R Brahem
email: rim.brahem@ifpen.fr

A Royon-Lebeaud

D Legendre

M Moreaud

L Duval

Experimental hydrodynamic study of valve trays

Keywords: Absorption, Hydrodynamics, Multiphase flow, Scale up, Valve trays, Emulsion height profiles

The most widely used sour gas sweetening process consists of treating the acid gas with aqueous amines solutions. Mass transfer between gas and liquid phases takes place in absorption columns where valve trays are commonly used as contactor devices. Design of industrial columns relies on empirical correlations developed on pilot units. Nevertheless, available experimental data and correlations on valve trays are scarce in comparison with works done on sieve trays. Optimal column design is hard to achieve because correlations provided in literature present large discrepancies. More thorough studies dedicated to valve trays are needed to develop reliable design correlations.

The present hydrodynamic study was carried out on a Plexiglas 1.26m per 0.1905 m absorption column containing four V-4 Glitsch valve trays. Water/air system was used at atmospheric pressure and ambient temperature. Liquid rate per weir unit length was varied between 3.2 10 -3 m 3 .(m.s) -1 and 24.3 10 -3 m 3 .(m.s) -1 and the kinetic gas factor between 0 and 3.5 Pa 0.5 .

The following hydrodynamic parameters were determined: tray pressure drop, valves pressure drop, clear liquid height, mean emulsion height and liquid mean hold up on the tray. Correlations for clear liquid height, liquid mean hold up and emulsion height are proposed.

Emulsion profiles characterisation was possible due to video records post-processing. Four different behaviours are identified for emulsion profiles according to liquid and gas velocities.

Significant behaviour changes on the hydrodynamic parameters allowed the identification of 3 system limits: dumping, weeping and pre-flooding. Correlations are proposed for these limits and an operational diagram is presented.

Nomenclature

Introduction

Valve trays are commonly used as internals in acid gas sweetening process. In this process natural gas, highly charged with acid compounds (mainly carbonic gas and hydrogen sulphide), is treated using aqueous amines solutions.

Industrial units design involves the determination of two main parameters which are column diameter and height. The diameter is controlled by two phase flow hydrodynamic behaviour (weeping, flooding). Total column height is controlled by mass transfer parameters.

Because of flow complexity on the tray, the design of absorption columns still relies on empirical correlations developed on pilot units. Available correlations in the literature present large discrepancies in shape, results, and choice of influent parameters because of their dependence to the pilot units. In addition, most experimental studies are done on sieve trays and few data is available on valve trays. For these reasons, extrapolation to industrial scale is not yet optimal.

Experimental works on hydrodynamic parameters are done in order to set hydrodynamic diagrams which are useful for diameter determination. These diagrams are equally important for total column height definition because certain hydrodynamic parameters are involved in the determination and prediction of mass transfer coefficients (e.g.: clear liquid height h Lc , liquid hold up α L …).

System limits determination is important for column operability. At a fixed liquid rate Q L , two main gas velocity thresholds exist: weeping and flooding. Both limits have to be well known before designing industrial units.

Weeping is the lower limit and it corresponds to a part of liquid flowing down through the plate perforations. Excessive weeping causes an important decrease of tray efficiency. Existing theoretical and semi-theoretical studies upon weeping from single orifice are well synthesised by [START_REF] Lockett | Distillation tray fundamentals[END_REF]).

The different models point out the importance of hole Froude number Fr h representing the ratio between gas inertia and liquid weight on the tray (see Equation ( 1)).

L L h G G h gh U Fr   2 ,  (1)
Several studies use this parameter to predict perforated trays weeping ( [START_REF] Mayfield | Perforated plate distillation columns[END_REF]; [START_REF] Wijn | On the lower operating range of sieve and valve trays[END_REF]). Since these correlations take into account only the static liquid weight they imply an increase of the weeping limit with increasing liquid load L. However, some studies stating the decrease of weeping with liquid load exist ( [START_REF] Fair | Chapt.13-Distillation and gas absorption[END_REF]; [START_REF] Qian | High-powered adaptative valve tray A new generation tray offers new advantages[END_REF]).

The upper limit of column operability is the flooding point. It corresponds to the entrainment of part of the liquid of the tray to an upper tray. As described by [START_REF] Kister | Equipement for distillation and gas absorption: tray columns[END_REF]) and [START_REF] Lockett | Distillation tray fundamentals[END_REF] , jet flooding limit is admitted to increase very slightly with increasing liquid load. A maximum is reached indicating the transition to downcomer flooding. This second flooding limit decreases sharply with liquid load. But though flood limit behaviour is commonly admitted correlations available in the literature are different in shape and results. For instance in Glitsch [START_REF] Glitch | Distillation tray fundamentals[END_REF] and Koch [START_REF] Koch | Distillation tray fundamentals[END_REF]) correlations liquid load L (see ( 2)) is used whereas in Nutter (Nutter Engineering Co. 1976) correlation it is liquid velocity toward active area U La (see (3)) that is considered and for [START_REF] Zuiderweg | Sieve trays: a view on the state of the art[END_REF]) the controlling factor is flow parameter with the same area considered for liquid and gas velocities FP' (see (4)).

W L L Q L  (2) a L La A Q U  (3) G L G L Q Q FP    ' (4)
The two phase flow on the tray is mainly described as a homogeneous flow. Francis equation type correlations for emulsion height over exit weir are thus used ( [START_REF] Colwell | Clear liquid height and Froth Density on seive trays[END_REF]; [START_REF] Bennett | New pressure drop correlation for sieve tray distillation columns[END_REF]; [START_REF] El-Azrak | Etude des plateaux à clapets -Caractérisation du transfert de matière et de l'hydrodynamique[END_REF]; [START_REF] Liang | The impact of valve tray geometry on the interfacial area of mass transfer[END_REF]

). 3 / 1 2            g L C h h h L D w L Fe L Lc    (5)

Where h

Lc is the clear liquid height, α L is the liquid hold up, h Fe is the emulsion height and C D is an empirical friction coefficient that differs according to experimental studies.

Influence of gas flow rate on clear liquid height is entirely projected in liquid hold up α L . Most usually, the latter parameter is determined using the ratio between clear liquid height and emulsion height.

Fe Lc L h h   (6)
The trajectory model is the other widely used description model for two phase flow on the tray. This model accords more importance to liquid-gas interaction and thus highlights the importance of flow ratio:

G L a G U L    ,  (7)
Where U G,a is the gas velocity per active area.

Available correlations usually use this parameter to describe hydrodynamics on valve trays ( [START_REF] Dhulesia | Clear liquid height on sieve and valve trays[END_REF]; [START_REF] Mustafa | Hydrodynamic Characteristics of Nutter Valve Trays, New Correlations[END_REF] This work aims at providing more experimental data on valve trays and better understanding flow behaviour for such devices. Experimental set up and methods are first described. A part is dedicated to video post-processing which allowed measurements of emulsion height profiles. Last, operating limits, namely dumping weeping and pre-flooding, are localised according to the observed hydrodynamic changes. Correlations and hydrodynamic diagram are proposed for these system limits.

Methods and materials

Experimental set up

The experimental set up used is a Plexiglas rectangular column of 1.26 m in length and 0.1905 m in width operated at atmospheric pressure and ambient temperature. Four Glitsch V-4 valve trays are installed in the column with a tray spacing of 0.455 m (more details are reported in Table 1). Air/water system is used.

Liquid is stored in two communicating tanks of 1 200 m 3 total volume and it circulates in a closed loop. Gas is supplied by an internal network and enters at the bottom of the column with no specific distributing device.

A grid demister is placed at the column top to prevent any liquid entrainment with gas flow and a separator is set after the column to collect liquid droplets in case of excessive entrainment (Figure 1).

Liquid rate was varied from 2.2 m 3 /h to 16.7 m 3 /h which means in term of liquid flow rate per unit weir length between 3.2 and 24.3 10 -3 m 3 /m.s. Gas flow rate was varied from 0 to 2 100 m 3 /h which in terms of kinetic factor Fa (see (8)), which represents the root square of gas inertia, represents a variation between 0 and 3.5 Pa 0.5 .

,a G

G U Fa   (8)

Measurements

Pressure drops

Both total tray pressure drop (ΔP Tray ) and emulsion pressure drop (ΔP Emulsion ) were measured. These measurements were made using Rosemount manometers. The pressure connection branches are large enough to neglect capillary effect.

For tray pressure drop ΔP Tray , the total pressure loss over three trays was measured. Considered ΔP Tray is the mean value of the 3 trays pressure loss. Emulsion pressure drop was measured at four different positions on the tray (e1 to e4) as shown in Figure 1. The considered ΔP Emulsion is the mean of these four measurements.

Valves pressure drop ΔP Valves is due to gas flowing through the valves. It was deducted from the two previous pressure drops by subtracting ΔP Emulsion from ΔP Tray .

In absence of liquid flow, valves pressure drop is supposed to be equal to tray pressure loss and is called dry pressure drop ΔP Dry .

Pressure connections are slightly elevated from bottom of the tray (2 cm). This elevation implies that valves pressure drop includes a part of liquid weight. This means that the emulsion pressure drop is slightly under estimated. The error implied by pressure connection elevation is at maximum about 1 mbar.

