Arij Ben

Roland Dauphin

Hassan Babiker

Yoann Viollet

Junseok Chang

Nicolas Jeuland

Amer Amer

Revisiting diesel fuel formulation from Petroleum light and middle refinery streams based on optimized engine behavior

Keywords: Naphtha, Straight-Run Gasoline, Design of Experiment DoE, formulation, optimization, pollutants, efficiency, NOx, particulates

The share of diesel fuel in European transport sector, which currently represents over 50% of total demand, is increasing, requiring massive imports of this product, while at the same time, gasoline fuels are today in surplus. In terms of air pollutant emissions, gasoline and kerosene streams have shown potential in achieving lower emissions in Compression Ignition (CI) engines, particularly nitrogen oxides (NOx) and particulates. A new fuel formulation approach through the use of light fractions within diesel technology could consequently address both questions of energy demand balance and reduction of diesel engines pollution footprint. In this study, a fuel formulation for a Diesel engine is optimized to achieve lower pollutants emissions and higher engine efficiency. The fuel matrix is based on seven refinery streams representative of gasoline (Hydrotreated Straight-Run Gasoline HSRG, Hydrotreated Fluid Catalytic Cracking HFCC and Reformate REF), kerosene (Hydrotreated Straight-Run Kerosene HSRK and Hydrocracked Kerosene HCKK) and diesel cuts (Hydrotreated Straight-Run Diesel HSRD and Hydrocracked Light Diesel HCKLD). A D-Optimal mixture design is applied to build, a 12-run, 7-factor fuel matrix and the fuels are thoroughly optimized on two engine conditions at light and mid-load representative of typical vehicle running conditions. The results show a high sensitivity and a good correlation of the engine efficiency and pollutants emissions with the volumetric contribution of each refinery stream to the fuel composition. The optimum fuel composition varies across the range of engine operating points. At light load for example, the addition of up to 50%v of gasoline streams (HSRG and HFCC) to diesel streams demonstrates a good potential to simultaneously reduce NOx and particulate emissions and an overall good engine performance. Reformate, a highly aromatic gasoline stream, did not offer an advantage at any of the tested conditions due to high particulate emissions. The two kerosene streams perform similarly to diesel streams in terms of engine efficiency and pollutants emissions. A compromise fuel, composed of 50%v HSRG and 50%v HSRD, is proposed that allowed halving NOx and particulate emissions and reducing the fuel consumption by 5%wt compared to reference diesel HSRD.

The optimized fuel represents an alternative for balancing diesel and gasoline demand and for pollutant emissions reduction.

Introduction

Energy and environmental concerns are driving several changes in the European transport sector, with increasing incentives towards alternative renewable energy sources and new refining processes. However, conventional fossil fuels remain the sector's major energy source. Conventional liquid fuels represented 95% of European demand in 2012 [START_REF]EU Transport in Figures -Statistical Pocketbook 2014[END_REF] and according to recent forecasts from the European Commission, are likely to remain dominant over the coming decades [START_REF] Capros | EU Energy, Transport and GHG emissions Trends to 2050, Reference scenario 2013[END_REF]. For transport applications, the share of diesel fuel represents over 50% of the European market (in comparison with gasoline and jet fuel, 32% and 14% respectively) and demand is rising [START_REF] Capros | EU Energy, Transport and GHG emissions Trends to 2050, Reference scenario 2013[END_REF]. The main drivers for such high demand are the high efficiency of diesel engines and recent improvements of after-treatment systems, noise and drivability. Conversely, light products like gasoline cuts, issuing from straight run distillation or Fluid Catalytic Cracking (FCC), a conversion process widely used in European refineries, are today in surplus. This has drawn much attention to their use as an alternative fuel in diesel engines to balance the energy demand and to meet future pollutant emissions legislation. To achieve pollutant targets for light duty diesel engines, several technological solutions are used to enhance the control of ignition timing and combustion rate [START_REF] Stanglmaier | Homogeneous charge compression ignition (HCCI): benefits, compromises, and future engine applications[END_REF] including variable compression ratio, adapted injection systems, improved piston geometry, increased exhaust gas recirculation (EGR) rates, and improved cooling or boosting capacity [START_REF] Christensen | Demonstrating the Multi Fuel Capability of a Homogeneous Charge Compression Ignition Engine with Variable Compression Ratio[END_REF], [START_REF] Pesant | IFP International consortium Effects of fuel and additives on diesel HCCI engines, 4th year, final report[END_REF], [START_REF] Muether | Advanced Combustion for Low Emissions and High Efficiency Part 1: Impact of Engine Hardware on HCCI Combustion[END_REF], [START_REF] Noehre | Characterization of partially premixed combustion[END_REF]. However, the physical and chemical characteristics of a fuel can have an important impact on mixture formation, ignition and heat release rate [START_REF] Kalghatgi | Partially Pre-Mixed Auto-Ignition of Gasoline to Attain Low Smoke and Low NOx at High Load in a Compression Ignition Engine and Comparison with a Diesel Fuel[END_REF], [START_REF] Warey | Fuel Effects on Low Temperature Combustion in a Light-Duty Diesel Engine[END_REF]. Higher volatility enhances the mixture formation during the ignition delay and a lower cetane number delays the ignition occurrence, mainly at low-to-mid loads [START_REF] Zannis | Effect of fuel aromatic content and structure on direct-injection diesel engine pollutant emissions[END_REF].

Increasing fuel volatility in fuels of similar chemical composition has shown a soot reduction potential in CI engines for diesel and kerosene cuts due to the reduction of over-rich areas, and a moderate effect for gasoline cuts [START_REF] Cracknell | Advanced Combustion for Low Emissions and High Efficiency Part 2: Impact of Fuel Properties on HCCI Combustion[END_REF]. Increasing diesel volatility at equal cetane number [START_REF] Cheng | Effects of Fuel Volatility on Early Direct-Injection, Low-Temperature Combustion in an Optical Diesel Engine[END_REF] leads to a reduction of liquid film formation on cylinder walls, and thus, a reduction in smoke and enhanced fuel-conversion efficiency.

The application of highly mixed combustion modes however presents the disadvantage of a load range limited by difficult combustion control at high load and an increase in HC and CO due to lower combustion efficiency [START_REF] Noehre | Characterization of partially premixed combustion[END_REF]. Fuel wall impingement, the crevices, boundary layers, and fuel-lean regions formed during longer auto-ignition delay may constitute additional sources of HC and CO emissions [START_REF] Miles | Sources and mitigation of CO and UHC emissions in low-temperature diesel combustion regimes: Insights obtained via homogeneous reactor modeling[END_REF], [START_REF] Musculus | End-of-injection over-mixing and unburned hydrocarbon emissions in low-temperature-combustion diesel engines[END_REF], [START_REF] Aceves | A multi-zone model for prediction of HCCI combustion and emissions[END_REF].

Petroleum-based formulations for Diesel engines and impacts on pollutants

The use of gasoline or kerosene as alternatives to diesel fuel has been studied by several groups for their pollutants reducing potential. Han et al. [START_REF] Han | Premixed low-temperature combustion of blends of diesel and gasoline in a high speed compression ignition engine[END_REF] proved a simultaneous reduction of NOx and soot emissions using up to 40% gasoline with low EGR requirement compared to diesel fuel. CO and HC emissions were comparable to diesel engines at light loads, however increased at high loads [START_REF] Han | Attainment and Load Extension of High-Efficiency Premixed Low-Temperature Combustion with Dieseline in a Compression Ignition Engine[END_REF]. Kerosene fuels are also attractive for their higher volatility and lower cetane number, generally between EU diesel and gasoline.

