D Guyonnet
email: d.guyonnet@brgm.fr

F Bodénan

G Brons-Laot

A Burnol

L Chateau

M Crest

J Méhu

P Moszkowicz

P Piantone

MULTIPLE-SCALE DYNAMIC LEACHING OF A MUNICIPAL SOLID WASTE INCINERATION ASH Waste Management (in press)

Keywords: Leaching, Waste, Incineration ash, Chromium, L/S ratio, Modelling

Predicting the impact on the subsurface and groundwater, of a pollutant source such as municipal solid waste (MSW) incineration ash, requires a knowledge of the so-called "source term". The source term describes the manner in which concentrations in dissolved elements in water percolating through waste evolve over time, for a given percolation scenario (infiltration rate, waste source dimensions, etc.). If the source term is known, it can be coupled with a model that simulates the fate and transport of dissolved constituents in the environment of the waste (in particular in groundwater), in order to calculate potential exposures or impacts. The standardized laboratory upward-flow percolation test is generally considered as a relevant test for helping to define the source term for granular waste. The LIMULE project (Multiple-Scale Leaching) examined to what extent this test, performed in very specific conditions, could help predict the behaviour of waste at other scales and for other conditions of percolation. Three distinct scales of percolation were tested: a laboratory upward-flow percolation column (30 cm), lysimeter cells (1-2 m) and a large column (5 m) instrumented at different depths. Comparison between concentration data collected from the different experiments suggests that for some non-reactive constituents (Cl, Na, K, etc), the liquid versus solid ratio (L/S) provides a reasonable means of extrapolating from one scale to another: if concentration data are plotted versus this ratio, the curves coincide quite well. On the other hand, for reactive elements such as chromium and aluminium, which are linked by redox reactions, the L/S ratio does not provide a means of extrapolation, due in particular to kinetic control on reactions. Hence extrapolation with the help of coupled chemistry-transport modelling is proposed.

Introduction

The acceptability of different methods of waste management (for example landfilling of incineration residues versus utilization as secondary materials) depends in part on predictions of potential environmental impacts. Impact predictions are often performed using models that simulate the fate and transport of pollutants in various environmental compartments (water, air, soil). With respect to the subsurface and groundwater, predicting potential impacts requires knowledge of the so-called "source term" (see CEN, 2006a). This source term describes, for a given waste and a specific scenario, for instance a percolation scenario, the manner in which pollutant emission varies as a function of time. If this source term is known, it can be coupled with a model that calculates pollutant fate and transport in the unsaturated zone underlying the waste or in the groundwater. But risk assessments for soil and groundwater often rely on simplifying assumptions regarding pollutant concentrations in the water emitted by the source (the waste) and in particular the assumption of constant concentrations, with values selected based on solubility limits. Such an assumption neglects in particular both the transient character of pollutant release from waste and also the finite character of available pollutant mass. In order to address the source term, various laboratory tests have been defined. While static batch tests (CEN, 2002(CEN, , 2006b)), whereby the source (the waste) is mixed with the solvent (water) at a specific liquid/solid ratio, provide information regarding the cumulative mass of pollutant that can be mobilized, dynamic percolation tests are considered to be more representative of source term behaviour for granular inorganic waste (see van der Sloot et al., 1997[START_REF] Fällman | Leaching tests for environmental assessment of inorganic substances in wastes, Sweden[END_REF][START_REF] Wahlström | Nordic recommendation for leaching tests for granular waste materials[END_REF]. The standardized upward-flow percolation test (CEN, 2005) consists schematically in flowing water in the upward direction through a 30 cm-height column at a certain rate and monitoring pollutant concentrations at the column outlet. The question then arises as to how representative are the results of this test with respect to source term behaviour at other scales and for other percolation conditions? In other words, how can the upward-flow column test help us to estimate the leaching behaviour of the waste for the problem at hand?

The influence of scale has been studied by previous authors. [START_REF] Van Der Sloot | Integration of lab-scale testing, lysimeter studies and pîlot scale monitoring of a predominantly inorganic waste landfill to reach sustainable landfill conditions[END_REF] compared leaching data from tests performed on a mixture of different waste residues at the laboratory scale, the scale of lysimeter experiments and a pilot test scale. They observed similarities in the leaching experiment data with differences originating from different redox conditions. [START_REF] Kylefors | A comparison of small-scale, pilot-scale and large-scale tests for predicting leaching behaviour of landfilled wastes[END_REF] studied the leaching of MSW at different scales and found that the tests were not reliable for predicting the liquid/solid (L/S) ratio required to reach a certain concentration. They suggest that predictions would require better knowledge regarding leaching mechanisms. This paper presents results of the LIMULE project (Guyonnet et al., 2005a;2005b) that examined the influence of scale and percolation conditions on the mobilization of soluble constituents from a municipal solid waste (MSW) incineration ash.

The focus of this paper is on the possible use of the L/S ratio as a means of extrapolating between scales and percolation conditions.

Materials and methods

The waste studied within the LIMULE project is a boiler ash from a fluidized-bed MSW incineration plant. This ash was selected primarily due to its particle size which is close to that of a sand. In order to limit the potential for material setting that might decrease the hydraulic conductivity and hence the ability to reach a significant value of L/S over the duration of the experiments, the ash was mixed in equal weight proportions with clean Loire river silica sand (0.1-1 mm), with a fraction < 80 m lower than 1% in weight. Thirty tons of the ash-sand mixture were produced using a horizontal rotating mixing tube.

The ash-sand mixture underwent detailed characterization with respect to physical properties influencing leaching behaviour and chemical properties. Procedures used to sample the initial ash-sand mixture and the final leached solid are described in Guyonnet et al. (2005a).

Chemical analysis of the solid included X-ray fluorescence (Philips PW2400; sequential) for the determination of major elements, ICP-AES spectroscopy for trace elements (and major elements in the leached samples), potentiometry for chloride and atomic absorption spectrometry (following extraction in a methanol bromine solution) for metallic aluminium (Al 0 ). Concentrations of Cr(VI) in the solid were determined colorimetrically (with the 1,5diphenylcarbonhydrazide method) after a hot alkaline extraction. It should be noted however [START_REF] Abbas | A study of Cr(VI) in ashes from fluidized bed combustion of municipal solid waste: leaching, secondary reactions and the applicability of some speciation methods[END_REF], that in the presence of reducing agents such as Al 0 , this method may lead to a systematic underestimation of Cr(VI) concentrations in the solid. The same problem occurs when Cr(VI) is measured following 24 h batch leaching tests. The quantification of Cr(VI) in the solid in presence of reducing agents is a particularly difficult task and the subject of ongoing research.

