Tailor-Made Fuels from Biomass for Homogeneous ... - ACS Publications

ARTICLE pubs.acs.org/EF

Tailor-Made Fuels from Biomass for Homogeneous Low-Temperature Diesel Combustion Andreas J. Janssen,*,† Florian W. Kremer,† Jan H. Baron,† Martin Muether,† Stefan Pischinger,† and Juergen Klankermayer‡ †

Institute for Combustion Engines, and ‡Institute for Technical and Macromolecular Chemistry, Rheinisch-Westf€alische Technische Hochschule (RWTH) Aachen University, 52062 Aachen, Germany ABSTRACT: The use of modern biofuels in mobile applications has an enormous potential to reduce greenhouse gases as well as engine pollutant emissions, such as soot or nitrogen oxides. This beneficial effect is directly related to the molecular structure of the biofuel as a product of an optimized production process. To understand the influence and emission reduction potential of the large variety of different fuel properties, this study aims to identify desirable fuel characteristics and define optimized biofuel components. In a first step, a literature survey is carried out, focusing on the impact of the cetane number, boiling characteristics, and aromatic and oxygen contents on the diesel combustion process. The incorporated investigations that analyze the combustion behavior, engine efficiency, and emission performance underline the potential of tailoring fuels to desired properties. From this foundation, a modelbased analysis of desired fuel properties was conducted, using a large database with 32 different fuels (single molecules and fuel mixtures). With multiple correlation methods, different fuel properties can be used to predict the emission performance of the engine. The following fuel optimization based on emission performance and engine efficiency results in ideal fuel properties for diesel engine combustion. As it turns out, a blend of 2-methyltetrahydrofurane (2-MTHF) (which can be derived from cellulose) blended with di-n-butylether complies with the desired fuel properties, which were defined before. In combination with an improved homogeneous low-temperature combustion process and an increased ignition delay, a nearly soot-free diesel combustion over a wide load range is realized. The oxygenated fuel enables increased exhaust gas recirculation (EGR) rates while maintaining the high engine efficiency of the diesel process.

1. INTRODUCTION The needs of future societies for continuous mobility necessitate the search for alternatives for fossil energy sources. One attractive option is the production and use of fuels from biomass, because they considerably reduce CO2 emissions as well as pollutant emissions, such as soot or nitrogen oxides. The holistic development of these fuels as a combination of biomass processing and combustion technology has proven to be a research area with a high need for interdisciplinary cooperation between natural and engineering sciences. The Cluster of Excellence “Tailor-Made Fuels from Biomass” (TMFB) at RWTH Aachen University was established in 2007 as part of the Excellence Initiative by the German Research Foundation to develop new, biomass-based, synthetic fuels for mobile applications. One of the long-term objectives of this research cluster is to determine the best possible combination of fuel components, whose properties are derived from the requirements of future combustion processes. The vision of the Cluster of Excellence is to optimize the entire process chain from biomass conversion to the combustion engine while not competing with the food chain. The TMFB Cluster of Excellence pursues an innovative research approach of using the synthesis capability of nature and converting and modifying biopolymers only as far as necessary. To do so, methods are developed within TMFB for the specific chemical conversion of biomass. First of all, the lignocellulose must be split up into its components: cellulose, hemicellulose, and lignin. Reaction media, such as ionic liquids, are used to break up these components. Ionic liquids are compounds, such as table salt, that r 2011 American Chemical Society

are made up of positively and negatively charged ions but have a melting point of below 100 °C, because of their molecular structure. Using various catalytic conversion methods, the individual components can then be converted into the desired fuel molecules. Figure 1 shows methods of converting organic matter into fuel components.1 The derived molecules have a different structure (see Figure 1) as well as a different behavior compared to diesel fuel. The knowledge of the effect of these structures on the fuel behavior is essential for the analysis of optimized fuel components. Therefore, the influence of the most important fuel properties, such as cetane number or boiling behavior, on the emission performance is investigated individually in a first step. However, the parametric study only shows isolated influences of one single fuel property. To define ideal fuel properties for the complete diesel process, a model-based analysis is conducted, in which multiple correlations are used to understand and define an ideal combination of fuel properties. On the basis of these results, the desired properties can be tailored in a specifically developed fuel design process.