Emulsion pressure drop can be used to determine clear liquid height h Lc as follows:

g P h L Emulsion Lc   
Emulsion pressure drop is divided into 3 contributions: liquid weight, gas acceleration pressure drop and liquid viscosity effect. Inside water-air emulsion, gas acceleration is considered negligible as well as the effect of liquid viscosity. Thus emulsion pressure drop can be used to evaluate clear liquid height.

Emulsion height

Emulsion height measurements were made using video records post-processing.

Firstly, each image from the video sequence is extracted. An air-emulsion separation profile is then estimated by image processing methods involving a marker controlled watershed operator [START_REF] Serra | Image Analysis and Mathematical Morphology[END_REF]. Two markers are imposed as first line and last line of the image, respectively. Markers are propagated on a topological function calculated from the image by morphological gradient and morphological closing using disk as structuring element [START_REF] Serra | Image Analysis and Mathematical Morphology[END_REF]) (cf. figure 2). All these profiles can be used to calculate a probability map indicating the number of times that a profile is at a certain height. From this height probability map, an average air-emulsion separation can be estimated for one condition video. Finally, in the third zone all valves are opened and ΔP Valves increases once more with gas velocity.

Results and discussion

Pressure drops

In absence of liquid flow (L=0 m 3 /m.s) and in theory, dry pressure drop ΔP Dry in the 3 rd zone is supposed to rise as the square of gas velocity [START_REF] Lockett | Distillation tray fundamentals[END_REF]). Experimentally the power on the velocity is actually found to be equal to 1,7. Correlation of ΔP Dry with kinetic gas factor could be given as follow: (El-Azrak 1988) and [START_REF] Piqueur | RESEARCH ON VALVE TRAYS. HYDRAULIC PERFORMANCE IN THE AIR-WATER SYSTEM[END_REF] worked on valves similar to those used in this work.

Compared to their results the present experimental results show good agreement (Figure 4).

In presence of liquid flow, dependence on gas velocity after OBP point varies between 1,5 and 1,9.

No experimental results were found in literature for comparison. Indeed, in presence of liquid flow, valves pressure drop is usually considered equal to dry pressure drop. The OBP point is dependent on liquid loading as shown in Figure 3. Increasing liquid velocity on the tray decreases minimum gas rate needed to open all valves. Correlating gas inertia for OBP points with liquid inertia gives the following expression:

2 3 2 , 10 9 1 . 2 L L L OBP U Fa      (10)
Where U L is liquid velocity calculated as:

Lc w L L h L Q U   (11)
Results of this correlation are presented Figure 5 and provide maximum deviation of 6%.

The depression generated by liquid velocity could so explain the decrease in kinetic gas factor at OBP point. This dynamic effect of liquid flow facilitates valves opening and so helps gas to access the tray.

Clear liquid height h Lc

From Figure 6, two main zones characterize h Lc variation. The first zone, in which h Lc rises with increased kinetic gas factor Fa, corresponds to an important weeping zone, usually called dumping zone. As hole gas velocity increases, it inhibits weeping and clear liquid height on the tray increases consequently until reaching a maximum indicating the dumping zone limit. For higher gas velocities, weeping exists but with a much lower rate than in the dumping zone(James R. [START_REF] Fair | Chapt.13-Distillation and gas absorption[END_REF] The value of kinetic gas factor corresponding to this limit decreases with increased liquid load. This highlights a depression on the tray caused by the increase in liquid velocity. This depression prevents excessive liquid flow through perforations.

In the second zone h Lc decreases very slowly until reaching a stagnation plate. Effect of liquid viscosity and gas acceleration which were neglected in emulsion pressure drop could not explain this decrease because both contributions would lead to increase emulsion pressure drop. This behaviour should be explained by the fact high gas velocities on the tray assist liquid evacuation (El-Azrak 1988). Some authors report that clear liquid height has little dependence on valve type ( [START_REF] Liang | The impact of valve tray geometry on the interfacial area of mass transfer[END_REF]; [START_REF] Piqueur | RESEARCH ON VALVE TRAYS. HYDRAULIC PERFORMANCE IN THE AIR-WATER SYSTEM[END_REF]). Comparison between experimental data issued from different studies using different valves is then possible. Figure 7 shows results for L=3.2 10 -3 m 3 /m.s in comparison with results of [START_REF] Dhulesia | Clear liquid height on sieve and valve trays[END_REF], [START_REF] Fasesan | Hydraulic characteristics of sieve and valve trays[END_REF] and [START_REF] Mustafa | Hydrodynamic Characteristics of Nutter Valve Trays, New Correlations[END_REF] on different valve trays. Present results are reasonably in accordance with these references. Because of a larger span of kinetic gas factor tested on this work, the first phase corresponding to weeping range is not reported by those authors. Dependence on Fa at fixed liquid rate is comparable between present results and those of [START_REF] Mustafa | Hydrodynamic Characteristics of Nutter Valve Trays, New Correlations[END_REF] and those of [START_REF] Fasesan | Hydraulic characteristics of sieve and valve trays[END_REF]. However, effect of kinetic gas factor is higher for [START_REF] Dhulesia | Clear liquid height on sieve and valve trays[END_REF] data. Flow ratio represents the quotient between liquid load times root square liquid density and kinetic gas factor. Interaction between the two fluids is thus pointed out.

Excluding the first region of dumping, we studied dependency toward the flow ratio ψ

2 . 0 3 . 6    Lc h (12)
Maximum deviation is of 20%.

The choice of flow ratio as a correlating parameter for clear liquid height describes conveniently the variation of clear liquid height with both liquid and gas velocities.

Emulsion height h results

Mean emulsion height h Fe

Results of mean emulsion height h Fe are shown in Figure 10 for different liquid loads. h Fe is an increasing function of both kinetic gas factor Fa and liquid load L.

Few data is found for mean emulsion height h Fe in the literature. In Figure 11 a, the present results for L=3.2 10 -3 m 3 /m.s are compared to those of [START_REF] Mustafa | Hydrodynamic Characteristics of Nutter Valve Trays, New Correlations[END_REF] at L=3.1 10 -3 m 3 /m.s and those of (El-Azrak 1988) at L=4 10 -3 m 3 /m.s for two different weir heights.

In both reference works, authors measured emulsion height directly on the column using measuring tapes.

As shown in Figure 11 a, present results are reasonably in accordance with those of [START_REF] Mustafa | Hydrodynamic Characteristics of Nutter Valve Trays, New Correlations[END_REF]. However, [START_REF] El-Azrak | Etude des plateaux à clapets -Caractérisation du transfert de matière et de l'hydrodynamique[END_REF] data are higher than the present results. This discrepancy is due to the fact he localized maximum emulsion height on trays and not mean height.

Considering equally maximum emulsion height, this work results in figure

Emulsion profiles

Emulsion height is not always homogeneous over the tray. Figure 12 shows an example of profiles evolution for a fixed L and variable Fa values.

Mean liquid hold up α L

Using h Lc and mean emulsion height h Fe we measured a mean liquid hold up on the tray α L . Results are reported on Figure 15 . Liquid hold up α L decreases sharply with Fa till reaching a saturation phase. Liquid hold up appears to be little dependent on liquid velocity. It is mainly a decreasing function of kinetic gas factor. This observation has been made by several authors. [START_REF] Bennett | New pressure drop correlation for sieve tray distillation columns[END_REF] working on sieve trays and [START_REF] Liang | The impact of valve tray geometry on the interfacial area of mass transfer[END_REF] working on different valve trays propose correlations independent of liquid velocity. [START_REF] Bennett | New pressure drop correlation for sieve tray distillation columns[END_REF]) [START_REF] Liang | The impact of valve tray geometry on the interfacial area of mass transfer[END_REF] Though small, dependence of liquid hold up on liquid rate is not negligible. Thus the choice of Froude number Fr as correlating parameter seems better suited than using only kinetic gas factor.

                   91 . 0 , 55 . 12 G L G a G L U Exp     (14)
Adopting the mathematical forma proposed by both [START_REF] Colwell | Clear liquid height and Froth Density on seive trays[END_REF] and [START_REF] Liang | The impact of valve tray geometry on the interfacial area of mass transfer[END_REF], influence of Froude number on liquid hold up is studied. The following correlation is obtained: Results are comparable to [START_REF] Colwell | Clear liquid height and Froth Density on seive trays[END_REF] correlation. This latter provides close results to present experimental points. As in this work no variation of perforated area was performed dependency to this parameter could not be verified.

Operational diagram

Observing behaviour changes for all preceding results, different system limits are recognized (Figure 17). The first transition corresponds to dumping limit determined according to clear liquid height results.

That corresponds also to the first transition identified of flatness deviation feature. When all valves oscillate simultaneously there is alternation between liquid and gas flowing through the tray perforations.