Tested alone in compression ignition engines, kerosene presents lower NOx emissions than diesel at a similar soot level [START_REF] Risberg | Auto-ignition quality of diesel-like fuels in HCCI engines[END_REF] [START_REF] Bergstrand | Effects on combustion by using kerosene or MK1 diesel[END_REF] and in mixture with diesel, it enhances the combustion efficiency [START_REF] Singh | Dieseline, Diesohol and Diesosene Fuelled HCCI Engine Development[END_REF]. The chemical effect of fuel formulation is difficult to separate from the physical effect, especially in complex engine configurations. Nevertheless, several general trends have been put forward in recent literature.

Most usually, fuels containing high level of aromatics increase soot formation [START_REF] Zannis | Effect of fuel aromatic content and structure on direct-injection diesel engine pollutant emissions[END_REF] [START_REF] Nakakita | Effect of hydrocarbon molecular structure in diesel fuel on in-cylinder soot formation and exhaust emissions[END_REF]. Diminishing the aromatic content generally correlates with particulate reduction for diesel, gasoline and kerosene distillation cuts [START_REF] Cracknell | Advanced Combustion for Low Emissions and High Efficiency Part 2: Impact of Fuel Properties on HCCI Combustion[END_REF]. Paraffinic saturated fuels have, in comparison, lower soot tendency regardless of their molecular structure [START_REF] Song | Fuel Property Impacts on diesel particulate morphology, nanostructures, and NOx emissions[END_REF] [START_REF] Takatori | Effect of Hydrocarbon Molecular Structure on Diesel Exhaust Emissions Part 2: Effect of Branched and Ring Structures of Paraffins on Benzene and Soot Formation[END_REF]. Note however, the exception of fuels containing a high proportion of long-chain normal paraffins, which may lead to enhanced soot formation through increasing fuel ignitability and the creation of local rich areas [START_REF] Nakakita | Effect of hydrocarbon molecular structure in diesel fuel on in-cylinder soot formation and exhaust emissions[END_REF]. Unsaturated hydrocarbons, namely, monoaromatics and short-chain olefins, can lead to over 2 and 5 folds higher NOx tendency respectively when compared to paraffinic saturated compounds [START_REF] Bachmaier | The formation of nitric oxide and the detection of HCN in premixed hydrocarbon-air flames at 1 atmosphere[END_REF]. Aromatic-rich fuels can have longer ignition delay times but can also form higher level of NOx towards the end of the combustion [START_REF] Kee | Effects of Aromatic Hydrocarbons on Fuel Decomposition and Oxidation Processes in Diesel Combustion[END_REF]. The safety question related to lighter fractions introduction in Diesel fuels has been recently addressed by Al-Abdullah et al. [START_REF] Al-Abdullah | Flash points and volatility characteristics of gasoline/diesel blends[END_REF] where the flash points (FP) and volatilities of blends of a commercial diesel and a commercial gasoline were measured. According to their results, the flash point decreases as the concentration of gasoline is increased. For a mixture of 16%vol of gasoline in diesel, FP reaches 40°C. These results suggest that blends with high gasoline fractions should present very similar behavior compared with gasoline which has a FP of 45°C.

Modeling approaches for fuel design

Optimizing a fuel's formulation for advanced combustion modes requires an accurate knowledge of the fuel's behavior over a wide range of engine operating conditions, both in steady state and transient modes.

To better address these complex physical and chemical phenomena involved, statistical modeling approaches can represent powerful tools. Especially, Design of Experiments (DoE), refers to the process of planning, designing and analyzing the experiments. It involves the development of statistical relations between the response variables and the input factors and their interactions. In engine applications, DoE have been widely applied in engine optimization processes [START_REF] Castagne | Comparison of different calibration methods based on design of experiments[END_REF]. DoE has also been used to optimize fuel properties in terms of cetane number (CN), volatility and total aromatics content [START_REF] Butts | Investigation of the Effects of Cetane Number, Volatility, and Total Aromatic Content on Highly-Dilute Low Temperature Diesel Combustion[END_REF]. However, to our knowledge, few studies have used DoE to optimize the fuel formulation with regards to combustion behavior. In this study, we propose to evaluate a DoE approach to optimize fuel formulation for diesel engines based on existing refinery streams, to improve engine efficiency and reduce main pollutant emissions. Engine outputs were modeled as a function of the fuel composition and an optimum fuel is proposed.

Materials and methods

Figure 1 presents the layout of the present study: first, seven refinery streams used in road and air transport were selected. A 12-run, 7-factor D-Optimal mixture design was then generated using Design-Expert® version 9. The seven refinery streams constitute the design variables while engine outputs correspond to the response parameters. Engine outputs were then modeled by first order models with regards to the volume fraction of the streams and the models presenting good quality were used to determine an optimum fuel composition. The CN of the streams ranges from 6 to 55. Detailed analysis of the streams is provided in Appendix A.

Fuel matrix definition by Design of Experiment approach

To optimize fuel composition, a 12-run, 7-factor D-Optimal mixture design was generated using Design-Expert® version 9 [START_REF] Anderson | Design-Expert Software V9 Tutorials[END_REF]. The seven refinery streams constituted the design variables while the engine outputs corresponded to the response parameters. Statistical first-order linear models with no interaction were defined and the matrix was built under constraints of the domain limits. Those limits consisted of the range of cetane number, fractions of diesel, gasoline and kerosene cuts and maximum total aromatics.

Hence, the CN range was defined from 35 to 51 (+/-2). The upper limit corresponds to current European diesel specification, and the lower was set to reduce the risk of combustion instability at light load based on previous studies from our group [START_REF] Pesant | IFP International consortium Effects of fuel and additives on diesel HCCI engines, 4th year, final report[END_REF]. The proportion of gasoline and kerosene streams was allowed up to 50%vol for each [START_REF] Cracknell | Advanced Combustion for Low Emissions and High Efficiency Part 2: Impact of Fuel Properties on HCCI Combustion[END_REF] [17] [16] [START_REF] Weall | Investigation into Partially Premixed Combustion in a Light-Duty Multi-Cylinder Diesel Engine Fuelled with a Mixture of Gasoline and Diesel[END_REF], and a minimum of 30%vol of diesel streams was required to allow for high load performance and a sufficient viscosity [START_REF] Pesant | IFP International consortium Effects of fuel and additives on diesel HCCI engines, 4th year, final report[END_REF]. In addition, total aromatic content was capped at 50% to limit smoke emissions. The matrix design was composed of 12 (Fuels 1-12) ranked in order of decreasing cetane number. These fuels included a reference diesel fuel (Fuel 4), binary blends of diesel/kerosene streams (Fuel 1 and 2), binary blends of diesel/gasoline streams: Fuels 3 and 12 containing 7%vol and 41%vol Reformate, Fuels 5 and 6 containing 50%vol HSRG and Fuels 10-11 containing 50% HFCC. Two ternary blends of diesel/kerosene/gasoline streams (Fuels 8-9) and a central point (Fuel 7) composed of all tested streams was set as repeatability point and tested 8 times throughout the study (Table 1). Finally, a validation fuel was also formulated (Fuel 13) composed of 50%HCKK, 30%HSRD and 20%HSRG. The matrix is detailed in Table 1. The fuels' properties cover a wide range of physical and chemical properties, some of which were outside the limits of European diesel fuel specifications EN590, especially in terms of density and viscosity. The fuels were treated with lubricity additive (OLI9000 from Innospec) and antioxidant additive (Butylated HydroxyToluene -BHT) to ensure mechanical component durability and a consistent fuel quality throughout the study. Figure 3 illustrates the density, viscosity, cetane number and PONA composition of the fuels in the matrix.

Diesel-rich and reformate-rich fuels (Fuel 3, 4, 8, 9 and 12) have the higher density, while the lowest density ones have higher fraction of HSRG and HFCC gasoline streams. Several matrix fuels are under the 172 range of the European specification EN590 (820-845 g/L). Due to the high volatility of several fuels, 173 viscosity could not be measured in the standard 40°C temperature. Pedersen model [START_REF] Pedersen | Viscosity of crude oils[END_REF] (validated at 10°C 174 with a correlation coefficient of 0.93) was used to estimate viscosity at 40°C. Calculated viscosity at 40°C 175 is significantly low for the entire matrix compared with European specification EN 590, except for diesel-176 rich fuels. 177 178 due to HFCC presence even at very low ratio, and Fuels 3 and 5 most likely due to the presence of HCKLD whose IBP is close to gasoline streams.