2. LITERATURE SURVEY ABOUT THE INFLUENCE OF DIFFERENT FUEL PROPERTIES A large number of fuel properties influence the emission performance of the fuel. Besides others, these are cetane number, Received: July 11, 2011 Revised: August 24, 2011 Published: August 24, 2011 4734

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Figure 1. Methods of converting new types of biofuels in the TMFB Cluster of Excellence.

boiling temperature, and oxygen and aromatic contents. The effect of these was analyzed in a literature survey as presented in this section. The impact of the cetane number on the emission performance has been largely investigated by numerous researchers. To determine the influence of the cetane number, several mixtures of iso-octane and n-heptane, so-called primary reference fuels (PRFs), were investigated, because the different mixtures have nearly the same density, low heating value, and boiling temperature, with only the cetane number varying from 30 to 58 for the considered mixtures. A decrease of the cetane number leads to an increased ignition delay for all fuels.2,3 The longer ignition delay causes a more homogeneous air/fuel mixture and increases the proportion of premixed combustion. In consequence, the combustion of fuels with a decreased cetane number results in reduced particulate matter emissions. In particular, rich mixture zones, which are responsible for soot formation, are reduced with increased ignition delays.4 Soot formation can be suppressed completely when operating a diesel engine homogeneously.5 Because of the faster combustion, noise emissions increase with longer ignition delays. At low engine loads, this behavior will be reversed because of different ignition reactions in the negative temperature coefficient (NTC) area as well decreasing combustion temperatures (cold combustion).6 This pre-ignition acts like a pilot injection. Investigations in two load points [n = 2000 min 1 and indicated mean effective pressure (IMEP) = 6.0 and 10.5 bar] with very high exhaust gas recirculation (EGR) rates have shown that hydrocarbon (HC) and carbon monoxide (CO) emissions increase with a decreasing cetane number.7 Areas with insufficient temperatures for a complete oxidation grow with longer ignition delays. Higher HC and CO emissions seem to be a constraint for the decrease of the cetane number.2 Additional vehicle tests in the new European driving cycle (NEDC) have proven to have a positive impact with an increased cetane number on nitrogen oxide (NOx), HC, and CO emissions.8 Engine efficiency will not be influenced by the cetane number when the beginning of injection is adjusted to a constant center of combustion, which is the point where 50% of the fuel is burned. Therefore, fuels with a lower cetane number require an earlier injection event.9 Generally, the influence of the cetane number on the ignition delay decreases with an increasing load point.

Thus, the impact of the cetane number on the emission performance increases with lower engine loads.8,10 Investigations with mixtures of diesel fuel, toluene, and nheptane have proven to have a significant reduction of soot emissions and a slight decrease of CO emissions with reduced boiling temperatures.11 Reduced particulate matter emissions are caused by a faster evaporation with higher volatile fuels.12 An influence of the boiling temperature on HC emissions could not be observed.13 A comparison of short alkanes with a low boiling temperature and long-chain alkanes leads to comparable conclusions.14 Additional investigations have shown that an increased boiling temperature causes, in particular, a higher number of small particles (nucleation mode) because of the condensation of unburned fuel.15 Also, the influence of aromatic fuel compounds has been investigated. It was shown that an aromatic-free fuel reduces the particulate emissions significantly over all load points compared to a fuel with aromatic compounds.16,17 That can also be shown in a cooperated fuel research (CFR) engine with a constant fuel mass flow rate. Increasing the aromatic content from lower than 1 to 27% leads to a 30% rise of particulate emissions, a 15% rise of NOx emissions, and a 35% rise of HC emissions.18 Generally, an increase of the aromatic content leads to an increase of all restricted harmful substances at all loads.19 The results can be confirmed by data of four European research programs.20 Furthermore, diverse investigations have proven the high potential of oxygen in the fuel to reduce particulate emissions.15,2125 The proportion of soot particles decreases linearly with an increasing oxygen content in the fuel. An oxygen content of 38 mass % in the fuel is sufficient to completely avoid the formation of particulates. With high EGR rates, NOx emissions can also significantly be reduced.26 The chemical kinetic modeling indicates that the bonding between the oxygen and carbon atom remains unchanged in partial oxidized hydrocarbons. As a result, the formation of soot precursors is fully prevented and the particulate emissions can be avoided.27 The measurements on a direct-injection, single-cylinder diesel engine show that, although the particulate mass decreases with an increasing oxygen content, the quantity of particulates stays nearly the same, which implies smaller particulates on average. Particulate emissions can differ for fuels with the same oxygen content, because of the molecular 4735