A micro-characterization of fresh samples and polished thin sections was performed using a powder X-ray diffractometer (Siemens D5000, cobalt K 1 radiation), an optical microscope, a scanning electron microscope (SEM) coupled with energy dispersive spectrometry (EDS) and an electronic microprobe coupled with wavelength dispersive spectrometry (WDS) for multielement micro-mapping and quantitative analyses. Standard procedures were followed for the chemical analysis of the waters collected from the leaching experiments (see below). Samples were filtrated at 0.45 µm and sub-samples for cation analyses were acidified. Dissolved concentrations (including Cr total ) were determined by ICP-AES with a sequential Jobin Yvon JY 38 device (NF EN ISO 11885 standard) and Ionic Chromatography (for Cl and SO 4 ; NF EN ISO 10304 standard). Hexavalent chromium in the liquid was analysed by colorimetry.

Results showed that Cr in the water samples was present exclusively as Cr(VI) and therefore ICP results on Cr total were taken as Cr(VI) concentrations because of the lower quantification limit (5 µg/l for the ICP-AES as compared to 10 µg/l for the colorimetric method). Additional details of analytical methods and results will be provided in an independent paper (in preparation) focused on the chemistry of the ash.

Standard batch tests were performed at a liquid/solid (L/S) ratio of 10: the leaching test for granular waste (CEN, 2002) and the pH-dependence leaching test (CEN, 2006b). Upward flow laboratory column tests were performed according to standard CEN (2005). Two pilotscale downward-flow percolation experiments were performed in lysimeter cells fed with demineralised water. One cell (2.5 m long, 1.75 m wide and 1 m high) was located indoors and equipped with a device for controlling infiltration (Fig. 1), while the other cell (2.5 m wide, 2.5 m large and 2 m high) was located outdoors and exposed to natural rainfall. A downward-flow percolation experiment was performed in a large column fed with demineralised water (Fig. 1). Thirty litres per day were sprayed with sprinklers at the top of the column by means of 10 litres every 8 hours. The column is 5 metres high and 1 metre in diameter and is made up of 5 superposed cylinders equipped with ports for the monitoring of temperature, water content (TDR probes), capillary pressure (tensiometer probes) and fluid composition (porous cups). Concentrations in fluids collected from 5 porous cups (noted PC1 through PC5) located at different depths within the column, as well as at the column outlet, were monitored over time. Tracer tests were performed in the lysimeter cells and the large column in order to obtain information regarding hydrodynamic parameters (effective porosities and dispersivities). Table 1 summarizes the main characteristics of the various experiments conducted under conditions of dynamic flow. The table illustrates the contrast of scales and infiltration rates between the experiments. A primary constraint regarding the choice of experimental conditions for the large column and indoor lysimeter experiments, was the need to reach a value of L/S ratio over the duration of the project (19 months of experimental monitoring) that allowed results to be compared with the laboratory column data, while avoiding infiltration rates that were several orders of magnitude larger than those encountered in natural field conditions. A compromise value of L/S = 3 was selected as a minimum objective.

Results

Physical and chemical characterization

Average particle density of the ash-sand mixture from pycnometry is 2.64 g.cm -3 , i.e. a value close to the density of silica sand (2.68 g.cm -3 ). Apparent dry density measured as the average of ten sub-samples is 1.32 g.cm -3 while BET surface area is 2.61 m 2 .g -1 . Particle size analysis of the fresh ash, before mixture with the Loire river sand, shows that particle size is lower than 500 µm with a fine fraction (< 63 µm) representing 9% in weight. A bulk measure of dry density was obtained from the measurement of the total mass installed in the large column: 5285 kg in a volume of 3.79 m 3 , yielding a dry density of 1.39 g cm -3 . Considering the particle density, the void ratio is 0.47. This void ratio was not saturated during the large column and lysimeter cell experiments. In Fig. 2a, the volumetric water content measured at a depth of 294 cm in the large column by a TDR probe shows a very stable value around 30%. This value, which was observed in all the TDR probes except the uppermost (closest to the sprinklers) can be compared to the value of 47% at saturation. The uppermost probe, located at 20.5 cm from the top, indicated an average water content of 23% and showed small fluctuations related to the cyclic water feed (every 8 hours). Overall steady-state flow conditions are illustrated by plots of cumulative inflow and outflow that are seen in Fig. 2b to be very linear. A slight difference between the inflow (30.1 l/day) and outflow (28.3 l/day) slopes is interpreted as being due primarily to evaporation at the top of the column (calculated as 2.4 mm/day from the difference in slopes)

Major elements in the ash-sand mixture are related to the presence of Si-rich oxidized phases and to a lesser extent to the presence of Al, Ca, Fe and K (see Table 2). Trace elements in the ash-sand mixture are, by order of decreasing importance, Zn, Cu, Ba, Pb, Sn, Sr. The proportion of zero-valent (metal) aluminium found in two analyses is 1.4 and 1.45 %. As will be shown below, this zero-valent aluminium has a major influence on the behaviour of chromium. From XRD analysis, the ash contains primarily quartz, silicates such as microcline, plagioclase, diopside, gehlenite, carbonates such as calcite, some sulphates (anhydrite) and iron oxides (hematite). Amorphous phases are also present and calcium phosphates were identified by microscopic analyses.

A characterisation of the waste after 580 days of leaching in the large column (average of 5 analyses; Table 2) shows a decrease in the proportion of metal aluminium: on average, 60% of metal aluminium has been altered. This result is consistent with SEM observations that show an alteration of aluminium particles. For other elements, comparison of the global analyses of the solid before and after leaching show differences that are not significant except for SiO 2 , although this is most probably due to the difference in analytical methods: unlike Xray fluorescence, ICP-AES is not an adequate technique for precisely measuring SiO 2 at such a high content (60-80 %; C. Crouzet, pers. comm.).

Scanning Electron Microscopy observations show the precipitation of aluminium hydroxides and the alteration of quartz and feldspath grains (development of micro-fissures).

There is also formation of hydrated phases such as ettringite (Ca 6 Al 2 (SO 4 ) 3 .26H 2 O) which, with the aluminium hydroxides (Al(OH) 3 ), help explain swelling that was observed in the lysimeter cells. Some degree of swelling occurred and resulted in some deformation of the lysimeters. However, it was not possible to detect a significant effect on the material's void ratio. No swelling was detected during the large column experiment, although some may have occurred as suggested by temperature variations measured during the first twenty days of leaching in the large column (Fig. 2c).

