Gabriel Ferro

Davy Carole

François Cauwet

Loren Acher

Hyewon Ji

Rodica Chiriac

François Toche

Arnaud Brioude

Thermochromic properties of some colored oxide materials

The thermochromic properties up to 500°C of some colored, commercial and non-toxic oxides were studied. It includes Bi 2 O 3 , Fe 2 O 3 , In 2 O 3 , WO 3 , Er 2 O 3 , YAG:Ce (Ce doped yttrium aluminum garnet Y 3 Al 5 O 12 ) and YInMn-Blue (Mn doped yttrium indium oxide YInO 3 ). From the CIELAB colorimetric parameters (L*a*b*), evolution of the color contrast ΔE with temperature was calculated. It allowed estimating that most of these oxides change color reversibly with increasing temperature. Two exceptions were identified: i) Er 2 O 3 which stayed almost identically pink on all the temperature range and ii) WO 3 which original color was not recovered upon cooling back to room temperature. While the color change of the other oxides was gradual with increasing temperature, YAG:Ce compound behaved differently since its thermochromicity abruptly started above 300°C. The obtained datasets for all these compounds can be used for anticipating the thermochromic properties of these powders and the blends made out of them.

Introduction

Reversible thermochromic materials share the property of changing color reversibly upon temperature variation, i.e. colored is restored upon coming back to the original temperature [START_REF] Day | Thermochromism of inorganic compounds[END_REF]. Several potential applications have been reported depending on the temperature range of interest: below 100°C for smart windows [START_REF] Cui | Thermochromic VO 2 for Energy-Efficient Smart Windows[END_REF], clothing [START_REF] Ramlow | Smart textiles: an overview of recent progress on chromic textiles[END_REF] or ink [START_REF] Kulcar | Colorimetric properties of reversible thermochromic printing inks[END_REF] and at higher temperatures for safety sensors or warning signals or simply temperature indicators [5]. For the high temperature applications, the thermochromic materials need to withstand the temperature change under air so that they should be thermally and chemically stable, in addition to be composed of non-toxic elements. The studied compounds are thus almost exclusively metal oxides. Their color change with temperature is usually not abrupt and rather moderate so that research focuses on complex and/or new oxides for trying to improve the color contrast, such as with pure or doped Y 3 Fe 5 O 12 [START_REF] Serier-Brault | Thermochromism in Yttrium Iron Garnet Compounds[END_REF], Er 3 Ga 5 O 12 [START_REF] Liu | Design Principles for 3d Electron Transfer in a Ga-based Garnet To Enable High Performance Reversible Thermochromic Material Color Maps[END_REF], (Bi 1- x Y x ) 2 O 3 [START_REF] Liu | Thermochromic Behavior of Yttrium-Substituted Bismuth Oxides[END_REF], Ca 14 Zn 6 Ga 10 O 35 [START_REF] Liu | Reversible thermochromic property of Cr[END_REF], SrZr 4 (PO 4 ) 6 [START_REF] Yanase | Substitution effect of V, Ca, and Ba on the thermochromic properties and thermal expansion of SrZr 4 (PO 4 ) 6[END_REF] or Sm 3-x Bi x Fe 5 O 12 [START_REF] Liu | In-situ optical and structural insight of reversible thermochromic materials of Sm 3-x Bi x Fe 5 O 12[END_REF].

But, do we really know the thermochromic properties of all the commercially available nontoxic colored oxides? For sure not. Some of them are known to be thermochromic (e.g. Bi 2 O 3 [START_REF] Gaudon | How to mimic the thermo-induced red to green transition of ruby with control of the temperature via the use of an inorganic materials blend?[END_REF][START_REF] Belykh | Thermochromic material[END_REF], Fe 2 O 3 [5] or WO 3 [START_REF] Weil | The beautiful colours of tungsten oxides[END_REF]) but detailed reports on their color change (showing photos and with CIE-Lab parameters) over a large temperature range can hardly be found. Some other commercial colored oxides have not been studied yet for their thermochromic properties. The goal of the present work is to fill the gap by characterizing the thermochromic properties up to 500°C of some commercially available colored oxides for sake of data-set completion. These data could be useful for anticipating the thermochromic properties of blends made out of these oxides as well as for identifying thermostable pigments (which do not change color with temperature) among them.

Experimental

The colored oxides studied in this work are commercially available powders displaying a certain color at room temperature (see Table 1 for more information on these powders). A homemade set-up was used for both qualitative and quantitative color estimation in combination with heating (see figure 1). It is composed of a heating stage (thermocouplecontrolled) positioned inside a closed box and uniformly illuminated from the top using commercial white light-emitting diodes (light temperature ~5900 K). This is pretty close to the CIELAB standard D65 illuminant used in L*a*b* measurements for the simulation of daylight. An external camera can take pictures of the samples by uncovering the central hole on the top side. The powders were placed on a dull-white boron nitride (BN) holder having shallow circular prints of 2 mm depth and 1 cm diameter. Each print was fully filled with a single powder and gently pressed in order to display an averagely flat and homogeneous surface. The BN holder was then placed on the heating stage before starting the heating procedure. Note that the use of a BN holder ensures good thermal conduction while avoiding both chemical reactivity with the metallic heating stage and metallic reflection of the light from the stage which could induce color estimation errors. Outside but close to the heating stage are placed a BaSO 4 pellet and a graphite powder respectively for fixing the white and black levels of the images. The heating procedure consisted in the succession of controlled heating ramps (5°C/min for 5 min) followed by temperature plateaux (3 min duration) every 25°C from room temperature to 500°C. Each picture was taken after 2 min of temperature stabilization for each plateau. A picture was also taken after cooling back to RT for reversibility check. After adjusting the color tunes using the white and black references, the CIE-Lab colorimetric parameters (L*a*b*) of the different powders were then extracted from these images using GIMP software considering an average area of 20x20 pixels. Color contrast versus temperature (ΔE) was determined using the following formula:

Eq ( Each powder has been characterized by UV-visible diffuse reflectance spectroscopy at room temperature using a Bruker UV-NIR 1050+ spectrophotometer equipped with an integrating sphere. For some oxides, Kubelka-Munk equation was used to transforms these reflectance spectra in absorption data in order to determine the band gap using the so-called Tauc plot. Some powders were additionally characterized by differential scanning calorimetry (DSC) with the DSC1 apparatus from Mettler Toledo in order to check for any physico-chemical event which could occur within the temperature range studied. Samples were analysed in 40 µL aluminium crucible, from room temperature up to 500°C, under air and with a rate of 10 K.min -1 . Prior to analyses, the DSC was calibrated in terms of melting temperature and enthalpy with pure In and Zinc. The relative errors were below 0.2 and 0.6 % for the melting temperature and enthalpy respectively.

Results

The resulting temperature dependences of the L*, a* and b* parameters for the studied commercial powders are listed in Table 2. From these data, the color contrast ΔE was calculated using Eq. ( 1) for each compound and at each temperature. The results are plotted in Figure 2. One can see that all oxides display a thermochromic behavior except for Er 2 O 3 which color remains almost unaffected by temperature. The maximum color changes (from RT to 500°C) are better seen in Figure 3. Obviously, the thermochromic compounds studied here can be separated into two families: the ones really changing color (Bi 2 O 3 , WO 3 , YAG:Ce and In 2 O 3 ) and the ones simply darkening (Fe 2 O 3 and YInMn). The latter family (composed of already rather dark powders at RT (with low L* values) gives smaller values of ΔE but with almost linear evolutions with temperature, while the former family displays the highest ΔE values associated with apparently more complex temperature dependences.

The values of ΔE at 25°C in Figure 2, referred to as "Back RT, correspond to the color contrasts obtained after coming back to room temperature. They thus allow estimating the contrast possibly induced by the thermal cycling and can be considered as a first check of reversibility. After cycling, one can see that almost all the studied compounds have ΔE values < 3.5 upon coming back to RT which means that the difference is hardly noticed by human eye according to [START_REF] Mokrzycki | Color difference Delta E -A survey[END_REF]. From our experience, taking into account the measurement errors and any possible thermally induced side-effect, such as residual water/humidity desorption, a ΔE values < 5 after cycling should corresponds to a fully reversible thermochromic material. The only exception among the studied oxides is WO 3 which leads to a ΔE value after cycling as high as ~14. This material does not seem to be reversibly thermochromic. This point will be discussed later. Finally, all the powders were characterized by UV-Vis reflectance spectroscopy at RT. The recorded spectra are shown in Figure 4 and separated in two sets: a) the semiconductors and b) the non-semiconductors. Let us consider now each case separately. Bi 2 O 3 . This oxide displays the highest values of ΔE among the studied compounds, passing from cream yellow at RT to deep orange at 500°C. Bi 2 O 3 was already previously identified as thermochromic material [START_REF] Gaudon | How to mimic the thermo-induced red to green transition of ruby with control of the temperature via the use of an inorganic materials blend?[END_REF][START_REF] Belykh | Thermochromic material[END_REF]. Its ΔE temperature dependence displays an important increase at low temperature (the most important among the studied materials) followed by a plateau at ΔE of ~52 the temperature range 275-400°C (figure 2). Looking to the effective color of Bi 2 O 3 powder at each temperature (figure 5), one cannot see any color stagnation in this specific temperature range. The UV-Vis reflectance spectrum (figure 4a) recorded at RT on Bi 2 O 3 powder is typical from a semiconductor material with a unique absorption threshold between 420-450 nm.

a) b)

In 2 O 3 . This pale yellow oxide at RT turns to light orange at 500°C and this colour change is fully reversible. Its ΔE temperature dependence has a similar shape as Bi 2 O 3 but shifted to lower ΔE value of ~40 (figure 2) despite showing only gradual color evolution in the full temperature range (Figure 5). The corresponding UV-Vis reflectance spectrum (figure 4a) is typical of a semiconductor material with a unique absorption threshold around 450 nm.