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Table 1. Single-Cylinder Engine Configuration single-cylinder unit benchmark

engine Euro 6

displacement

cm3

stroke

mm

88.3

bore diameter

mm

75

390

compression ratio

15

valves per cylinder

4

maximum peak pressure fuel injection system specifications

bar

220 Bosch piezo common rail system

maximum injection pressure

bar

2000

hydraulic flow rate (HFR)

cm3/30 s

310 at 100 bar

nozzle hole diameter

μm

109

number of spray holes spray cone angle

8 deg

153

structure of the fuel.28 The increase in oxygen effects the other fuel properties; thus, adoptions are necessary to increase particulate matter and NOx emissions. However, the NOx/particulate trade-off can be improved with the dominant reduction of the particulate emissions. The improvement of the trade-off becomes more significant with decreasing engine loads.24,29 Studies with diethylenglycoldimethylether (DGM) on a six-cylinder commercial vehicle engine show that, in addition to the particulate emission, the HC emissions and, at high load points, the CO emissions can also be reduced with the oxygen content in the fuels.30 Kinetic modeling approaches confirm these results.27 Overall, the described parametric study underlying this investigation shows an explicit correlation between fuel properties and emission behavior in a compression ignition engine. In particular, the cetane number, boiling characteristics, and oxygen and aromatic contents of a fuel show the highest potential to decrease engine-out emissions. Investigations have shown that a reduction of the cetane number, a higher volatility, and a decrease of the aromatic content leads to reduced soot emissions. Because of an improved oxidation of soot precursors, oxygenated fuels show significantly reduced particle emissions. HC, CO, and noise emissions are mainly influenced by the cetane number, whereas a decreased cetane number results in higher emissions of these species.

3. EXPERIMENTAL SECTION The single-cylinder engine used for the diesel engine tests with a swept volume of 0.39 L was designed for lowest emission levels while at the same time featuring high fuel efficiency.31 A compression ratio of 15:1 was selected to keep the NOx emissions low despite the increased charge density, following typical EURO 6 development strategies. The combustion system reached a specific output of 80 kW/L at maximum peak firing pressures of 220 bar. A common rail system with a maximum fuel injection pressure of 2000 bar was used as the injection system. To optimize the flow characteristics, one intake port was designed as a filling port and the second one was designed as a classic swirl port. Creating charge movement was supported by seat swirl chamfers on both intake valves. The combustion chamber geometry was designed with a conventional recess shape, which was further optimized together with the nozzle geometry (eight hole, ks = 1.5) to achieve the best possible air use.

Figure 2. Investigated load points. The low compression ratio of 15:1, early injection, and high injection pressures, as well as improved EGR cooling, make lowest particulate emissions possible, and as a result, the research engine meets the discussed Euro 6 standard. Table 1 shows a summary of the parameters of the test engine used. Additional information on the single-cylinder research engine can be found in earlier publications.3133 For all fuel investigations carried out on the described engine setup, the center of combustion was kept constant. With this method, a large database of 34 different fuels was established that allows, because of equal calibration parameters, for a direct comparison of the emission performance of the fuel. The database consists of engine results from single-component fuels, such as n-heptane or 1-decanol, as well as mixtures of different diesel type fuels. Thus, the fuels cover a wide range of different fuel properties: (i) cetane number from 30 to 70, (ii) temperature, where 50% of the fuel volume is vaporized (T50%), from 100 to 250 °C, (iii) aromatic content from 0 to 30%, and (iv) oxygen content from 0 to 10% All fuels were analyzed in four load points, three of which are within the NEDC range for an inertia weight class of 1590 kg (see Figure 2). The fourth load point is of interest for future downsizing concepts. Table 2 shows the calibration. All fuels were analyzed with a single injection and at a constant center of combustion, which was chosen differently for the respective load points, whereby in each case, the start of injection was adjusted accordingly. The tolerance for the center of combustion is (0.1 °CA. The constant indicated specific NOx emissions (ISNOx) level was obtained by adjusting the EGR rate accordingly. The other calibration parameters, such as intake manifold pressure, fuel injection pressure, and charge air temperature, had been optimized in earlier studies for a realistic four-cylinder engine with a twostage boosting device, all in compliance with the Euro 6 standard.34 The target of these investigations is to conduct a model-based analysis to investigate multiple correlations of different fuel properties. The model boundaries are chosen to be inside of the range of the properties of the investigated fuel.