Leaching tests performed at L/S=10 (CEN, 2002) on the fresh ash-sand mixture show a pH of 10.3 (Table 3). The leachate contains primarily Ca, Na, K, Cl, SO 4 , and Al as well as traces of Ba, Cr(VI) and Sr. A leaching test was also performed on the waste sampled from the large column after the leaching experiment and dried at 105°C. As could be expected, proportions of Na, Cl, K and Ca are lower than prior to leaching (Table 3). Hexavalent chromium concentrations appear higher at the end of the experiment which suggests some oxidation of Cr(III) to Cr(VI) during the experiment, in accordance with the observed trend towards more oxidizing conditions (Fig. 3b;[START_REF] Brookins | Eh-pH diagrams for geochemistry[END_REF]. It is reminded, however, that in the presence of Al 0 , Cr(VI) concentrations tend to be underestimated. Results of leaching tests are compared in Table 3, for reference, to the landfill waste acceptance criteria [START_REF] Ojc | Council Decision of 19 December 2002 establishing criteria and procedures for the acceptance of waste at landfills pursuant to Article 16 of Annexe II to Directive 1999/31/EC[END_REF]. The ash-sand mixture complies with the non-hazardous waste criteria and following the leaching experiment, it also complies with inert waste criteria for all parameters except for chromium which is close to the limit.

Results of dynamic leaching tests as a function of L/S ratio

In this section, concentration data collected from the various dynamic leaching tests are plotted as a function of L/S ratio and compared. Note that for a constant infiltration rate, the L/S ratio can be written (see Hjelmar et al., 2003): experiments (except for Na lab-column data). The characteristic shape of the curves suggests that these constituents are rapidly dissolved and then gradually diluted by incoming demineralised water. The plots for Na and K display "bumps" that are remarkably consistent between the indoor cell and the large column. These bumps, observed at the same values of L/S, are interpreted as the effect of an ion-exchange mechanism: it is well known [START_REF] Drever | The geochemistry of natural waters[END_REF]) that from thermodynamic considerations the dilution of a solution enhances the adsorption of divalent cations (such as Ca) to the expense of monovalent cations (such as Na and K). It is unsure however which surface is responsible for the adsorption (possibly ettringite). Figure 5 compares results for non-reactive constituents, including data from the outdoor lysimeter, over a limiting L/S window. Data are seen to coincide reasonably well.

d h I t S L  / ( 1
Figure 6 compares the evolutions of concentrations for reactive constituents. In particular for chromium, there is not a good correspondence between data from different experiments.

Notice the inverse correlation between aluminium and chromium: chromium appears when aluminium decreases. The same trends were observed in the data from all five porous cups of the large column (see Guyonnet et al., 2005b) and suggests the simultaneous oxidation of metal aluminium (Al 0 ) to Al(III) with a reduction of Cr(VI) to Cr(III). So long as there is Al 0 to oxidize, chromium is in its less mobile trivalent form. These results are consistent with those of Chen et al. ( 2003) and [START_REF] Astrup | Geochemical modeling of leaching from MSWI air-pollution-control residues[END_REF] who performed batch tests on incineration residues and showed the oxidation of Al 0 in presence of Cr(VI).

Sulphate behaviour also varied with experimental conditions. As shown in Fig. 6a, in the lab column experiment, SO 4 decreased with increasing L/S ratio, which is consistent with a dissolution of Ca-sulphates. But in the large column and indoor cell, concentrations were found to increase with time (Fig. 6a). This could be explained by a late release of neoformed sulphates that had time to accumulate (Ca-sulphates but also neoformed ettringite). The same type of delayed behaviour was observed during leaching of a bottom ash heap by [START_REF] Freyssinet | Chemical changes and leachate mass balance of municipal solid waste bottom ash submitted to weathering[END_REF].

The plots of concentrations as a function of L/S ratio were found to be very useful: for the case of non-reactive constituents, the relatively good correspondence between the data collected during the experiments performed at different scales and using different percolation rates suggests that the L/S ratio might be used as a tool to extrapolate between percolation scenarios. Data from experiments performed by [START_REF] Crest | Experimental percolation under intermittent conditions and bases of a fuzzy rule-based model to describe pollutant emission from waste[END_REF] on the same ash-sand mixture in column tests with alternating flow conditions also suggest a good correspondence for non-reactive constituents. On the other hand, for constituents that are influenced by reactions and reaction kinetics (see next section), the L/S ratio does not provide a means of extrapolation. This is observed here for aluminium and especially chromium, elements that are influenced by redox reactions, with specific reaction kinetics.

Modelling results

Since extrapolation between experiments using the L/S ratio is not satisfactory for the case of reactive constituents such as Cr and Al, a modelling approach was adopted. In accordance with the principle of parcimony that states that the simpler model explaining observations should be preferred to the more complex model, several models of varying complexities were tested.

Exponential decay model

The exponential decay model is a simple model that is often used for fitting dynamic leaching experiments (see [START_REF] Hjelmar | Development of acceptance criteria for landfilling of waste : an approach based on impact modelling and scenario calculations[END_REF]. The conceptual model is that of a continuously stirred and diluted reactor: the model assumes that at time t = 0, all available mass is instantaneously dissolved (yielding an initial concentration noted « C o ») and that this concentration is then gradually diluted by incoming water. The exponential decay model is written:

) exp( k LS C C o s   (2)
where k is a kinetic parameter which, for a non-reactive constituent, is shown in the appendix to be the waste mass per unit pore volume. If the L/S ratio has units of l/kg, then the k parameter has units of kg/l. An attempt to reproduce concentrations measured at the outlet of the large column is presented in Fig. 7a. The match between measured and calculated concentrations, obtained for a value k = 10 kg/l and presented in cartesian scale, might appear satisfactory, but a plot in semi-logarithmic scale (Fig. 7b) shows that the measured data display a curvature that an exponential model cannot, by definition, reproduce.

CSTR cascade model

A continuously-stirred tank reactor (CSTR) cascade model [START_REF] Tiruta-Barna | Distributed mass transfer rate for modelling the leaching of porous granular materials containing soluble pollutants[END_REF], with exchanges between mobile and immobile porosities, was used to reproduce the behaviour of non-reactive constituents (Na, K, Cl). Primary model fitting-parameters are the number of reactors (noted n ; which controls dispersion), the proportion of mobile versus total porosity (noted a) and the rate coefficient controlling exchange between mobile and immobile porosities (noted b).

Application of the model to the different experiments suggests that inclusion of a double porosity allows the "tails" observed in the non-reactive constituent data to be reproduced.

However, it was always necessary to readjust fitting parameters when passing from one scale to another. This is illustrated by Fig. 8 which shows fits to chloride data from the lysimeter cell (Fig. 8a) and from the large column (Fig. 8b). The match to the lysimeter cell data was obtained using: n = 6, a 1 = 0.15, a 2 = 0.05, b 1 = 0.7, b 2 = 0.0003, the subscripts indicating that two immobile zone porosities were required to match the data. The match to the large column data was obtained using: n = 6, a 1 = 0.048, a 2 = 0.95, b 1 = 0.02, b 2 = 0.00003. While unlike the exponential model, the long "tails" displayed by the measured data can be reproduced in semi-logarithmic scale when multiple porosity is included, the necessity to readjust model fitting parameters when simulating the data from each experiment underlines limits in predictive capability and therefore in the ability to extrapolate between percolation scenarios.

