To reduce the amount of carbon dioxide released from transportation the EU has implemented legislation to mandate the renewable content of petrol and diesel fuels. However, due to the complexity of the combustion process the addition of renewable content, such as biodiesel and ethanol, can have a detrimental e ect on other engine emissions. In particular the engine load can have a signi cant impact on the emissions. Most research that have studied this issue are based on steady state tests that are unrealistic of real world driving and will not capture the di erence between full and part loads. This study aims to address this by investigating the e ect of renewable fuel blends of diesel, biodiesel and ethanol on the emissions of a compression ignition engine tested over the World Harmonised Light Vehicle Test Procedure (WLTP). Diesel, biodiesel and ethanol were blended to form binary and ternary blends, the ratios were determined by Design of Experiments (DoE). The total amount of emissions for CO, CO 2and NO xas well as the fuel consumption, were measured from a 2 :4 L compression ignition (CI) engine running over the WLTP drive cycle. The results depicted that percentages smaller than 10 % of ethanol in the fuel blend can reduce CO emissions, CO 2emissions as well as NO xemissions, but increases fuel consumption with increasing percentage of ethanol in the fuel blend. Blends with biodiesel resulted in minor increases in CO emissions due to the engine being operated in the low and medium load regions over the WLTP. CO 2emissions as well as NO xemissions increased as a result of the high oxygen content in biodiesel which promoted better combustion. Fuel consumption increased for blends with biodiesel as a result from biodiesel's lower heating value. All the statistical models describing the engine responses were signi cant and this demonstrated that a mixture DoE is suitable to quantify the e ect of fuel blends on an engine's emissions response. An optimised ternary blend of B2E9 was found to be suitable as a 'drop in' fuel that will reduce harmful emissions of CO emissions by approximately 34 %, NO xemissions by 10 % and CO 2emissions by 21 % for transient engine operating scenarios such as the WLTP drive cycle. Keywords: biodiesel, ethanol, engine emission, ternary blend, design of experiment, WLTP. 1. Introduction The European Union (EU) has implemented suc- cessive emission standards to reduce the envir- onmental impact of harmful emissions from road transport and to help the transition towards a low- carbon economy. These measures include a limit on CO 2emissions as well as a separate Euro 6 le- gislation, which enforces limitations on permissible quantity of harmful gasses in the vehicle exhaust [1]. Corresponding author Email address: adriaan.vanniekerk@uwe.ac.uk (A.S. van Niekerk)Additionally, the EU has set out climate and energy targets for 2020 to combat climate change, increase energy security and strengthen its competitiveness. These targets can be summarised as follows [2]: 20 % reduction in EU greenhouse gasses from 1990 levels. 20 % improvements in EU's energy eciency. 20 % of EU energy from renewable energy. The nal target is enforced by the EU by giving all the member countries binding targets to raise their share of renewable energy in their energy consump- tion by 2020. These targets are determined based Preprint submitted to Applied Energy 9th September 2019 on the individual country's use of renewable energy and the potential to increase their production. Tar- gets range from 10 % for Malta to 49 % for Sweden. The UK has a target of 15 %. Ultimately, all the in- dividual targets will ensure the EU meets its target of 20 % by 2020. Included in the individual targets of each country, a 10 % share of renewable energy in the transport sector is required. This can be achieved using a combination of biofuels, hydrogen or 'green' electricity [2]. The UK government hopes to reach it's 2020 target of 10 % renewable energy in transport with the promotion of ultra low emis- sion vehicles (ULEV) as well as increasing its use of renewable fuels in the transport sector [3]. Biofuels, which include biodiesel and bioethanol, are a renewable source of energy in the transport industry [4{6]. Currently renewable fuels, such as biodiesel, can be used as a 'drop in' fuel together with diesel up to a maximum of 7 % at pump sta- tions [5]. Studies have been conducted on biodiesel and ethanol blends ranging from pure fuels (e.g. B100) to binary blends with petroleum diesel (e.g. B20, E10, etc.) and ternary blends with ethanol or bioethanol (e.g. B20E2, B40E5, etc.). A sum- mary of the previous research in this area will be presented below. Numerous studies have been conducted to study the e ect of renewable fuels on harmful exhaust emissions for engines running at steady state con- ditions. Ge et al. [7] found that CO emissions can be reduced by approximately 60 % and PM emis- sions by approximately 65 % when using canola oil methyl ester (COME) in a 2 L compression ignition engine. Palash et al. [8] reported an approximate 10 % and 22 % drop in HC emissions using a 2.5 L compression ignition engine for B5 and B10 respect- ively. A drop in CO and HC emissions due to the high oxygen content of biodiesel and the absence of aromatic content was reported in a research re- view done by Tuner [5] speci cally looking at heavy- duty engines. Research done by Durve et al. [9] also found a decrease in CO and HC emissions of 18 % and 12 % respectively when using a B20 blend. Cheikh et al. [10] reported that the reduction of CO emissions with a biodiesel binary blend is not as pronounced for low and medium engine loads, whereas for high engine loads signi cant reduction in CO emissions are possible. Increasing the