The second limit identified corresponds to open balance point OBP. This limit corresponds as well to the second transition of flatness deviation criterion. It represents the end of valves instability and thus the establishment of a regime of totally opened valves which means little weeping. For this reason this transition is called weeping limit. [START_REF] Zanelli | Perforated Plate Weeping[END_REF] studied weeping phenomena (weeping and dumping domains) on perforated trays. They related weeping to oscillations of clear liquid height and gas pressure drop on tray. Oscillations are caused by the competition of liquid weight on the tray with the sum of gas pressure drop and surface tension. Surface tension at the valve hole is negligible in comparison with sieve trays because of much larger hole diameter. Forces in competition in the valve case are liquid weight and gas inertia. Therefore, the Froude number is used in most weeping correlations. This dimensional number is used to correlate dumping limit as follow: With maximum deviation of 9% For weeping limit, corresponding to OBP point, a correlation using liquid inertia has been already introduced:

2 3 2 , 10 9 1 . 2 L L L OBP U Fa      (10)
Maximum deviation of this correlation is of 6%.

Several correlations for sieve trays weeping exist in literature ( [START_REF] Fair | Chapt.13-Distillation and gas absorption[END_REF]; [START_REF] Lockett | Distillation tray fundamentals[END_REF][START_REF] Mayfield | Perforated plate distillation columns[END_REF]; [START_REF] Wijn | On the lower operating range of sieve and valve trays[END_REF]) but hardly any could be found for valve trays. [START_REF] Fair | Chapt.13-Distillation and gas absorption[END_REF] proposed a graphical hydrodynamic diagram for an industrial sieve tray column of 1.52 m diameter. [START_REF] Wijn | On the lower operating range of sieve and valve trays[END_REF] proposed a theoretical study of weeping limits for both sieve and valve trays.

Comparison of present dumping and weeping correlations with existing correlations is reported on Figure 18.

The dumping limit is in accordance with [START_REF] Fair | Chapt.13-Distillation and gas absorption[END_REF] diagram (Figure 18.a). However present weeping limit shows a different trend from what is proposed in the literature for sieve trays (Figure 18.b). This could be inherent to valve trays which are supposed to lessen weeping phenomena. [START_REF] Qian | High-powered adaptative valve tray A new generation tray offers new advantages[END_REF] underlined this fact when working on valve trays. Indeed, almost all weeping correlations are based on a critical hole Froude number Fr h . Presence of the moving valves on the tray and the important difference in hole diameter range compared to perforated plates implies a different critical value of Froude number at the weeping point between valve and sieve trays. Therefore the use of weeping correlations issued from sieve trays works appears to be irrelevant. [START_REF] Fair | Chapt.13-Distillation and gas absorption[END_REF] diagram presents two weeping limits showing dependencies on liquid loading that are opposite. The importance of weeping definition is highlighted since dependency to liquid load depends on weeping relative severity.

Applying the model proposed by [START_REF] Wijn | On the lower operating range of sieve and valve trays[END_REF] on valve trays to the present clear liquid heights shows a weeping limit that is decreasing with increased liquid loading. The present results are in accordance with this trend though their dependency to liquid loading is higher than the results of [START_REF] Wijn | On the lower operating range of sieve and valve trays[END_REF] model. More experimental data on valve trays is needed for better suited comparison.

The last transition identified in accordance to the emulsion height results is the third transition of flatness deviation criterion. This limit refers to the reappearance of inhomogeneities on emulsion profile due to gas channelling. This is considered here as a pre-flooding limit because further increase of gas velocities leads inhomogeneities to reach upper tray and cause flooding.

Correlation using both Froude number and flow ratio parameters is proposed here for this preflooding limit: Flooding correlations in literature are available for sieve trays. For valve trays, correlations are usually given by the manufacturers. Some of available correlations are presented in Figure 19.

Dependency of pre-flooding correlation on liquid load seems to be comparable to flood correlation trend.

Present experimental results do not allow identifying the effective flooding limit. Actually in the literature, flooding is more usually determined according to entrainment results [START_REF] Kister | Entrainment from sieve trays in the froth regime[END_REF]). Valve tray absorption columns are commonly used for acid gas treatment process. Few experimental data on pilot units are available which represents a hindrance to optimal design of industrial units.

Figure 1 :

 1 Figure 1 : Sketch of experimental set up schema pressure drop connections.

Figure 2 :

 2 Figure 2 : from left to right and top to bottom: image sequence extracted from a video; air-

  3.1.1. Dry and valves pressure drops ( ΔP Dry , ΔP Valves ) Three zones characterizing ΔP Valves are shown in Figure 3. The first zone corresponds to closed valves. Gas flows through minimum opened surface area and ΔP Valves increases linearly with square gas velocity. In this zone, few negative pressure drops can be noticed. The weeping is very important for this range of kinetic gas factor Fa and it may explain these values. The second zone corresponds to a stagnation region where valves open progressively. Gas velocity at the orifice is almost constant and this explains the stagnation of pressure drop. This second zone is delimited by the closure balance point (CBP) for which all valves are closed and the open balance point (OBP) for which all valves are opened.

  of 1.5%.Experimental points published by[START_REF] El-Azrak | Etude des plateaux à clapets -Caractérisation du transfert de matière et de l'hydrodynamique[END_REF] for the same valve kind (Glitsch V-4) show dependence to gas velocity comparable to present results (power =1,7). The deviation from theoretical dependency to gas velocity was reported by other authors for different valves ([START_REF] Piqueur | RESEARCH ON VALVE TRAYS. HYDRAULIC PERFORMANCE IN THE AIR-WATER SYSTEM[END_REF] with A1 Glitsch and KV Koch valves;[START_REF] Mustafa | Hydrodynamic Characteristics of Nutter Valve Trays, New Correlations[END_REF] with Nutter valves;[START_REF] Fasesan | Hydraulic characteristics of sieve and valve trays[END_REF]) with Koch T).

Figure 3 :Figure 4 :

 34 Figure 3 : Valve pressure drop ΔP Valves for deferent L.

Figure 5 :

 5 Figure 5 : Dependence of the Open Balance Point (OBP) on liquid load L.

Figure 6 :

 6 Figure 6 : Clear liquid height h Lc for deferent L.

Figure 7 :

 7 Figure 7 : Comparison between our measurements and literature data.

Figure 8 :Figure 9 :

 89 Figure 8 : Clear liquid height correlation in comparison with experimental data.

Figure 10 :

 10 Figure 10 : Mean emulsion height h Fe for different L. Continuous lines represent correlation 20.

Figure 11 :

 11 Figure 11 : Comparison of mean emulsion height results with literature (a) Comparison to

Figure 12 :Figure 14 :

 1214 Figure 12 : emulsion profiles for L=8.2 10 -3 m 3 /m.s and different Fa. Blue arrow indicates liquid

Figure 15 :

 15 Figure 15 : Mean liquid hold up for different L.

Figure 16

 16 Figure16shows a comparison between these different correlations and present results. The

  this correlation is 16% and results are reported Figure16.

Figure 16 :

 16 Figure 16 : Liquid hold up correlations.

Figure 17 :

 17 Figure 17 : Experimental system limits.

  Figure 18 : Comparison of present correlations with correlations from literature (a) Dumping

Table 1 : Geometrical characteristics of the column Tray characteristics

 1

	L T (m)	1.26
	L w (m)	0.1905
	L P (m)	0.96
	L D (m)	0.15
	A T (m²)	0.24
	A a (m²)	0.18
	A h (m²)	0.032
	h w (m)	0.065
	L w (m)	0.1905
	Trays number	4
	T s (m)	0.455
	Valve characteristics
		V4
	Type
		GLITSCH
	Wweight (kg)	0,025
	Valves/ tray	27
	Minimum valve lift	0.001 m
	Maximum valve lift	0.009 m
	d V	0.0475 m
	d h	0.039 m

Table 2 : Geometrical characteristics of pilots columns used in reference works Reference This work

 2

					(Mustafa &
							(Piqueur &
			(Dhulesia	(Fasesan	Békássy-	(El-Azrak
							Verhoeye
			1984)	1987)	Molnár	1988)
							1976)
					1997)
	column	rectangular	rectangular	circular	circular	circular 0.45	circular
	dimension	1.26m*0.1905m	1.2m*0.61m	0.63 m	0.4m	m	0.3m
	A a (m²)	0.183	0.559	0.237	0.098	0.084	0.063
	A h /A a (%)	17.6	19.5	14.3	18.5	11.9	14
	valve type	GlitschV-4	GlitschV-1	KochT	Nutter	GlitschV-4	GlitschV-1
	h w (mm)	65	50/75	50.8	50	29/42/72	4.5/6/7.5/9

In this work, new experimental data for Glitsch V-4 valve trays is reported. Tray and valves pressure drops, clear liquid height, emulsion height and liquid hold up are analysed over a large scope of liquid and gas flow rates. Behaviour changes are observed for these different parameters.

Valves pressure drop results appears to be dependent on liquid velocity. Indeed, valves opening is assisted by the dynamic depression created by liquid velocity on tray. After total valves opening, valves pressure drop increases with gas velocity according to a power less than 2, the expected value for turbulent flows.

Clear liquid height is determined by emulsion pressure drop. Comparison to literature showed good agreement. A correlation using flow ratio is presented and describes conveniently the variation of h Lc with gas and liquid velocities.

Tray pressure drop variations are described in accordance to both valves and emulsion pressure drops.

Relative importance of these two parameters controls total tray pressure drop evolution.