Fuel optimization procedure

The experiments were conducted in a DV6D 4-cylinder light duty diesel engine from PSA Peugeot Citroën (PSA), compliant with Euro 5 specification. The engine details are given in Table 2. The engine oil and water temperatures were set to 90°C during the experiment. The engine was equipped with a Bosch CRI2.2 common rail direct fuel injection with a maximum rail pressure of 1800 bar, a turbocharger with a fixed geometry and a high pressure EGR system. The engine was tested without the after-treatment system fitted and an open Engine Control Unit (ECU) was used to monitor injection parameters and air/EGR settings. 3) compliant with levels obtained in Euro 5-6 regulations, using similar test equipment [START_REF] Warey | Fuel Effects on Low Temperature Combustion in a Light-Duty Diesel Engine[END_REF]. The final optimized heat release profile was used as a baseline for the optimization of the fuels belonging to the same cetane group. An overview of both methodologies is provided in Figure 4. Note that the injection pressure was optimized for each fuel separately. The initial rail pressure for the "central" fuels (used in the optimization methodology) was that of the standard map, i.e. 800 bar for EP1 and 1146 bar for EP2, while, for the fuels belonging to the same cetane group, the initial rail pressure corresponds to the optimized pressure of the "central" fuel. It should be noted that fixed engine settings such as the intake pressure correspond to diesel like-fuels, and may influence the results of high volatility and low-cetane number fuels. For example, a higher intake may better highlight the potential of high volatility and low-cetane number fuels according to previous work of Han et al. [START_REF] Han | Attainment and Load Extension of High-Efficiency Premixed Low-Temperature Combustion with Dieseline in a Compression Ignition Engine[END_REF]. Further details on the optimization methodology are provided in Appendix B.

NOx -PM tradeoff characterization

To evaluation was checked through a variation of the integration limits. This method for NOx-PM tradeoff characterization aims to complete the more conventional optimum selection and point-to-point comparison. The latter may not be representative of the overall fuel behavior and may not be indicative of a fuel's sensitivity to small variation in engine settings, especially intake O 2 concentration. This method is usually poorly discriminating in the case of small differences.

Results

Fuel matrix evaluation

This section presents the results of the fuel matrix evaluation. To highlight the main fuel effect observed, only matrix fuels composed of binary mixtures with a significant percentage of light gasoline and kerosene streams are presented. Namely, Fuels 2, a diesel/kerosene blend and Fuels 5, 6, 10 and 12, which are gasoline/diesel blends. Fuel 4 composed of 100% HSRD was the reference fuel. Fuels are compared on engine conditions EP1 and EP2. Figure 5 illustrates the comparison of the NOx-PM tradeoff and cumulated particulates (C PM ) over the NOx optimum range, i.e. from 0.7 to 1.5 g/kW.h and from 1 to 2 g/kW.h on EP1 and EP2, respectively (Appendix C). To evaluate the robustness of this approach, the limits of the NOx range were varied by +/-0.5 g/kW.h, and results show negligible variation of the main trends discussed hereafter. Moreover, optimum results summarized in Figure 6 are discussed to illustrate the variation of fuel consumption and other regulated pollutants with respect to streams composition.

Engine results synthesis using the entire fuel matrix is summarized in Appendix C.

Effect of the addition of Hydrotreated Straight Run Gasoline (HSRG) to Diesel

On EP1, the addition of 50% HSRG to both diesel streams (HSRD or HCKLD, in Fuel 6 and Fuel 5, respectively) allows over 5 times lower C PM compared with reference diesel Fuel 4. Through the combination of fuel's higher volatility and increased ignition delay, HSRG addition shifts the combustion towards the lower temperature range, thus reducing the NOx emissions. The decrease of PM emissions may be associated with the fuel composition through lower fractions of aromatics and soot precursors especially in Fuel 5, in agreement with the results of Weall et al. investigating gasoline-diesel blends [START_REF] Weall | Investigation into Partially Premixed Combustion in a Light-Duty Multi-Cylinder Diesel Engine Fuelled with a Mixture of Gasoline and Diesel[END_REF].

Similar conclusions were obtained by Han et al. [START_REF] Han | Attainment and Load Extension of High-Efficiency Premixed Low-Temperature Combustion with Dieseline in a Compression Ignition Engine[END_REF], [START_REF] Han | Premixed low-temperature combustion of blends of diesel and gasoline in a high speed compression ignition engine[END_REF] showing that the addition of gasoline up to 40%vol allowed the simultaneous reduction of NOx and soot emissions. Optimum results comparison (Figure 6) shows a reduction in fuel consumption and HC emissions (5% and 25%, respectively).

However, carbon monoxide increased by 30% and 85%, for Fuel 5 and Fuel 6, respectively compared with Fuel 4. Lower fuel consumption can be associated with the shorter combustion duration (CA90-CA10 is half of that of diesel fuel), while the CO increase is mainly due to lower combustion efficiency typical of low cetane number fuels especially at high EGR rates [START_REF] Jeihouni | Relationship between fuel properties and sensitivity analysis of non-aromatic and aromatic fuels used in a single cylinder heavy duty diesel engine[END_REF], usually due to over-mixing leading to fuel-lean regions acting as carbon monoxide sources [START_REF] Musculus | End-of-injection over-mixing and unburned hydrocarbon emissions in low-temperature-combustion diesel engines[END_REF]. At the higher engine load of EP2, Fuels 5 and 6 present similar trends and NOx-PM tradeoff is comparable to Fuel 4. This result is closely related to fuel characteristics. Later SOI, applied for diesel fuel allows for shifting the combustion towards the lower temperature range, whereas, the EGR rate is limited by the soot constraint. In the case of gasoline/diesel blends (Fuels 5 and 6), SOI is limited by stability, NOx emissions are lowered through the higher EGR rate possible by virtue of their low sooting tendency. Both strategies lead to a similar NOx-PM tradeoff, however, Fuels 5 and 6 present the advantage of reduced fuel consumption, through better combustion phasing and shorter combustion duration favored by better air fuel mixing.

Effect of the addition of Hydrotreated Fluid Catalytic Cracking (HFCC) to Diesel

HFCC addition to HSRD studied in Fuel 10 reduces by over 4 times C PM compared with HSRD. Low CN contributes to increase the autoignition delay, shifting the combustion towards lower temperatures which reduces NOx emissions. Additionally, the high volatility reduces the formation fuel rich areas and high olefin content ensures a high reaction rate [START_REF] Starck | Impact of fuel characteristics on {HCCI} combustion: Performances and emissions[END_REF] thus reducing PM emissions. Both effects allow for achieving very good NOx-PM tradeoff at EP1. Optimum results at EP1 (Figure 6), illustrate 13% lower fuel consumption and 23% lower HC emissions compared with Fuel 4. In fact, the higher rate of combustion of the olefin-rich HFCC stream reduces the combustion duration and favors thermal efficiency. CO emissions increase by over 45% comparably with the HSRG effect (Fuel 6). We note that noise level was very high (91 dB) and could not be reduced with the settings variation, most likely due to the increased autoignition delay and mixture-controlled combustion. At higher load at EP2, Fuel 10 presents roughly similar results to Fuel 4 in terms of NOx-PM tradeoff, CO, HC and noise. However, a better fuel economy is achieved mainly due to better phasing and shorter combustion promoted by better mixing.