biod- iesel content also increases the NO xemissions, as its higher oxygen content improves combustion which results in higher combustion temperatures. Lahane and Subramanian [11] found that for blends of upto B15, NO xemissions increases marginally; bey- ond B15 the NO xemissions will increase signi c- antly. Binary blends of biodiesel with conventional diesel, especially B15 and lower, is seen as the best option for use in diesel vehicles without the need to modify the engine while still achieving a reduction in regulated emissions [11, 12]. Binary blends of diesel and ethanol also have the bene t of reducing regulated emissions. de Oliveira et al. [13] found that binary blends of E10 reduced CO emissions by approximately 7 %, but by increas- ing the percentage of ethanol to E20, caused the CO emissions to increase by approximately 10 %. Mo jur et al. [4] reported similar trends when in- creasing the ethanol content in the fuel and contrib- uted the rise in CO emissions due to ethanol's high latent heat of evaporation. By increasing the eth- anol content, the cooling e ect of ethanol causes incomplete combustion. High percentages of eth- anol can also cause an increase in ignition delay due to its low cetane number, which also results in incomplete combustion. The cooling e ect of eth- anol's high latent heat of evaporation has a positive e ect on NO xemissions. By increasing the ethanol content in the fuel blend, the combustion temper- ature is reduced and therefore less thermal NO x is produced during combustion. Shahir et al. [14] also found that the addition of ethanol to diesel in- creases the fuel consumption of the engine due to ethanol's lower calori c value. As the percentage of ethanol increases in the blend, the fuel consump- tion also increases. Table 1 shows a summary of the e ects of binary blends of diesel and biodiesel and diesel and ethanol and their e ects on harm- ful emissions. In the table a '+' represents an in- crease, '-' represents a decrease and 'o' represents an insigni cant change. From Table 1 it is evident that the bene ts of using renewable fuels to reduce harmful emissions are dependent on engine opera- tion. Biodiesel has a signi cant e ect on harmful emissions reduction when the engine is operated at full load, but the reduction is less pronounced for part load conditions. The same applies for binary blends between diesel and ethanol. The addition of ethanol to reduce harmful emissions is only e ective when the engine is operating at high loads. Emis- sions such as CO and HC are increased at low and medium load conditions when running with binary blends of diesel and ethanol. The summary of the literature for binary mixtures reveals that because the emissions are a ected by engine load then the application of steady state tests to real world tran- 2 sient driving behaviour is limited. Biodiesel Ethanol Part load Full load Part load Full load NOx - - - - CO o - - ++ - - HC - - ++ - FC + + ++ + CO2 N/A + - - PM - - - - - - - Table 1: Summary of e ects of binary blends on harmful emissions [4, 8, 15{18]. Binary blends of ethanol with diesel have some technical barriers due to the miscibility of ethanol in diesel fuel. The addition of additives (emulsi - ers) are required in order to run a compression igni- tion (CI) engine with a blend of ethanol and diesel. Biodiesel acts as an emulsi er for ethanol; the ad- dition of biodiesel drastically improves the miscib- ility of ethanol in diesel [19]. Studies of binary blends of biodiesel and diesel show that, generally, as the biodiesel is increased then the CO and HC emissions decrease and NO xincreases. Whereas binary blends of ethanol and diesel decrease NO x and increase CO. Therefore the ternary blends of these fuels result in inconsistent results depending on the blend and the speed and load of the engine. The ethanol in the ternary blend also improves the atomisation performance of the fuel, because the ethanol blended fuel has a low kinematic viscos- ity and surface tension [20]. Ethanol's high oxygen content improves combustion which can reduce the CO and HC emissions even further when compared to binary blends of biodiesel and diesel. Imtenan et al. [21] found that using ternary blends of diesel, biodiesel and ethanol with an ethanol fraction of 5 % decreased HC, CO and NO xemissions. Simil- arly, Guarieiro et al. [22] reported a decrease of CO and NO xemissions when adding small amounts of ethanol of up to 5 %, but HC emissions increased with the addition of ethanol to the blend. Mo jur et al. [4] found that ternary blends sig- ni cantly decrease the HC, PM and smoke emis- sions, while NO xemissions increase slightly. An optimum amount of ethanol was found to be a max- imum of E5 as per the results of Shahir et al. [14] which reduces soot and HC emissions. The lower heating value of ethanol and biodiesel as well as biodiesel's high density and high viscosity resultsin an increase in fuel consumption [14]. Table 2 shows a summary of the e ects of ternary blends on the harmful emissions. In the table a '+' rep- resents an increase, '-' represents a decrease and 'o' represents an insigni cant change. The table shows that the chemical interaction e ects between biodiesel and ethanol in a ternary blend is complex and also depends on load and/or speed of the en- gine. With such complex interactions between en- gine conditions and ternary fuel blends on the emis- sions, studies based on steady state results cannot be easily translated to the real world, where the engine speed and load is varied based on driver be- haviour. To illustrate this, Figure 1 compares the variation of engine Brake Mean E ective Pressure (BMEP) with engine speed for the steady state en- gine points cited in this paper in the literature and the current