WO 3 . This compounds passes from light green at RT to deep orange at 500°C. Compared to Bi 2 O 3 , the perceived color change is also very effective but in different hues, despite smaller ΔE values. ΔE temperature dependence of WO 3 is more regular than for the two previous oxides, i.e. without a plateau (figure 2). Another difference is the moderate reversibility of WO 3 thermochromicity since its ΔE after coming back to RT was calculated as 14, value allowing distinguishing easily the color change by naked eyes. The UV-Vis reflectance spectrum of WO 3 is typical of a semiconductor material with a unique absorption threshold between 450-500 nm (figure 4a). YAG:Ce. This bright yellow oxide at RT turns reversibly to bright orange at 500°C. However, its thermochromicity is by far the most uncommon of the series. Indeed, its L*a*b* parameters remain almost constant up to 300°C at which point they start varying (Table 2), generating thus a sudden increase in ΔE (figure 2). The temperature dependence slope above 300°C is the highest in this temperature range, being similar to that of Bi 2 O 3 but at lower temperature (< 150°C). From the UV-Vis reflectance spectrum (figure 4b), one can confirm that YAG:Ce is not a semiconductor and that its color is the consequence of its broad absorption band around 450 nm.

Fe 2 O 3 . This oxide is a well-known dark-red pigment at RT, reversibly passing to almost blackred at 500°C. It is thus a thermochromic material with moderate but almost constant increase of ΔE with increasing temperature. Its thermochromicity mainly consists in continuous darkening of its original color rather than in a real color change. From its UV-Vis reflectance spectrum, one can see that its light absorption is different from other semiconductors of this study. Firstly, its reflectance does not exceed 50% over the entire spectral range (which is to be directly related to its rather dark color). Secondly, its absorption at low energy is not flat and displays large bands.

YInMn. This deep-blue oxide at RT passes reversibly to dark-blue at 500°C. Its temperature dependency of color contrast is rather similar to that of Fe 2 O 3 but shifted to lower values (figure 2). Likewise, its thermochromicity mainly consists in continuous darkening of its original color rather than in a real color change (figure 5). From the UV-Vis reflectance spectrum (figure 4b), one can confirm that YInMn is not a semiconductor and that its color is the consequence of its broad absorption band covering the red and yellow spectral region ( > 500 nm). Note that, like for dark colored Fe 2 O 3 , its reflectance value does not exceed 50% over the entire visible spectral range.

Er 2 O 3 . This compound does not really change color with temperature. It simply passes from light pink at RT to slightly deeper pink at 500°C (Figure 3). Its ΔE temperature dependence is as monotonous as for the two previous oxides. But in the case of Er 2 O 3 , ΔE values remain always < 10 for the entire temperature range while keeping a high color lightness (L* ≥ 89). As a matter of fact, this compound can be considered as a good thermostable pink pigment.

From the UV-Vis reflectance spectrum (figure 4b), one can see that Er 2 O 3 is not a semiconductor and that its color is the consequence of a complex combination of many narrow absorption bands occurring at various positions of the visible spectral region.

Discussion

The ΔE plateaux observed in Figure 2 for Bi 2 O 3 and In 2 O 3 are obviously not correlated to the real color evolution since in both cases this evolution is gradual (figure 5). Though no phase transition or other physico-chemical evolution is expected for In 2 O 3 in the studied temperature range, this could be different for Bi 2 O 3 since several polytypes of this oxide are known to be stable at RT [START_REF] Drache | Structures and oxide mobility in Bi-Ln-O materials: heritage of Bi 2 O 3[END_REF]. XRD analysis of the Bi 2 O 3 powder (not shown here) demonstrates that it is of the most stable monoclinic polytype. From DSC analyses, no sharp physico-chemical event has been detected upon heating Bi 2 O 3 powder up to 500°C and cooling back to RT (Figure 6). We can thus rule out the possibility of phase transformation for this oxide in the studied temperature range. The expected polytype should be thus the most stable α (monoclinic), as confirmed by its band gap value of 2.86 eV obtained from the Tauc plot shown in figure 7. The band gap values obtained for the other semiconducting oxides (Fe 2 O 3 , In 2 O 3 and WO 3 ) are in good agreement with the ones reported elsewhere [START_REF] Zheng | Tuning of the crystal engineering and photoelectrochemical properties of crystalline tungsten oxide for optoelectronic device applications[END_REF][START_REF] Ren | Visible light enhanced extracellular electron transfer between a hematite photoanode and pseudomonas aeruginosa[END_REF][START_REF] Walsh | Nature of the band gap of In 2 O 3 revealed by first-principles calculations and x-ray spectroscopy[END_REF].

- We believe that the plateaux obtained in Figure 2 for Bi 2 O 3 and In 2 O 3 are due to the ΔE calculations using Eq. 1 which can generate some slope variations in case of uneven evolution of each L*, a* and b* values with temperature. This is illustrated in Figure 8 which plots the generic term from Eq. 1 (X* (T) -X* (RT) ) 2 as a function of temperature, with X* corresponding to L*, a* or b* colorimetric parameters for these two oxides. One can see that the effect of b* parameter, which variation is the main one contributing to ΔE at low temperature, saturates then decreases at higher temperature. This is not the case for L* and a* parameters which are rather constant at low temperature while they start increasing rapidly at high temperatures. These dissymmetric evolutions of parameters, when put together in Eq. 1, are thus at the origin of these plateaux. Coming now to the case of YAG:Ce powder, its ΔE temperature dependence is peculiar with a sharp increase at ~300°C. However, the DSC analysis (Figure 6) did not show evidence of any phase transition or other physico-chemical event at this temperature that could be expected for such abrupt evolution. Yet, one can notice a little bump at ~230°C associated with a slight change in the slope of the DSC curve upon heating, while nothing is seen upon cooling. A second DSC analysis on the same sample did not display anymore this bump both upon heating and cooling. Finally, using another sample of the same YAG:Ce powder, the bump was again seen by DSC upon heating but disappeared upon cooling. All these results suggest some surface desorption/elimination of volatile species occurring around 230°C. If so, it should not participate to the unusual thermochromic properties found here for YAG:Ce powder, especially when considering that such desorption temperature is a bit far from the expected one for thermochromic transition around 300°C.

Looking closer to the L*a*b* values in Table 2, one can see that in the low temperature range these parameters remain almost constant so that ΔE is almost zero. Observing such an abrupt and reversible thermochromism without clear physico-chemical modification of the material is very uncommon. Usually the colour change is gradual, as can be seen for all the other compounds of this study. However, this YAG:Ce is known to be an efficient yellow phosphor. This is probably the reason for such a highly bright yellow color at temperatures < 300°C. Above this temperature, it was shown that the luminescence quenches while thermoluminescence appears and gradually increases [START_REF] Ueda | Insight into the thermal quenching mechanism for Y 3 Al 5 O 12 :Ce 3+ through thermoluminescence excitation spectroscopy[END_REF]. This is probably the red-shifting of such thermoluminescence which cause the observed thermochromicity for this material. Note that the thermochromicity of YAG:Ce powder as shown in the present study was never reported before. It should though require deeper investigations for better understanding of the color variation.

Finally, Let us comment the low reversibility observed for WO 3 powder. This compound is known to have different colors, from yellow (stoichiometric), green, blue up to black, depending on its O deficiency (amount of O vacancies) [START_REF] Weil | The beautiful colours of tungsten oxides[END_REF][START_REF] Chen | Facile strategy for synthesizing non-stoichiometric monoclinic structured tungsten trioxide (WO 3-x ) with plasma resonance absorption and enhanced photocatalytic activity[END_REF][START_REF] Liu | Surface oxygen vacancy and defect engineering of WO 3 for improved visible light photocatalytic performance[END_REF]. XRD analyses performed before and after thermal cycling at 500°C (not shown here) did not evidence any structural change of the powder which remains of the monoclinic polytype. This is confirmed by the DSC analysis which did not detect any phase transition or other physico-chemical event. The color contrast observed for this compound after thermal cycling could thus originate from a variation or deficiency vacancies after heating. One may speculate some photo-thermal assisted reaction with air moisture or with some volatile organic molecules (not detectable by DSC since it operates in darkness), which could have partially reduced the original WO 3 powder.

Conclusion

Among the studied oxides, most of them display reversible color change with increasing temperature up to 500°C. There are two exceptions: i) Er 2 O 3 which can be considered as a good thermostable pink pigment and ii) WO 3 which thermal cyclability is questionable. YAG:Ce compound displays the most uncommon thermochromicity since it is observable only above 300°C while for other oxides it is more gradual and starts from RT. The strongest thermochromicity is for Bi 2 O 3 powder with ΔE value as high as ~65 at 500°C. The darker powders at RT (YInMn and Fe 2 O 3 ) display the lowest thermochromic properties by simply darkening rather than by true color changing.

Figure 2 .Table 2 :

 22 Figure 2. Temperature dependence of color contrast ΔE for each oxide studied in this work. The points at 25°C correspond to the color contrasts obtained after coming back to room temperature, i.e. contrast induced by the thermal cycling.

Figure 3 . 3 YInMnFigure 4 .

 334 Figure 3. Comparison of the colors obtained at RT and 500°C for each studied oxide.

Figure 5 .

 5 Figure 5. Evolution of the color from RT to 500°C for all the compounds of this study.

Figure 6 .

 6 Figure 6. DSC analyses of Bi 2 O 3 , YAG:Ce and WO 3 powders from RT to 500°C, recorded upon heating and cooling.

Figure 7 .

 7 Figure 7. Tauc plots calculated from the reflectance spectra shown in Figure 4a using direct bandgap assumption (n=2) for Bi 2 O 3 and indirect one (n=1/2) for Fe 2 O 3 , In 2 O 3 and WO 3 . The peak at ~3.3 eV (strong in Bi 2 O 3 spectrum but also visible in Fe 2 O 3 and WO 3 ones) is a measurement artifact, already visible in Figure 4a, due to the change in source lamp of the spectrometer.

Figure 8 .

 8 Figure 8. Temperature dependence of each (X* (T) -X* (RT) ) 2 subgroup in Eq. 1, with X* = L*, a* or b* colorimetric parameters obtained in this work on Bi 2 O 3 (left) and In 2 O 3 powders.