4. MODEL-BASED ANALYSIS OF FUEL PROPERTIES A statistical model based on linear regression is set up to analyze the impact of different fuel properties. The linear regression is based on the following least-squares problem:

∑i ðyi ^yi Þ2 f minimum

ð1Þ

where yi is a measured fuel property, in this case a pollutant emission compound, ^yi is the corresponding value, predicted by 4736


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Table 2. Engine Calibration pressure load point 1

center of combustion (°CA BTDC)

rail (bar)

boost (bar, absolute)

exhaust manifold (bar, absolute) 1.13

and IMEP = 4.3 bar

6.6 at 0.5 g/kWh ISNOx

720

1.07

(B) n = 1500 min1 and IMEP = 6.8 bar


900

1.5

1.6

(C) n = 2280 min1 and IMEP = 9.4 bar


1400

2.29

2.39

(D) n = 2400 min1 and IMEP = 14.8 bar


1800

2.6

2.8

(A) n = 1500 min

Figure 3. CO and HC emissions at low load point, n = 1500 min1, IMEP = 6.8 bar, and ISNOx = 0.5 g/kWh.

the model, and (yi ^yi) is the residuum of the model. To model the emissions, the following function was chosen: ^yi ¼ c þ

∑i ðai xi þ bi xi 2 Þ

ð2Þ

In this equation, c, ai, and bi are constant coefficients, to be determined in the least-squares problem, whereas xi is a physical property of the fuel and an input parameter to the model. The advantage of the chosen function is that it allows for quadratic behavior of the inputs together with linear coefficients, hence making linear regression possible. A measure for the quality of a linear regression model is the coefficient of determination, R2 SSReg SSRes R2 ¼ ¼ 1 ð3Þ SSTot SSTot SSTot ¼

∑i ðyi yÞ̅ 2

ð4Þ

where yi is the measured fuel property and y is the average of the data SSRes ¼

∑i ðyi ^yi Þ2

ð5Þ

where yi is the measured fuel property and ^yi is the model prediction SSReg ¼

∑i ð^yi yÞ̅ 2

ð6Þ

where ^yi is the model prediction and y is the average of the data.

The coefficient of determination, however, tends to converge toward 1 if the number of coefficients is approaching the number of model points. Therefore, a more adapted measure was chosen, the adjusted coefficient of determination, R2adj 2 Radj ¼ 1

SSRes ðn 1Þ SSTot ðn pÞ

ð7Þ

where n is the measured points and p is the summation of the model coefficients (including constant). The adjusted coefficient of correlation takes into account the degrees of freedom in the sums of squares, thus attaining high values only for a sufficient overhang of data compared to the model coefficients. This present study only considers models with R2adj > 0.9. A better fit of the data and, thus, a higher R2adj is obtained by removing outliers. The literature survey above shows that most studies focus on the fuel properties of aromatic content, oxygen content, cetane number, and T50%, the temperature where 50 vol % of the fuel is vaporized. Hence, these four parameters were identified as suitable model inputs. While most studies investigate the influence of a single property on the emissions, here, the correlation of the different fuel properties is investigated. The reason is to determine the significance of the different fuel properties on the emission itself and their variation significance at different load points. The results of the model-based analysis with four inputs are shown below. Because two-dimensional plots can only describe the direct influence of two input parameters, the other two input parameters are kept constant in the center of the model boundaries. 4737


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Figure 4. Noise emissions at high load point, n = 2280 rpm, IMEP = 9.4 bar, and ISNOx = 0.5 g/kWh.