transient drive cycle (WLTP) imple- mented on the engine used in this study. The World Harmonised Light Vehicle Test Procedure (WLTP) was implemented in 2017 and addresses the issues of it's predecessor in terms of real world driving be- haviour [25]. As can be seen from Figure 1, the studies from the literature only cover a small pro- portion of the driving test cycle. Some attempts have been made to study the e ects of engine speed and load using statistical methods [24], but the res- ults cover a limited area of an engine's operating envelope. This study aims to address the short- comings of previous studies by investigating both the variation of engine conditions and ternary fuel blends on exhaust emissions. In order to determine 500 1000 1500 2000 2500 3000 3500 4000 Engine speed (rpm)050010001500Brake Mean Effective Pressure (kPa) Drive cycle From literature Figure 1: Comparison of engine operating points as discussed in literature compared to the operating points in the WLTP drive cycle. a suitable blend of diesel, biodiesel and ethanol that 3 Hulwan and Joshi [19] Zhu et al. [23] Khoobbakht et al. [24] Yilmaz et al. [15] %B 10 10 10 15 15 20 49 43 %E 20 20 20 15 15 10 3 15 BMEP (MPa) 0.2 0.4 0.6 0.2 0.7 1.15 0.37 0.37 Speed (rpm) 1600 1600 1600 1800 1800 1900 3000 3000 NOx o o o - - + - - CO + o o + o - o + HC N/A N/A N/A - o - - - FC + + + ++ + N/A N/A N/A CO2 + + + N/A N/A + N/A N/A PM N/A N/A N/A - { N/A N/A N/A Table 2: Summary of e ects of ternary blends on harmful emissions. will reduce engine emissions a multivariate analysis is needed to provide a clear and thorough know- ledge on the combustion characteristics of the en- gine. The use of non-linear techniques like Design of Experiment (DoE) is suitable to explore the in- teraction e ects of diesel, biodiesel and ethanol and its e ect on engine responses. DoE is the most cost e ective and economical technique to evaluate the individual e ects and combined e ects of the blend components on the output response [26]. DoE tech- niques have been used successfully in other studies to investigate the e ects between engine speed and load and blends between diesel, biodiesel and eth- anol [24], to quantify the e ects of fuel compositions on GDI-derived particle emissions using a mixture DoE [27] and to optimise a double pilot injection strategy to improve diesel performance and emis- sions [28]. Although multiple studies used DoE to optimise engine system parameters, the instances where mixture DoEs were used to optimise ternary fuel blends over a given drive cycle is scarce. This paper investigates the use of a mixture DoE to characterise the response of a CI engine fuelled with ternary blends of diesel, biodiesel and eth- anol while being tested over the WLTP. Engine responses that were considered are CO emissions, CO2emissions, NO xemissions and engine fuel con- sumption. The results for each exhaust emission are analysed, before the holistic improvement of the en- gine emissions and performance is discussed, thus demonstrating how future biofuel energy can be op- timised whilst simultaneously mitigating engine ex- haust emissions.2. Materials and Methods 2.1. Experimental set-up A 2.4 L Euro IV compression ignition (CI) en- gine with a programmable after-market ECU was used as the test engine to collect the data. Fig- ure 2 shows a schematic of the CI engine testing facility that was used for studying the engine emis- sions. The engine, who's speci cations are listed in Table 3, was connected to a Froude FO271 dynamo- meter. Two gas analysers were used; one (NOVA 7466K) for measuring CO 2and NO xemissions and the other (TESTO 350) for measuring CO emis- sions. This was done to ensure the highest accur- acy in the measurement, as the two gas analysers have di erent accuracy levels for di erent exhaust gasses. Both were located upstream of any exhaust after treatment systems. A summary of the ana- lysers is presented in Table 4. The factory tted mass air ow sensor (MAF), calibrated with a Su- per ow SF-120 ow bench, was used to measure the intake mass air ow. Cumulative mass fuel con- sumption was measured by weighing the fuel before and after the test on a calibrated digital scale. Data such as engine speed, throttle position, cooling wa- ter temperature and oil sump temperature were re- corded from the ECU as well as from the dynamo- meter control system. The fuel supplier provided physiochemical properties of diesel, biodiesel and ethanol as well as the binary and ternary blends are listed in Table 5. 2.2. Experimental design A mixture design approach was adopted to ex- plore the individual e ects of diesel, biodiesel and ethanol and their interactions in a blend for di er- ent engine responses. The selection of the mixture 4 Engine parameter Characteristics Bore (mm) 89.9 Stroke (mm) 94.6 Volume (cc) 2402 Compression ratio (CR) 17.5 Number of cylinders 4 Method of cooling Water cooled Table 3: Engine parameters used for experimentation. Exhaust gas Range Accuracy Method CO (ppm) 0 10000<10 electrochemical CO2(%) 0 20<0:2 infra-red NO (ppm) 0 2000<20 electrochemical NO2(ppm) 0 800<8 electrochemical Table 4: Method and accuracy of the instruments used to measure the engine emissions. A D BEF CIG HAir flow path Signal wiring Figure 2: Layout of the engine test cell together with meas- uring equipment; A: air lter; B:Mass air ow sensor; C: en- gine; D: Turbo charger; E: gas analyser; F: exhaust outlet; G: load cell; H: dynamometer; I: DAQ.DoE is appropriate as the sum of the input vari- ables, in this case the blend components, must be unity [29]. As opposed to a response surface design, the factors in a mixture design is not independent from each other. If x1;x2;:::;x pdenote the pro- portions of pcomponents of a blend, then 0xi1i= 1;2;:::;p (1) and x1+x2++xp= 1 (2) For a mixture design with three components, the design space is a triangle with vertices correspond- ing to formulations that are pure blends (100 % of one blend). Figure 3 shows an extreme ver- tices design, where upper limits have been set to the amount of biodiesel and ethanol. The upper limits are based on previous research [11, 19] where the maximum addition without engine modi cation was determined: xD+xB+xE= 1xB;xE0:2 (3) wherexDis the fraction component of diesel, xB is the fraction component of biodiesel and xEis the fraction component of ethanol in the blend. Centroid and axial points have been added to the design to increase its capability to t models for ternary blends. The composition of the centroid point and axial points were calculated using: xD= 0:83;xB= 0:14;xE= 0:03 xD= 0:83;xB= 0:03;xE= 0:14 xD= 0:86;xB=xE= 0:07 xD= 0:94;xB=xE= 0:03(4) The whole mixture design was replicated once and the runs were randomised to ensure experimental errors are independently distributed. The mixture design consists of one centroid point, three axial points and three vertices points. With the replica- tion, a total of 14 runs of mixture experiments were used to analyse the data acquired from the experi- mental runs. 2.3. Desirability approach The optimisation of the fuel blend is dependent on more than one engine response which include CO emissions, CO 2emissions, NO xemissions and fuel consumption. The desirability approach was used for the optimisation of the fuel blend parameters (diesel, biodiesel and ethanol) for the properties of 5 Cetane LHV Density ( kg=m3) Viscosity ( mm3=s) CFPP (C) Flash point (C) number (MJ/kg) at 15C at 40C Diesel 51.7 42.8 831.1 2.686 -26 65 Biodiesel 52.8 38.0 883.2 4.372 -6 179 Ethanol 7.0 26.8 790.0 1.200 -38 40 B20 51.9 41.8 841.5 3.023 -22 87 E20 42.8 39.6 822.9 2.388 -28 60 B14E3 50.5 41.6 837.2 2.877 -24 80 B3E14 45.5 40.4 826.9 2.529 -27 65 B3E3 50.4 42.2 831.4 2.692 -26 67 B7E7 48.7 41.3 831.9 2.700 -25 71 Table 5: Physiochemical properties of diesel, biodiesel, ethanol and their blends. Diesel 0.81.0 Biodiesel0.20.0 Ethanol0.20.0 Figure 3: Extreme vertices simplex design plot of the mix- ture DoE. the engine response mentioned above. The soft- ware transforms each response to a dimensionless desirability value d. The value ranges from d= 0, which indicates that the response is unacceptable, tod= 1 which shows that the response is more de- sirable. The goal of this study was to minimise all engine emissions and the desirability of each of the responses was calculated using [30]: di(^Yi) =8 >< >:1 if ^Yi(x)<Ti ^Yi(x)Ui TiUiifTi^Yi(x)Ui 0 if ^YiUi(5) wheredi(^Yi) is the desirability function of response ^Yi(x),TiandUiare the target and upper values re- spectively that are desired for response ^Yi(x). For minimising the response, Tiwill denote a small enough value for the response. The individual de- sirability functions are combined using the geomet- A B CDEHG F Supply line Return lineFigure 4: Layout of the engine's fuel delivery system; A: fuel tank; B: fuel primer pump; C: fuel lter; D: main fuel pump; E: common rail; F: engine; G: safety valve; H: fuel return line. ric mean, which gives the overall desirability: D= (d1(Y1)d2(Y2))0:5(6) It is noticeable that if any response di(^Yi) is com- pletely undesirable, di(^Yi) = 0, then the overall de- sirability is zero. 2.4. Mixture homogeneity The diesel, biodiesel and ethanol fuel were mixed manually in batches of 5 L in the determined blend ratios based on the mixture DoE. The fuels were mixed together using lab equipment with an accur- acy of 10 ml to make the homogeneous fuel blends. Each blend was then kept in a sealed glass container for a maximum of 24 hours to observe its physical appearance. All blends, except E20, exhibited a homogeneous mixture, with no observable separa- tion between the di erent fuels evident. The ob- served blended fuel samples also did not form any sediments and the mixtures were deemed as stable. 6 Figure 5 show the E20 fuel sample, where it is evid- ent that the ethanol and diesel has separated from each other. Ethanol is immiscible with diesel fuel over a wide range of temperatures, leading to sep- aration. Consequently, in many cases the presence of a surfactant and co-solvent additive in the bin- ary blends between diesel and ethanol becomes ne- cessary. Lapuerta et al. [31] found that the water content in ethanol, temperature of the fuel and per- centage of additive used can in uence the homogen- eity of the binary blend between diesel and ethanol. Binary blends with a maximum of approximately 10 % ethanol in the blend can be used in diesel engines in countries where temperatures rarely fall below 5C. The addition of additives will increase miscibility of the fuel thus extending the use of bin- ary blends of diesel and ethanol to colder countries. Hansen et al. [32] also found that the homogeneity of the fuel blend depends on temperature and eth- anol's water content. For blends between dry eth- anol and diesel, the blend was homogenous for tem- peratures higher than 10C. Fernando and Hanna [33] and Kwanchareon et al. [34] found that biod- iesel can be used successfully as an amphiphile (a surface-active agent) prevent separation between a binary blend of ethanol and diesel. They reported that the addition of biodiesel to the binary blend between ethanol and diesel increases the lubricity characteristics of the blend and makes it homogen- eous well below sub-zero temperatures. For the testing of E20, no additives were added to the blend to improve its homogeneity as the addition of ad- ditives could in uence the results [35]. Qualitative checks showed that after approximately 25 minutes, signs of separation was evident in the E20 blend. The homogeneity of the blend was ensured by act- ively stirring the fuel blend during testing with a magnetic stirrer. 