Table 1 .

 1 List of the commercial oxide powders studied in this work

	Material	Color at RT	Grain size	Purity	Supplier
	Bi 2 O 3	Cream yellow	NS #	≥98%	Sigma-aldrich
	Fe 2 O 3	Dark red	< 5 µm	≥96%	Sigma-aldrich
	In 2 O 3	Pale yellow	NS #	99.5%	Merck
	Er 2 O 3	Light pink	NS #	≥99.99	Aldrich
	WO 3	Light green	≤ 25µm	99%	Aldrich
	YAG:Ce *	Bright yellow	20-40 µm	99%	Merck
	YInMn-Blue **	Deep blue	1.3 µm	NS	Kremer Pigmente
	* Ce-doped yttrium-aluminum garnet (Y 3 Al 5 O 12 ) / ** YIn 1-x Mn x O 3
	# NS = not specified

Electronic copy available at: https://ssrn.com/abstract=4065706


Eric Meistermann

Aude Vernhet

Philippe Cottereau

Frédéric Charrier

Patrick Chemardin

Céline Poncet-Legrand

Tuning the

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Tuning the pH during the fermentation has a strong effect on the wine protein composition and the stability of the resulting white wines.

E. Meistermann 1 , A. Vernhet 2 , P. Cottereau 1 , F. Charrier 1 , P. Chemardin 2 , C. Poncet-Legrand 2 AIM: The context of global warming implies increases of ethanol content and pH in wines. Elsewhere, previous results have shown the impact of pH on the stability of proteins in white wines. Our aim was to asses for different varieties the effect of pH on the protein content of wines at the end of the alcoholic fermentation. Fermentations were performed using the same starting must, to avoid any other difference in terms of matrix composition (polyphenols, polysaccharides, ions...).

On the whole and in accordance with previous works, protein concentrations in wines decreased during fermentation.

However, this decrease was more marked for the lowest pHs (3.0 and 3.3).

Furthermore, two families of proteins (chitinases and b-glucanases) were particularly fragile, but their behavior also depended on the initial pH of the must and the matrix composition : for instance chitinases were eliminated in Gewurztraminer and Sauvignon, but not in Pinot Gris, nor Riesling and Sylvaner.

On the contrary, invertases and lipid transfer proteins remained stable when they were initially present.

All these behaviors are in agreement with previous results [START_REF] Dufrechou | Stability of White Wine Proteins: Combined Effect of pH, Ionic Strength, and Temperature on Their Aggregation[END_REF][START_REF] Lambri | Effect of pH on the Protein Profile and Heat Stability of an Italian White Wine[END_REF][START_REF] Dufrechou | White Wine Proteins: How Does the pH Affect Their Conformation at Room Temperature?[END_REF].

METHODS:

Fermentations were carried out using musts from different grape varieties (Sauvignon, Sylvaner, Riesling, Gewurztraminer and Pinot Gris), adjusting the pH of the initial musts to 3.0, 3.3, 3.6 and 3.9 either with hydrochloric acid or sodium hydroxide. For each wine thus obtained, the proteins were analyzed and quantified by gel electrophoresis. Heat tests (heating at 40°C during 4 hours) were carried out and the differences between the initial and final turbidities (DNTU) were calculated.

SDS -PAGE analysis of proteins in musts and wines

CONCLUSIONS:

This study confirms that pH has a decisive impact on protein composition in white wines, but the latter is not directly related to the risk of haze formation. Other parameters such as the composition in ions, polyphenols, and polysaccharides play a part. They are themselves strongly dependent on the grape variety and cultural practices.

Even though the concentration of proteins either decreased or remained stable when the pH decreased, the turbidity measured after the heat tests evolved differently: a maximum was observed at pH 3.6. This is probably due to a balance between different phenomena:

-at higher pHs (>3,6) the temperature of denaturation of most proteins is increased above 50°C and are thus not unfolded at 40°C; -at lower pH (3.0 and 3.3), proteins unfold at lower temperature, but once unfolded, there are more charged and their aggregation is limited due to electrostatic repulsions, limiting the visual haze formation.

  Invertase 70 kDa b-glucanase ~35 kDa Chiti27 : chitinase 27 kDa Chiti 24: chitinase 24 kDa TL22: Thaumatin like protein 22 kDa TL21: Thaumatin like protein 21 kDa TL19-20: Thaumatin like protein 19-20 kDa LTP: Lipid transfer Proteins ~13-

  Evolution of DNTU as a fonction of the pH


Elias Fekhari

Bertrand Iooss

Vincent Chabridon

Joseph Muré

Kernel-based quadrature applied to offshore wind turbine damage estimation

HAL is

Industrial context & Problem statement

• EDF Renewables operates ∼10 000 MW of wind turbine (WT) worldwide • New technologies (e.g., offshore floating WT), wind farms reaching end-of-life → Need probabilistic tools to optimize safety margins and asset management Chained simulation model Expensive-to-evaluate deterministic computer model [START_REF] Capaldo | Design brief of HIPERWIND offshore wind turbine cases: bottom fixed 10MW and floating 15MW[END_REF] (distributed on a cluster):

g : R p × R q → R, (x, z) → g(x, z) (1)
Fig. 1: Numerical simulation chain for WT damage assessment (simulates 10min)

Random inputs • X ∈ D X ⊂ R p : environmental random vector with its joint distribution f X (•)

• Z ∈ D Z ⊂ R q : system random vector with its joint distribution f Z (•)

• The random vectors are considered mutually independent

Uncertainty propagation

Two nested quantities of interest to estimate (see [START_REF] Müller | Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines[END_REF]) Damage Equivalent Load (DEL): expected value of the damage over the environmental conditions (conditionally to a sample of system variables Z = z)

E[Y|Z = z] = E[g(X, Z)|Z = z] = D X g(x, z) f X (x) dx = ϕ(z) (2)
Probability of threshold exceedance: for a given threshold

y th ∈ R p f = P (ϕ(Z) > y th ) = D Z 1 {ϕ(z)>y th } f Z (z) dz (3)
→ How to efficiently estimate the DEL?

Environmental measured data

SCADA data collected over a period of four years at the Teesside (UK) offshore wind farm Kernel-based probabilistic integration

1. Select integration nodes ⇒ E[g(X)] ≈ 1 n ∑ n i=1 g x (i)
Candidate set: S is a fairly dense finite subset of R d with size N ≫ n that emulates the target distribution (e.g., a large Sobol' sequence, available data as in Fig. 2) Kernel herding criterion [START_REF] Chen | Super-samples from kernel herding[END_REF][START_REF] Pronzato | Bayesian quadrature and energy minimization for space-filling design[END_REF]: at iteration n + 1, for a given design of experiments (DoE) X n = {x (1) , . . . , x (n) }, and a given kernel k

x (n+1) ∈ arg min x∈S   1 n n ∑ i=1 k(x, x (i) ) - 1 N N ∑ j=1 k(x, x ′(j) )   (4)
2. Compute optimal weights for integration ⇒ E[g(X)] ≈ ∑ n i=1 w * i g x (i) Optimal weights for quadrature [START_REF] Briol | Probabilistic Integration: A Role in Statistical Computation?[END_REF]: for a given DoE X n and a given kernel

k w * = P(X n )K -1 n (5)
with potentials P(X n ) = k(x, x (1) ) f X (x) dx, . . . , k(x, x (n) ) f X (x) dx and variance-covariance matrix {K n } i,j = k(x (i) , x (j) ) → New Python package on pypi: otkerneldesign (using OpenTURNS [START_REF] Baudin | Title: Open TURNS: An industrial software for uncertainty quantification in simulation[END_REF])

Conclusions & Perspectives

• Combining kernel herding with optimal weights is an efficient integration method • This method is sensitive to the chosen kernel and its hyper-parameters • This method allows direct sampling from available empirical distribution • Active learning methods might be more efficient but are harder to distribute (HPC) → Determine influential system variables using advanced sensitivity analysis methods → Adapt kernel-based sampling methods to reliability analysis problems

Numerical results: DEL estimation by kernel herding

Fig. 2 :

 2 Fig. 2: Teesside wind farm environmental data (in grey, N = 10 5 ) and a kernel herding sample (in blue, n = 520)

Fig. 3 :

 3 Fig.3: Kernel-based sampling on a bivariate random mixture (markers' sizes indexed to the optimal weights)

Fig. 4 :Fig. 5 :Fig. 6 :

 456 Fig. 4: Offshore wind turbine structure diagram


Simon Kamerling

Valéry Vuillerme

Sylvain Rodat

MILP Algorithm for outlet temperature optimization of a hybrid concentrated solar thermal system for SHIP considering a 1D solar field and a dual phase thermocline storage

Keywords: MILP, SIPH, SHIP, concentrated solar thermal, concentrated solar heat, operating temperature optimization, flexible heat integration, process heat, storage, system modelling, control strategy, dual-phase thermocline

Solar Heat for Industrial Processes (SHIP) is in an increasing trend in research for industrial decarbonization. Process heat demand and solar energy can both present large intermittencies. Storage gives a degree of freedom on which mathematical optimization can be performed. Lowering the working temperature of the solar field decreases the heat losses and therefore maximizes the production; it also reduces heat losses from the storage while increasing its use in the operational phase. In this paper, the investigated system is a solar field consisting of low-cost Parabolic Trough Collectors in series with Linear Fresnel Receivers, in series with a dual-phase thermocline storage. A MILP (Mixed Integer Linear Programming) algorithm is used in order to obtain a control trajectory of the system; then, this control trajectory is injected in a non-linear and more accurate plant model to evaluate its performances. Comparison is performed with another control approach, consisting in producing at the process temperature whenever possible. The MILP control shows an absolute increase of the solar fraction of 2.5%. The way the thermocline is handled is not standard (as compared to what is done for electricity production) but shows a great interest for heat applications.