Figure 5. Particulate emissions at high load point, n = 2400 rpm, IMEP = 14.8 bar, and ISNOx = 0.3 g/kWh.

As indicated in Figure 3, the cetane number shows the highest influence on both CO and HC emissions, although the influence decreases with an increasing cetane number. A higher aromatic content influences both emissions negatively. While a higher oxygen content and a higher T50% have a positive impact on HC emissions, their influence on the CO emissions is negligible. Both models fit the given data well with R2adj = 0.92; also, the overhang of data is sufficient because there are 3 times as many model points as coefficients. A comparable effect can be observed for the other load points. As seen in Figure 4, also noise emissions mainly depend upon the cetane number, while the impact increases with a decreasing cetane number. The noise emissions were calculated from the measured cylinder pressure traces and converted into the one-dimensional indicator “combustion sound level (CSL)”.35 Generally, the combustion of fuels with very low cetane numbers will result in the highest noise emissions, independent of the aromatic or oxygen content of the fuel. Only the boiling characteristic shows a minor impact on the noise value. With increasing T50%, noise emissions tend to be higher. This behavior can also be seen for the other load points, except at very low load, where an increasing cetane number leads to higher noise emissions. The sums of squares with R2adj = 0.93 remain on a high level. Figure 5 shows the influence of the four input parameters on the particulate emissions at n = 2400 min and IMEP = 14.8 bar. The particulate emissions were calculated from smoke numbers according to the following formula: ISPM ¼

EAVL PIND

ð8Þ

where mass flow particles according to AVL correlation (EAVL) are in g/h and indicated power (PIND) is in kW EAVL ¼

RAVL MPAB RAFB 298 101300

ð9Þ

Figure 6. Critical air/fuel ratio for different fuel molecules.

where mass flow exhaust gas (humid) (MPAB) in in kg/s RAVL ¼ 4051 5:32 103 FSN e0:31FSN

ð10Þ

where gas constant humid exhaust gas (RAFB) is in J kg1 K1. Generally, the oxygen content has the highest impact on soot emissions. An increase of the oxygen amount leads to a reduction of particulate emissions. Furthermore, a clear trend can be seen for the influence of the aromatic content and the boiling characteristic of a fuel. The higher the aromatic content, the more soot emissions will be formed. Soot emissions can be decreased with a lower cetane number, although the reduction is smaller compared to the other input parameters at this high load point. An ideal fuel concerning particulate emissions at high engine loads should have low aromatic content, high volatility, a reduced cetane number, and high oxygen content. Because particulate emissions are on a very low level, a model-based analysis was not feasible for the two lower engine load points. The corresponding values of R2adj indicate that the shown models fit the data well. This fact allows us to draw conclusions from the detected influences of the different fuel properties. One disadvantage of the models is the lack of predictability and 4738


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Figure 7. CO and HC emissions at low load point, n = 1500 rpm, IMEP = 6.8 bar, and ISNOx = 0.5 g/kWh.

Figure 8. Noise emissions at high load point, n = 2280 rpm, IMEP = 9.4 bar, and ISNOx = 0.5 g/kWh.

Figure 9. Particulate emissions at high load point, n = 2400 rpm, IMEP = 14.8 bar, and ISNOx = 0.3 g/kWh.

accuracy for random inputs within the design space. Hence, different model inputs where chosen, still using the same modeling method. As new modeling parameters, the ignition delay and the critical air/fuel ratio were chosen. The critical air/fuel ratio is defined as the quotient of the amount of oxygen required for an oxidation of the fuel to CO and hydrogen (H2) and the amount required for a complete oxidation to carbon dioxide (CO2) and water (H2O)