Coupled chemistry-transport modelling

As was shown previously, the L/S ratio did not provide a means of extrapolation in the case of reactive constituents when reaction kinetics influence the behaviour. This was the case for Al and Cr that are linked by redox reactions. In order to attempt to reproduce the behaviour of these elements and provide a means of extrapolation, a coupled chemistry-transport model (PHREEQC ; [START_REF] Parkhurst | User's guide to PHREEQC (version 2) -A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculation[END_REF] was used. The model couples dissolved constituent transport by advection and dispersion with chemical speciation reactions. The first step for performing such modelling is the identification of phases controlling solubilities and the selection of adequate thermodynamic data. Gibbsite (Al(OH) 3 ) is generally considered to control the solubility of aluminium [START_REF] Hsu | Aluminium hydroxides and oxi-hydroxides[END_REF][START_REF] Eary | Geochemical and equilibrium trends in mine pit lakes[END_REF]. Comparison between geochemical simulations (Guyonnet et al., 2005a) Oxygen and water were included as oxidants because, from mass balance considerations, experimental data suggest that another oxidant than Cr(VI) must be involved. If one assumes that Cr(VI) is the sole oxidant of metal aluminium Al 0 (Eq. 4), then the release of one mole of Al(III) in solution would require the reduction of one mole of Cr(VI) in solution. But from results in Table 3, the initial quantity of available Cr(VI) in the ash-sand mixture is estimated to be on the order of 1 mg/kg. This estimate is based on the value of 0.84 mg/kg measured in the leaching test performed on the waste sampled from the large column (Table 3) after leaching and on the fact that very little Cr(VI) was exported from the large column during the experiment (only 0.03 mg/kg). Considering the volumetric water content of 30% and the particle density of 2.64 g/cm3, the total dissolution of 1 mg/kg Cr(VI) would yield a concentration of approximately 10 -4 mol/l. But such a concentration cannot account for the maximum Al concentration observed in the large column (Fig. 6b; 140 mg/l, i.e., 52 x 10 -4 mol/l). The model therefore takes explicitly into account the transport of aqueous oxygen (10 mg/l in the inflowing water) and the quantity of aqueous oxygen in solution is controlled by the oxidation of reactive Al, following (see also [START_REF] Astrup | -Chromium Release from Waste Incineration Air-Pollution-Control Residues[END_REF]: Cr-red = kinetic constant for Cr(VI)(aq) reduction, p = surface exponent and q = oxygen exponent.

      q p r ox Al r aq O M Al k dt Al d ) ( / 2 0 0 0    (
These Stoichiometries of reactions (3) through (6) and kinetic rates were specified in the PHREEQC input file and the advective-dispersive equation was solved using a finite difference algorithm. Discretization was optimized using the Peclet and Courant criteria to minimize numerical dispersion. Figure 9 shows the results of a match to data collected from the large column in porous cups located at two depths (PC3 at 254 cm and PC4 at 356 cm) and using the same values of fitting parameters: M 0 = 300 mg/kg, initial available Cr(VI) in the solid = 1 mg/kg, k Al-ox = 2 x 10 -6 mol l -1 s -1 , k Cr-s = 10 -12 mol l -1 s -1 , k Cr red = 4 x 10 -4 mol l -1 s -1 , p = 2/3, q = 1/2. It is seen that the overall observed behaviour of aluminium decrease, coinciding with Cr(VI) increase, is reproduced. Figure 10 shows results of a "blind" simulation of laboratory column data using the same fitting parameters as previously. The overall behaviour is reproduced reasonably well, suggesting that the model may have some potential for scale extrapolation.

Note that the value of reactive metal aluminium (300 mg/kg) used to fit the measured data is very close to the difference between Al leached at L/S = 10 before and after the experiment (see Table 3). The rates of aluminium oxidation, chromium reduction and dissolution are fitting parameters that are adjusted during the process of matching measured and calculated Al and Cr concentrations. Although there are, to our knowledge, no examples in the literature of such rates for this type of waste material, one can compare the value of aluminium oxidation to the rate of pyrite oxidation observed in batch experiments by [START_REF] Williamson | The kinetics and electrochemical rate-determinig step of aqueous pyrite oxidation[END_REF]. These authors measured a rate of 10 -8.19 mol m -2 s -1 which, considering the surface area of their material (0.051 m 2 g -1 ) and the liquid-solid ratio (4 g/l), yields a value of 10 -6.3 mol l -1 s -1 , which is of the same order of magnitude as the value obtained here for aluminium oxidation.

Discussion and conclusions

The LIMULE project provided data regarding leaching of a MSW incineration ash mixed with Loire sand, at different scales of observation and for periods up to 19 months. The particular emphasis of this paper is on the ability to extrapolate between scales and conditions of percolation using the L/S ratio. It was found that this ratio provided a means of extrapolation for the case of non-reactive constituents such as soluble salts (Cl, Na, K, Ca): when the concentration data are plotted versus L/S ratio, the curves coincide reasonably well suggesting that one curve can be used as a type-curve to provide a concentration versus time relationship for other percolation scenarios. For reactive constituents on the other hand, the L/S ratio does not provide a means of extrapolation. This was found to be the case for aluminium and chromium, two elements that are linked by redox reactions with specific reaction kinetics: oxidation of metal aluminium and reduction of Cr(VI) to Cr(III).

If the evolution of an element in solution is controlled by reaction kinetics rather than by the rate at which water is percolating through the waste, then using the L/S ratio to extrapolate from the small scale to the large scale will have the effect of overestimating times of element breakthrough (unconservative estimates). This is illustrated schematically in Fig. 11 which represents two columns (noted 1 and 2) of different lengths, filled with the same waste and fed with water at the same constant rate. If we measured the evolution of concentrations (noted C1 and C2) at the column outlets over time, we might obtain the curves depicted in Fig. 11a: column 2 being longer, the element appears later than in column 1, but the delay is not very large because it is a reaction kinetic that is controlling breakthrough (as was the case for Cr ; Fig. 6d) rather than the amount of water that has percolated through the column. We now plot the data as a function of L/S ratio rather than time (Fig. 11b). As cumulative volumes are divided by a much smaller mass in the case of column 1 (full line), the curve for column 1 is offset to the right with respect to column 2 (dashed curve). Let us now assume that we have not "measured" the concentration versus time relationship of column 2 in Fig. 11a but wish to "predict" it using the concentration versus L/S of column 1 in Fig. 11b: we obtain the dashed curve in Fig. 11c; times are offset to the right with respect to the "measured" times in Fig. 11a, implying that breakthrough times are overestimated.