Introduction

Heat, this low exergy energy often discredited in public debates, where electricity has the foreground, represents 50% of the final energy consumed in 2019 [START_REF] Iea | Renewables 2020 -Analysis and forecast to[END_REF], with electricity and mobility representing 20% and 30% respectively. Heat production, in 2019, emitted 13.3 Gt-CO2-eq., which corresponds to 40% of total emissions. Among this 50% of heat, half is used by industrial processes and 47% for heating and domestic hot water, the 3% remaining being for various uses. It is consequently urgent to decarbonize heat for industry if humanity wants to keep the actual level of production without suffering from the consequences. Many renewable solutions allow a decarbonization of heat; the most used today are biomass (~79% of renewable heat in industry), electricity from renewable sources (~10%) and heat from heat networks (~3%). Solar thermal is unfortunately not much used today. Indeed, solar thermal has often been criticized for its price and the space required for its implementation.

In Europe, gas prices are currently under market pressure, having reached €108/MWh in October 2021; the resilience and independence of our industrial systems are thus naturally questioned.

It seems more and more obvious that, for temperate climates and installations in non-desert areas (and, as a result, where space is limited), the solar energy cannot be a unique source of energy along the year. Consequently, it must be hybridized. Hybridization with wind energy, by mechanical wind turbine and Joule effect [START_REF] Okazaki | Concept study of wind power utilizing direct thermal energy conversion and thermal energy storage[END_REF], as well as biomass, would enable an effective form of resilience to be achieved. Production could be adapted to the availability of energy, if too many external constraints appear in future supplies. Investment in these energies and research is therefore increasingly seen as a modern emergency. We assume in this work that the industry must run at the planned schedule: the heat demand is considered immutable, although some works [START_REF] Fitsum | Design Optimization and Demand Side Management of a Solar-Assisted Industrial Heating Using Agent-Based Modelling (ABM): Methodology and Case Study[END_REF] showed the interest of managing demand.

In this article, we will discuss the hybridization of a solar field with a boiler. As the boiler operates at high temperature (and thus with higher exergy), it is interesting to reduce the temperature at the exit of the solar field, thus increasing the solar performance and reducing the fuel requirements. This concept of Flexible Heat Integration was proposed by [START_REF] Rashid | Process intensification of solar thermal power using hybridization, flexible heat integration, and real-time optimization[END_REF]. A previous article [START_REF] Kamerling | Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications[END_REF] has been published on a similar optimization, with two-tank storage. MILP (Mixed-Integer Linear Programming) algorithms have been widely used in the optimization of solar thermo-electric power plants, such as [START_REF] Pousinho | Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach[END_REF][START_REF] Yang | Operation optimization strategy for wind-concentrated solar power hybrid power generation system[END_REF].

In this paper, a MILP algorithm is used to obtain a control trajectory, which is then applied to a more complex model, taking into account nonlinearities. The purpose of this paper is to share model developments, considering a 1D solar field and a dual phase thermocline storage.

The first part (section 2) presents the investigated system and the general co-simulation approach used in this paper. The second part describes the implemented solar field and the thermocline 1D models; the third part describes the MILP algorithm and the associated control, as well as an alternative control approach enabling a comparison of the results. In the fourth part, the results of a case study are given.

Proposed system and modeling approach

2.1. System under consideration 2.1.1. Diagram and description of the studied solar concentration system

The system chosen in this study is a solar field consisting of low-cost parabolic trough collectors (PTC) in series with higher efficiency Linear Fresnel Receivers (LFR), the solar field being in series with a thermocline storage, although it is possible to bypass the storage and to go directly to the boiler. In Figure 1, we can see on the left, the solar field (described in section 3.1); in the middle, the thermocline storage (section 3.2); on the right, the boiler. The variables in black correspond to the input data of the model, those in red correspond to the control trajectory obtained from control algorithms (section 4) and those in blue are deduced from the control trajectory (section 3.3).

The Heat Transfer Fluid (HTF) enters the system with a mass flow rate 𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 , at a temperature 𝑇 𝑟𝑒𝑡𝑢𝑟𝑛 . It has to be sent to the process at the temperature 𝑇 𝑑𝑒𝑚𝑎𝑛𝑑 , both being constants along the year. If the storage is in discharge mode (outlet temperature 𝑇 𝑜𝑢𝑡,𝑑,𝑠𝑡 ), some or all of the fluid may enter the thermocline storage; part of this flow (𝑚̇𝑎 𝑢𝑥 ) comes from the control trajectory, whereas the other part is deduced from the operating mode (𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 ). From the point of view of control algorithms, 𝑚̇𝑎 𝑢𝑥 allows the solar field to be bypassed. Passing it through storage resets the thermocline. If the storage is in charge mode (outlet temperature 𝑇 𝑜𝑢𝑡,𝑐,𝑠𝑡 ), part of the flow comes from the storage 𝑚̇𝑆 𝐹→𝑠𝑡 , which means that the inlet temperature of the solar field 𝑇 𝑖𝑛,𝑆𝐹 has to be recalculated, in case the outlet temperature of the storage is significantly higher than the process return temperature 𝑇 𝑟𝑒𝑡𝑢𝑟𝑛 .

Main assumptions

Different assumptions and approximations have been made in this model: the main objective is to compare the different control approaches:

 the quality of the forecast: this is not taken into account, i.e. the meteorological data are assumed to be known in advance. This means, de facto, that we are on a probabilistic trajectory corresponding to the median of the probabilities. One way to overcome this assumption would be to use Robust MILP algorithms, such as [START_REF] He | Optimal Offering Strategy for Concentrating Solar Power Plants in Joint Energy, Reserve and Regulation Markets[END_REF], which allows taking into account the quality of the forecast and making decisions upon the robustness of the solution,  constant efficiency of the boiler (taken at 100%): this assumption is very accurate in a wide range of operation, the value of 100% is taken as it does not influence the results presented here,  solar modules' inertia is taken as the contained fluid's inertia,  no headers were considered in this study, although they add some inertia to the system,  the chosen HTF is Therminol 66, and its properties are all taken at 1 bar; dependence on temperature is taken into account through interpolation of fluid properties,  time discretization is hourly.

Modelling approach

Figure 2 shows the modelling approach followed by the algorithm. To calculate the outcomes over the year, a loop is performed every 24h.

Before entering the loop, the algorithm reads the input data, and chooses the design if specified. The outlet temperatures of the solar field are discretized.

At the beginning of the loop, the solar field model calculates the producible mass flow for the different discretized temperatures, considering inertia.

Then, an optimization algorithm chooses the optimal control trajectory upon 48h. The control trajectory is then fed into the non-linear model, which then calculates the heat production and boiler requirements, as well as the storage state and other variables of interest (in blue in Fig. 1.), upon 24h. Both storage and solar field final states are then passed as input to the loop. The optimization is run over 48h in order to optimize the storage dispatch for the next day as well (else, the storage is just emptied by the optimization algorithm). This kind of control strategy is commonly referred to as Rolling Horizon [START_REF] Moretti | MILP and MINLP models for the optimal scheduling of multienergy systems accounting for delivery temperature of units, topology and non-isothermal mixing[END_REF].

1D Models

Solar Field Model

In the present study, the solar field consists of low-cost Parabolic Trough Collectors (from Absolicon) followed by Linear Fresnel Receivers (from Industrial Solar), permitting to reach higher temperatures. This association allows some installations to gain on the cost of the heat produced [START_REF] Friendship | Deliverable: D1.2 "List of specifications for all components subsystems and global SHIP 200 & SHIP 300 parameters[END_REF].

The solar field is modelled in one dimension, with calculations on each module, and this in a quasi-static approach: a stationary calculation is made, then the thermal inertia is translated into a loss or a gain in terms of mass flow, in the manner of the SAM model [START_REF] Wagner | Technical Manual for the SAM Physical Trough Model[END_REF].

Fluid properties are obtained by interpolation. They are assumed to only depend on the temperature.

Optical efficiency and thermal losses

The calculation of the optical efficiency is done in two different ways depending on the collector technology:

 The PTC via a longitudinal 𝐼𝐴𝑀 𝐿,𝑃𝑇𝐶 (Incidence Angle Modifier), multiplied by the cosine of the longitudinal angle : 𝜂 𝑜𝑝𝑡 (𝜃 𝐿 ) = 𝜂 0,𝑃𝑇𝐶 * 𝐼𝐴𝑀 𝐿,𝑃𝑇𝐶 (𝜃 𝐿 ) * cos (𝜃 𝐿 )

 The LFR via a longitudinal 𝐼𝐴𝑀 𝐿,𝐿𝐹𝑅 and a transversal 𝐼𝐴𝑀 𝑇,𝐿𝐹𝑅 , in which is already included the cosine effect: 𝜂 𝑜𝑝𝑡 (𝜃 𝐿 , 𝜃 𝑇 ) = 𝜂 0,𝐿𝐹𝑅 * 𝐼𝐴𝑀 𝐿,𝐿𝐹𝑅 (𝜃 𝐿 ) * 𝐼𝐴𝑀 𝑇,𝐿𝐹𝑅 (𝜃 𝑇 )

With 𝜃 𝐿 and 𝜃 𝑇 the longitudinal and transversal angles respectively, and 𝜂 0 the optical efficiency at normal incidence, with subscripts corresponding to the concerned module. The end losses are not taken into account, although they are probably significant in the case of small or medium size installations, located in latitudes corresponding to continental Europe. The module incident energy is then written as: 𝑞̇𝑖 𝑛 = 𝐺 𝑏 * 𝜂 𝑜𝑝𝑡 (𝜃 𝐿 , 𝜃 𝑇 ) * 𝐴 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 , with 𝐺 𝑏 the beam irradiation in 𝑊. 𝑚 -2 and 𝐴 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 the aperture area in m² of a module.

The heat losses are written in a general way:

𝑞̇𝑙 𝑜𝑠𝑠𝑒𝑠 (Δ𝑇) = 𝐴 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 * ∑ 𝑎 𝑖 Δ𝑇 𝑖 4 𝑖=1
, with:

Δ𝑇 = 𝑇 𝑖𝑛 +𝑇 𝑜𝑢𝑡 2 -𝑇 𝑎𝑚𝑏
being the average fluid temperature difference with the ambient, 𝑇 𝑖𝑛 being the inlet temperature of a module, 𝑇 𝑜𝑢𝑡 the outlet temperature and 𝑇 𝑎𝑚𝑏 the ambient temperature. The 𝑎 𝑖 , 𝑖 ∈ ⟦1; 4⟧ are the heat loss coefficients (from correlations) and are in 𝑊. 𝑚 -2 . 𝐾 -𝑖 .

