF graph of URI u t if u t appears in G r t as a node, i.e., u t ∈ V . Definition 5. (Referring URI) Given a target URI u t , we call "referring URI" and note u r the RDF-relevant URI that was dereferenced into a representation deref (u r ) containing the context RDF graph G r t in which we found the target URI u t . Definition 6. (Sibling Set) The sibling set of u t in the context RDF Graph G r t denoted by Sib ut is the set of all other URIs that have common subject-property or property-object pair with u t in G r t . Sib ut is formally defined as:

Sib ut = {s|∃p, o ∈ G r t , s.t.(u t , p, o) ∈ G r t ∧ (s, p, o) ∈ G r t } ∨ {o|∃s, p ∈ G r t , s.t.(s, p, u t ) ∈ G r t ∧ (s, p, o) ∈ G r t } Definition 7. (Direct Property Set)
The direct property set P S t is the set of properties that connect u t in the context graph G r t :

P S t = {p|∃s, o ∈ G c t , s.t.(u t , p, o) ∈ G r t ∨ (s, p, u t ) ∈ G r t } Prediction Task.
Using these definitions we can now define our prediction task. Suppose that a hard URI u t is discovered from the context graph G r t obtained by dereferencing a referring URI u r . We want to predict if u t is RDFrelevant based on some features extracted from u t , u r , P S t and Sib ut . Our task is to learn the mapping Relevant :

U → {0, 1} with Relevant(u) equals 1 if u is RDF-relevant ( Relevant| U R → 1) and equals 0 if u is not RDF-relevant (Relevant| U I → 0)

Feature Extraction

We distinguish between two kinds of features that can be exploited for the prediction intrinsic and extrinsic. Definition 8. (Intrinsic URI Features) The intrinsic features of a URI u are features obtained by an extraction F int : U → F that relies exclusively on the URI identifier itself.

An example of an intrinsic feature is the protocol e.g. http. Definition 9. (Extrinsic URI Features) The extrinsic features of a URI u are features obtained by performing some network call on the URI F ext : U → F .

The URI generic syntax consists of a hierarchical sequence of several components. An example of URI is shown in Fig. 1.

The intrinsic features F int (u) consider that the different components of a URI u are informative and contain helpful information for prediction. The components include: scheme, authority, host, path, query, fragment information. We generate the intrinsic features of u based on these components.

We further distinguish two kinds of extrinsic features: Definition 10. (URI Header Features) These extrinsic features of a URI u are obtained by an HTTP HEAD call F head : U → F and do not require to download the representation of the resource identified by u. Definition 11. (URI Representation Features) These extrinsic features of a URI u are obtained by an HTTP GET call F get : U → F and characterize the content obtained after a complete download of the representation of the resource identified by u.

We distinguish F head and F get because they have different costs in terms of network access time. The URI header feature we will consider in this paper is the content type e.g. feature u t contentType='text/html'.

URI representation features come from the content of the referring URI u r , namely the context graph G r t of u t which includes the direct properties and siblings information of u t . Definition 12. (URI Similarity Features) The similarity between two URIs u s and u t denoted by simV alue(u s , u t ) is defined using the Levenshtein distance [Levenshtein, 1966] between the two strings representing these URIs. In order to reduce the feature space, a threshold τ is set for the similarity value and if the similarity value is larger than τ , we discretize the similarity as simV alue(u t , u s ) = high and otherwise simV alue(u t , u s ) = low. Definition 13. (RDF Relevance Features) The boolean characteristic of being RDF-relevant for a URI u is noted F RDF rel (u) and returns true if u is RDF-revelant and false otherwise.

With the types of features explained above we can now define four atomic feature sets based on the sources the features come from to explore and evaluate when training and predicting the RDF-relevance of a target URI u t :

• F +t = F int (u t ) + F head (u t ) is a feature set considering the intrinsic and header features of the target URI u t . • F +r = F int (u r ) is a feature set considering the intrinsic features of the referring URI u r .

• F +p = p∈P St F int (p) is the set of intrinsic features of each direct property of the target URI u t . • F +x = us∈Sibu t F x (u s ) is a feature set including fea-
ture crosses that combine the intrinsic, header, similarity and relevance features of the sibling URIs of u t . This supports predictive abilities beyond what those features can provide individually and can be interpreted as using a logical conjunction 'AND' to combine these features

F x (u s ) = F int (u s )× F head (u s ) × F sim (u s , u t ) × F RDF rel (u s )
With these definitions we can now consider and evaluate any combination of feature sets. We note F +a the feature set with a being a combination of the atomic feature sets as defined above. For instance an experiment using the feature set Algorithm 1: Online prediction with the FTRL-Proximal algorithm Data: F +t+r will only consider as inputs the intrinsic and header features of the target URI u t and the intrinsic feature of referring URI u r . In Section 4, the predictive abilities of different combinations of the feature sets are examined.

URIs u 1 , u 2 , • • • , u T Result: ŷ1 , • • • , ŷT 1 for t = 1 to T do

Online Prediction

During the process of crawling, the crawler will encounter URIs belonging to millions of domains, and the number of properties could be tens of thousands. Obviously, the potential feature space will be huge. Thus, we use feature hashing technique [START_REF] Weinberger | [END_REF] to map high-dimensional features into binary vectors.

We assume that data becomes available in a sequential order during the crawling process. The predictor makes a prediction at each step and can also update itself: it operates in an online style. Compared to batch methods, online methods are more appropriate for the task of crawling since the predictor has to work in a dynamic learning environment. FTRL-Proximal [McMahan et al., 2013;[START_REF] Mcmahan | [END_REF] developed by Google has been proven to work well on the massive online learning problems. FTRLproximal outperforms the other online learning algorithms in terms of accuracy and sparsity, and has been widely used in industry, e.g., recommender systems and advertisement systems. We adopt this learning algorithm in our work.