2.5. Flushing procedure In order to ensure that the the old fuel blend from the previous test, in the fuel system does not in u- ence the next test, the fuel system was ushed with the next test's blend of fuel before formal testing began. It was necessary to determine the amount of ushes required that will successfully remove all remaining fuel blend from the previous test. This was done using red fuel dye. The following pro- cedure was followed using the engine's fuel delivery system (Figure 4): Step 1. Run engine with fuel which contains the red dye. (a) Fuel sample of a E20 fuel blend. (b) Grayscale of E20 fuel sample. Figure 5: Qualitative check for blend homogeneity for E20 fuel blend. (a) Fuel with red dye added. (b) Fuel after en- gine was ushed four times. (c) Fuel without any dye added. Figure 6: Fuel samples used in compiling the ushing pro- cedure. Step 2. Use fuel primer pump to pump out all fuel from the fuel system. Step 3. Replace current fuel lter with an empty fuel lter. Step 4. Replace fuel in the fuel tank with clean fuel and run the fuel primer pump for 5 minutes. Step 5. Idle engine for 5 minutes. Step 6. Run engine at 2500 rpm for one minute. Step 7. Use fuel primer pump to pump out all the fuel from the fuel system. Step 8. Repeat steps 4-7. A sample of the fuel in the fuel tank was taken after each ush iteration. The samples were photo- graphed with a Canon EOS 700D under homogen- eous light conditions (Figure 6). The red spectrum 7 of each sample was calculated using computer soft- ware. The red spectrum of the clean fuel was sub- tracted from the other sample's spectrum to elim- inate any red colours that were already present in the clean fuel. After the fourth ush, the majority of the dye has been removed from the fuel as seen in Figure 7. For the mixture design the engine fuel system was ushed four times before every test was conducted. 2.6. Data collection The engine was run on the WLTP as shown in Figure 8. The WLTP shows the variation of vehicle speed with time. Since only the engine and not the whole vehicle was tested it is necessary to relate the vehicle speed to the engine speed and load, based on the vehicle characteristics such as gear ratio etc. The method used is presented elsewhere [36]. 3. Results In this present study, the e ects of di erent blends of fuel on di erent engine responses were considered. Fuel blends included binary and tern- ary blends between diesel ( xD), biodiesel ( xB) and ethanol (xE). Engine responses include CO, CO 2, NOxemissions as well as fuel consumption when tested over the WLTP drive cycle. The experi- mental response of 14 runs in the design matrix (Figure 3) along with their corresponding points of the tted mixture design are shown in Table 6. All 14 runs were cold start runs, with the engine oil and cooling water temperature at approximately 20C (= 2) at the start of each test. The principal model analysis was based on the analysis of vari- ance (ANOVA) which provided statistical inform- ation including the p-values of the di erent model terms as well as the lack of t terms (Table 7). P- values of less than 0.02 are deemed as highly signi- cant, which means that the probability of this phe- nomena is due to chance, is less than 2 % [37, 38]. The residual error of the mixture DoE is made up of the pure error and the lack of t error terms. The pure error term re ects on the variability of the observations within each response and the lack of t term measures the error due to de ciency in the statistical model. The mean square of each term can be used to test the signi cance of the lack of t relative to the pure error term. If it is deemed non-signi cant, then the model is acceptable. The lack of t term's signi cance is listed in Table 7. (a) Red spectrum of fuel with red dye added. (b) Red spectrum of fuel after the rst ush. (c) Red spectrum of fuel after the second ush. (d) Red spectrum of fuel after the third ush. (e) Red spectrum of fuel after the fourth ush. Figure 7: Change in red spectrum for the fuel samples taken after each fuel system ush. 8 0 500 1000 1500 Time (s)0102030405060708090Vehicle speed (km/h)Figure 8: The World Harmonised Light Vehicle Test Pro- cedure used in the type approval of new vehicles as per EU regulation 2017/1151 [25]. No issues with laboratory equipment were ob- served with the use of the di erent fuel blends. Fuel lters and fuel injectors operated normally. Each engine response is discussed in more detail in the sections below. 3.1. Evaluation of CO emissions The quadratic model developed for CO emissions as tted based on the mixture design corresponds to: ZCO=xD35:34xB+ 70:00xE + 44:7xDxB79:86xDxE 13:78xBxE(7) whereZCOis the CO emissions in the exhaust gas in grams per kilometer and the variables xD,xB andxEare the fraction percentages of the com- ponents in the overall fuel blend. The e ect of all the linear terms as well as the diesel/biodiesel and diesel/ethanol quadratic terms were found to be sig- ni cant on CO emissions with p-values less than 0.02. Other terms such as the interaction between biodiesel and ethanol did not have a signi cant ef- fect on CO emissions as indicated by a p-value of 0.249. Furthermore, the regression statistics good- ness of t ( R2) and the goodness of prediction (adjustedR2) showed high values of 98.65% and 97.81% respectively for the presented model indic- ating a high correlation between the observed and the predicted values of CO emissions. The variation of CO emissions response with fuel blends based on Figure 9: Contour plot of the engine response over the WLTP for CO emissions in grams per kilometer. the model developed from DoE is shown in Fig- ure 9. As shown in the