Considering that the L/S ratio does not provide a means of extrapolation in the case of reactive elements, it was proposed to extrapolate using modelling. Attempts to fit measured data using a simple exponential decay model showed that even for a non-reactive constituent such as chloride, this model fails to reproduce the observed behaviour: when plotted in semilogarithmic scale, the data from the different experiments display a curvature that the exponential model cannot reproduce. With respect to application of this model, it is recommended that it be applied to concentration data (rather than to cumulative emission data) and that the quality of the fit be judged on the basis of a plot in semi-logarithmic rather than in cartesian scale. With respect to predicting non-reactive constituent behaviour for other percolation scenarios, results presented here suggest that rather than fitting the exponential model to measured laboratory data, it would seem preferable to use the measured data, plotted versus L/S, directly as a type curve.

Modelling of non-reactive constituents using a CSTR cascade model with mobile and immobile porosities show that sets of model fitting parameters can generally be found such that measured data are reproduced (in particular the observed curvature in semi-logarithmic scale). However, the modelling also illustrates the difficulty to extrapolate between scales since reproducing the data from each experiment required a new adjustment of model fitting parameters and in particular of the proportions of mobile versus immobile porosities and the constants controlling mass exchange between these porosities.

Difficulty in simulating the behaviour of reactive constituents, such as aluminium and chromium, using a coupled chemistry-transport model, resides primarily in the number of parameters involved: stock of reactive metal aluminium that can be oxidized, availability of oxygen in the system, kinetics of the reactions of Al 0 oxidation and Cr(VI) reduction, choice of mineral phases controlling the solubility of aluminium and chromium, selection of thermodynamic data for theses phases, etc. While the modelling tool was found to be extremely valuable for exploring different hypotheses regarding mechanisms controlling the behaviour of Al and Cr in this system, truly predictive capability remains limited due to the complexity involved. However, the predictive capability of such modelling is increasing, provided it relies upon detailed characterization of the waste (see for example [START_REF] Kosson | An integrated framework for evaluating leaching in waste management and utilization of secondary materials[END_REF]. For example, it was found in this project that reproducing the results of the pHdependent solubility test (CEN, 2006b) with a geochemical speciation model, could help considerably in guiding choices with respect to the selection of mineral phases to be included in the predictive modelling (Guyonnet et al., 2005a). As the waste is solicited during this test over a large range of pH values, it yields a global response that provides information regarding the mineral assemblage controlling solubilities. By coupling this information with mineralogical observations, the model can be constrained and hence its predictive capability improved.

With respect to the source term, one essential piece of information for predictions of potential impacts is the maximum concentration value in the water emitted from the waste. Table 4 summarizes maximum values of concentration measured during the various dynamic leaching experiments performed in this project, and the L/S ratio at which these maximum concentrations were observed. For soluble salts it is seen, as expected, that the highest concentrations correspond to the lowest values of L/S. For a constituent such as chloride which has an enormous solubility and in spite of the equilibration period included in the standard test (CEN 2005), the first value of concentration measured in the laboratory test (at L/S = 0.1) does not provide an adequate estimate of the maximum concentration that can be expected during flow through a larger column of waste, where the first significant values of L/S may be much lower. Taking for example a 20 m high column of waste with a density of 1500 kg/m 3 and considering a typical value of net infiltration of 300 mm/year, a L/S ratio of 0.1 corresponds (Eq. 1) to 10 years. As shown in this study, chloride concentrations at lower L/S ratios and hence smaller times may be significantly higher. As a prospect, it would seem useful to define a procedure that would allow the identification of maximum concentrations that are more representative of potential field conditions. If several points are measured during the laboratory column test at low values of L/S (0.1, 0.2, 0.3, 0.4, 0.5), it might be possible to identify a meaningful curvature that could help extrapolate to even lower values of L/S (for example 0.01) that cannot easily be reached experimentally.

Considering the complexity of source term behaviour for the case of reactive constituents, the question remains as to the extrapolation of the standardized upward-flow laboratory column test to field conditions. Several options might be considered. Tests could be performed at different rates in laboratory columns of different sizes, thus providing direct information regarding extrapolation. Pilot-scale tests, that are more representative of true field conditions, can be performed, especially if laboratory results indicate that there is an environmental issue.

In any case, characterization should be performed in parallel with the modelling effort since the ultimate objective is generally to be able to predict a potential environmental impact; such predictions necessarily rely at some stage on model calculations.

Appendix: derivation of the exponential decay equation and meaning of the k parameter

We consider flow at a constant rate Q through a column of volume V, height h and section S.

Waste dry density is d, volumetric porosity is n and total available mass of a given constituent is M m . Constituent concentration in inflowing water (C in ) is zero. At time t = 0 it is assumed that all the available mass M m is instantaneously dissolved, yielding a concentration C o that is subsequently diluted by inflowing water at zero concentration. It is also assumed perfect mixing throughout the column porosity. We derive expressions for concentration (C out ) and cumulative emission (E out ) at the outlet.

Mass flux into the column is:

0   in in C Q F (1)
Mass flux out of the column is:

out out C Q F  (2)
From mass balance, the difference between the flux in and out of the column is the in mass in the column. For a non-reactive constituent:

t C nV F F p in out      (3)
where C p is solute concentration in the column pore water. Since perfect mixing is assumed:

C p = C out , hence: t C nV C C Q out in out ) (      (4)
Assuming C in = 0 and dividing by the column section yields:

out out C n h I t C     ( 5
)
where I is infiltration per unit section.

Integrating:

      t n h I C C out 1 (6) yields:   t n h I C out o C C out   ) ln( (7) hence: ) exp( t n h I C C o out   (8)
The initial concentration C o is the result of the instantaneous dissolution of available mass M m into pore volume n V :

n V M C o m  (9)
Expressing available mass per unit mass of dry waste:

V d M A m  ( 10
)
where A is dubbed "availability".

yields:

V d A M m  (11)
Substituting into (9):

n d A V n V d A C   o (12) From (8): ) exp( t n h I n d A C out   (13)
Cumulative emission E out per unit mass of dry waste is obtained by integrating the flux out with respect to time:

t C Q V d E out out    1 (14)
Substituting ( 13) and simplifying by the section S yields:

t t) n h I ( n d I A h d E out     exp 1 (15) Cnt t) n h I ( h n I I A h n E out     exp ( 16
)
where Cnt is an integration constant.