Figure 7 shows the impact of the critical air/fuel ratio and the ignition delay on HC and CO emissions for n = 1500 rpm and IMEP = 6.8 bar. Both models show a good fit with the given data. Generally, both emissions significantly depend upon the ignition delay time. Additionally, the molecular fuel structure has an inferior impact on CO emissions. These can be reduced with a short ignition delay and a low critical air/fuel ratio, while HC emissions only depend upon the ignition delay. Noise emissions mainly depend upon the ignition delay, as seen in Figure 8. The influence of the molecular structure on the noise value is negligible. Because of a faster combustion, an increase of the engine ignition delay results in higher noise emissions. Figure 9 displays the particulate emissions as a function of the critical air/fuel ratio and the ignition delay. The particulate emissions decrease with an increase in the ignition delay. This is due to the fact that more time is available for the mixture preparation; hence, the combustion is more homogeneous. This effect, however, stagnates for higher ignition delays, as shown in Figure 9. Once the mixture has reached a certain level of homogeneity, the particulate emission cannot be reduced any further by this aspect. The particulate emissions are much more sensitive to a variation in the critical air/fuel ratio, as can be seen by the gradient in the direction of the y axis. To validate and compare the quality and explanatory power of the two different models shown before a cross-validation was applied, a model based on all model fuels, except for one, was built and then used to predict the emissions of the omitted fuel. This procedure was consecutively repeated for all of the fuels investigated. Subsequently, the relative error of the prediction was calculated ^yi 1 100% relative errorprediction ¼ yi

λcrit

C O mair, CO MC MO ¼ ¼ 2C H O mair, st þ MC 2MH MO

ð11Þ

where mair,CO is the air mass required for oxidation to carbon monoxide, mair,st is the air mass required for stoichiometric combustion, C, H, and O are the mass fractions of carbon, hydrogen, and oxygen, respectively, and Mi is the molar mass of the corresponding element. The critical air/fuel ratio as defined above can be understood as an indirect indicator for the relationship of hydrogen, carbon, and oxygen contents of the fuel. It rises with C content and increasing compactness of the molecules, from paraffines via olefins to aromatic compounds. The oxygen content of a fuel leads to significantly lower critical air/fuel ratios. Figure 6 depicts the critical air/fuel ratios of several different molecules. The ignition delay as input is advantageous because it implicitly takes load point parameters into account, such as cylinder pressure and temperature of a specific load point. The critical air/ fuel ratio (λcrit) on the other hand combines different structural properties of the fuel. In the following, the results for the same load points and emissions as before are shown. Here, the model was based on two inputs, the critical air/fuel ratio and the ignition delay.

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Table 3. Accuracy of the Model Prediction for Different Input Parameters two inputs

emission

load point

accuracy of prediction



accuracy of prediction for

for 50% of the sample (%)



100% of the sample (%)