Stationary calculation of the outlet temperature of a loop

Knowing the mass flow rate and the inlet temperature, and after calculating the optical efficiency, the outlet temperature of a module is calculated by solving the following equation:

𝑚̇ * 𝐶 𝑝 * (𝑇 𝑜𝑢𝑡 -𝑇 𝑖𝑛 ) = 𝑞̇𝑖 𝑛 -𝑞̇𝑙 𝑜𝑠𝑠𝑒𝑠 (1),
with 𝑚̇ the mass flow rate in 𝑘𝑔. 𝑠 -1 , 𝐶 𝑝 the fluid specific heat capacity in 𝐽. 𝑘𝑔 -1 . 𝐾 -1 , 𝑇 𝑖𝑛 and 𝑇 𝑜𝑢𝑡 being respectively the inlet and outlet HTF's temperatures in the SF. This equation is a quartic equation in 𝑇 𝑜𝑢𝑡 . The fluid properties are taken at the inlet temperature.

The calculation of all temperatures in the loop is then performed from one module to another, considering for the module n the outlet temperature of the module n-1.

Quasi-static calculation of the mass flow rate

The output temperature being fixed, the calculation is done in two steps: first, the stationary mass flow rate is calculated, by dichotomy on the outlet temperature, via Eq.(1). Then, the energy needed to heat the content of the modules (thermal inertia) to reach their temperature is accounted for, and converted to a mass flow rate before being subtracted from the stationary flow. This is calculated as follows:

𝐸 𝑖𝑛𝑒𝑟𝑡𝑖𝑎 = ∑ 𝑉 𝑖 * 𝜌(𝑇 ̅ 𝑖,ℎ ) * (ℎ(𝑇 ̅ 𝑖,ℎ ) -ℎ(𝑇 ̅ 𝑖,ℎ-1 )) 𝑖 (2) ,
with 𝑉 𝑖 the HTF volume contained in module i in 𝑚 3 , 𝜌 the density in 𝑘𝑔. 𝑚 -3 , h the enthalpy of the HTF in 𝐽. 𝑘𝑔 -1 , 𝑇 ̅ the average temperature of the module (calculated by averaging inlet and outlet temperatures), the subscripts h and h-1 referring to the time h and h-1.

The conversion to mass flow rate is then done in the following way:

𝑚̇𝑖 𝑛𝑒𝑟𝑡𝑖𝑎 = 𝐸 𝑖𝑛𝑒𝑟𝑡𝑖𝑎 ℎ(𝑇 𝑆𝐹 ) * 3600 (3)
The mass flow rate of the solar field is then written 𝑚̇𝑆 𝐹 = 𝑚̇𝑠 𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑟𝑦 -𝑚̇𝑖 𝑛𝑒𝑟𝑡𝑖𝑎 (4).

Validation

The validation of the solar field 1D model was done using the "Linear Fresnel with Molten Salt" model of the open-source software SAM. Using SAM inlet and outlet temperatures, the hourly mass flow rates were calculated with the above model and compared to SAM's hourly mass flows.

The average relative error of the daily energy production is 2.6% -highest peaks in relative error occur when the daily production is very low. The average relative error on the hourly flow rate is 6.9%. The main reason for the deviations is that in SAM the molten salt/water heat exchanger before entering the turbine has a large thermal inertia, while there is no heat exchanger in the present model. Figure 3 shows the daily heat output of the two models, as well as the relative error between both. (5)

Then the parallel defocusing, which consists in defocusing a certain number of loops, in order not to exceed a certain mass flow (full storage, etc.). It is calculated as a ratio between the chosen mass flow and the one that can be produced at the desired outlet temperature: 𝑑𝑒𝑓𝑜𝑐𝑢𝑠 // = 1 -𝑚̇𝑆 𝐹 𝑚̇𝑝 𝑜𝑠𝑠𝑖𝑏𝑙𝑒 (6).

Note that the real defocus is not continuous; however, we are working on a large solar field, which makes the approximation acceptable.

Input parameter values

Dual-phase thermocline

It was chosen to work with a dual-phase thermocline because this storage is cheap, as it consists of only one tank and a large proportion of the often expensive HTF is replaced by a low-cost solid.

Implemented model description

The thermocline model implemented is the one of a dual-phase thermocline, from [START_REF] Hoffmann | A thermocline thermal energy storage system with filler materials for concentrated solar power plants: Experimental data and numerical model sensitivity to different experimental tank scales[END_REF], with the correlations from [START_REF] Esence | A versatile one-dimensional numerical model for packed-bed heat storage systems[END_REF]. It is a model with three partial differential equations: one for the fluid temperature, one for the solid temperature and one for the wall temperature. The resolution of this system involves a one-dimensional discretization over the tank's height. The discretization chosen for the convection term depends on the fluid flow direction: The use of the QUICK scheme would be preferable in general cases, but the above formulation is sufficient to have a convergence of the model with liquid fluids. The time discretization is implicit, because the explicit discretization showed very quickly a divergence in the results.


The model is solved using the open-source library DAE-CPP [START_REF] Ikorotkin | dae-cpp: A simple but powerful C++ DAE[END_REF], itself based on the free IBM library Math Kernel Library.

Validation

This model has been validated with an in-house code, itself validated on experimental data from the STONE installation at CEA Grenoble [START_REF] Esence | Study of thermocline development inside a dual-media storage tank at the beginning of dynamic processes[END_REF].

The validation was done in the following way: a sequence of charges and discharges at different temperature levels were simulated in both models, then a calculation of the temperature difference was done using interpolations, in order to measure the deviation due to the numbers of elements:

𝑒𝑟𝑟 = 1 Δ𝑡 * 1 𝐿 * ∫ ∫ |𝑇 𝑟𝑒𝑓 (𝑧,𝑡)-𝑇(𝑧,𝑡)| 𝑇 𝑟𝑒𝑓 (𝑧,𝑡) 𝑑𝑡 𝑑𝑧 Δ𝑡 0 𝐿 0
For a number of elements equal to 100 (the reference number of the in-house model), we found a difference of 0.9%. For 50 elements, this difference rises to 2.6%, but we have chosen to work with 50 elements as the computation time of the storage depends strongly on this parameter.

Nonetheless, varying the temperature at the inlet (as done further) can create modelling inaccuracies, as the stratification may be disturbed; furthermore, this model does not take into account buoyancy. This would need a deeper modelling for better validation.

Calculation of a threshold HTF mass

Hot fluid should not be sent back in the solar field. There should therefore be a temperature threshold, traduced as an HTF injected mass threshold which should not be exceeded by the control trajectories. This mass is not obvious as the thermocline is filled with solid particles. Taking the temperature threshold as:𝑇 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 𝑇 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 + 1 3 (𝑇 𝑐ℎ𝑎𝑟𝑔𝑒 -𝑇 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 ), a serie of simulations (charge mode) with a constant mass flow rate showed that the HTF mass threshold that could be injected to reach the temperature threshold was about 𝑀 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 𝜌 𝐻𝑇𝐹 * 𝑉 𝑡𝑎𝑛𝑘 . This result does depend on the mass flow rate but appeared to be a good enough value.

Definition of the charge percentage

The energy contained in the storage is defined as: In this section, the solar thermal system response to the control trajectory is explained; this latter is therefore assumed to be known, i.e., (𝑚̇𝑆 𝐹 , 𝑇 𝑆𝐹 , 𝑚̇𝑎 𝑢𝑥 ).

Input parameters

Determination of the storage operating mode

The mass flow rate of the demand must be assured, which gives:

𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 = 𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 + 𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 + 𝑚̇𝑎 𝑢𝑥 (10)
The flow coming from the solar field can go either to the storage or to the boiler, which gives:

𝑚̇𝑆 𝐹 = 𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 + 𝑚̇𝑆 𝐹→𝑠𝑡 (11)
We can then write the variation of the inlet and outlet mass flow of the storage:

Δ𝑚̇= 𝑚̇𝑆 𝐹→𝑠𝑡 -𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 (12)
The reinjection of Eq.( 10) and Eq(11) in Eq(12) gives:

Δ𝑚̇= (𝑚̇𝑆 𝐹 -𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 ) -(𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 -𝑚̇𝑎 𝑢𝑥 -𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 ) = 𝑚̇𝑆 𝐹 + 𝑚̇𝑎 𝑢𝑥 -𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 (13)
The sign of Δ𝑚̇ then gives the operating mode of the storage.

Case 1: Storage load: 𝚫𝒎 ̇> 𝟎

It can be deduced that 𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 = 0 , and thus from Eq.( 12) that 𝑚̇𝑆 𝐹→𝑠𝑡 = Δ𝑚̇. Moreover, one must then necessarily have 𝑚̇𝑎 𝑢𝑥 = 0, because no control model attempts to load the storage while not producing at least the demand mass flow rate.

We deduce from Eq.( 12) and Eq.( 13) that 𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 = 𝑚̇𝑆 𝐹 -𝑚̇𝑆 𝐹→𝑠𝑡 = 𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 .

The outlet temperature of the storage is then calculated according to 𝑚̇𝑆 𝐹→𝑠𝑡 , which gives us an outlet temperature of the storage, noted 𝑇 𝑜𝑢𝑡,𝑐,𝑠𝑡 . The inlet temperature of the solar field can then be calculated:

𝑇 𝑖𝑛,𝑆𝐹 = 𝑚̇𝑆 𝐹→𝑠𝑡 * 𝑇 𝑜𝑢𝑡,𝑐,𝑠𝑡 + 𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 * 𝑇 𝑟𝑒𝑡𝑢𝑟𝑛 𝑚̇𝑆 𝐹→𝑠𝑡 + 𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 ( 14
)
This last one can be quite high if the thermocline zone starts to exit the tank. This is why a mass threshold should be set for those control models. Nonetheless, verification is performed, as well as reactualization of mass flow rates:

 If 𝑇 𝑖𝑛,𝑆𝐹 + 10°𝐶 is higher than the outlet temperature of the solar field, an attempt is made to run the solar array at the demand temperature, and the flow rates are updated accordingly. Note that this shows a flaw in the control trajectory.