Effect of the addition of Reformate (REF) to Diesel

The addition of around 40% REF to HCKLD in Fuel 12 leads to a significant degradation in the C PM performance (8 times higher than the HSRG/HCKLD mixture and 50% higher than reference diesel). This trend can be attributed to the low CN of Fuel 12 and the low combustion stability that limits EGR capacity. Both contribute to NOx formation through increased mixture-controlled high temperature combustion, thus, confirming a negative effect of excessive ignition delay on exhaust emissions observed by other groups [START_REF] Kitano | Effects of Fuel Properties on Premixed Charge Compression Ignition Combustion in a Direct Injection Diesel Engine[END_REF]. Moreover, the higher aromatic fraction provided by the REF stream contributes to an increase in the soot precursors and can also play a role in increasing NOx formation [START_REF] Bachmaier | The formation of nitric oxide and the detection of HCN in premixed hydrocarbon-air flames at 1 atmosphere[END_REF]. Contrary to the HSRG and HFCC effect on NOx-PM, which drops off at higher load, the negative effect of REF addition to diesel is more pronounced at higher load (Figure 5). A fair linear relationship between C PM and REF volume fraction is obtained. Optimum results show a similar positive trend on fuel consumption on EP1 and EP2 and CO emissions increase on EP1 in agreement with the other gasoline streams.

Effect of the addition of Hydrocracked Kerosene (HCKK) to Diesel

The addition of HCKK kerosene stream to HCKLD in Fuel 1 leads to an increase in C PM by over 7 times compared with HSRG in the same proportion (Fuel 5) at EP1. Its lower volatility and higher cetane number reduces the mixing efficiency and favors the formation of fuel-rich areas which increases the sooting tendency. The fuel chemistry can also contribute to a NOx rise through lower paraffin and higher naphthenes fractions. The NOx relationship to the molecular structure of alkanes studied in literature shows higher NOx tendency for cyclic paraffins compared to normal and branched ones [24][36]. At higher load, the addition of HCKK has a relatively small impact on NOx-PM tradeoff. Optimum results presented in Figure 6 illustrate higher fuel consumption at EP1 and EP2, and reduced CO emissions at EP1 which confirms the previous hypothesis. Overall, we observe that kerosene/diesel blends behave similarly to diesel streams in terms of engine efficiency and regulated pollutants.

Summary

The analysis of engine results allows a first evaluation of both the direct and synergistic effects of refinery streams on engine outputs. The results suggest a significant impact of gasoline streams composition on the NOx/PM/fuel economy trade off. REF addition to diesel streams appears to degrade the NOx-PM tradeoff at both engine loads. HSRG illustrates a good potential for simultaneously reducing NOx, PM and fuel consumption with an acceptable increase of carbon monoxide at light load. The addition of olefins-rich HFCC to HSRD diesel streams is also favorable for NOx / PM trade-off at light load. Finally, the HCKK stream shows diesel-like behavior at light and high load, with rather degraded CO emissions. Results show a sensitivity of the NOx-PM tradeoff to streams composition which varies with the engine load: lower load engine conditions, characterized by higher homogeneity, are more sensitive to the fuel properties than higher load conditions.

Modeling of engine outputs and optimization function of the refinery streams composition

Linear models with no interaction were built linking optimum results obtained at EP1 and EP2 to the refinery streams' volume fractions. HFCC streams reduces heat rejection through lower combustion temperature, while at mid load, they induce later and higher temperature premixed combustion, thus increasing the combustion efficiency (Figure 6). NOx coefficients vary significantly between EP1 and EP2. EP 1 presents a higher sensitivity to streams composition. The lowest coefficients are associated with HSRG and REF, then HFCC, while diesel and kerosene streams are more similar, confirming previous conclusions. At higher load, HFCC and HSRG streams' coefficients become higher than both kerosene and diesel, although of a similar magnitude. REF has the worst effect on NOx emissions, three times higher than standard diesel and over twice as high as the other gasoline streams. PM coefficients are lower for gasoline streams HSRG and HFCC at light load and tend to increase at mid load. Only REF presented high PM coefficients for both EP1 and EP2. HSRD and kerosene streams present a good tradeoff with average levels at EP1 and EP2.

The coefficients of the C PM model, in agreement with the previous analysis of NOx and PM coefficients, underline the negative effect of REF stream on EP1 and more significantly on EP2, and the suitability of gasoline streams HSRG and HFCC at light load with low coefficients and kerosene and diesel streams at mid load.

To summarize, modeling of the engine outputs allows qualitative representation of the main streams effects described in section 2.1. The models' accuracy was fair for several engine outputs over the tested conditions, however not sufficiently predictive for the results of the validation fuel. This may be associated to the relatively low number of fuels used to build the model. Besides, the model did not take into account streams' interactions, that may have an influence as well. Therefore, two approaches were evaluated, for fuel optimization, first, through the minimization of pollutants (NOx and PM trade off), then through a comparative evaluation of the optimized fuel with matrix fuels. . The optimization was carried out under the same constraints on EP1 and EP2 for PM, CO, HC and stability adopted for the matrix fuel evaluation methodology (Table 3). Interestingly, the optimum composition is one of the matrix fuels tested: Fuel 6, a diesel/gasoline blend composed of 50%v HSRD and 50%v HSRG, in good agreement with the ranking of the fuels according to average C PM displayed in Figure 9. This fuel allows, at light load, a drastic reduction in NOx emissions and a low sooting tendency, and a fairly good behavior at mid load. This study allowed to put forward an interesting potential of using gasoline and kerosene streams in diesel fuels on a commercial light-duty diesel engine (PSA DV6D). The optimization methodology was based on reduced number of parameters and interactions. A more elaborated engine calibration would be necessary to confirm the observed trends on larger range of operating conditions. Besides, the upgrading of the engine hardware (especially, in terms of boost pressure and combustion chamber design) may allow to further explore the full potential of these streams in terms of engine performance and emissions.

Figure 1 :

 1 Figure 1: Scheme of the study outline

Figure 2 :

 2 Figure 2: Main fuel properties of the seven selected refinery streams

Figure 3 :

 3 Figure 3: Density, Viscosity at 40°C (Pedersen model), cetane number and chemical composition of the 179 fuel matrix Fuels 1-12 and the validation fuel (Fuel 13) 180 PONA composition of the matrix fuels was calculated by linear correlation from the data of single streams 181

  characterize the NOx-PM tradeoff, a quantified evaluation developed. Based on the NOx and PM emissions measured during the final EGR sweep, exponential curves representing PM emissions as a function of NOx (Figure 5) were fitted according to the following equation: 𝑃𝑀(𝑔 𝑘𝑊ℎ ⁄ ) = 𝐴(𝑖, 𝑗) exp (𝐵(𝑖, 𝑗) 𝑁𝑂𝑥 (𝑔 𝑘𝑊ℎ ⁄ ) ⁄ ), where A and B are fitting coefficients determined for the fuel i and operation point j. The correlation coefficients are above 0.95 for all matrix fuels optimized at different speed-load conditions. The integral of the curves (C PM ), calculated over a determined NOx range, allowed the characterization of the NOx-PM tradeoff. Fuels having a better NOx-PM tradeoff would have a lower level of PM integral (or cumulated particulates) over the integration domain. The robustness of this

Figure 5 :

 5 Figure 5: NOx-PM tradeoff during EGR sweeps (experiment and model) for several matrix fuels, CPM calculation a) EP1: 1500 rpm-6bar. b) EP2: 2280rpm-8.2 bar

Figure 6 :

 6 Figure 6: Optimized engine outputs of several matrix fuels tested at two engine conditions: a) EP 1: 1500 rpm-6bar and b) EP2: 2280rpm-8.2 bar

Figure 7

 7 presents predicted and experimental results for several engine outputs of matrix fuels (Fuels 1-12) along with the validation fuel (Fuel 13). Results are normalized from 0 to 1 at the scale of all the experimental results. Details of the model coefficients and statistical data are summarized in Appendix D.Fuel consumption has a determination coefficient (R²) above 0.97 for both EP 1 and EP2; Results of Fuel 13 overestimate fuel consumption by 8% for EP1 and are fairly predictive for EP2. NOx and PM models have low determination coefficients (0.78 and 0.62 respectively for EP1 and 0.93 and 0.45, respectively for EP2). The poor performance of the PM model at EP2 can be associated with the quasi-equity of particulate emissions at the optimum point for most fuels (Appendix C). The models accurately predict EP2 NOx level for Fuel 13 and within twice the standard deviation interval (σ) for EP1. PM emissions are within twice (σ) at both operating points. The quality of the CO and HC emissions models was good for EP2 and fair for EP1 (Appendix D). Fuel 13 results were fairly predicted for both. Finally, the model of C PM , was fair on EP1 (R²=0.65) and good on EP2 (R²=0.93). The prediction of Fuel 13 level was fair only for EP2 and poor for EP1.