from 0.4 to 0.4

from 0.9 to 1.1

from 0.5 to 0.5

from 1.3 to 1.4

from 0.6 to 0.5

from 1.1 to 0.7

from 0.4 to 0.4

from 1.1 to 2.0

from 9.9 to 8

from 20.5 to 23.8

from 10.5 to 16.2

from 22.1 to 22.5

n = 1500 rpm and IMEP = 6.8 bar

from 5 to 3.4

from 11.5 to 12.3

from 9.4 to 8.3

from 23.1 to 18.4

n = 2280 rpm and

from 5.4 to 3.8

from 10.5 to 17.8

from 4.4 to 11.3

from 35.6 to 23.9

from 29.3 to 11.2

from 39.7 to 26

from 7 to 8.8

from 26.2 to 27.2

from 8.8 to 14.5

from 19 to 26

from 10.1 to 17.3

from 15.2 to 26.8


from 11.8 to 12.8

from 19.4 to 45.8

from 14.2 to 37.8

from 42 to 37.8

n = 2280 rpm and

from 20.8 to 18

from 35.6 to 43.2

from 10.1 to 15.8

from 327.3 to 135.9

from 14.9 to 11.3

from 26.3 to 36.8

from 11.6 to 16.8

from 51 to 47.4

n = 1500 rpm and CSL

four inputs

IMEP = 4.3 bar n = 2280 rpm and IMEP = 9.4 bar n = 1500 rpm and IMEP = 4.3 bar

ISCO

IMEP = 9.4 bar n = 1500 rpm and IMEP = 4.3 bar ISHC

ISPM


IMEP = 9.4 bar n = 2400 rpm and IMEP = 14.8 bar

where ^yi is the model prediction and yi is the measured fuel property. Thereafter, two different error spreads were determined. One ranges from the smallest negative to the smallest positive relative error, which could explain the relative error of 50% of all of the modeled fuels. The other spread ranges from the largest negative to the largest positive error, occurring in all predictions. The results are shown in Table 3. The predictability of the models as indicated by the error spreads is at a high level for all considered properties. The lowest error spreads are found for the combustion noise expression, which is due to the logarithmic nature of that characteristic. A big difference between the 50 and 100% error spread as found for the particulate emissions at the four input model states a big influence of single outliers. In this case, the models are not to be used at their boundaries because these effects might lead to a false prediction. As seen before, there is a trade-off between HC, CO, and noise emissions and particulate matter emissions. To reduce HC, CO, and noise emissions, the fuel should be tailored toward a short ignition delay. Particulate matter can be reduced with a long ignition delay and low critical air/fuel ratio. Both trends are indicated with arrows pointing toward an optimized area for each respective property in Figure 10 for one specific load point (n = 2280 rpm and IMEP = 9.4 bar). By the clearly marked, optimum areas for HC, CO, and noise emissions on the one hand and particulate emissions on the other hand, the challenge of the definition of the optimized fuel is underlined. As a possible fuel for minimum soot emissions, the later discussed 2-methyltetrahydrofurane (2-MTHF)/di-n-butylether blend is shown. Dependent upon the engine load, different parameters have different impacts on emission performance. At high engine, load soot emissions are important, while at lower load, HC and CO emissions are dominant. Therefore, ideal fuel properties directly

Figure 10. Optimized fuel properties with regard to different emissions.

depend upon the engine load. The variance in emissions as a function of the ignition delay and critical air/fuel ratio is given in Table 4 for different load points. The variation is calculated for the optimized fuel properties for each load point and emission, respectively. A variance within the ignition delay has a higher impact than a variance in the critical air/fuel ratio for noise, HC, and CO emissions, whereas the total impact of the ignition delay is reduced with an increasing load for HC and CO emissions. Considering particulate emissions, the critical air/fuel ratio becomes more important for optimized fuel parameters with an increasing load, while the 4740


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Table 4. Variation in Emissions for Given Input Parameters at Optimized Properties Inside Model Boundaries emission CSL

ISCO

ISHC

ISPM

Δemission/Δignition delay

load point

Δemission/Δλcrit


0.1 db/0.1 ms

0.005 db/0.01


0.6 db/0.1 ms

0.005 db/0.01


0.9 g/kWh/0.1 ms

0.037 g/kWh/0.01


0.6 g/kWh/0.1 ms

0.002 g/kWh/0.01


0.6 g/kWh/0.1 ms

0.003 g/kWh/0.01


0.2 g/kWh/0.1 ms

0.0002 g/kWh/0.01


0.2 g/kWh/0.1 ms

0.002 g/kWh/0.01

n = 2280 rpm and IMEP = 9.4 bar n = 2280 rpm and IMEP = 9.4 bar

0.1 g/kWh/0.1 ms 0.001 g/kWh/0.1 ms

0.001 g/kWh/0.01 0.0001 g/kWh/0.01


0.0002 g/kWh/0.1 ms

0.002 g/kWh/0.01

Table 5. Properties of Diesel Fuel and the Tailor-Made Fuel properties

unit

cetane number

EN590 diesel fuel 52.6

EN590 diesel fuel limits

70% 2-MTHF/30% di-n-butylether ∼30

minimum of 51

boiling characteristic 10%

°C

195.4

maximum of 65 vol % at 250 °C

84.6

50%

°C

274.9

minimum of 85 vol % at 350 °C

95.1

90%

°C

328.9

minimum of 95 vol % at 370 °C

147.1

aromatic content

%

24.9

no limit

0

carbon content hydrogen content

% %

86.4 13.6

no limit no limit

71.8 12.7

oxygen content

%

0.1

no limit

15.5

0.340

no limit

0.287

critical air/fuel ratio

Figure 11. Engine results with tailor-made fuel from biomass.

ignition delay dominates at lower engine loads. Overall, the results show that, by optimizing only the fuel structure, the

emission rate of the diesel fuel of today can be significantly reduced. 4741


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Figure 12. Burning characteristics of tailor-made fuel from biomass. The temperatures are mass-averaged temperatures derived from heat release analysis.