Simplifying:

Cnt t) n h I A E out     exp( (17)
The integration constant is obtained by setting time to infinity:

Cnt = E  = A Hence: ) exp 1 ( t) n h I ( A E out    (18)
Previous researchers express Equations ( 8) and ( 18) as a function of the liquid/solid (L/S) ratio, defined as:

d h I t LS  (19)
and introduce a parameter k such that:

) exp( k LS C C o out   (20)
and:

)

) exp( 1 ( k LS A E out    (21)
Comparing equations ( 8), ( 18) and ( 20), ( 21) it follows that:

n d k  (22)
Relationships below, which follow from the above, are also sometimes used:

k A C o  (23)
)

) exp( 1 ( k LS k C E o    ( 24
)
Parameter k is therefore the ratio between waste dry density and volumetric porosity (units of kg/l). It is the waste mass per unit pore volume. It follows that in this model, the rate of concentration decrease is just a function of the flushing rate (I) and the "reactor volume" (h n). It is worth noting that Equation ( 20) is identitical to Equation (7) of [START_REF] Belevi | Long-term behaviour of municipal solid waste landfills[END_REF] Note that for a solute that is assumed to undergo reversible and instantaneous adsorption, with a retardation coefficient R, Equations ( 20) and ( 21) require that:

n R d k  (25)
And Equation ( 12) requires that Equation (23) be written:

R k A C o  (26)

  ) where: I = constant infiltration rate, t = time, d = waste density, h = height of the waste column. This simple equation has been used by previous authors to provide a means of extrapolation from one percolation scenario to another. The concentration versus L/S ratio measured for a certain scenario (in particular the laboratory column test) is used as a type-curve and the concentration versus time relationships for other scenarios are calculated by setting I, d and h in Eq.(1) accordingly.

Figure 3

 3 Figure3compares the evolution of pH, Eh and electrical conductivity (Eh measurements are available for the large column experiment only). The pH is slightly higher in the large column(11.5 -12) than in the laboratory column (11) while the values from the indoor cell appear to fluctuate. As shown in Fig.3b, the redox potential (Eh) at the outlet of the large column increased gradually from values lower than 0 mV to values above 100 mV at the end of the experiment. Figure4shows non-reactive (or poorly reactive) soluble constituent (Cl, Na, K, Ca) concentrations, as a function of L/S ratio (based on collected outflow volumes).The curves show a typical decreasing shape and a relatively good correspondence between

  and aluminium solubility observed in the large column (see Fig.6b) suggest that the microcrystalline form of Gibbsite (Gibbsite(c)) is a possible controlling phase. Next, several hypotheses were tested with respect to oxidoreduction of the Al-Cr couple, by confronting model results with the measured data. At the elevated values of pH observed (pH~11.25), it was hypothesized as in[START_REF] Astrup | -Chromium Release from Waste Incineration Air-Pollution-Control Residues[END_REF], that chromium concentrations were the result of the following simultaneous and and (aq) indicate solid and aqueous phases.

  with: k = c o /m o in the notation of these authors. Correspondence with the parameter notation herein is: c o = C o and m o = A.

Fig. 1 .Fig. 11 .

 111 Fig. 1. View of the large column (left) and automatic spraying system of the indoor lysimeter cell (right)

Table 1

 1 Characteristics of the experiments performed under conditions of dynamic flow

	Scale of experiment	Infiltration	Infiltration	Duration	L/S
		(cm/day)	conditions		reached
	1: Upward-flow percolation column (30 cm)	15	Controlled	60 days	20
	2: Indoor lysimeter cell (1 m)	0.55	Controlled	590 days	2.5
	2: Outdoor lysimeter cell (2 m)	0.11 on average Uncontrolled 610 days	0.3
	3: Large column (5 m)	3.8	Controlled	580 days	3*
	*: 3 at the outlet, but up to 24 in the uppermost porous cup

Table 2

 2 Global chemical composition of the fresh ash-sand mixture (2 analyses) and ash-sand mixture after leaching in the large column (average and standard deviation of 5 analyses)

	Analysis method	XF/ICP-AES	ICP-AES
				Leached
	%	Fresh 1	Fresh 2	(average & stand. dev.)
	Al 2 O 3	11.5	10.7	10.8 (0.46)
	CaO	7.6	6.7	8.7 (0.58)
	Fe 2 O 3 total	3.64	3.1	2.9 (0.17)
	K 2 O	3	3.1	2.8 (0.08)
	MgO	1.3	1.3	1.4 (0.08)
	MnO	0.07	0.07	0.1 (0.00)
	Na 2 O	1.7	nm	nm
	P 2 O 5	1.25	1.102	1.1 (0.04)
	SiO 2	67.4	70	60.4 (0.7)
	TiO 2	0.69	0.64	0.6 (0.04)
	S total	0.26	nm	0.22 (0.07)
	Cl	0.43	nm	nm
	LOI (1000°C)	1.4	2.16	3.0 (0.29)
	Traces*	0.56	nm	0.6
	Total **	100.8	101.8	94.3
	Al° (%)	1.40	1.45	0.6 (0.05)
	Traces mg/kg
	As	46	nm	27.5 (2.12)
	Ba	955	nm	969.8 (19.73)
	Cd	4	nm	3.4 (0.89)
	Co	61	nm	59.2 (15.99)
	Cr total	168	nm	185.2 (16.62)
	Cu	1021	nm	988.8 (102.21)
	Mo	8	nm	< 5
	Ni	63	nm	55.2 (3.70)
	Pb	473	nm	473.2 (23.19)
	Sn	155	nm	233.8 (11.73)
	Sr	235	nm	187 (11.38)
	Zn	2053	nm	2268 (183.05)

Notes: nm = non measured; * 25 traces analysed **total = main elements + traces + LOI (1000°C).

Table 3

 3 Batch leaching test at L/S = 10 l/kg: fresh ash-sand mixture and ash-sand mixture after leaching in the large column. All units in mg/kg dry weight unless indicated otherwise.

		Fresh	Leached		NHW
	Parameter	ash-sand	ash-sand HW limit	limit	IW limit
	pH (pH units)	10.3	9.9
	Conductivity (µS/cm)	1 580	661
	Soluble fraction	14 307	6 280	100 000	60 000	4 000
	Al	844	477.9
	Ca	2 312	1 389
	Mg	< 5	< 10
	Na	448	12
	K	500	24
	Inorganic Carbon	718	82
	Cl	2 697	16	25 000	15 000	800
	SO 4	1 938	1 760	50 000	20 000	1 000
	As	< 0.1	< 0.1	25	2	0.5
	Ba	2.5	0.92	300	100	20
	Cd	< 0.02	< 0.02	5	1	0.04
	Cr total	0.27	0.84	70	10	0.5
	Cu	0.03	0.07	100	50	2
	Ni	< 0.05	< 0.05	40	10	0.4
	Pb	< 0.02	0.03	50	10	0.5
	SiO 2	< 5	13
	Sr	8.3	2.4
	Zn	< 0.05	0.1	200	50	4
	Notes: HW = Hazardous Waste, NHW = Non-Hazardous Waste, IW = Inert Waste. Limits from OJC
	(2002).