Figure 7 :

 7 Figure 7: Modeling of the engine outputs based on optimum results at EP1 (1500rpm -6 bar) and EP2

Figure 8

 8 Figure 8 presents the normalized correlation coefficients. They illustrate the contribution of the different

Figure 8 :

 8 Figure 8: Normalized correlation coefficient of the linear model for a. EP 1 and b. EP 2 function of the

Figure 9 : 4 Conclusions

 94 Figure 9: Ranking of the fuel matrix according to NOx-PM Tradeoff: Average of C PM on EP1and EP2

Table 1 :

 1 Fuel matrix: Fuel 1-12, Fuel 7 corresponds to the central fuel and was tested 8 times throughout the study, Fuel 13 is the validation fuel (used for models validation)

	Unit	Fuel 1	Fuel 2	Fuel 3	Fuel 4	Fuel 5	Fuel 6	Fuel 7	Fuel 8	Fuel 9	Fuel 10	Fuel 11	Fuel 12

Table 2 :

 2 Details of the DV6D 4-cylinder engine model from PSAThe experiment was conducted in double injection mode (i.e. pilot and main injection) with EGR. To cope with the high dispersion of fuels properties, particularly volatility and reactivity, the engine settings were adapted for each fuel. Matrix fuels were optimized separately on six operating points ranging from 1350 to 2400 rpm engine speed and from 1 to 13.4 bar of Brake Mean Effective Pressure (BMEP). These operating points are representative of the New European Driving Cycle (NEDC) and have been used to evaluate new formulation or engine concepts for reduced pollutants emissions by other groups [32],[START_REF] Warey | Fuel Effects on Low Temperature Combustion in a Light-Duty Diesel Engine[END_REF].

	Engine specifications	Value
	Bore	75 mm
	Stroke	88,3 mm
	Displacement	1560 cm3
	Connecting rod length	136,8 mm
	Compression ratio	16:1
	Intake valve opening	377 °CA
	Intake valve closing	550 °CA
	Intake valve maximum lift	9,25 mm
	Exhaust valve opening	164°CA
	Exhaust valve closing	350°CA
	Exhaust valve maximum lift	9,25 mm
	Maximum torque	230 Nm at 1750 rpm
	Maximum Power	68 kW at 4000 rpm

are optimized using the same methodology for each fuel, allowing the definition successively of the optimum settings of main and pilot injections phasing, pilot quantity, EGR rate and rail pressure. The intake pressure was fixed to PSA's standard calibration matching the maximum engine capacity. Pilot quantity was fixed to 1.5mg/stroke, corresponding to the average pilot quantity used in the stock calibration. To reduce the optimization time, the fuels were categorized into three cetane ranges (Group 1: 47-52, Group 2: 41-46 and Group 3: 35-40). Within each range, a "central" fuel was defined: Fuel 4, Fuel 7 and Fuel 11, respectively. Central fuels were optimized with a detailed method consisting of successive single parametric variations of main and pilot injection phasing, rail pressure and EGR rate. The optimum parameters are defined to achieve targets of NOx, PM, noise, CO, HC and IMEP stability (summarized in Table

Table 3 :

 3 Engine optimization criteria at 1500 rpm -6 bar (EP1) and 2280 rpm -8.2 bar (EP 2). (*) Fuel consumption is minimized if the pollutant targets are respected

	Parameter	Unit	Precision	Fixed settings and optimization criteria
	Engine speed	rpm	+/-3	1500	2280
	Engine load	bar	+/-0.1	6	8.2
	Intake pressure	bar	+/-0.015	1.03	1.46
	NOx	g/kWh	+/-0.03	Minimize	Minimize
	Particles	g/kWh	+/-0.03	< 0.15	<0.2
	CO	g/kWh	+/-1	< 8	<4
	HC	g/kWh	+/-0.1	< 0.8	<0.4
	COV	%	+/ -0. 5	3	3
	Noise	dB	+/-1	85	89

Figure 4: Overview of the optimization methodologies (*) optimum levels were determined in agreement with the target and constraints described in

Table 3 Rail pressure sweep at optimum EGR rate* Optimum Rail pressure 5. Variation of EGR rate* Optimum EGR rate 4. SOI main sweep at fixed Main-Pilot offset with EGR* Final settings of Main and Pilot phasing Optimum Heat Release profile w/o EGR 3. SOI pilot sweep with EGR* Optimum Main-Pilot offset 2. SOI main sweep in single injection with EGR* First Main SOI phasing 1. SOI main sweep in single injection w/o EGR 5. Rail pressure sweep at optimum EGR rate* Optimum Rail pressure 4. Variation of EGR rate* Optimum EGR rate 3. Pilot quantity and Rail pressure sweeps w/o EGR Fitted height of Pilot and Main HR 2. SOI Main / Pilot sweeps w/o EGR Fitted phasing of main and pilot HR: Optimum SOI 1. SOI main sweep in single injection w/o EGR Multiple sweep methodology Heat release methodology

 3

5 Acknowledgement

 5 Authors acknowledge the valuable support of Saudi Aramco to this research work and would like to thankRomain CHERBLAND and Yoan LEMONNIER from IFP Energies Nouvelles for conducting the engine experiments and Frederic NICOLAS from IFP Energies Nouvelles for engine calibration support.

	528	7 Appendices
		Appendix A:

Main properties of the studied refinery streams 529

		Appendix D:
					BSFC	NOx		PM	CO		HC		CO2	noise stab C PM
					(g/kWh) (g/kWh) (g/kWh) (g/kWh) (g/kWh) (g/kWh)	dB	%	g/kWh
		Analyses Fuel 1	Method 271	1.48	Unit	REF 0.12	HFCC HSRG 4.00 0.55	HCKK HSRK HCKLD HSRD 848 0.85 0.228
		Density at 15°C Fuel 2	ASTM D4052 271 1.46	kg/m3 868.4 0.09	691.9 3.80	741.1 0.49	812.2	787.7 849	818.7	836.6 0.78 0.121
		Viscosity 40°C Fuel 3	EN ISO 3104 281 1.22	mm²/s 0.5958 0.36 0.12 7.10	0.5685 1.342 0.59	1.122 878	2.634	3.297 0.96 0.217
		LHV		Fuel 4	ASTM D240 274 1.40	MJ/kg 0.09 41.03	44.46 3.65	44.29 0.71	43.89	40.73 860	43.57	42.57 1.90 0.171
	549	Sulfur CN (CFR) Fuel 5 Fuel 6 H/C/N (IFPEN) Fuel 7 C Fuel 8 H Fuel 9 N Fuel 10 O Fuel 11 Total Fuel 12 H/C Fuel 13 Composition	NF EN ISO 20846 257 ASTM D613 1.05 260 1.08 255 1.29 CG 265 1.25 261 1.29 238 1.21 235 1.31 257 1.27 242 1.50 GCxGC	<0.5 0.02 mg/kg 6.1* 0.04 -0.12 90.5 0.10 % wt 9.5 0.09 % wt 0.04 % wt 0.13 % wt 100.0 0.11 % wt -1.255 0.12 8535	5.7 4.86 16.5* 6.74 4.29 85.9 6.04 14.1 5.07 5.61 4.22 100.0 8.07 1.970 1.39 8533	<0.5 0.55 34.8 0.56 0.64 85.4 0.64 14.6 0.50 0.55 0.58 100.0 0.62 2.056 0.26 8534	<0.5 45.4 86.0 13.8 0.11 100.0 1.930 8532	221 799 49.0 806 802 85.8 834 14.1 824 0.09 742 733 100.0 814 1.971 761 8531	<0.5 54.9 85.8 14.0 0.12 0.11 100.0 1.958 8536	1.5 0.82 0.030 51.3 0.93 0.035 1.21 0.242 86.4 1.04 0.169 13.6 2.14 0.143 0.94 0.042 1.09 0.231 100.0 1.15 0.254 1.09 0.492 1.889 8537
		Paraffins			% wt	2.15	33.1	59.0	36.3	57.2	43.4	29.9
		Naphthenes			% wt	0.14 EP 2 : 2280 rpm -8.2 bar 7.0 27	43	18.5	40.2	25
		Olefins	Fuel	BSFC	NOx	% wt	0.17 PM	45.0 CO	0	HC	0	0 CO2	0 noise stab C PM 0
		Aromatics reference	% wt (g/kWh) (g/kWh) (g/kWh) (g/kWh) (g/kWh) (g/kWh) 97.5 14.8 13.9 20.7 24.3	16.4 dB	45.3 %	g/kWh
		Monoaromatics Fuel 1	279	1.15	% wt	96.7 0.15	14.8 3.72	13.9 0.18	19.5	23.3 876	14.7	41.7 0.69 0.123
		Polyaromatics Fuel 2	272	1.02	% wt	0.8 0.10	0.0 3.10	0.0	0.18	1.2	1 852	1.7	3.6 0.68 0.081
		Distillation Fuel 3	EN ISO 3405 283 1.06		0.20	4.05		0.17		891	0.74 0.109
		0		Fuel 4	277	1.25	°C	105.8 0.11	30.4 2.37	81	0.15	166.9	149.9 872	123.9	187.9 0.67 0.123
		5		Fuel 5	253	1.26	°C	119.5 0.19	36.9 1.73	98	0.26	184.6	165.8 792	207.4	201.3 0.69 0.143
		10		Fuel 6	253	1.25	°C	121.5 0.20	39 1.68	102.1 0.27	190.4	169.7 793	229.8	206.7 1.08 0.135
		15		Fuel 7	257	1.30		122.9 0.21	40.3 1.57	104.9 0.30	193.4	173 811	241.1	213.4 0.65 0.209
		20		Fuel 8	257	1.39	°C	124.1 0.21	41.4 2.09	107.6 0.28	196.2	175.6 815	247.6	220.8 0.90 0.284
		30		Fuel 9	258	1.52	°C	126.2 0.20	43.7 1.30	112.4 0.24	200.7	181.1 822	257.7	240.6 0.90 0.276
		40	Fuel 10	251	1.24	°C	128.7 0.20	46.3 1.96	117 0.25	204.3	186.2 790	264.9	268.5 0.74 0.142
		50	Fuel 11	246	1.18	°C	131.6 0.18	49.6 1.62	122.9 0.20	207.5	191.5 774	271.7	296.7 1.17 0.149
		60	Fuel 12	254	2.04	°C	135.1 0.17	54.1 1.12	129.1 0.33	210.9	197.5 816	278.5	320.1 0.60 0.687
		70	Fuel 13	258	1.27	°C	140.2 0.22	61.1 1.79	135 0.20	214.5	205.4 812	284.7	344.7 0.62 0.223
	550	80					°C	146.8	72.8	143	219.4	215.9	292.5	362.7
		90					°C	156.7	88.9	152.7	226.4	231.6	304	373.6
		95					°C	166.9	98.8	160.1	232	244.8	312.4	378.8
		100					°C	198.7	115.4	171.2	243.4	267.8	320.2	381
		Recovery at 250°C			% v/v					>98	96.3	22.6	33.9

Synthesis of the models data of main engine outputs. Modeling was made on optimum results of EP1 (1500rpm -6 bar) and EP2 (2280 rpm-8.2 bar): Correlation coefficients, standard deviations and determination coefficients

	Refinary stream	EP 1: 1500 rpm -6 bar BSFC NOx PM g/kWh g/kWh g/kWh	CO g/kWh	HC g/kWh	Noise dB	Stability %
	HSRG	238	0.77	-0.03	6.41	0.48	89	0.46
	HFCC	192	1.17	0.07	4.52	0.50	98	0.73
	REF	228	0.96	0.11	12.71	0.68	85	1.95
	HSRK	269	1.44	0.09	2.98	0.51	81	0.49
	HCKK	263	1.50	0.10	2.23	0.43	80	1.66
	HSRD	277	1.35	0.08	4.72	0.68	83	1.74
	HCKLD	280	1.37	0.12	5.25	0.60	82	0.87
	std dev	3.24	0.09	0.03	1.28	0.06	1.25	0.36
	R²	0.97	0.78	0.62	0.64	0.54	0.93	0.68
	Refinary stream	EP 2: 2280 rpm -8.2 bar BSFC NOx g/kWh g/kWh	PM g/kWh	CO g/kWh	HC g/kWh	Noise dB	Stability %
	HSRG	223	1.4		0.3	-0.1	0.4	88	1.1
	HFCC	213	1.3		0.3	-0.1	0.3	86	1.2
	REF	207	3.3		0.3	-3.0	0.6	81	0.8
	HSRK	262	0.9		0.1	2.9	0.2	93	0.9
	HCKK	269	1.1		0.2	2.7	0.2	90	0.9
	HSRD	279	1.2		0.1	2.8	0.2	89	0.7
	HCKLD	286	1.0		0.1	4.1	0.1	89	0.6
	std dev	3.03	0.10		0.04	0.55	0.03	0.33	0.22
	R²	0.97	0.93		0.45	0.84	0.90	0.97	0.31

Viscosity is measured using the standard test method (EN ISO 3104) at 40°C except HFCC (due to high volatility; IBP=30°C). The viscosity of HFCC is calculated using thermodynamic

Pedersen model [START_REF] Lugo | A Compositional Representative Fuel Model for Biofuels -Application to Diesel Engine Modelling[END_REF].

Appendix B: Engine optimization methodology and targets

In this section, the detailed and simplified method for fuels optimization are evaluated on the same Fuel in order to quantify the sensitivity to the fuels classification into cetane groups of the engine results. Fuel 5 having a central cetane number of 46.1 is tested using multiple sweeps method, and Iso-heat release method in Group 1 and Group 2 (i.e. through the imitation of optimum heat release of Fuel 4 and Fuel 7, respectively) at 3 different engine conditions. The comparison of optimum results shows almost 10% variation of BSFC on average. The amplitude of NOx and PM variation according to the optimization methodology is on average 0.14 and 0.03 g/kW.h, respectively. Optimization methodology impacts mostly the noise level due to its tight relationship with the injection settings. As an initial evaluation of the approach, the sensitivity of engine results to the methodology is taken into account as if it were an engine measurement dispersion error.