Emulsion height measurements are presented with a detailed description of emulsion profile evolution. Emulsion profiles are very little studied in previous works and almost no data exists on valve trays. In the present work, two criteria are identified for quantitative description of emulsion profiles: flatness deviation related to the shape and fluctuation related to evolution in time. Four main domains are identified using these results.

Liquid mean hold up is evaluated and a correlation based on Froude number is proposed.

Defining the different behaviour changes of hydrodynamic parameters and comparing them allows the definition of the system limits : dumping, weeping and pre-flooding. An operational diagram is proposed with correlations of the identified 3 limits. This diagram can be improved by the determination of the upper system limit (flooding) and regime transitions (emulsion, mixed and spray regimes). In order to do that other experimental measurements are needed (e.g.: entrainment measurements).

Based on such diagram, mass transfer coefficients can be investigated and column design optimization considered with less uncertainties.


Kwon-Young Choi
email: kwon-young.choi@irisa.fr

Bertrand Coüasnon
email: bertrand.couasnon@irisa.fr

Yann Ricquebourg
email: yann.ricquebourg@irisa.fr

Richard Zanibbi

CNN-Based Accidental Detection in Dense Printed Piano Scores

Keywords: Optical Music Recognition, Deep Learning, Symbol Detection, Data Augmentation

The recognition of mid-18th to mid-20th century piano scores presents segmentation challenges caused by touching and broken symbols produced by imprinting techniques and time degradation. We present a new notehead accidental dataset containing 2955 images from dense and damaged piano scores. We address this detection problem with very small training samples using a simple Spatial Transformer (ST)-based Convolutional Neural Network detector improved through bootstrapping and contextual information, and more powerful deep learning detectors (Faster R-CNN, R-FCN, and SSD) with transfer-learning on the COCO dataset. We trained all our detectors using 5 fold cross-validation and obtain 98.73% mean Average Precision (mAP) for an Intersection over Union (IoU) threshold of 0.75 with our best detector. Our STbased detector obtains a slightly lower mAP of 94.81%, but runs 40 times faster, and uses 18 times less memory.

I. INTRODUCTION

The recognition of mid-18th to mid-20th century dense and damaged piano scores presents unique segmentation problems of touching and broken music symbols as shown in Figure 2 due to their imprinting techniques and time degradation. Segmentation and classification of music symbols is an early task of the pipeline and should be highly precise and reliable because a segmentation or classification error could propagate and ruin the latest stages like music notation reconstruction. The segmentation of music symbols is the most challenging task because of the lack of previous work/datasets to test and compare approaches. With the introduction of recent deep learning architectures for object detection, we can now apply an end-to-end approach to segmentation and classification of music symbols. However, deep learning architectures generally need a lot of annotated training, which we do not have for old printed scores.

In this work, we consider the problem of detecting a single accidental symbol with the a priori knowledge of the position of the associated note head (Figure 1). We address this task using a new Spatial Transformer-based detector that is both small and fast, and compare this with three stateof-the-art object detectors . Our objective is to train these detectors with a limited number of annotated samples and to design methods general enough to be easily adapted for other symbols. In summary, we describe the semi-automatic generation of ground-truth data for our task in Section III-A. We then present two ways of tackling the problem of training deep detectors with limited data. In Section III-B, we use a relatively simple detector with fast inference time but low out-of-the-box accuracy, and improve its results using bootstrapping techniques and contextual information. In Section III-C, we use three different state-of-the-art object detectors to produce highly precise accidental detectors using transfer learning. Finally, we evaluate and discuss the various tradeoffs of speed vs accuracy for different detectors in Section IV and Section IV-C, and conclude in Section V.

II. RELATED WORK

In this Section we review the field of OMR with a focus on symbol detection and classification. We also present the grammar-based DMOS system for generating document recognition systems -we used this system to semiautomatically generate the dataset published with this work. Finally, we review the Spatial Transformer network and state-of-the-art detectors for object detection.

A. Optical Music Recognition

OMR studies by [START_REF] Fornés | Analysis and Recognition of Music Scores[END_REF] or [START_REF] Rebelo | Optical music recognition: State-of-the-art and open issues[END_REF] typically present the OMR workflow as multiple consecutive stages: image preprocessing, staff detection with possible removal, music symbol segmentation/classification and finally music notation reconstruction. However, many works reorganize, merge or remove some of these stages.

Preprocessing and Staff Line Detection: Existing work in OMR tends to use common document pre-processing operations. Binarization is used to isolate connected components from the background, and often score pages are skew-corrected and have noise removal applied. Next, staff line detection has been performed using combinations of filters, pixel projection profiles, run-length analysis, contourline tracking and graph path search. The height between two staff lines is an important feature in OMR, and this interline distance is estimated for later use. Recent work like [START_REF] Calvo-Zaragoza | Staff-Line Detection on Grayscale Images with Pixel Classification[END_REF] has used Convolutional Neural Networks (CNNs) to do pixelwise classification to locate staff lines.

In this work, we use a graph-based Kalman filtering method [START_REF] Andecy | Kalman filtering for segment detection: Application to music scores analysis[END_REF] to detect and remove staves from the original image and is able to process broken or curved lines accurately. The symbol detection methods that we present are generally robust to remaining staff removal artifacts.

Symbol Detection: Music scores are constructed using a lot of relatively simple shapes like lines and blobs in a complex bi-dimensional structure. This fact has pushed OMR systems to use simple extraction algorithm like graphical primitive detection or connected components, and then use complex adhoc rules to merge or over-segment primitives [START_REF] Fornés | Analysis and Recognition of Music Scores[END_REF]. The classification of music symbols can be done using a variety of techniques like simple filters, template matching or classifiers like HMM, neural network, K-NN and SVM as presented in [START_REF] Rebelo | Optical recognition of music symbols[END_REF].

More recently, convolutional-based neural network detectors [START_REF] Pacha | Handwritten Music Object Detection: Open Issues and Baseline Results[END_REF] that merge the segmentation and classification steps have been applied to a variety of dataset like the newly annotated handwritten dataset of modern music, the MUS-CIMA++ dataset [START_REF] Hajič | The MUSCIMA++ Dataset for Handwritten Optical Music Recognition[END_REF] or on mensural music scores by [START_REF] Pacha | Optical Music Recognition in Mensural Notation with Region-Based Convolutional Neural Networks[END_REF]. Fully convolutional neural networks have also been used by [START_REF] Hajic | Towards Full-Pipeline Handwritten OMR with Musical Symbol Detection by U-Nets[END_REF] and [START_REF] Tuggener | Deep Watershed Detector for Music Object Recognition[END_REF] which allows for pixel wise segmentation of music symbols.

Reconstruction: Finally, the last step of the recognition process is to reconstruct the music notation and validate the structure produced. [START_REF] Rebelo | Optical music recognition: State-of-the-art and open issues[END_REF] shows that because of the strong structure and graphical rules of music notation, it makes sense to model this organization using a grammar. Most of these methods are used at the end of the OMR pipeline, to check the validity of the recognized music structure. One exception is the DMOS method described in Section II-B, where the grammar drives both the recognition and validation of the structure produced.

We believe that low-level symbol segmentation problems caused by the density, noise or pre-processing of a music score (see Figure 2) should not be part of the grammar for an OMR system, as it is too complex to be modeled explicitly. We wish to devise a method that delegates the segmentation task to a statistical model, in our case a Convolutional Neural Network (CNN) designed to do both segmentation and classification. We developed our method with the goal that it could be applied to any kind of structured document.

B. The DMOS Syntactical Method

The DMOS syntactical method was introduced by [START_REF] Coüasnon | DMOS : A generic document recognition method, application to an automatic generator of musical scores, mathematical formulae and table structures recognition systems[END_REF], and is a general off-line method for recognizing structured documents. The first version of the system included a grammar for musical scores. DMOS uses attributed twodimensional grammars to define the symbolic and graphical representation of documents, producing constituent parse trees. The contextual information produced by the grammar can also be used to restrict the search space of our detector, as explained in Section III.

The hierarchical graphical structure produced, for example a simple music note as illustrated in Figure 3, is described by a set of rules that can search through the use of backtracking and check the coherence of different note elements. This ability to pinpoint inconsistencies can be used to efficiently produce semi-annotated data by reducing the amount of manual verification. Although the grammar is tailored to deal with complex polyphonic orchestral scores, segmentation had to be addressed using dedicated rules, which are difficult to produce and maintain. This detection of music symbol is the task we are proposing to resolve using Convolutional Neural Network-based detectors.

C. Convolutional Based Object Detector

In this work, we use two different approaches to detect a single accidental using a small labeled training sample: 1) a novel Spatial Transformer-based network, and 2) state-ofthe-art general object detectors (Faster R-CNN, R-FCN, and SSD) using transfer learning.

Spatial Transformer: First, we use a simple convolutional neural network (CNN) architecture based on Spatial Transformer (ST) networks proposed by [START_REF] Jaderberg | Spatial Transformer Networks[END_REF]. The ST network is composed of two stacked CNNs: a localization network and a classification network. The localization network has the task to output a 2D affine transformation for a given input image. The Spatial Transformer Layer applies this transformation to the input image, that will be then fed to the classification network. In the original work of [START_REF] Jaderberg | Spatial Transformer Networks[END_REF], the ST was intended as an attention model and not as a localization model. However, in our context of single symbol detection, we view this affine transformation as an opportunity to build a very simple music symbol detector.