Table 4

 4 Synthesis of highest measured concentrations (C max ) and corresponding values of L/S (l/kg)

	Element	Large Column Outdoor Cell	Indoor Cell Laboratory Column
		C max	L/S	C max	L/S	C max	L/S	C max	L/S
	Cl (mg/l)	96 100 0.008 28 493 0.022 27 600 0.032 6 612	0.1
	Na (mg/l)	32 100 0.008 8 660 0.022 5 285 0.032 1 172	0.1
	K (mg/l)	25 000 0.008 8 401 0.022 5 045 0.032 1 049	0.1
	Ca (mg/l)	30 000 0.008 11 969 0.022 8 020 0.032 2 385	0.1
	SO 4 (mg/l)	306	0.008	42	0.2	170 0.032	95	5
	Al (mg/l)	146	0.67	98	0.29	119	0.51	138	2
	Cr (g/l)	41*	3.02	9	0.26 122* 2.43	32	20

*: increasing at the end of the experiment

Acknowledgements

We thank Pierre Gallé, Marc Gamet (BRGM) and Mohammed Abdelghafour (POLDEN) for their valuable contributions to the LIMULE project, ADEME (the French Agency for Environment and Energy Management) for its financial support, CIDEME for providing the incineration ash and an anonymous reviewer.


F Taglino

M Lezoche

Business Process driven solutions for innovative enterprise information systems

Existing limitations and problems in the current life-cycle of software applications will encourage new development paradigms. New technological trends, aiming at responding to current needs, such as flexibility, dynamicity, scalability, will certainly drive the envisaged changes. In this paper, possible solutions for the development and maintenance of innovative enterprise information systems (IS) are outlined. In particular, it will be argued about business process driven approach to information systems development, and how, through the support of new and emerging technologies, it can address nowadays requirements and overcome current limits. Business process driven approach to IS development is here characterized by: (i) business process modelling for representing the business logics; (ii) enhanced SOA paradigm for business process execution; (iii) business rules-based approach to software applications maintenance and evolution.

Introduction

Enterprise information systems (IS) are continuously evolving over the time. This is due to their increasing diffusion, emerging of new needs, and evolution of the supporting technologies. Nowadays, the traditional model of information systems development is increasingly problematic with a poor reflection of the needs of real information systems use. Analysis from well known market watchers, such as Gartner Group and Standish Group, report that software applications development still suffers high probability of failure [START_REF]Research shows high failure rate on IT projects[END_REF] [START_REF]Extreme Chaos[END_REF]. Furthermore, it has long been known that the maintenance phase of the enterprise IS lifecycle can consume more than half of the total lifetime costs of an information system [START_REF] Bennett | Software evolution: past, present and future[END_REF].

Consequently, the nowadays needs for new generation of enterprise IS are evolving towards:

• Flexible, agile, and non monolithic applications;

• Adaptability with capability of dynamically reflecting new business requirements; • Openness, to ensure interoperability among applications and support the development and sustainability of business ecosystems.

The current technological trend, in particular due to the evolution of the Internet, towards the so called Future Internet, appears in line with the above needs to support new enterprise information systems. In fact, the intention of the Future Internet, recently embraced by the European Union with the Bled Declaration2 (31 March 2008), will be characterized by openness, scalability, dynamicity, and proactiveness that should make easier to support new, flexible and dynamic information systems.

On these premises, it is unavoidable to think about significant changes in the development of next generation enterprise IS, and a significant contribution will come from a business process driven approach.

Business Process design involves the identification and sequencing of work activities, tasks, resources, exchanged messages, decisions, and responsibilities, deployed across time and space, with clearly identified inputs and outputs.

In traditional approach for development of information systems, business process modelling is mostly used as a means for specifying high level business requirements to be implemented by IT people. This means not an immediate correspondence between designed business processes and the final applications. Furthermore, this traditional approach also suffers the cultural gap between business experts and software analysts/developers. Traditionally, they belong to two separate communities and operate without a systematic interaction and cooperation, causing the problem of Business/IT alignment. The OMG MDD/MDA (Model Driven Development/Architecture) [4] approach has been proposed to close this gap. However, real integrated solutions have not been provided yet With business process driven IS, here a possible approach for software applications generation proposed, which could change the traditional model of information systems development lifecycle. The Business Process driven IS is characterized by the following key aspects:

• the application business logics is represented by business processes designed by using graphical notations, to ease the development, and reduce the need of writing code. • execution of software applications is left to specific business process execution engines, which are able to run business processes and keep track of the control flow. • execution of process tasks is accomplished by accessing available services over the Internet. Business process execution engines are able to invoke external services when it is required. This approach is supported by Software Oriented Architectures (SOA), and new concepts like Software as a Service (SaaS) 3 which is towards the delocalization of software services and an on demand service provision. Semantics-based techniques will help for service discovery and composition. • business rules based approach for supporting IS maintenance and evolution.

Figure 1 sketchily represents a business process centric view in the development and execution of software applications. In the rest of the paper the above aspects will be briefly outlined. The need for a tighter cooperation between business experts and IT specialists is growing in momentum, and it is unavoidable. If software industry wants to reduce costs and risks in developing more complex and powerful applications. Today, there is a great number of methods and tools to model BPs. There are also international organizations, such as OMG 4 and WfMC 5 , very active in the field.

New business process modeling notations and languages like BPMN (Business Process Modelling Notation) [START_REF]Business Process Modeling Notation, V1.1[END_REF] and BPEL (Business Process Execution Language) [START_REF] Andrews | Business Process Execution Language for Web Services[END_REF], for design and execution respectively, are emerging. New open source and commercial tools, supporting such languages, are now available (i.e., INTALIO 6 , uEngine 7 ). For instance, there are several BP editor tools based on different graphical notations (around 70 adopt the XPDL, from WfMC)

In particular, such tools, allow business process diagrams building, and the automatic generation of an executable form. Business processes are modelled by using graphical notations which make their construction more intuitive and available also to non technology people (say business people).

Even if such an approach is still in a preliminary stage, it is already possible to envisage some interesting enhancements.

Ontology-based semantic support An ontology is defined as "a formal, explicit specification of a shared conceptualisation" [START_REF] Gruber | A translation approach to portable ontologies[END_REF]. This means that an ontology is: (i) the result of a consensus reached among a group of domain experts (shared conceptualisation) and more in general (by using special tools, like a folksonomy) of a wider community; (ii) a formal specification and as such it can be interpreted by a machine for reasoning and querying activities.