Appendix C: Optimum results obtained on the fuel matrix: Fuels 1-12 and on validation fuel (Fuel 13) 548

EP 1 : 1500 rpm -6 bar Fuel reference


From me do back receive the flour of all, And leave me but the bran.'. 1 When Menenius Agrippa recited his fable, medical arts were still far from developing the technique of scintigraphy. Such diagnostic technique makes it possible to follow the movements and accumulation of radioactive isotopes administered to a patient. With the help of scintigraphy, the body's members personified in the fable would have been able to know not only how the stomach distributed the nutrients, by which channels and in what proportions, but also how each member cooperates with the others in the human body. If they had known all this, Agrippa's story might have ended quite differently... It is often said that digital technologies are revolutionizing the social sciences, much as they revolutionized the natural sciences a few decades ago. This is not the case. The situation in the social sciences is more like that of the natural sciences in the 15th century, just after the introduction of the printing press. In The Printing Press as an Agent of Change, Elizabeth [START_REF] Eisenstein | The Printing Press as an Agent of Change: Communications and Cultural Transformations in Early Modern Europe[END_REF] thoroughly describes how the introduction of the press triggered the Scientific Revolution of the XVI century. Yet, as Eisenstein makes very clear, the revolution did not broke overnight. Several decades after Gutenberg invention, naturalists were still printing the same mistakes they used to hand-copy. It was only after press attained a certain level of maturity and circulation that it started to have a real impact on the natural sciences.

Social sciences are today in a similar position. Rather than drawing on digital data to revitalize their practices, they are still trying to pass them off as new terrains for old methods. Cyberculture, virtual communities, online identities, computer-mediated communication: all these notions have been developed to quarantine the novelty of electronic media. And yet, the very speed at with which digital technologies infiltrate modernity makes such resistance more and more untenable. It is no longer possible to circumscribe digital interactions to a specific sector, as their threads are now ubiquitously woven into the fabric of collective existence. Digital media offer much more than just another field to apply existing methods: they offer the possibility of restructuring the study of social existence [START_REF] Rogers | Information Politics on the Web[END_REF].

Up to now, access to collective phenomena has always been both incomplete and expensive. Compared to their colleagues in the natural sciences, social scientists have always been relatively poorly equipped. While physicists could follow billions of atoms in their accelerators and biologists could grow millions of microbes under their microscopes, social scientists could not simultaneously maintain breadth and depth in their observations. Their methods offered them a bipartite view of social existence, as they could either focus on specific interactions or skim the surface of global 1 "Tempore quo in homine non ut nunc omnia in unum consentiant, sed singulis membris suum cuique consilium, suus sermo fuerit, indignatas reliquas partes sua cura, suo labore ac ministerio uentri omnia quaeri, uentrem in medio quietum nihil aliud quam datis uoluptatibus frui; conspirasse inde ne manus ad os cibum ferrent, nec os acciperet datum, nec dentes quae acciperent conficerent. Hac ira, dum uentrem fame domare uellent, ipsa una membra totumque corpus ad extremam tabem uenisse. Inde apparuisse uentris quoque haud segne ministerium esse, nec magis ali quam alere eum, reddentem in omnes corporis partes hunc quo uiuimus uigemusque, diuisum pariter in uenas maturum confecto cibo sanguinem," Ad Urbe Condita, II, 32.

structures.

Among other things, this explains why in social sciences the notion of emergence has taken a different meaning than in the natural sciences. In physics, biology, and medicine, the notion of emergence is used to describe phenomena of self-organization due to the spontaneous coordination of many agents. In the social sciences, emergent phenomena become structures, classification tables hosting and influencing interactions. This structuralist vision is due to a great extent to the fact that the social sciences have never had methods to reconnect micro and macro and show how global phenomena are built by the assemblage of local interactions. Digital technology promises to revolutionize this situation, providing the social sciences the possibility of following each thread of interaction and showing how social life is woven together by their assemblage.

The Problem of Social Life

The problem of social life is that every situation involving more than two people can quickly become unbelievably complicated. Take the complexity of a human being and multiply it by the number of people involved in the interaction and then by the number of interactions needed to organize the simplest of social phenomena. Add to that the fact that interactions can affect and be affected by any kind of non-human agent (technologies, elements of nature, pieces of art…) and that these agents can carry the consequences of other interactions stretching far into time and space. If we are able to make anything of this complexity, it is because our societies can count on a large number of simplifying devices such as institutions, norms, standards, procedures, etc. Despite what we might think, the so-called emergent social structures are in reality simpler than the interactions of which they are made.

Emergence is a very useful notion, an idea that benefits both social actors and social scientists: it allows actors to assume the stability of collective phenomena and allows researchers to presume that these phenomena make sense [START_REF] Johnson | Emergence: The Connected Lives of Ants, Brains, Cities, and Software[END_REF]. However, the notion of emergence conceals entirely different meanings in these two cases. For social actors, there is no other way to access emergent properties than constructing them. Everyone who has tried to gather a rock band, organize a chess tournament, or start a knitting circle known this all too well: social phenomena do not exist by themselves. Collective phenomena emerge through the work of participants, extend as far as the actors carry them, and last as long as they keep them up.

To be sure, social facts can be materialized: decisions can be transcribed, borders reinforced by barriers, relationships sealed with symbols. Social structures can be stabilized by material infrastructures [START_REF] Latour | Reassembling the Social: An Introduction to Actor-Network Theory[END_REF], but it is only through actors' coordinated work that collective phenomena can emerge and last [START_REF] Weisman | The World Without Us[END_REF]. This labor, however, is only rarely recognized by the social sciences that, since their earliest days, never had the resources necessary to explore the construction of social structures. They can follow a few specific interactions, but breaking down social facts into the thousands of constituent interactions has always been beyond their reach. And so, they preferred to avoid the question.

In the 19th century, a young sociology found the workaround it needed in statistics. By combining the two separate mathematical traditions of probability calculus and error estimation, statistics provided the social sciences with a convenient shortcut [START_REF] Desrosières | The Politics of Large Numbers: A History of Statistical Reasoning[END_REF]. Instead of tracing the way emergent structures were built by simplifying thousands of local exchanges, the researchers could gain access to social phenomena through a mathematical leap. Where social actors assembled, the researchers could aggregate. For example, instead of studying how criminal organizations selected and socialized their recruits, the researchers could calculate the profile of the "average criminal." Instead of studying how crime rings spread their investments through the coordination of a multitude of individual criminals, the researchers could estimate the average incidence of each crime. The gain in efficacy was tremendous. Not only could the social sciences finally portray social structures, they could even contribute to their solidification.

Of course, the adoption of statistics came at a cost came with a cost. In bypassing the work of collective construction via the statistical shortcut, researchers introduced a fictive distinction between micro-interactions and macro-structures. Though such distinction has never been anything more than an optical illusion reflecting the leap of statistical aggregation, social scientists always found it very convenient. Through the micro/macro distinction, researchers who were interested in local interactions could observe them without having to mess with the tangle of influences that surround each social event. And,those who wanted to observe the global picture of social life were could do it without bothering about single brush-strokes . By separating micro and macro, the social sciences simultaneously validated qualitative and quantitative methods.

Still, the dualistic approach based on the micro/macro distinction has a major disadvantage: it only works after the actors have finished their work of simplification and to until that they manage to maintain it. One cannot cross-sample, if there are no clear boundaries among classes, groups or genders or. One cannot rely on averages or normal curves, if actors do not conform to norms or normality. One cannot estimate deviance, if deviant behaviors are deterred or sanctioned. Statistical analyses can approximate collective facts only to the extent that they have been defined, normalized and stabilized by social actors. The dualistic approach can describe emerged phenomena rather well, but it is useless with emerging phenomena.

Learning from Controversies

The weakness of the dualistic approach becomes particularly clear if, instead of concentrating on areas of consensus, we turn to the study of controversies, observing social life where agreement is no longer (or not yet) reached. In the study of disputes, we can depend neither on quantitative methods (because the local positions are so varied that there is nothing to be gleaned from aggregating them into a general average) nor on qualitative methods (because even the most specific controversy is always part of a global network of influences). Where disputes rage, where actors cannot come to agreement, where social ties came undone or, quite simply, where change unfolds so quickly that old institutions dissolve before new ones can be created, quantitative and qualitative methods reach their limits. This is why the social sciences have always been uncomfortable with controversies: their methods demand that they reduce them to residual phenomena or, at best, to transitional stages between periods of stability. This is particularly evident in the sociology of science and technology. For ages, arguments among scholars were considered to be insignificant events on the road to scientific advancement. Even authors like Thomas [START_REF] Kuhn | The Structure of Scientific Revolutions[END_REF] who took scientific controversies very seriously ended up defining them as passing phases between two periods of "normal science."