gure, there is an area of CO emissions less than 0 :85 g=km for a binary blend of diesel and ethanol: for blends between approx- imately E2 and E10. The decrease in CO emissions can be contributed to a prolonged oxidation process even in the exhaust phase, which is possible due to the fact that ethanol has a high oxygen content. Figure 9 also shows that for high concentrations of ethanol ( >15 %) in the fuel blend, can result in CO emissions higher than 1 :6 g=km. Similar studies [15, 35] have also found that for high percentages of ethanol in binary blends, the ignition delay can be increased due to the low cetane number of eth- anol which causes the fuel to resist auto-ignition in diesel engines and inhibit complete combustion. The cooling e ect of the ethanol on the gas temper- ature due to ethanol's high latent heat of evapora- tion can also in uence the oxidation process, even though enough oxygen is available for combustion [19]. This is also evident for ternary blends with a high percentage of ethanol. In Figure 9, CO emissions increase for binary blends between B5 and B15 and then start to de- crease again for percentages of higher blends above approximately B16 [39]. Binary blends of diesel and biodiesel less than B20 reduce CO emissions by a small amount at low engine loads and the reduc- tion becomes more pronounced at high load scen- 9 Run Parameter settings Experimental response (g/km) xDxBxE CO CO 2 NOx FC 1 0.80 0.00 0.20 2.0598 244.52 1.0812 134.00 2 0.83 0.14 0.03 1.2046 258.74 1.0841 122.75 3 0.83 0.03 0.14 1.4141 232.37 1.0584 128.75 4 0.86 0.07 0.07 1.1409 251.43 1.0566 119.27 5 1.00 0.00 0.00 1.0182 252.03 1.1148 120.64 6 0.83 0.14 0.03 1.0624 257.02 1.0841 121.11 7 0.80 0.20 0.00 0.8844 238.07 1.0669 123.23 8 0.83 0.03 0.14 1.2837 232.37 1.0370 129.98 9 0.94 0.03 0.03 0.9863 244.98 1.0741 121.79 10 0.80 0.20 0.00 0.8868 247.06 1.0714 126.50 11 0.94 0.03 0.03 0.9724 247.48 1.0948 126.57 12 1.00 0.00 0.00 0.9874 251.08 1.1518 116.41 13 0.86 0.07 0.07 1.0638 241.27 1.0635 121.18 14 0.80 0.00 0.20 1.9894 245.00 1.0764 135.50 Table 6: The experimental values of the engine response for the mixture DoE. CO CO 2 NOxfc Linear <0:02<0:02<0:02<0:02 Quadratic <0:02<0:02<0:02 0.078 xDxB<0:02<0:02 0.351 0.973 xDxE<0:02<0:02<0:02 0.297 xBxE 0.249 0.392 0.894 0.030 Lack-of-Fit 0.948 0.782 0.829 0.068 Table 7: P-values of the engine response for the mixture DoE. arios. For blends of biodiesel greater than B20, CO emission reduction is more pronounced at low en- gine loads [34, 40]. Engine operating conditions as well as oxygen content of the fuels play a signi c- ant part in the formation of CO emissions [15, 41]. The engine operates more regularly in the low and medium load regions when tested over the WLTP, which results in minor CO emission changes for bin- ary blends. For ternary blends, small additions of biodiesel and ethanol will decrease the engine's CO emis- sions. A maximum addition of B5E5 will result in similar CO emissions compared to using just diesel. 3.2. Evaluation of CO 2emissions The quadratic model developed for CO 2emis- sions as tted based on the mixture design corres-ponds to: ZCO2= 251 xD2721xB+ 3768 xE + 3660 xDxB4438xDxE + 634 xBxE(8) whereZCO2is the CO 2emissions in the exhaust gas of the engine in grams per kilometer. The lin- ear terms as well as the quadratic terms in Equa- tion 8 are signi cant with both having a pvalue< 0:02. The interaction between the diesel and biod- iesel terms and the diesel and ethanol terms are also signi cant. Interaction between the biodiesel and ethanol terms are not signi cant, as shown in Table 7. High values of R2(88.30%) and adjus- tedR2(80.99%) respectively for Equation 8 indic- ate a high correlation between the observed and the predicted values of CO 2emissions. The vari- ation of CO 2emissions response with fuel blends based on the model developed from DoE is shown in Figure 10. A region of minimum CO 2emis- sions are evident for binary blends between diesel and ethanol. For blends of approximately E5 to E15, CO 2emissions of less than 225 g =km were pre- dicted. Ethanol has a very low hydrocarbon atomic ratio which reduces the CO 2emissions because of the limited carbon content per unit energy of the fuel when burned. The high oxygen content in eth- anol also contributes to better combustion, which increases CO 2emissions, yet it is o set by the smal- ler amount of carbon atoms available for combus- tion in ethanol. 10 Figure 10: Contour plot of the engine response over the WLTP for CO 2emissions in grams per kilometer. The region of highest CO 2emissions are for bin- ary blends of diesel and biodiesel in the region of B10. Increasing the binary blend between diesel and biodiesel beyond B10, results in a reduction of CO 2emissions of less than 255 g =km. The higher oxygen content in biodiesel promotes com- plete combustion which results in an increase of CO2emissions. The majority of ternary blends between diesel, biodiesel and ethanol resulted in CO2emissions between 225 g =km and 270 g =km. An increase in CO 2emissions towards binary blends between diesel and biodiesel as well as a reduction in CO 2emissions towards binary blends between diesel and ethanol was also reported in other liter- ature [4, 41, 42]. 