Faster R-CNN: The Faster R-CNN [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF] is one of the pioneer object detection architectures in Deep Learning and is now widely used in very diverse tasks. The detection process happens in two steps. First, in a Region Proposal Network (RPN) stage, a feature extractor (VGG-16 or resnet 101) is used to process input images. Then, at some intermediate layer of the feature extractor, anchor boxes are used in a sliding window manner to predict class agnostic box proposals. This RPN is trained using a multi-loss function taking into account both localization and objectness score produced by the RPN. Secondly, some of these proposals (usually 300) are cropped from the feature layer used to predict them, and the rest of the feature extractor is processed. Unlike the RPN stage, the second stage outputs class-specific bounding boxes refinement for each of the proposals. Finally, a similar multitask loss is used to optimize the second stage detection.

R-FCN:

The R-FCN detector proposed by [START_REF] Dai | R-FCN: Object Detection via Region-based Fully Convolutional Networks[END_REF] is an adaptation of the Faster R-CNN architecture designed for even faster detection. While the Faster R-CNN avoids a lot of computation by sharing a single network for both RPN and full detection stages, it still needs to process each region proposal until the end of the feature extractor. That is why the R-FCN architecture proposes to extract region proposals only at the last layer of the feature extractor and therefore reduces the amount of computation for each proposal. They also propose a position-sensitive cropping mechanism using position-sensitive score maps in order to retain the localization information for each proposed region. R-FCN is much faster than the Faster R-CNN, while maintaining comparable accuracy.

SSD: The third object detector we propose to use is the Single Shot Detector (SSD) [START_REF] Liu | SSD: Single Shot MultiBox Detector[END_REF]. Unlike the Faster R-CNN and R-FCN that use two stage predictions, the SSD architecture predicts directly class and bounding boxes of objects from a single pass of the feature extractor. This model is typically significantly faster than two stage detectors like Faster R-CNN and R-FCN.

III. METHODOLOGY

In Section III-A, we propose a new dataset for detecting accidental symbols. Using this dataset, we propose two main approaches of producing a CNN based detector with enough precision to be used in an OMR system: a contextual approach in Section III-B and an end-to-end approach in Section III-C. An overview of our different detectors is presented in Table II along with the main experimental conditions such as transfer learning from the COCO dataset, detection of a single object or multiple objects, use of the note head centroid as an input feature and use of bootstrapping to augment training data.

A. Dataset Construction

OMR surveys declare that the recognition of contemporary printed music scores is in practice already done by stateof-the-art OMR system. We believe that this statement does not concern more ancient printed music of the romantic and classical period, from around 1750 to 1950 period. These music scores are typically produced using engraving techniques that present very different graphical characteristics and segmentation problems as opposed to scores produced using computer software. That is why we propose a new small dataset for single accidental detection in dense and noisy piano scores 1 .

We used three different scores from the composers Friedrich Kuhlau, Felix Mendelssohn, and Richard Wagner edited in the 19th century. The constitution of this dataset was semi-automated by using DMOS to analyze the layout of the score. Using the produced structure, we were able to extract potential locations of accidental by looking to the left of note heads. Connected components in the location were then classified using a simple CNN based classifier trained on isolated printed music symbols. Finally, we manually verified every potential accidental symbol, obtaining 2955 examples containing three accidental classes and a reject class (for when a note has no accidental), see Table I. In Figure 1, we show how we position our detection window, with the target notehead on the right side, using four times the size of the space between stafflines as estimated by DMOS (the interline distance) as the window side length.

Given the omnipresence of the target note head, we require that the detector localize the note head if there is no accidental associated with it. This allows us to define rejection using a concrete symbol detection goal, rather than trying to detect missing accidentals in background noise.

Bootstrapping Strategies: Having a small initial number of training examples, we use a translation-based data augmentation method, randomly moving the window framing the accidental (or note head for rejection). We propose four different variations shown in Figure 5. The Figure shows how we set boundaries on possible positions for randomly located windows. The unconstrained model in Figure 5a requires the accidental, or note head for rejection, to always be entirely in the image. We avoid introducing the vertical displacements not present in the original data using the vertical model in Figure 5b, where the window must be vertically centered around the centroid of the note head. We still allow a small range of 10 pixels of vertical variation.

The note head being a strong visual cue linked to the accidental, we propose a third generation model called note head (see Figure 5c) where at least half of the current note head should always be inside the sampling window. Finally, we combined the vertical and note head model constraints (the vertical note head model in Figure 5d). For each of these bootstrapping strategies, we augment the dataset in different quantities: 25k, 50k, 100k, 200k, 400k.

We also use this data augmentation opportunity to balance our dataset and over-sample less frequent classes like the flat and natural. Our previous use of the centroid position of the current note head can now be used to distinguish between multiple accidentals, and help the network to pick the right accidental to localize anywhere in the image.

B. Modified Spatial Transformer Detector

We modify a Spatial Transformer Network to construct an accidental detector as shown in Figure 4. We use only four parameters for affine transformations instead of the original six in [START_REF] Jaderberg | Spatial Transformer Networks[END_REF] by zeroing out the two shearing parameters to produce axis-aligned bounding boxes. The main modification of the ST architecture is the forwarding of the affine transformation produced by the initial localization network to the new multi-task network that produces both classification and a localization correction for a symbol. This localization correction is added to the initial localization to produce the final detection. Localization and classification is learned jointly using a weighted multi-task loss 1 composed of a mean squared loss for the localization L reg and categorical cross-entropy for classification L cls . A weighting coefficient λ is used to normalize the localization loss with the classification loss.

L(t, t corr , p) = L cls (p, p * ) + λ • L reg (t + t corr , t * ) (1)
Here, p and p * are respectively predicted class and ground-truth class. t, t corr and t * are respectively the initial transformation produced by the localization network, the transformation correction produced by the multi-task network and the ground-truth transformation.

Use Of Contextual Information: To this localization and multi-task network, we propose an improvement in order to use more contextual knowledge available during a typical OMR workflow. Knowing that the position of the note head is strongly correlated to the position of the accidental, we provide this coordinate as an input feature using two additional neurons in the first feed-forward layer of the localization network and multi-task network. Similarly, the affine transformation produced by the localization network is forwarded to the first feed-forward layer for the multi-task network to provide more contextual information.

We chose the origin to be the upper left corner of the window and normalize the coordinates using the size of the window, that is the bottom right corner has a coordinate of (1, 1). In the original training samples, every note head centroid will have the same coordinate (1, 0.5), because the window is positioned relative to the note head.

C. Multiple ROIs Object Detector Approach

Instead of specializing our detection model to the data we want to process, we show in this section the use of more complex and general CNN based detectors like the Faster R-CNN, R-FCN and SSD. These detectors typically extract multiple Region Of Interests (ROIs) from the input image and make either two step detection (Faster R-CNN and R-FCN) or single step detection (SSD). This strategy has the advantage of dramatically improving detection precision at the expense of adding a lot of computation, as discussed in Section IV-C. We use the official implementation of [START_REF] Huang | Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors[END_REF] that can be used to train and compare Faster R-CNN, R-FCN and SSD models. For the Faster R-CNN and R-FCN models, input images are typically scaled to 600 pixels on the shorter edge while keeping a maximum width or height of 1024 pixels. The SSD models only takes fixed size input images of 300x300 pixels. We propose to reuse our cropping strategy presented in III-A for the input of the network. This strategy produces relatively small images of around 130x130 pixels. However, we note that the up-sampling to the normal input size of the different object detector models does not deteriorate the image like a down-sampling strategy and it also allows us to use umodified Resnet 101 and MobileNet v1 feature extractors, without having to change ratio and scales of anchor boxes.

Faster R-CNN: Our objective is to build the most precise music symbol detector possible in order to minimize errors early in the OMR pipeline. That is why we use the Resnet 101 feature extractor that produces excellent accuracy while providing pre-trained weights on the Common Objects in Context (COCO) dataset for both Faster R-CNN and R-FCN models. The COCO dataset is a large object detection dataset containing around 200K images with 1.5 million object instances and is currently one of the major dataset used to train and evaluate object detection models. The ability of using pre-trained weights is essential because our dataset of 2955 examples is far too small to train these complex architectures from scratch. By using transfer learning, we can reduce over-fitting and benefit from the start of powerful feature extractors learned on the COCO dataset. It is therefore a way for us to reduce the amount of training data needed to produce an accurate detector.

Using the Faster R-CNN, we also propose to combine this complex object detector architecture with our previous propositions of bootstrapping and concatenation of contextual information (section III-B). We experiment using the best performing bootstrapping method, which is vertical as shown in Figure V, in order to augment the number of training samples. In combination with the bootstrapping method, which could lead to a confusion for the object detector of the correct accidental to localize, we concatenate the (x, y) coordinate information of the center of the note head to the first fully-connected layer after the crop and resize operation of the selected ROIs. Originally, the coordinates of the center of the note head are relative to the top left corner of the original image and scaled relatively. Because of the crop operation of ROIs, we duplicate the note head centroid position of one dataset example for each ROI and translate and rescale the coordinate relatively to the cropped area. The Faster R-CNN, R-FCN and SSD, are trained using the classical multi-loss function, combining a classification loss L cls (Softmax) and localization loss L reg (Smooth L1):

L(a i , I) = L cls (p i , p * i ) + λ • [a i > 0] • L reg (t i , t * i ) (2)
For each anchor a i of image I, we search for the best matching predicted box t i . If such a box exist, a i is assigned to be positive and enable the localization loss L reg . p i and p * i are respectively the predicted class and the ground-truth class, t * i is the ground-truth bounding box associated with the anchor a i . Here, λ = 2 meaning that the localization loss has twice as much weight as the classification loss. All other parameters are left to their default values.