We use x t to denote the feature vector of URI u t and y t ∈ {0, 1} the true class label of u t . Given a sequence of URIs

u 1 , u 2 , • • • , u r , • • • , u t ,
the process of online prediction based on FTRL-Proximal algorithm is shown in Algorithm 1. We adapted the original algorithm to make it output binary values by setting a decision threshold τ . If the predicted probability p t is greater than the decision threshold τ ∈ [0, 1], it outputs prediction ŷt = 1; otherwise ŷt = 0.

At round t + 1, the FTRL-Proximal algorithm uses the update formula (1):

w t+1 = argmin w ( t s=1 w • g s + 1 2 t s=1 σ s w -w s 2 2
+ λ 1 w 1 ).

(1) In equation ( 1), w t+1 is the target model parameters to be updated in each round. In the first item of equation (1), g s is the gradient of loss function for training instance s. The second item of equation ( 1) is a smoothing term which aims to speed up convergence and improve accuracy, and σ s is a non-increasing learning rate defined as

t s=1 σ s = √ t.
The third item of equation ( 1) is a convex regularization term used to prevent over-fitting and induce sparsity. Subsampling. Not all URIs are considered as training instances since we are interested in hard URIs. We exclude from training URIs with the extensions such as *.rdf/owl. Inversely, URIs with the file extension *.html/htm are included in the training set since they may contain RDFa data. The true class label y t is required to update the predictor online. In our scenario, to observe the true class label of a URI we have to download it and check whether it contains RDF data or not. We cannot afford to download all URIs because of the network overhead, and our target is to build a prediction model that avoids downloading unnecessary URIs. There, an appropriate subsampling strategy is needed. We found that the positive URIs are rare and relatively more valuable. For each round, if URI u t is predicted positive namely ŷt = 1, we retrieve the content of u t and observe the real class label y t . Then the predictor can be updated by new training instance (y t , x t ). For those URIs predicted negative, we only select a fraction ∈]0, 1] of them to download and observe their true class label. Here is a balance between online prediction precision (which requires as many URIs as possible for online training) and downloading overhead. To deal with the bias of this subsampled data, we assign an importance weight 1 to these examples.

Empirical Evaluation

In the first experiment, we evaluate the predictive ability of different combinations of feature sets introduced in Section 3.2 by using offline classifiers including SVM, KNN and Naive Bayes. The dataset including 103K URIs and 9,825 different hosts is generated by operating a Breadth-First Search (BFS) crawl. As described in Section 3.2, the feature sets include F +t derived from the target URI u t , F +r derived from the referring URI u r , F +p derived from the direct properties of u t and F +x derived from the siblings of u t .

In Figure 2(a) we report the results for the 4-element combination of feature sets ( F +t+r+p+x ), all 3-element combinations and the 2-element combinations ( F +t+x , F r+p , F +p+x ) which have the best performance in their class. Among all combinations, the 3-element combination F +t+r+p outperforms the other combinations with F-measure 0.8216 and accuracy 0.7902. The 4-element combination F +t+r+p+x as the second best combination scores F-measure 0.7944 and accuracy 0.7722. We found that augmenting sibling features to F +t+r+p is not helpful to improve the performance in the cases of three classifiers. We also found that the performance of F +r+p+x which is derived by excluding feature set F +t from F +t+r+p+x decreases a lot (the worst in all 3-element combinations) compared to the performance of F +t+r+p+x . It indicates that the features from target URI u t are important.

In the next experiment, we evaluate the performance of the proposed online prediction method against several baseline methods. We implemented the proposed crawler denoted by LDCOC (Linked Data Crawler with Online Classifier). The implementation detail of LDCOC is provided in [Huang and Gandon, 2019]. The decision threshold τ in Algorithm 1 is set to 0.5 and the parameter of LDCOC is set to 0.17 according to the ratio of the number of positive URIs to the number of negative URIs in the previous training set. We also implemented three crawlers with offline classifiers including SVM, KNN and Naive Bayes to select URIs. The classifiers are pre-trained with two training sets (with size 20K and 40K). The BFS crawler is another baseline to be compared to. As suggested above, we use the feature set F +t+r+p for the experiment. Figure 2(b) shows the percentage of retrieved RDF relevant URIs by different crawlers after crawling 300K URIs. Since the Linked Data Web is a dynamic environment, the crawlers with offline classifiers pre-trained by a small size training set would not improve performance a lot and our proposed crawler LDCOC outperforms these crawlers.

Conclusion

We have presented a solution to learn URI selection criteria to improve the crawling of Linked Open Data. The method is able to predict whether a newly discovered URI contains RDF content or not by extracting features from several sources and building a prediction model based on FTRL-proximal learning algorithm. The experimental results demonstrate that the coverage of the crawl is improved compared to baseline methods. Our method can also be generalized to crawl other kinds of Linked Data such as JSON-LD, Microdata, etc.

Figure 1 :

 1 Figure 1: Example of a URI with component parts.

Figure 2 :

 2 Figure 2: (a) Performance of the combinations of feature sets; (b) Percentage of retrieved RDF relevant URIs by different crawlers.

  do 2 get feature vector x t of u t ; 3 probability p t = sigmoid(x t • w t );

	4	if p t > τ then
	5	output ŷt =1;
	6	else
	7	output ŷt =0;
	8	end
	9	observe real label y t ∈ {0, 1};
	10	update w t+1 by equation (1);
	11 end

 Huang and Gandon, 2019] that won the best paper award at ESWC-2019. This work is supported by the ANSWER project PIA FSN2 N • P159564-2661789/DOS0060094 between Inria and Qwant.