 If not, we recalculate the production of the solar field. If the calculated flow is lower than the flow of the trajectory 𝑚̇𝑆 𝐹 , the latter is lowered, and the mass flows are updated. If the calculated mass flow rate is greater than the trajectory mass flow rate 𝑚̇𝑆 𝐹 , this is considered as parallel defocusing.

Case 2: Storage discharge: 𝚫𝒎 ̇< 𝟎

We then deduce that 𝑚̇𝑆 𝐹→𝑠𝑡 = 0, and thus that 𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 = -Δ𝑚̇. It was chosen, given the thermocline type of storage, that 𝑚̇𝑎 𝑢𝑥 goes through the thermocline storage (see section 2.1), in order to reset the thermocline and recover a maximum of energy.

The mass flow rate through the storage to the boiler (𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 ) becomes:-Δ𝑚̇+ 𝑚̇𝑎 𝑢𝑥 .The storage outlet temperature is then determined by the thermocline model; it is noted 𝑇 𝑜𝑢𝑡,𝑑,𝑠𝑡 .

Case 3: Storage inactivity: 𝚫𝒎 ̇= 𝟎

In this case, the control system does not discharge the storage, and we have: 𝑚̇𝑆 𝐹 = 𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 and 𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 = 𝑚̇𝑎 𝑢𝑥 + 𝑚̇𝑆 𝐹 . Due to the implemented logic of thermocline's reset, i.e., that 𝑚̇𝑎 𝑢𝑥 passes through the storage, we take: 𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 = 𝑚̇𝑎 𝑢𝑥 .

The storage outlet temperature is then determined by the thermocline model.

From this data, the boiler requirement is deduced:

𝑞̇𝑏 𝑜𝑖𝑙𝑒𝑟 = 𝑚̇𝑆 𝐹→𝑏𝑜𝑖𝑙𝑒𝑟 * (ℎ(𝑇 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 ) -ℎ(𝑇 𝑆𝐹 )) + 𝑚̇𝑠 𝑡→𝑏𝑜𝑖𝑙𝑒𝑟 * (ℎ(𝑇 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 ) -ℎ(𝑇 𝑜𝑢𝑡,𝑑,𝑠𝑡 )) (15)

Control models

Two control approaches are considered: one from MILP optimization and one from a simple control approach so that the benefit of the MILP algorithm can be evaluated.

MILP algorithm

For the sake of brevity, it was chosen not to expose the full MILP algorithm here, as it may be found in [START_REF] Kamerling | Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications[END_REF]. Nonetheless, a summary is given here, as well as the thermocline specific approach. The cost function is the boiler use. The implemented constraints represent the constraints of the system.

This MILP algorithm can be considered as a water-flow MILP, as opposed to energy-flow MILP. [START_REF] Moretti | MILP and MINLP models for the optimal scheduling of multienergy systems accounting for delivery temperature of units, topology and non-isothermal mixing[END_REF] summarizes well the difference: the second approach only considers energy balances without taking into account temperatures and mass flow rates, whereas the first one directly implements mass flows and temperatures; the non-linear term formed by the product of mass flow rate and temperature is linearized through the use of binary variables and a discretization of the temperature levels, as is described below.

The main subtlety lies in the pre-calculation of the solar field flows at discretized output temperatures, and the use of binary variables for the choice of production. Since inertia must be taken into account, a binary variable spanning two time slots is considered:

𝑧 ℎ,𝑖,𝑗 = { 1 𝑖𝑓 𝑇 𝑆𝐹,ℎ = 𝑇 𝑖 𝑎𝑛𝑑 𝑇 𝑆𝐹,ℎ-1 = 𝑇 𝑗 0 𝑒𝑙𝑠𝑒
, with 𝑇 𝑖 and 𝑇 𝑗 in the set of discretized variables.

The mass stored by the algorithm must not exceed a maximum mass, which we have defined by 𝑀 𝑚𝑎𝑥 = 𝜌 𝐻𝑇𝐹 (𝑇 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 ) * 𝑉 𝑡𝑎𝑛𝑘 , in order not to overpass a threshold temperature at the bottom of the storage, and not to send a too hot fluid in the solar field (see section 3.2.3.).

The notion of bypass flow (𝑚̇𝑎 𝑢𝑥 ) is necessary because of the MILP algorithm: the thermal losses in the tank must be taken into account to get closer to the real operation, which forces to add in the cost function a cost proportional to the mass in the storage (virtual in the case of a thermocline storage). Therefore, this mass cannot be negative, even though it is virtual. Therefore, it is necessary to define an exit gate to the MILP to respect the constraint of a positive mass. This is written by the following constraints: ∀ℎ, 0 ≤ 𝑀 𝑠𝑡,ℎ ≤ 𝑀 𝑚𝑎𝑥 et 𝑀 𝑠𝑡,ℎ+1 = 𝑀 𝑠𝑡,ℎ + 3600. * (𝑚̇𝑆 𝐹 + 𝑚̇𝑎 𝑢𝑥 -𝑚̇𝑑 𝑒𝑚𝑎𝑛𝑑 ).

The mass present in the storage at the beginning of the MILP calculation (i.e., at midnight), was calculated as follows: 𝑀 0 = 𝑀 𝑚𝑎𝑥 * 𝜏, with τ the charge percentage of the storage defined in 3.2.4.

Control approach 1 (CA1)

This control approach tries to always operate at the process temperature. It therefore heats the solar field in the morning and then switches it on. If the mass flow rate producible at the demand temperature is higher than the demand mass flow rate, the excess is stored. If it is lower, the storage is emptied; if the storage is empty, the bypass flow 𝑚̇𝑎 𝑢𝑥 allows to complete the need. If the storage is full, in the sense that the overall mass injected into the thermocline storage is greater than 𝑀 𝑚𝑎𝑥 , the solar field is defocused. The mass present in the storage at the beginning of the simulation is calculated in the same way as above.

Case Study

A case study is presented here: production of chemical and intermediate products at Tarragona (Spain). The demand is considered constant at 280°C, with a flow rate of 20 kg/s. The return temperature is assumed to be 50°C. This corresponds to a power demand of 11.4 MW, and a daily heat demand of 273.6 MWh. The storage is designed to deliver heat for 6 hours at nominal rate and process temperature. The meteorological data are from PVGIS (EU SCIENCE HUB, 2017). The number of discretization of the outlet temperature of the solar field is 8. The yearly simulation time is of 23 minutes on a computer with a 3GHz processor and 32Go of RAM.

Design

The design of the plant is as follows:

 A loop structure consisting of a "rectangle" of PTC: 10 in series and 5 in parallel, followed by a line of LFR: 8 in series, 1 in parallel. This gives an aperture area for a loop of 519m².

 67 loops, for a total aperture area of 34.773m²

 A storage volume of 256𝑚 3 , which makes an HTF volume of 69 𝑚 3 and a solid volume of 187𝑚 3 , or 56.8t of HTF and 467t of solids, for a storage capacity between 𝑇 𝑟𝑒𝑡𝑢𝑟𝑛 = 50°𝐶and 𝑇 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 = 280°𝐶 of Δ𝐸 = 𝐸 𝑚𝑎𝑥 -𝐸 𝑚𝑖𝑛 = 34.18 𝑀𝑊ℎ

Hourly results

In this section, hourly results are commented. These results correspond to February 25 th and 26 th , and June 19 th and 20 th , to compare two different solar conditions. shows that the mass flow rate is increased in MILP strategy, whereas its outlet temperature is decreased. This explains why the heat losses are lower in the Solar Field, as can be seen on Fig. 6.i, as compared to CA1. Fig. 6.k. shows that the MILP algorithm smooths the boiler energy need, which is a side effect (i.e., it was not implemented straightforward) that might be interesting if the variability of the efficiency of the boiler with the thermal load is taken into account. Figures 6.g. et 6.h. show that the MILP makes a better use of the storage as compared to CA1. 7. presents the same variables as in Fig. 6, but at a different time of the year, in which irradiation and optical efficiency are higher. Here, the main contribution of the MILP algorithm as compared to CA1 appears in the period of the defocused energy: the MILP algorithm prefers to defocuse in the morning, and then fills the storage to pass the night. Indeed, the MILP algorithm lowers the heat losses of the storage. The maximum filling of the storage is explained by the mass threshold (or temperature threshold) beyond which the storage cannot be charged anymore.

Discussion

We can see in Figure 6.h. that the outlet temperature of the storage increases and then decreases: the thermocline is not really a thermocline, the hot/cold zone separation is not clear.

In electricity production, keeping a constant inlet temperature to the turbine is of primary importance, for which it is much more interesting to keep a high quality thermocline. For SHIP with hybridization and a large difference in ΔT between the return and process temperature, it is shown here that it may be advantageous not to favour the quality of the thermocline but rather the quantity of stored energy. There is indeed a loss in exergy because of mixing, but the number of calories from the solar field being higher and the boiler completing the exergy loss most of the time; it appears to be in the end favourable.

Nevertheless, if the SF inlet temperature is close to the SF outlet temperature, the impact of the optimization on the temperature is very small; moreover, the objective in this case should be to keep a thermocline in order to have access to the desired storage temperature. It is highly likely that a parallel architecture of the boiler is more interesting.

Yearly results

In this section, monthly results are presented over the year. The annual solar fraction of the MILP is 30.9% and that of the CA1 approach is 28.5%. During the summer, the two models behave differently: in fact, the MILP prefers to defocus in the morning and to fill the storage later, thus minimizing the thermal losses in the storage. This is why more energy is defocused by the MILP while keeping a higher solar fraction. In winter, the difference is explained by the difference in thermal losses.