R-FCN: R-FCN is similar to the Faster R-CNN except for in how the ROIs are computed and extracted from the feature extractor. This led to a significant speed-up as shown by [START_REF] Huang | Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors[END_REF] and our own results in section IV. The loss function is the same as the Faster R-CNN, see equation 2.

SSD: By doing detection in single step fashion, the Single Shot Detector is able to produce multiple detection with much faster speed than the Faster R-CNN and the R-FCN. We also use a different, more lightweight, feature extractor known as MobileNet v1. We resize all input images at 300x300 pixels as the model does not accept variable size input. No bootstrapping and contextual information was used for the R-FCN and SSD detectors. We use the same multi-task loss function as the Faster R-CNN, but use a Weighted sigmoid function for the classification loss. All parameters are left by default and use α = 1 meaning that both classification and localization loss has the same weight.

D. Training Protocol

Contextual Bootstrapping Approach: The training of our Spatial Transformer architecture, shown in Figure 4, used in our contextual bootstrapping approach is done in a single end-to-end approach using a multi-task loss function composed of a categorical cross-entropy loss for classification and mean-squared error loss for localization. The normalization of the two losses is done by multiplying the localization. After a quick grid search for this parameter in {1,5,10,15,20}, the best results were obtained using a value of 20. The network is trained using the Adam backpropagation algorithm with a learning rate of 0.0001 and a batch size of 50.

Multiple ROIs Object Detector Approach: For training the Faster R-CNN, R-FCN and SSD models, we mainly reuse the recommended parameters by [START_REF] Huang | Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors[END_REF]. We chose to use pre-trained weights on the COCO dataset as mentioned before in section III-C for all feature extractors used: Resnet 101 and MobileNet v1. We train the Faster R-CNN and R-FCN with SGD configured with a learning rate of respectively 0.0001 and 0.0003. For the SSD, we use RMSProp with a learning rate of 0.004 and a batch size of 24.

Cross-Validation: Our dataset consists only of 2955 original images with very imbalanced classes. We do a 5 fold cross-validation in order to test our different approaches to produce reliable results. We implement this strategy by splitting the original dataset of 2955 images into 5 folds of ∼593 examples. We iterate 5 times and each time we choose a different fold to be the testing fold and use the remaining 4 folds for training. In the context of bootstrapping as seen in section III-B, we make sure that the data augmentation only operates on the training folds and happens only after the cross-validation splitting is done. That way, there are no possibilities that different bootstrapped images coming from the same original image are present in both training and test set. Using this cross-validation method, we therefore propose both the mean and standard deviation for every results presented in the next section.

IV. RESULTS

Using the cross-validation protocol described in the previous section, we evaluate our detectors using the mean Average Precision (mAP) metric proposed by the PASCAL VOC Challenge in [START_REF] Everingham | The Pascal Visual Object Classes (VOC) Challenge[END_REF]. This metric allows us to jointly evaluate classification and localization accuracy and compare the impact of bootstrapping of our ST-based detector in Table V. We also compare with state-of-the-art detectors in Table III. However, we make two small modifications to this metric. The mAP metric uses an IoU threshold in order to decide if a detection is a True Positive (TP) or a False Positive (FP). It is common to use 0.5 as the IoU threshold for object detection. In our context of precise music symbol detection, we propose to add a second threshold of 0.75, which is much more strict and more representative of the level of precision we want to obtain. Also, rejection (i.e., the absence of any detection target) is not considered in the original mAP metric. That is why we propose to ignore the localization if the model correctly predict the input image to be a rejection (no accidental). Although, we define our rejection task to localize the note head, our objective is to give the network something stable to localize in order to simplify the rejection.

A. Detector Comparison

Using the mAP metric, Table III shows the performance of our different approaches. We can see that results are very good with an mAP of ∼99% with an IoU threshold of 0.5 except for our ST detector which only performs at ∼97%. Using an IoU threshold of 0.75 more clearly distinguish the detectors and place first the R-FCN with a mAP of 98.7%, then Faster R-CNN followed by SSD and finally the ST based detector. These results show the superiority of using multiple ROIs generated from different part of the images instead of a single ROI from the whole image. However, more complex detectors come with additional overhead, as shown in Table IV. The Faster R-CNN is about 90 times slower than the ST based detector, and takes about 18 times more memory. Given that this detector will be intensively used by the OMR system (more than thousand of calls by page of music score), using a Faster R-CNN will provoke a significant slow-down of the recognition process.

B. Impact Of Bootstrapping And Contextual Information

In the case of the ST based detector, we found that the augmentation of training data almost always leads to better localization as shown in Table V. Another interesting observation is that different sampling strategies led to different results. The unconditional inclusion of the note head in the sampled image does not lead into an improvement, which seems to correlate with the property of translation invariance found in classical CNN architecture based on convolution and pooling operation. We also found that reducing the vertical displacement of the sampled images relatively to the vertical position of the note head lead to better results than allowing an unconstrained positional sampling. This seems to confirm our starting hypothesis that introducing variation in a very stable characteristic of our data does not help the ST based detector to converge.

In case of the use of the Faster R-CNN, we found that bootstrapping techniques actually hurt the precision of the detection. To better analyze this result, we divided the dataset into four categories: single accidental where only one accidental is visible in the image, multi accidental where multiple accidentals are visible, reject without accidental where no accidental are visible and finally reject with accidental where an accidental is visible in the image but should not be detected as it is not associated with the correct note head. We found that when using bootstrapping the results for both multi accidental and reject with accidental decreased significantly: 11% decreased for mAP with IoU > 0.5 and 17% decreased for mAP with IoU > 0.75. Our conclusion is that the architecture of the Faster R-CNN, designed for multi-object detection with strong translation invariance using the anchor boxes mechanism, is not suitable to be used in combination with bootstrapping for our particular task of contextual detection.

Again, for the ST based detector, we found that the use of contextual information like the centroid position of the current note head always helps the detector improve the detection results. In contrast, this add of information for the Faster R-CNN did not change anything to the results.

Finally, for the ST based detector, combining bootstrapping techniques and contextual information lead to an improvement of 9.3% mAP for an IoU threshold of 0.5 and 30.8% mAP for an IoU threshold of 0.75 (line 1 and 6 comparison in Table V). In the contrary, for more complex detectors like the Faster R-CNN, the use of contextual information or bootstrapping techniques did not improve the already very good results.

C. Discussion

After seeing the results of our experiments, we show the clear superiority of the Faster R-CNN and R-FCN for the task of detecting an accidental. However, we also propose less powerful models like the SSD and ST based detector for having more efficient and faster inference time for less accuracy. Also, if we consider using semi-supervised or unsupervised architecture in order to resolve the detection of music symbols, the Spatial Transformer should be simpler to integrate as it was designed as an attention model and integrates in any kind of neural network architectures.

In regards of the full OMR task, we only show here how to resolve a small subset of the pipeline. Future works will be oriented towards two main points: extend detection to other symbols, further reduce the number needed of training samples and propose a new corpus of dense and complex orchestral printed scores.

We plan to extend the detection of music symbols in a bottom up fashion, first adding multiple note heads detection and then gracefully integrate accidentals, followed by articulation marks, ornaments. . . We also have done some preliminary experiments by further reducing the quantity of training samples to 1/5th of what is used in this work and the Faster R-CNN, R-FCN and SSD models show only a small degradation of the results. More investigation is needed in order to characterize the relation between the accuracy of the detectors relative to the number of training samples and the size of the window in which we want to detect a symbol. Our focus on reducing the number of needed training samples is based on the observation of the fact that manually annotating data is very slow and costly. Moreover, it has to be done again each time we work in a new corpus/type of documents.

Even though the situation in OMR recently improved by the introduction of the MUSCIMA++ dataset which contains localization, class and relationship information of music symbols in handwritten scores, the dataset is still extremely homogeneous because the corpus was originally designed for the staff lines removal task. We feel that the OMR community is neglecting printed scores because of recent software printed music scores with very good impression quality. However, there is still a huge quantity of music scores printed or engraved from the 18th to the early 20th century. These scores present a lot of challenges because of their printing techniques, time degradation, bad scanning qualities and complexity of the classic or romantic music style. While the dataset used in this work is available here2 , we will propose to the OMR community a new corpus of printed scores with the optic of automatically generating ground-truth data instead of manually annotating scores.