3.3. Evaluation of NO xemissions The quadratic model developed for NO xemis- sions as tted based on the mixture design corres- ponds to: ZNOx= 1:13xD1:19xB+ 8:97xE + 2:50xDxB10:13xDxE + 0:33xBxE(9) whereZNOxis the NO xemissions in the exhaust gas of the engine in grams per kilometer. Both the linear and the quadratic models in Equation 9 are signi cant with p-values less than 0.02. Interactionbetween the diesel and ethanol terms is signi cant withp<0:02 whereas the interaction between the diesel and biodiesel terms and the biodiesel and ethanol terms are not signi cant. High values of R2(88.24%) and adjusted R2(80.89%) respectively for Equation 9 indicated that there is a high cor- relation between the observed and the predicted re- sponse of the engine. The variation of NO xemis- sions response with fuel blends based on the model developed from DoE is shown in Figure 11. A low NOxemissions region is evident for binary blends between diesel and ethanol of approximately E7 to E15. The decrease in NO xemissions for binary blends between E7 and E15 can be contributed to ethanol's high latent heat of evaporation which de- creases the combustion temperature as well as NO x formation[43]. By increasing the ethanol content beyond E15, results in NO xemissions increasing due to ethanol in uencing the combustion charac- teristics of the engine as well as lowering the cetane number of the fuel blend considerably [19, 35]. A ternary blend between diesel, biodiesel and ethanol manages to achieve an equilibrium between the e ects of better combustion due to the higher oxygen content in biodiesel and ethanol, as well as the advanced injection of the fuel because of biod- iesel's increased bulk modulus. This can be seen in Figure 11 where there is a plateau of NO xemis- sions between 1 :06 g=km and 1:08 g=km for ternary blends with xD= 0:8. 3.4. Evaluation of fuel consumption The quadratic model developed for fuel consump- tion as tted based on the mixture design corres- ponds to: fc= 119 xD+ 166 xB268xE 18xDxB+ 586 xDxE 1333xBxE(10) wherefcis the fuel consumed by the engine when run over the WLTP in grams per kilometer. The linear model is signi cant with a p-value of less than 0.02 and the interaction terms approaches statist- ical signi cance with a p-value of 0.078. Of the three interaction terms of the quadratic model, only the interaction between biodiesel and ethanol are deemed signi cant with a p-value of 0.03. High values ofR2(87.48%) and adjusted R2(79.65%) respectively for Equation 10 show that there is a high correlation between the observed and the pre- dicted response of the engine. The variation of 11 Figure 11: Contour plot of the engine response over the WLTP for NO xemissions in grams per kilometer. the engine's fuel consumption over the WLTP with fuel blends based on the model developed from DoE is shown in Figure 12. The region of min- imum fuel consumption of less than 118 g =km is achieved when approximately 10 % biodiesel and 10 % ethanol are blended with diesel. This region of improved fuel consumption can be attributed to better fuel atomisation as a result from the eth- anol in the fuel [44]. The additional oxygen from the biodiesel and ethanol in the blend also con- tributes to improved combustion and better fuel economy. A plateau region for fuel consumption between 118 g =km and 122 g =km is evident for most of the binary blends between diesel and biodiesel as well as ternary blends with xE3%. High concen- trations of ethanol in binary blends between diesel and ethanol ( xE7%) and in ternary blends with the percentage ethanol in the blend being higher than 15%, results in fuel consumption of more than 126 g=km. An increased concentration of ethanol in the blend reduces the energy content of the blend, which causes fuel consumption of the engine to rise. This is also true for binary blends between diesel and biodiesel with biodiesel content higher than 10 %. Biodiesel and ethanol have lower heating val- ues (38 MJ=kg and 26:8 MJ=kg respectively) com- pared to diesel (42 :8 MJ=kg) and as the content of biodiesel and ethanol increases in the fuel blend, so does the fuel consumption [16, 32, 41, 45]. The Figure 12: Contour plot of the engine response over the WLTP for fuel consumption in grams per kilometer. increase in fuel consumption was minimal for con- centrations of xB0:1, but became signi cant for higher concentrations of biodiesel in the fuel blend. The plato region for ternary blends in Figure 12 is also reported by Shahir et al. [14] where small per- centages of ethanol in the ternary blend does not contribute signi cantly to the fuel consumption in- crease. With higher concentrations of ethanol in the ternary blend, the fuel consumption increases signi cantly [19]. 3.5. Mixture optimisation As there is more than one response to be optim- ised, it is necessary to set requirements for each response, that the optimisation tool will optimise towards. The desirability approach was used to de- termine if the optimisation was able to meet the re- quirements. For each response, an upper and lower value is required. If the response needs to be min- imised by the optimisation tool, the lower limit is given as the target to optimise towards. The lowest and highest values of each response from the tests conducted (Table 6) where taken and set as lim- its for the optimisation tool. All lower and upper limits used by the optimisation tool can be seen in Table 8. Figure 13 shows the desirability plot when optimising towards a reduction in all engine emission responses. The plotted lines are known 12 Engine emissions Lower Upper limit limit CO (g/km) 0.889 1.990 CO2(g/km) 232 258 NOx(g/km) 1.04 1.15 Fuel consumption (g/km) 116 136 Table 8: Optimisation lower and upper limits. as the prediction lines of the di erent engine emis- sions. The vertical solid lines for each variable is the current blend ratio setting. By changing the vertical solid line for each blend fraction, the hori- zontal dashed lines were updated by re-computing the predicted engine emissions for the new blend fraction between diesel, biodiesel and ethanol. The horizontal dashed lines show the nal predicted en- gine emissions according to the blend fractions. The optimisation gives a blend of E11 with the highest desirability percentage of 100 %. Diesel