Much of the interest of Science and Technology Studies, as developed by the Edinburgh [START_REF] Bloor | Knowledge and Social Imagery[END_REF] and Bath [START_REF] Collins | The Golem: What Everyone Should Know about Science[END_REF] Schools, lies in having shown that, far from being residual or transitional phenomena, controversies are the true engine of science. In science and technology (Bijker and Law, 1992), disagreements and conflicts are the norm and consensus can only be reached through tremendous efforts of coordination and stabilization [START_REF] Callon | Acting in an Uncertain World: An Essay on Technical Democracy[END_REF]. Scientific paradigms are not coherent systems that channel scholars' work. Instead, they are the fragile product of this work. They require endless negotiations for their upkeep and their existence is constantly threatened by the risk that these negotiations might fail [START_REF] Latour | Science in Action: How To Follow Scientists and Engineers through Society[END_REF].

Working with students at the Paris Institut d'Études Politiques in a course entitled Cartography of Controversies,2 we came to understand that, far from being limited to the sciences, this situation is common to all collective phenomena. Controversies have taught us that nothing in our social lives endures without changes. Communal life resembles less a long, quiet river than a restless sea of thousands currents that no social contract could ever definitively calm. Thus, social institutions are not structures imposing themselves on individuals but a sort of "Law of War" always subject to negotiation. Sure these institutions exist, and sure they have effects, but these effects are the result of interactions rather than being their premise.

The combination of stability and change that characterizes controversies cannot be untangled using a dualistic approach (Venturini, 2010a). As they keep us from breaking down social disputes into the network of multiple oppositions that characterize them, quantitative methods can only address the few elements that create consensus among the actors (and which are typically the least interesting part of a dispute). As they keep us from showing how every quarrel is tied to a broader network of opposition, qualitative methods inhibit any understanding of how equilibrium points can be temporarily negotiated while leaving the controversy open. Studying controversies requires a new set of "quali-quantitative" methods that allow us to trace social phenomena throughout the processes of construction, deconstruction, and reconstruction that constitute them.

The Promise of Computerized Methods

Quali-quantitative methods are not merely the juxtaposition of statistical analysis with ethnographic observation. Erasing the micro/macro border implies being prepared to collect and process an unprecedented amount of data. Being interested in the construction of social phenomena implies tracking each of the actors involved and each of the interactions between them. This was an impossible goal just a dozen years ago, but it starts to become more and more realistic as digital technologies spreads.

The interest of electronic media lies in the fact that every interaction that passes through them leaves traces that can be easily recorded, massively stored and inexpensively retrieved. Each day, researchers discover new pools of digital data: public and private archives are sucked into computer memory, economic transactions migrate online, social networks take root on the web. Digital mediation spreads out like a giant roll of carbon paper, offering the social sciences more data than they ever dreamt of.

Thanks to digital traceability, researchers no longer need to choose between precision and scope in their observations: it is now possible to follow a multitude of interactions and, simultaneously, to distinguish the specific contribution that each one makes to the construction of social phenomena. Born in an era of scarcity, the social sciences are entering an age of abundance. In the face of the richness of these new data, nothing justifies keeping old distinctions. Endowed with a quantity of data comparable to the natural sciences, the social sciences can finally correct their lazy eyes and simultaneously maintain the focus and scope of their observations. The potential of this quali-quantitative approach has been illuminated by the MACOSPOL (Mapping Controversies on Science for POLitics) project. Funded by the European Union, this project coordinated eight European labs in an effort to inventory and test the digital tools available for controversy mapping [START_REF] Venturini | Building on Faults: How To Represent Controversies with Digital Methods[END_REF].

While the quantity of the identified resources was encouraging, project results also revealed the relative immaturity of digital methods. Today, more and more tools are available to extract and analyze electronic data and many researchers have begun using them [START_REF] Lazer | Computational Social Science[END_REF]. Still, the success of these initiatives remains limited by their intermittent nature. In most cases, the scholars restricted themselves to digitizing one piece or another of their research without reconsidering the whole research process. The results of this approach are certainly interesting and have the advantage of being easily comparable with those of classical methods. Yet, a true transition to quali-quantitative methods will not be possible until the social science research chain is entirely digitized. For the new methods to realize their innovative potential, it is necessary that each step in the research chain be rethought in a coherent manner:

1. The identification of data sets should privilege digital archives, the web, online media and networks, digital documentation and literature, and, more generally, all sorts of digital traces. 2. The extraction of data should be based on assisted browsing or on different techniques of data collection (crawling, querying, scraping, parsing...). 3. Data from diverse sources should be integrated in a common relational database (data mashing). 4. The analysis and modeling of data should lean on web, digital, and, where possible, open source tools. 5. Research results should be published on the web, preferably in open archives and in a standard format in order to make their reuse easier.

It is easy to see that the process we describe is in fact a loop. Available on the web, the results of a digital investigation can become the data for a new analysis. Even more important: the digital data as well as their extraction and analysis tools can be published alongside the results, making accessible to the public not only the conclusions of a research project, but all the path that have led to them. This is the true appeal of digital methods: instead of just describing the experimental procedures it is now possible to give direct access to them. In a digital research chain, no path is blocked, no transformation is irreversible, no result is given. Every element in the chain is made available to the public and is open to scrutiny. In a digital research project, navigating the data and navigating the chain of data processing are ultimately the same thing.

The Quali-quantitative Oligopticon

The promise of digital methods lies in the possibility of opening the research chain of social sciences. To keep this promise, however, the whole research chain must be digitized. As long as they will be confined to one part of the research process, digital methods will not deploy their innovative potential and will risk generating all sorts of incompatibility effects.

For example, the question of representativeness perfectly illustrates the difficulty of mixing digital and traditional methods. A criticism that has often been addressed at the analysis of digital traces is that these traces regard a non-representative sample of society. From the point of view of the traditional social sciences, this is certainly true. It is well known that digital literacy is not uniformly diffused in society (men, young people, and those with high levels of education are generally overrepresented in online samples). Still, this disproportion is a problem only as long as we insist on treating digital data as if they were survey data. The advantage of the new methods is that they allow tracing the assemblage of collective phenomena instead of obtaining them through statistical aggregation. The question of representativeness is thus posed in an entirely different way.

Digital data are representative and interesting only if their processing chain (identification, extraction, integration, analysis, publication) remains close to the work of social actors. To be sure, we are not saying that quali-quantitative methods will allow us to smooth out all the complexity of collective life. Quite the contrary, the advantage of these methods is that they are flexible enough to follow some social phenomena along each of their folds.

No research method offers a panoptic vision of collective existence and quali-quantitative methods are no exception. Digital methods can only offer an oligoptic vision of society [START_REF] Latour | Paris ville invisible[END_REF], exactly as traditional methods. However, for the first time in the history of the social sciences, this vision will at least be continuous spanning from the tiniest micro-interaction to the largest macro-structure.

This is why we opened this chapter with the story of Menenius Agrippa and the technology of scintigraphy. The advantage of scintigraphy is not that it allows observing any biological phenomenon, but that it can follow some of them in a continuous fashion, without jumps or breaks. Social existence is not divided in two levels, as traditional methods led us to believe. Microinteractions and macro-structures are only two different ways of looking at the same collective canvas, like the warp and weft of the social fabric. There -in the unity generated by the multiplication of differences, in the stability produced by the accumulation of mutations, in the harmony hatching from controversies, in the equilibrium relying on thousands on fractures -lie the marvel of communal existence. Qualitative and quantitative methods have too long hid this spectacle from us. Digital methods will open our eyes.

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