V. CONCLUSION

A renewed interest is shown toward OMR in the computer vision and pattern recognition research communities, because many interesting challenges remain to be overcome. In this work, we concentrated on designing a method that produces an accurate segmentation and classification of the accidental associated with a note head, without a priori rules concerning segmentation problems. We propose four different detectors: a Spatial Transformer based detector, SSD, R-FCN and Faster R-CNN. We show the tradeoff in speed over accuracy in different detectors with the best detector having 98.73% mAP for an IoU threshold of 0.75. The fastest ST based detector shows very bad out of the box performance. However, by using contextual information like the position of the note head and bootstrapping techniques, we improve significantly the accuracy of the detection by 9.3% mAP for an IoU threshold of 0.5 and 30.8% mAP for an IoU threshold of 0.75. Much more work is still needed in order to implement the whole OMR pipeline. In our future work, we are planning to further extend our detection models to other type of symbols like note heads, further reduce training data and propose a new corpus of printed music scores to the OMR community in order to research automatic ground-truth data generation.

Figure 1 .

 1 Figure 1. Task definition: detector should predict the position (red box) and class of an accidental (flat, natural, sharp or no accidental (rejection)) using raw image pixel and possibly the centroid of the note head (blue cross).

Figure 2 .

 2 Figure 2. Hard segmentation problems on accidentals

Figure 3 .

 3 Figure 3. Grammar workflow. Recognized elements are red. Violet squares are zones where recognizable elements are searched for. The construction of a musical note starts (a) find a potential stem, (b) two possible locations (top-right and bottom-left) are searched for a note head, (c) a potential accidental is searched at the left of the note head, (d) an alignment check is done between the note head and accidental.

Figure 4 .

 4 Figure 4. Accidental detection using a Spatial Transformer (ST). The localization network takes an 80x80 image as input, and produces an axis-aligned bounding box represented by an affine transformation in 4 outputs nodes. The sub-image in the bounding box, resized to 40x40 pixels, is then classified by the Multi-task network into four classes III-A. The Multi-task network refines the output of the Localization network by producing offset for theta. We train the whole architecture end-to-end using a weighted multi-task loss composed of a classification loss and localization loss.

Figure 5 .

 5 Figure 5. Four randomized sample bootstrapping techniques. The red square shows possible areas where the blue square, which is the sampling window, can be positioned. The green zone is always inside the blue sampling zone.

Table I DATASET

 I PRODUCED BY THE XXX PIPELINE DRIVING A SIMPLE CONNECTED COMPONENT BASED SEGMENTATION, A SIMPLE MUSIC SYMBOL CLASSIFIER AND A MANUAL CHECK

	No accidental (Reject)	Natural	Sharp	Flat	Total
	968	968	777	242	2955

Table II ARCHITECTURE

 II AND DATA USAGE FOR ACCIDENTAL DETECTORS. THE

TABLE SHOWS WHETHER

 SHOWS DETECTORS USE TRANSFER LEARNING, CAN DETECT MULTIPLE OBJECTS, MAKE USE OF THE ASSOCIATED NOTEHEAD LOCATION, OR USE BOOTSTRAPPED SAMPLES. VERSION LABELS (V1, V2, . . . ) DIFFERENTIATE DIFFERENT EXPERIMENTAL CONDITIONS FOR THE SAME DETECTOR AND ARE REUSED IN TABLE III.

	Detector	Transfer Learn.	Mult. Objs	Note Head	Bootstrap
	CNN + ST v1
	CNN + ST v2
	CNN + ST v3
	CNN + ST v4
	Faster R-CNN v1
	Faster R-CNN v2
	Faster R-CNN v3
	R-FCN
	SSD

Table III RESULTS

 III COMPARING THE BEST SPATIAL TRANSFORMER (ST) BASED DETECTOR, FASTER R-CNN, R-FCN AND SSD. RESULTS SHOWN ARE MAP (IN %) WITH AN IOU THRESHOLD OF EITHER 0.5 OR 0.75. SEE TABLE II FOR AN OVERVIEW OF EACH DETECTORS.

	Detectors	mAP with IoU >0.5	mAP with IoU >0.75
		µ(%)	σ(%)		µ(%)	σ(%)
	ST v4	97.25	1.68		94.81	2.99
	Faster R-CNN v1	98.73	0.94		98.34	0.73
	Faster R-CNN v2	98.85	0.67		98.65	0.59
	Faster R-CNN v3	86.91	3.79		84.80	3.86
	R-FCN	99.17	0.30		98.73	0.40
	SSD	98.93	0.67		97.81	0.92
			Table IV
	SPEED AND MEMORY CONSUMPTION OF THE ST BASED DETECTOR,
	FASTER R-CNN, R-FCN AND SSD. MEASURES WERE TAKEN ON A
		NVIDIA GPU K80.
	Detectors	ST	SSD	R-FCN	Faster R-CNN
	Speed (ms/image)	2	14	80		180
	Memory (Mb)	260	300	4800		4800

Table V MEAN

 V AVERAGE PRECISION RESULTS (IN %) WITH IOU >0.5 OR >0.75 (5-FOLD CROSS-VALID., 2,955 SAMPLES) FOR THE SPATIAL TRANSFORMER BASED DETECTOR, FIGURE 4. WE EXPLORE THE USE OF: nh WHICH MEANS THE NOTE HEAD CENTROID IS PROVIDED TO THE INPUT OF THE NETWORK, DIFFERENT DATA quantity, DIFFERENT bootstrapping methods AND DIFFERENT LOCALIZATION loss weight. Spatial Transformer-based Detector Conditions mAP with IoU >0.5 mAP with IoU >0.75

	nh quantity bootstrapping method loss weight µ(%)	σ(%)	µ(%)	σ(%)
	400k	vertical	20	97.3	1.7	94.8	3.0
	200k	vertical	20	96.0	2.4	92.0	2.8
	100k	vertical	20	94.6	3.5	86.1	6.3
	25k	vertical	20	92.9	3.1	68.0	1.5
	original		20	90.0	4.0	62.5	4.3
	original		20	88.0	3.6	64.0	3.7
	400k	vertical	20	94.3	3.5	86.2	5.9
	400k	vertical note head	20	96.0	1.6	92.2	1.1
	400k	unconstrained	20	94.4	2.1	92.4	2.3
	400k	note head	20	95.7	1.9	88.4	2.5
	400k	vertical	10	96.6	1.9	93.8	2.2
	400k	vertical	1	95.6	1.9	89.7	3.5

https://www-intuidoc.irisa.fr/en/choi accidentals/

https://www-intuidoc.irisa.fr/en/choi accidentals/


Innocent J Macha
email: innocent.macha@uts.edu.au

Besim Ben-Nissan
email: bbesim.ben-nissan@uts.edu.aucjerran.santos@uts.edu.au

Jerran Santos

Sophie Cazalbou
email: sophie.cazalbou@univ-tlse3.fr

Bruce Milthorpe
email: ebruce.milthorpe@uts.edu.au

Hydroxyapatite/PLA biocomposite thin films for slow drug delivery of antibiotics for the treatment of bone and implant-related infections

Keywords: drug delivery, hydroxyapatite, antibiotics, infections, thin film composites, stem cells, biocompatibility

Drug delivery systems were developed from coralline hydroxyapatite (HAp) and biodegradable polylactic acid (PLA). Gentamicin (GM) was loaded in either directly to PLA (PLAGM) or in HAp microspheres. Drug loaded HAp was used to make thin film composites (PLAHApGM). Dissolution studies were carried out in phosphate buffered saline (PBS). The release profiles suggested that HAp particles improved drug stabilization and availability as well controlled the release rate. The release also displays a steady state release. In vitro studies in human Adipose Derived Stem Cells (hADSCs) showed substantial quantities of cells adhering to hydroxyapatite containing composites. The results suggested that the systems could be tailored to release different clinical active substances for a wide range of biomedical applications.

Introduction

Tissue engineering and advanced medical technologies have been very effective tools to improve the quality of human life. The benefits of bioceramic materials used for the repair and reconstruction of disease or damage afflicted areas of the musculoskeletal system have been broadly implemented [START_REF] Hench | Bioceramics: From Concept to Clinic[END_REF]. During the last two decades the development of bioceramic composites capable of releasing clinical active materials by reproducible and predictable kinetics has been a clinical and scientific endeavour. Drug release systems have been proved to provide outstanding alternative to conventional clinical therapies. The advancement in both science and biomaterial design and engineering, more sophisticated therapeutic agent release systems have been developed with improved capabilities and performances for the treatment of resilient diseases such as musculoskeletal disorders and bone related diseases. There are also possibilities to develop drug release systems with more efficiency and rational drug delivery platforms featuring up-to-theminute technological capabilities [START_REF] Santos | Drug-releasing implants: current progress, challenges and perspectives[END_REF]. Drug delivery technology presents an interesting interdisciplinary challenge for pharmaceutical, chemical engineering, biomaterials and medical communities [START_REF] Rao | Preparation, characterization and in vitro release of gentamicin from coralline hydroxyapatite-gelatin composite microspheres[END_REF]. In general, a biomaterial that will act as a drug carrier must have the ability to incorporate a drug, to retain it in a specific site, and to deliver it progressively with time to the surrounding tissues. Furthermore, it would be advantageous if the material is injectable or alternatively coatable on an implant and most importantly a biodegradable [START_REF] Choi | Calcium phosphate nanocoatings and nanocomposites, part I: recent developments and advancements in tissue engineering and bioimaging[END_REF] Usage of biodegradable materials in designing drug release devices addresses these challenges by providing outstanding capability of performing localized and controlled delivery of drugs at different parts of the host body. Biodegradable polymer films loaded with gentamicin have been developed to serve as "coatings" for fracture fixation devices and prevent implant-associated infections [START_REF] Aviv | Gentamicin-loaded bioresorbable films for prevention of bacterial infections associated with orthopedic implants[END_REF]. The use of biodegradable polymer films is advantageous due to its propensity to uptake and release antibiotics, as a consequence of its degradability. Although their drug release rates are high, they could be tailored to form biocomposites with different biodegradability rates by incorporating other materials. Biodegradable polymer-bioceramic composites suits this hypothesis a great deal because of the bioactive nature of ceramic materials, specifically calcium phosphate based materials, which promotes bone tissue growth. Incorporation of bioceramics derived from coral in the polymer will improve not only controlled drug release but also bioactivity and tissue regeneration, especially in orthopaedic and maxillofacial applications.