Ontologies can contribute in several ways to the development of business process driven information systems. Some of them are briefly reported below. As the result of the consensus of domain experts, an ontology represents a common reference and, as such, it can establish a shared playground which different kinds of actors, with different know how and roles (i.e., business and IT people), can play on. Furthermore, as a formal representation, an ontology can be used for:

• Supporting business process design by verifying semantic alignment of a business process against a reference ontology. This goal can be achieved by performing consistency checking through the use of semantic reasoning techniques. • Supporting business process design and reengineering providing suggestions to business experts during the modelling phase of a BP. Business experts can be supported in finding, for instance, alternative elements of a business process by performing semantic search and similarity reasoning over the business ontology.

Intelligent enterprise knowledge base

Modeling a business process (as developing a software business application) is a complex task that needs a lot of information. For instance, knowledge about internal procedures, interaction with external entities, rules, responsibilities, goals. When this documentation is coded within an organization, most of the times it is fragmented and available in heterogeneous formats and representations (some even not digitalized), such as technical reports, organizational memos, meeting minutes, forums and blogs, best practices, laws and regulations, available in different contexts: from the enterprise to the industry sector, to national and international levels (typically reachable over the web). Obviously, such information has much more value when it is reachable, understandable, sharable and re-usable. Essentially, an extended and intelligent enterprise knowledge base, able to make available the enterprise knowledge, would be also ideal to support modeling and maintenance of business processes. For instance, modeling a process having an intelligent??? access to related best practices, or laws regulating specific activities, would be ideal. Innovative semanticbased (i.e., ontology-based semantic annotation and search) and knowledgeenhanced (i.e., NLP-based knowledge extraction and classification) solutions will be useful to automatically achieve large part of the enterprise documental resources, to build the desired enterprise knowledge base and to support its access and use.

Service oriented Business Process execution

The development of software applications driven by business process is based on the fact that once a business process has been modelled it can be executed. However, since business process modelling notations like BPMN are not executable, a business process can be automatically transformed into an executable format, for instance BPEL (or another execution language like XML Process Definition Language, XPDL [START_REF]Ver 1.0 XML Process Definition Language[END_REF]), to be then executed by a BP execution engine (e.g., Ac-tiveBPEL 8 ). Nevertheless, a BP says what each task should do, but no how to perform the task. Due to that, a BP execution engine needs to invoke, in correspondence of an automatically executable task 9 , a software component in charge to accomplish that task. This need perfectly matches the capabilities offered by the Service Oriented Architecture paradigm which can be synthesized as follows: wherever a given functionality is needed it can be executed by a Web Service. According to that, a business process links and sequences services, in a process called web services orchestration.

According to the emerging wave of the Internet of Service, the above approach will be facilitated and encouraged. The Internet of Services will take the enterprise SOA approach to the next level by making services easy to implement, consume, and trade.

Furthermore, in an open scenario like the one that is going to configure with the advent of the Internet of Services, the binding of services to business processes will more and more need:

• intelligent search methods for the localisation of suitable services needed for executing a certain BP; • interoperability solutions for allowing the coexistence of services which, spread worldwide, have not been designed to work together. For both these aspects, emerging semantics-based solutions (i.e., WSMO [START_REF] Mocan | An Ontology-Based Data Mediation Framework for SemanticEnvironments[END_REF] for search and retrieval of web services (Semantic Web Services, SWS) and the Athena Semantic Reconciliation suite and [START_REF] Bagnato | Semantic Reconciliation of Business Documents in a SOA Framework[END_REF] for heterogeneous message reconciliation) certainly represent promising solutions.

From this brief description, it emerges how Service Oriented execution of business processes represents a step forward to agile, flexible and non monolithic software applications. In fact, the accomplishment of each of tasks is demanded to services which have been properly selected. Whenever the selected services are not anymore available, other suitable available services will be searched and invoked.

Rule-based approach to IS maintenance and evolution

In a dynamic enterprise, BPs need to be periodically revised and updated. Such BP evolution may be necessary for different reasons, as for instance the introduction of company policies which requires the update of non conformant BPs. Company policies are often represented in the form of business rules (BRs). Business experts usually tend to formulate a BR in natural language (NL), but it is well-known that NL is often ambiguous and error-prone. For this reason, there are interesting proposals of using structured (controlled) natural language. Recently, OMG promoted the use of structured English in the business rules framework SBVR [START_REF]Semantics of Business Vocabulary and Business Rules Specification[END_REF]. Another interesting proposal is ACE [START_REF] Fuchs | Discourse Representation Structures for ACE 6.0[END_REF] (Attempt to Controlled English): a rich subset of standard English, designed for specification and knowledge representation.

Independently of its representation language, a BR can say, for instance: "all expenses greater than 5.000€ require the approval of the Head of Unit". Another BR may involve the way business operations are performed, for instance: "the receiving of a quotation must precede the issuing of a purchase order".

In the context of BP evolution, a challenging problem is to automatically identifying the BPs that violate at least one BR and, possibly, to make the former evolving according to the latter.

In a previous work [START_REF] Lezoche | Business Process Evolution: a Rule-based Approach[END_REF], one of the author of this paper, presented an ontological approach to BP modeling, proposing a method to verify if, given a BR, a process is consistent with it. Such a method is characterized by the concepts of BP Schema (BPS), which is a set of predicative atoms, and BP Instance (BPI) origi-nated from the actual execution of a BPS. In order to be valid a BP Schema, all its instances must satisfy the rules. Consistency table to test coherence between BR vs BPS, and without exploding every single instance are used.

However, independently of the specific solution, a BR-based approach appears a valid support to the maintenance and evolution of business process driven applications, especially with respect to agility and flexibility of enterprises. In fact, the possibility to express BRs in a declarative way, avoiding hard coded control procedures, makes enterprise policies more easily configurable and adaptable, especially in the context of an open scenario where enterprises dynamically change and join together in virtual organizations.

Conclusions

In this paper we have focused on innovative approaches to development, execution, maintenance, and evolution of enterprise software applications. In particular, a business process driven approach has been presented.

The approach is based on some characterizing features: (i) representing the business logics by means of a business process by using graphical notations; (ii) service oriented execution of business processes; (iii) business rules for supporting evolution and management.

Figure 1 :

 1 Figure 1: Business process driven software development

Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti" -Consiglio Nazionale delle Ricerche. Rome, Italy. francesco.taglino@iasi.cnr.it, lezoche@iasi.cnr.it.

http://www.future-internet.eu/index.php?id=47

http://en.wikipedia.org/wiki/Software_as_a_service

http://www.omg.org

http://www.wfmc.org

http://www.intalio.com

http://www.uengine.org/web/guest/home

http://www.activebpel.org/

For not automatically executable tasks, BPEL4People[START_REF] Agrawal | WS-BPEL Extension for People (BPEL4People), Version 1.0[END_REF] extends BPEL from involvement of Web services alone to role-based human activities as well.