Conclusion

This paper presented the results of a MILP algorithm applied to temperature optimization in a solar field with thermocline storage for industrial process heat. This optimization showed a 2.5%-point increase in the solar fraction as compared to a control trying to reach the process temperature as soon as possible.

Despite the risk of affecting stratification, varying the temperature of the storage inlet, has shown to be interesting in the case of hybridization with a high temperature difference between the return temperature and the process temperature. The use of the storage is maximized by the MILP algorithm, and it is interesting, in the case presented here, not to respect the order imposed by the stratification, because the storage is emptied by the constant demand all night long. These optimization results seem very interesting, but limited to cases of large temperature difference between the input and the output, which opens a range of optimization. Another dimension of freedom can be found in the case of an industrial site with different heat requirements, and different production sites, approaching the notion of Total Site Analysis. This will be investigated in future works.

Fig 1 .

 1 Fig 1. Diagram of the solar thermal system studied and the variables of interest

Fig. 2 .

 2 Fig. 2. Algorithm structure

Fig. 3 :

 3 Fig.3 : Daily heat production of SAM model (yellow) and the model presented here (blue), with relative error in % on the right (red)3.1.5. Definition of defocusingDefocusing has been defined in two ways: firstly, defocusing in series, i.e. in a loop, in order not to exceed the maximum temperature of a collector or not to exceed the output temperature. The defocusing fraction is then defined by: 𝑑𝑒𝑓𝑜𝑐𝑢𝑠 𝑆𝑒𝑟𝑖𝑒 = 𝐴 𝑑𝑒𝑓𝑜𝑐𝑎𝑙𝑖𝑠𝑒𝑑 𝐴 𝑙𝑜𝑜𝑝

Fig. 4 .

 4 Fig.4. IAM of the collectorstransversal IAM of the LFR (blue), longitudinal IAM of the LFR (yellow), longitudinal IAM multiplied by cosine of the longitudinal angle of the PTC (green)



  Downwind differencing scheme for tank's discharge (rising fluid): Upwind differencing scheme for tank's charge (dropping fluid):

Fig. 5 .

 5 Fig.5. Storage outlet temperature (continuous line) during a load, with three different mass flow rates; mass injected into the storage (dashes), with the colours corresponding to the flow rates; 𝑴 𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅 = 𝝆 𝑯𝑻𝑭 * 𝑽 𝒕𝒂𝒏𝒌 with the pink crosses; and 𝑻 𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅 with the green crosses Figure 5 shows the outlet temperature of the storage under charging, at a constant mass flow rate, for a storage volume equal to 465 𝑚 3 . The second vertical axis indicates the mass injected into the storage. It can be seen that the storage outlet temperature increases by about 1/3 of the temperature difference when a mass of 𝑀 𝑡ℎ𝑟𝑒𝑠𝑔𝑜𝑙𝑑 = 𝜌 𝐻𝑇𝐹 (𝑇 ℎ𝑜𝑡 ) * 𝑉 𝑡𝑎𝑛𝑘 has been injected in the storage. This remains dependent on the mass flow rate and not perfectly precise.

  𝑇 𝐻𝑇𝐹 (𝑧)) * ℎ 𝐻𝑇𝐹 (𝑇 𝐻𝑇𝐹 (𝑧)) + (1 -𝜖) * 𝜌 𝑠𝑜𝑙𝑖𝑑 * ℎ 𝑠𝑜𝑙𝑖𝑑 (𝑇 𝑠𝑜𝑙𝑖𝑑 (𝑧)) * 𝐴 𝑡𝑣 𝑑𝑧 (7) With 𝜖 the rock bed global void fraction, 𝜌, ℎ, 𝑇 the density, enthalpy and temperature respectively, the subscripts HTF and solid referring to the different materials involved, and 𝐴 𝑡𝑣 the cross-sectional area of the tank. By defining 𝐸 𝑚𝑎𝑥 the stored energy when ∀𝑧, 𝑇 𝐻𝑇𝐹 (𝑧) = 𝑇 𝑠𝑜𝑙𝑖𝑑 (𝑧) = 𝑇 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 and by defining 𝐸 𝑚𝑖𝑛 the stored energy when ∀𝑧, 𝑇 𝐻𝑇𝐹 (𝑧) = 𝑇 𝑠𝑜𝑙𝑖𝑑 (𝑧) = 𝑇 𝑟𝑒𝑡𝑢𝑟𝑛 , we can define the charge percentage 𝜏 = 𝐸 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 -𝐸 𝑚𝑖𝑛 𝐸 𝑚𝑎𝑥 -𝐸 𝑚𝑖𝑛 (8).

Fig. 6 .

 6 Fig. 6. Hourly results of the simulation on the 25 th and 26 th of February Fig. 6.a presents the DNI in W/m² which is intermittent on the 25 th and closer to a clear-sky day on the 26 th ; Fig 6.b. shows the power on the receiver, in 𝑊/𝑚 𝑎𝑝𝑒𝑟𝑡𝑢𝑟𝑒 2 ; Fig.6.c. gives the ambient temperature in °C; Fig. 6.d. shows the demand mass flow rate in kg/s; Fig.6.e. presents the mass flow rate from both control strategies, i.e. MILP and CA1; Fig.6.f. presents the outlet temperature of the Solar Field; Fig.6.g. presents the stored energy in %; Fig.6.h. presents the outlet temperature of the storage; Fig.6.i. presents the heat losses in the solar Fig.6.j. shows the heat collected by the solar field; Fig.6.k. shows the heat produced by the boiler; Fig.6.l. presents the defocused energy in the Solar Field.

Fig. 6

 6 Fig.6.e. shows that the mass flow rate is increased in MILP strategy, whereas its outlet temperature is decreased. This explains why the heat losses are lower in the Solar Field, as can be seen on Fig.6.i, as compared to CA1. Fig.6.k. shows that the MILP algorithm smooths the boiler energy need, which is a side effect (i.e., it was not implemented straightforward) that might be interesting if the variability of the efficiency of the boiler with the thermal load is taken into account. Figures 6.g. et 6.h. show that the MILP makes a better use of the storage as compared to CA1.

Fig. 7 .

 7 Fig. 7. Hourly results of the simulation on the 19 th and 20 th of June Fig.7. presents the same variables as in Fig.6, but at a different time of the year, in which irradiation and optical efficiency are higher. Here, the main contribution of the MILP algorithm as compared to CA1 appears in the period of the defocused energy: the MILP algorithm prefers to defocuse in the morning, and then fills the storage to pass the night. Indeed, the MILP algorithm lowers the heat losses of the storage. The maximum filling of the storage is explained by the mass threshold (or temperature threshold) beyond which the storage cannot be charged anymore.

Fig. 8 .

 8 Fig. 8. Yearly metrics. The Solar Fraction (𝑆𝐹 𝑎 , Fig.8.a.) is calculated as 𝑆𝐹 𝑎 = 1 -𝑄 𝑏𝑜𝑖𝑙𝑒𝑟 𝑄 𝑑𝑒𝑚𝑎𝑛𝑑 , with 𝑄 𝑏𝑜𝑖𝑙𝑒𝑟 the heat from the boiler for the month, and 𝑄 𝑑𝑒𝑚𝑎𝑛𝑑 the heat demand during the month. The average charge percentage (Fig.8.b.) is calculated as the hourly average of the charge percentage. The monthly heat losses of the solar field (Fig.8.c.) correspond to the sum of the thermal losses when the solar field is operating. The monthly defocused energy (Fig.8.d.) correspond to the sum of the defocused energy.

Table 1 . Parameters of the PTC

 1

	Parameter (unit)	Value	Description
	𝜂 0	0.697	Optical efficiency at normal incidence
	𝐴 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 (m²)	5.51	Collector surface area
	𝑎 1 (𝑊. 𝑚 -2 . 𝐾 -1 ))	0.73	Heat losses coefficient
	𝑚̇𝑛 𝑜𝑚 (𝑘𝑔. 𝑠 -1 )	0.126	Nominal mass flow rate

Table 2 . Parameters of the LFR

 2

	Parameter (unit)	Value	Description
	𝜂 0	0.686	Optical efficiency at normal incidence
	𝐴 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 (m²)	30.45	Collector surface area
	𝑎 1 (𝑊. 𝑚 -2 . 𝐾 -1 )	0.033	Heat losses coefficient
	𝑎 4 ( 𝑊. 𝑚 -2 . 𝐾 -4 )	1.48e-9	Heat losses coefficient
	𝑚̇𝑛 𝑜𝑚 (𝑘𝑔. 𝑠 -1 )	0.6	Nominal mass flow rate

Table 3 . Parameters of the dual phase thermocline

 3

	Parameter (unit)	Value	Description
	𝜖 (-)	0.27	Rock bed global void fraction/ HTF Volumetric fraction
	𝑑 𝑝 (m)	0.015	Equivalent diameter of solid particles
	𝑈 𝑒𝑞 (W/m².K)	0.1	Heat losses coefficient
	𝑒 𝑤𝑎𝑙𝑙 (m)	0.012	Thickness of the metal wall
	𝐶 𝑝,𝑠𝑜𝑙𝑖𝑑 & 𝐶 𝑝,𝑤𝑎𝑙𝑙 (J/kg.K)	900 & 500	Specific heat capacity of the solid filler and the wall's metal
	𝜌 𝑠𝑜𝑙𝑖𝑑 & 𝜌 𝑤𝑎𝑙𝑙 (kg/𝑚 3 )	2500 & 7200	Density of the solid and the wall
	𝑘 𝑠𝑜𝑙𝑖𝑑 & 𝑘 𝑤𝑎𝑙𝑙 (W/m.K)	5 & 20	Thermal conductivity of the solid and the wall
	𝑁 𝑑𝑖𝑠𝑐𝑟	50	Number of elements of the thermocline's discretization
	3.3. System model


K Dejaeger
email: karlien.dejaeger@ugent.be

J Criquet

M Vanoppen

E R Cornelissen

G Billon

C Vignal

How do haloacetamides and haloacetic acids affect human intestinal epithelial cells?