This research is aimed at developing and testing gentamicin (due to its widely used for the treatment of bacterial infection) loaded hydroxyapatite particles (HAp) within thin film polylactic acid (PLA) biocomposites as slow drug delivery devices for the treatment of bone and implantrelated infections.

Experimental

Materials. Coral skeleton samples were obtained from the Great Barrier Reef shoreline, QLD Australia. Gentamicin sulfate, Clodronate (Dichloromethylenediphosphonic acid disodium salt), Chloroform diammonium hydrogen phosphate (NH 4 ) 2 HPO 4 , 98%), and sodium hypochlorite (NaClO), were obtained from Sigma Aldrich, Castle Hill, Australia.

Methods. Coralline materials were hydrothermally converted to hydroxyapatite based on the procedures described in [START_REF] Macha | Marine structure derived calcium phosphate-polymer biocomposites for local antibiotic delivery[END_REF][START_REF] Ben-Nissan | Natural bioceramics: from coral to bone and beyond[END_REF]. Drug loading and in-vitro drug release from PLAHAp composites followed the procedures described in [START_REF] Macha | Marine structure derived calcium phosphate-polymer biocomposites for local antibiotic delivery[END_REF][START_REF] Macha | Development and dissolution studies of bisphosphonate (clodronate)-containing hydroxyapatite-polylactic acid biocomposites for slow drug delivery[END_REF]. Bioceramic composites were produced by solution casting method. Briefly, the film composites were loaded with 10 % (w/w) and the release was studied in buffer solutions at physiological conditions. Drug release study from PLA thin film composites was conducted under SINK conditions in phosphate buffered saline (PBS) ((0.1 M, Na 3 N 0.1%, pH 7.4) at 37 ± 0.1 °C) for gentamicin and in Tris-HCl buffer ((0.1 M, pH 7.4) at 37 ± 0.1 o C) for clodronate (because PBS will interfere with 31 P quantification) in a temperature controlled water bath shaker running at constant speed of 100 rpm. Each sampling time had its own independent samples under the same conditions and experiments were respectively terminated after sampling. Gentamicin concentrations in the solution were determined by Cary 100 UV-Vis spectrophotometer (Agilent Technologies, Victoria, Australia, Cary Series UV-Vis Spectrophotometer) at the maximum absorbance of gentamicin-o-phthaldialdehyde complex, λ max = 332 nm, using procedures described in [START_REF] Aviv | Gentamicin-loaded bioresorbable films for prevention of bacterial infections associated with orthopedic implants[END_REF][START_REF] Chou | Antibiotic delivery potential of nano-and micro-porous marine structure-derived beta-tricalcium phosphate spheres for medical applications[END_REF]. Ophthaldialdehyde reagent was prepared by dissolving 2.5 g ophthaldialdehyde in 62.5 mL methanol and adding with 3 mL 2hydroxyethylmercaptan to 560 mL 0.04 M sodium borate in distilled water. 2 mL gentamicin solution, 2 mL o-phthaldialdehyde reagent were reacted for 45 min at room temperature. The absorbance, which corresponds to the gentamicin concentration, was then measured at 332 nm.

Stem Cell attachment. Adult hADSCs tissue culture was conducted under asceptic sterile conditions in a class II laminar flow hood (Clyde-Apac BH2000 series). hADSCs were cultured till sub-confluence at 5x10 4 cells/cm 2 T25 culture flask (Nunc) in D-MEM Glutmax/F12 (Gibco) with 10% FBS (Invitrogen) and incubated at 37 o C at 5% CO 2 . Subsequently cells were passaged and diluted to for the seeding density 1x10 4 cells/cm to be placed on PLA and PLAGM for 10 days for continual growth and adherence test. Non-adherent cells are eliminated by replacing the media every 2 days for normal cellular growth. At 10 days media was decanted, cells washed in PBS, fixed in 4% formalin, washed in di-ionized water (dH 2 O) and dehydrated for SEM.

Results and Discussion

Drug release from PLA and PLA-HAp composite is not only due to PLA degradation but also the diffusion of the drug entrapped into the polymer matrix, which plays an important role depending on the extension of the experiments. Fig. 1 shows drug release profiles composed of five stages for PLAGM and PLAHApGM thin film composites. It is possible to appreciate different shapes of the release curves depending on the type of drugs and release medium. The release time was also enough to appreciate si gn ificant de gradation of the pol ym er matrix as shown in Fig. 2. The assessment of kinetic release from drng delive1y devices provides the confidence to predict the release behaviour before the release systems are realized. The release kinetic study was assessed by model dependent method. Based on number of kinetic models available in literature, the selected model described the overall release of drng from the dosage fonns. . . --------L!.. 1------+ 1 1 In the release profiles of drngs from degradable polymer matrix the most sustained release paii is by degradation of the polymer matrix. Table 1 shows time range of each release stage for gentamicin (these values ai•e an approximation considering the shape of the release profiles, the final time of one stage occurs with the initial time of the next one). Each stage time goes with the assumptions that the dmg is homogeneously distributed in the matrix and therefore homogeneously release of the drngs from the matl'ix. The release of gentamicin from these devices seem to follow semi-empirical equation describe by Korsmeyer-Peppas model. Neve1theless, the 'n' coefficient obtained for PLAGM and PLAHApGM (Table 2), indicates that somehow a number of different mechanisms might control the release. Thus, the release of gentamicin contained from PLA matrix seems to be mainly contrnlled by diffusion whereas for PLAHApGM is possibly mixture of diffusion, super case II mechanism and possible combination of other mechanisms of trnnsp01t which control drng release.

1 1 � ------ 1 1-■----• . vr � ---• /ï v• •••PLAGM
Fig. 3 presents SEM pictures showing in vitro studies using stem cell investigations. The m01phology and attachment of (hADSC) seeded on PLA thin film composites. The results show high density of cellular attachment on hydroxyapatite containing composites such as PLAHAp and PLAHApGM samples but none on PLA and PLAGM (not shown) composites. PLA has an alkyl pendant group (CHr) in its backbone, which makes the pol ym er more hydrophobie, and tends to denaturalize proteins responsible for cell binding and adhesion, while hydroxyapatite is osteoconductive material. Gentamicin has NH2-group, which with CH3-on the pol ym er backbone reduce any chance for protein binding on the surface. This was evident because these samples (PLA and PLGM) do not show any cell on their surfaces. In order to confirm the lack of protein adsorption on the surface of PLA and PLAGM samples, we coated them with poly-L-lysine (Sigma Aldrich, Australia) as an attachment factor to enhance the electrostatic interaction between negatively charged ions of the cell membrane and positively-charged ions of the culture surface by increasing the number of positively-charged sites available for cell binding. The results suggest that cells were able to attach on the PLA and PLAGM surfaces after coating in less than 24 hours (Fig. 4).

Conclusions

Controlled drugs release successfully achieved for gentamicin containing composites with defined dissolution kinetics. This in-vitro study shows the potential of PLA-bioceramic film composites as effective local drug delivery devices for biomedical applications. In addition, they can generally be used in surgery to prevent infections caused by bacterial.

Figure 1 :

 1 Figure 1: Release profile of gentamicin from PLAGM and PLAHApGM thin films

Table 1 :

 1 Specific time frames for different release stages and their numerical values for gentamicin (Five stages) NameofDrug Gentamicin WeekO .. . ! Week3 Stages Stage I (burst ext. release) Stage II (Intemal release) Stage III (lag phase) Stage IV (2 nd burst int. release) Stage V (Intemal release)

Figure 2 :

 2 Figure 2: SEM picture of gentamicin loaded PLAGM and PLAHApGM before and after three weeks of drng release in PBS revealing degraded m01phologies.

Figure 3 :

 3 Figure 3: SEM pictures of stem cell cultured PLA thin film composites for 10 days, showing attachment and morphology of cells.

Figure 4 :

 4 Figure 4: SEM picture of cell cultured samples coated with polylysine a) PLA b) PLAGM

Table 1 :

 1 Modelled dissolution char acteristics of the mean dissolution profile

	Model	Model expressi on		PLAGM	PLAHApGM
	Korsmeyer-Peppas	F = kt n	r 2	0.992	0.962
			n	0.282	1.315