Biodiesel EthanolDesirability D = 1.00NOxemissions y = 1.003 g/kmCO emissionsy = 0.796 g/km 0.89 0.11CO2emissions y = 204.0 g/km 0 Figure 13: Optimisation plot for engine emissions for the fuel blend E11. The 100 % desirability factor is achieved as all the responses are below the target values set out during optimisation. The comparisons between en-Engine emissions E11 B2E9 CO -41 % -34 % NOx -12 % -10 % CO2 -26 % -21 % Fuel consumption 11 % 8 % Table 9: Summary of the impact of optimised binary and ternary blends on exhaust emissions. gine emissions for diesel and the engine emissions if the engine would run on a E11 blend is an ap- proximate reduction in CO emissions of 41 %, a reduction in NO xemissions of 12 % and a reduc- tion in CO 2emissions of 26 %. The E11 fuel blend, when composed of bioethanol, achieves the EU's re- newable content in fuel target, but as mentioned in 2.4, blends between ethanol and diesel for percent- ages of ethanol higher than 10 % cannot be used in countries where ambient temperatures fall be- low5C. Even though E11 does decrease engine emissions signi cantly, it will be impractical to use in European countries, due to the non-miscibility of the mixture. Another blend of B2E9 also achieved a desirability percentage of 100 % when all responses were minimised. The B2E9 fuel blend achieved en- gine emission responses below the targets given dur- ing the optimisation process, which resulted in the same desirability factor as E11. The comparisons between engine emissions for diesel and the engine emissions if the engine would run on a B2E9 blend is an approximate reduction in CO emissions of 34 %, a reduction in NO xemissions of 10 % and a re- duction in CO 2emissions of 21 %. Table 9 shows the di erences in emissions for the di erent blends compared to diesel with 5 % biodiesel in the fuel blend, that is available at most fuel pumps [46]. The addition of 2 % of biodiesel to the fuel blend improves the miscibility of the fuel blend with 9 % of ethanol in the fuel. B2E9 also surpasses the EU's target of 10 % renewable content in fuel as well as achieves signi cant reductions in engine emissions. The high content of ethanol in the blend does im- pact fuel consumption in a negative way, which can in uence its uptake in the commercial market. 4. Conclusion This study investigated the e ect of fuel blends between diesel, biodiesel and ethanol on the emis- sion characteristics of a diesel engine tested over 13 the World Harmonised Light vehicle Test Proced- ure (WLTP). Based on a mixture design of experi- ment (DoE), 7 di erent fuel blends between diesel, biodiesel and ethanol were formulated and 14 ran- domised runs were designed, including one replicate of each test. The main conclusions are: 1. An optimised ternary blend of B2E9 can be used as a 'drop in' fuel that will reduce harm- ful emissions, compared to 100 % pump diesel, of CO emissions by approximately 34 %, NO x emissions by 10 % and CO 2emissions by 21 % over the WLTP drive cycle. 2. The use of the ternary blend B2E9 will also contribute to the UK meeting the EU target of 10 % renewable energy in the transport sector, compared to the current blend of B5. 3. This work demonstrated that the mixture DoE is a useful tool to quantify the e ects of fuel blends between diesel, biodiesel and ethanol on the engine's emissions response when tested over the WLTP. 4. The addition of ethanol to the fuel blend can reduce CO emissions, but higher concentra- tions of ethanol in the blend can reduce the cetane number of the fuel blend, which neg- atively impacts combustion and increases CO emissions. 5. The addition of higher concentrations of biod- iesel of more than 15 % to the fuel blend will also reduce CO emissions. Lower concentra- tions of biodiesel have a smaller e ect on CO emissions at low engine loads and can even in- crease CO emissions as a result of biodiesel's poor atomisation properties. 6. Engine CO 2emissions can be reduced by ap- proximately 19 % with the use of a binary blends of E10. The low hydrocarbon atomic ra- tio of ethanol results in a reduced carbon con- tent per unit energy of the fuel which reduces CO2emissions. The high oxygen content in ethanol also contributes to better combustion, which increases CO 2emissions, yet it is o set by the smaller amount of carbon atoms avail- able for combustion in ethanol. The higher oxygen content in biodiesel also promotes com- plete combustion which results in an increase of CO 2emissions of approximately 11 % for blends containing about 10 % biodiesel. 7. The increase in NO xemissions for blends with biodiesel is a result of increased combustiontemperatures due to the higher oxygen con- tent in biodiesel. The increase is also associ- ated by the advancement of the injection tim- ing, caused by the higher bulk modulus of com- pressibility of biodiesel. A decrease of approx- imately 9 % in NO xemissions for blends with ethanol is contributed to ethanol's high latent heat of evaporation which decreases the com- bustion temperature as well as NO xformation. 8. An increased concentration of ethanol (between 10-15 % in the blend) reduces the energy content of the blend, which causes fuel consumption of the engine to rise by approximately 9 %. This is also true for binary blends between diesel and biodiesel. 5. Future work Future work will investigate how engine emissions can be further reduced, focussing on novel combus- tion techniques such as Low Temperature Combus- tion (LTC) with ternary biofuel blends. The de- sired outcome will conform with future EU renew- able targets as well as emission legislation. Funding The authors would like to thank the University of the West of England for the use of the laboratory equipment to conduct this research. Declarations of interest None