Keywords: cytotoxicity, gene expression, Caco-2

In this research, human intestinal epithelial cells (Caco-2) were exposed to haloacetamides and haloacetic acids. A 24, 48 and 72h exposure was conducted to determine the cytotoxicity of these compounds. The unregulated haloacetamides were clearly more cytotoxic than the regulated haloacetic acids. Moreover, changing the halogen from Cl to I increased the toxicity extensively. A 6h stimulation with 1 µM of each compound and subsequent RNA extraction showed high upregulation of genes involved in both oxidative stress and inflammatory pathways with haloacetamide exposure, while no significant changes were seen for haloacetic acid exposure.

INTRODUCTION

Disinfection of drinking water is a widely used technique to inactivate pathogens. However, harmful disinfection by-products (DBPs) are formed due to the reaction with organic matter (Pals et al., 2013). Unregulated DBPs, such as haloacetamides (HAcAms), are found in lower concentrations compared to the regulated trihalomethanes and haloacetic acids (HAAs), but it is suggested by several studies that their toxicity is much higher (Richardson et al., 2007).

Research with Chinese Hamster Ovary (CHO) cells showed that HAcAms are about 140 times more cytotoxic and 10 times more genotoxic than the regulated HAAs (Ding et al., 2019). Only very few research has been conducted on human cells, although the CHO results cannot necessarily be extended to human cell lines (Ding et al., 2019). For example, studies on colonic epithelial cells (CCD 841 CoN) showed that iodoacetic acid is 4 times more cytotoxic than iodoacetamide, whereas iodoacetamide has 2 times higher cytotoxicity than iodoacetic acid in CHO cells (Sayess et al., 2017;Wagner and Plewa, 2017).

Furthermore, reactive oxygen species and the activation of oxidative stress pathways are at the basis for the genotoxic and carcinogenic properties of the DBPs. Nonetheless, limited studies have investigated inflammatory responses despite the well-established relationship between these responses and the progression of cancer (Procházka et al., 2019). This research will therefore, (i) compare the cytotoxicity between HAAs and HAcAms and (ii) investigate the expression of various genes for both families on human intestinal epithelial cells (Caco-2).

MATERIALS AND METHODS

Caco-2 cells were grown at 37°C under a 5% CO 2 humidified atmosphere in DMEM medium supplemented with 100 U/ml penicillin, 100 mg/ml streptomycin, 2mM Lglutamine and 20% fetal calf serum. medium containing increasing concentrations of either chloroacetamide (Cl-AcAm), bromoacetamide (Br-AcAm), iodoacetamide (I-AcAm), dichloroacetamide (Cl 2 -AcAm), trichloroacetamide (Cl 3 -AcAm), chloroacetic acid (Cl-AA), bromoacetic acid (Br-AA) or iodoacetic acid (I-AA) for 24h, 48h and 72h. 10 µL of 5 mg/mL MTT (3-[4,5-dimethyl thiazol-2-yl]-2,5-diphenyl tetrazolium bromide) stock solution was added at the end of the stimulation period and incubated for 4h at 37°C in a 5% CO 2 humidified atmosphere. The MTT is reduced by metabolic active cells and converted into formazan crystals. These crystals were dissolved with 100 µL DMSO and absorbance was measured at 570 nm. The results were used to plot dose-response curves.

5x10 5 cells/well were seeded in a 24-well plate and incubated with 300 µL cell medium containing a 1µM concentration of a certain compound for 6h. RNA was extracted using the Nucleospin RNA II kit (Macherey Nagel) and subsequently converted into cDNA. qPCR was conducted for the detection of genes involved in inflammation and oxidative stress pathways.

RESULTS AND DISCUSSION

Figure 1 shows the half maximal inhibitory concentration (IC 50 ) deduced from the dose-response curves (r² > 0.75) for HAcAms and HAAs. From this, it is concluded that for both families, the I-DBP is the most cytotoxic followed by Br-DBP. Cl-DBP appears to be the least cytotoxic. This is in agreement with studies exerted on CHO cells (Plewa et al., 2010;Plewa et al., 2008). Cl 2 -AcAm and Cl 3 -AcAm caused least cell death, with IC 50 values up to 40 000 µM.

Furthermore, the HAcAms have overall lower IC 50 values compared to the HAAs, which is similar to the conclusions drawn from CHO cells (Wagner and Plewa, 2017). However, the IC 50 values found in this study were generally higher than the ones reported for CHO (Wagner and Plewa, 2017). A preliminary assessment on gene expression for inflammation and oxidative stress pathways with Caco-2 cells was exerted and is shown in Figure 2. The 1 µM dose was chosen, since this concentration appeared to be non-cytotoxic for all compounds after 24h.

Figure 2a shows a prominent change in gene expression from Cl-AcAm to I-AcAm. No change in gene regulation was seen for Cl-AcAm, but Br-AcAm shows an upregulation for oxidative stress genes. On the other hand, I-AcAm shows high upregulation for all inflammatory genes tested. Although Cl 2 -AcAm and Cl 3 -AcAm did not show high cytotoxicity, they did have an effect on several genes. No significant changes were seen for HAAs (Figure 2b)

Although it is believed that DBPs are toxic through the activation of oxidative stress pathways, this preliminary study shows high expression in inflammatory pathways. This confirms previous work performed on FHs74Int cells after a 4h stimulation and subsequent global gene microarray analysis (Procházka et al., 2019).

Figure 1 :

 1 Figure 1: IC 50 values (µM) determined after 24h, 48h and 72h for, (a) chloroacetamide (Cl-AcAm), bromoacetamide (Br-AcAm), iodoacetamide (I-AcAm), dichloroacetamide (Cl 2 -AcAm), trichloroacetamide (Cl 3 -AcAm) and, (b) chloroacetic acid (Cl-AA), bromoacetic acid (Br-AA) and iodoacetic acid (I-AA). Error bars represent the 95% confidence interval.

Figure 2 :

 2 Figure 2: Heatmap showing the expression of inflammatory genes (TNFα, IL1β, IL8, IL6) and oxidative stress genes (HMOX1, iNOS) in (a) chloroacetamide (Cl-AcAm), bromoacetamide (Br-AcAm), iodoacetamide (I-AcAm), dichloroacetamide (Cl 2 -AcAm), trichloroacetamide (Cl 3 -AcAm) and, (b) chloroacetic acid (Cl-AA), bromoacetic acid (Br-AA) and iodoacetic acid (I-AA).Upregulation compared to the control group is visualized with red (>100%), downregulation with green (<100%). **=Significant different compared to the control group.

The following step in this research will be performing a global gene microarray on human intestinal epithelial cells. This will yield information about the entire set of genes expressed during the exposure of different DBPs and thereby identifying the several pathways that are affected by DBP exposure (Procházka et al., 2019).

Ding, X., Zhu, J., Wang, X., Zhou, W., Wu, K., Zhou, Z., Zhou, K., Wu, D., Jiao, J., Xia, Y. and Wang, X. 2019


Thuan Quang

Jacques Tran

Michal De Sanoit

Christine Pomorski

P Mer-Calfati

Bergonzo

Optimization of the

come

CLIQUEZ POUR MODIFIER LE STYLE DU TITRE

SOUS-TITRE 1

CONTACT : xx.xx@cea.fr

SOUS-TITRE 2 SOUS-TITRE 3

Cliquez pour modifier les styles du texte du masque

• Exemple 1 • Exemple 2
Cliquez pour modifier les styles du texte du masque

• Exemple 1 • Exemple 2
Cliquez pour modifier les styles du texte du masque

Actinides precipitation

Optimum conditions: 120 min, -3 mA/cm 2 , 15 ml of 0.3 M NaNO 3 pH 3

▪ Fast cleaning with high efficiency > 99.9 % by anodic courant of +2mA/cm 2 in acid solution (Na 2 SO 4 0.3M pH 3)

Sensor stability

Spectrum of 241 Am precipitated onto B-NCD/Si PIN

Multi-actinides detection

Conclusion

▪ Fabrication of an innovative detector based diamond/silicon for direct alpha detection in solution with limit of detection up 0.24 Bq/L ▪ Sensor could be reused after a fast cleaning by applying a weakly anodic current of +2 mA/cm 2 ▪ Sensor showed very high stability due to unique properties of diamond electrode ▪ Precipitation efficiency is improved up about 80 % for 241 Am and 244 Cm, and 50 % for 239 Pu ▪ Actinides electroprecipitation in NaNO 3 is 1.4 times more efficient than in Na 2 SO 4 due to nitrate reduction generate the limitation of hydrogen gas than water reduction

Introduction▪

  Nuclear industry or nuclear accident (e.g. Tchernobyl 1986 and Fukushima 2011) generate non negligible amount of radioactive sources in the environment. Among of radionuclides, actinides are very hazardous due to their radiation emission and chemical toxicity, and more than their long half-life to hundreds thousands years. ▪ We propose herein an innovative device consisting of a diamond electrode and a conventional silicon alpha sensor, directly detecting alpha activity in solutions [1]. Diamond electrode is dedicated to collect alpha emitter onto sensor by electroprecipitation technique. ▪ This work aims at optimizing electroprecipitation step by fine tuning parameters of electrochemical process such as: pH of electrolyte, current density, electrolyte nature, geometry of electrochemical cell, sensor area and precipitation times. Talvitie 1972; [3] Hallstadius 1984; [4] Tsoupko-Sitnikov et al. 2000

Si PIN 239 Pu 241 Am 244 Cm Optimization of the Efficiency of Diamond Based Alpha Spectrometers in Solutions Q.T. TRAN, J. de SANOIT, M. POMORSKI, C. MER-CALFATI and P. BERGONZO thuan-quang.tran@cea.fr

  Spectrum of 239 Pu, 241 Am,244 Cm precipitated onto B-NCD/CEA, LIST, Diamond Sensors Laboratory, Bât. 451, Centre d'Etude de Saclay, 91191 Gif-sur-Yvette, France

	Actinides	Precipitation yields	Sensor cleaning (%)
		(%)
	241 Am (5.96 Bq)	81.5	> 99.9
	239 Pu (5.13 Bq)	49.9	> 99.9
	244 Cm (4.40 Bq)	78.2	> 99.9