Figure 13. Potential of tailor-made fuel for homogeneous low-temperature combustion. The temperatures are mass-averaged temperatures derived from heat release analysis.

5. ENGINE RESULTS WITH TAILOR-MADE FUEL According to the TMFB fuel production approach (see Figure 1) and the Tailor-made fuel requirements from above, 2-MTHF was chosen to be investigated in the engine. 2-MTHF can be synthesized via molecular transformation of levulinic acid to γ-valerolactone and 1,4-pentanediol.36 Thus, 2-MTHF can easily be derived from biomass. Voll et al. have analyzed the pathways from cellulose to 2-MTHF. These results show that an energetic reasonable large-scale production is possible.37 Because

the self-ignition properties of 2-MTHF do not fit all desirable requirements, a blend component is required. Generally, ether molecules show high self-ignitability; thus, di-n-butylether was selected and blended 30% by volume to design fuel that meets the defined requirements. However, an easy and direct access to di-n-butyhlether from lignocellulose via the TMFB approach as shown in Figure 1 for 2-MTHF has to be explored in the future. Properties of the tailor-made fuel blend are given in comparison to diesel fuel in Table 5. 4742


Energy & Fuels Figure 11 shows the results of the four-part load points for the Euro 6 NOx emission level. The potential of the TMFB approach with respect to soot emissions can be seen throughout the entire engine map. Nearly soot-free diesel fuel operation can be implemented with the tailor-made fuel blend in the entire NEDC area. The results confirm the model-based analysis for all engine-out emissions. The longer ignition delay results in increased HC and CO emissions, in particular, at lower engine loads. In comparison to diesel fuel, the CO emissions are 17.45 g/kWh (239%) higher with the TMFB fuel in the lowest load point. HC emissions follow the same trend. When maintaining a constant center of combustion, no major differences within the engine efficiency can be detected. The lower cetane number of the tailor-made fuel leads to an increased maximum burning rate, as shown in Figure 12. Because of the longer ignition delay, the more homogeneous mixture results in a fast combustion with higher peak temperatures. Finally, the higher part of premixed combustion leads to a higher combustion noise level at higher loads. To use the full potential of the tailor-made fuels, one objective was to transfer the idealized low-temperature combustion to the actual engine operation. Because of the lower polytropic exponent, recirculated exhaust gas reduces the combustion temperature and, therefore, NOx emissions. The principal increase of particulate, HC, and CO emissions and the reduction of the engine efficiency causes a limitation of the maximum EGR rate for conventional diesel fuel. As seen in Figure 13, an approximation to low-temperature combustion becomes possible with the tailor-made fuel in very high engine loads (n = 2400 rpm and IMEP = 14.8 bar). The NOx/particle trade-off indicates the high potential for future homogeneous combustion systems when using tailormade fuels instead of diesel fuel. Even with the highest EGR rates, soot emissions can be avoided entirely at high engine loads, while HC and CO emissions remain at a constant level. Additionally, also the engine efficiency can be kept constant even at the highest exhaust recirculation rates. The results indicate that, with tailor-made fuels from biomass, a nearly soot- and NOx-free combustion without any drawbacks is possible. Results derived with this specific engine setup are expected to be qualitative compared to different engine characteristics, because the change in engine hardware (e.g., different injection systems) effects all chosen fuels in the same manner. However, a quantitative conclusion about the influence of the injection system on mixture formation cannot be drawn because it was not investigated.

6. CONCLUSION Tailor-made fuels from biomass have the potential to drastically reduce local emissions as well as greenhouse gases. In this study, different fuel requirements for different engine loads were identified with a model-based analysis. The results support the following conclusions for an application in a diesel combustion process: (1) Generally, a tailor-made fuel should have a low critical air/fuel ratio. This implies that the fuel molecule should consist of a simple chain, without double bonds or ring structures, and should be oxygenated. Thus, soot emissions can be reduced significantly, while HC and CO emissions are not negatively affected. (2) To reduce HC and CO emissions at low engine loads, fuels with a shorter ignition delay are appropriate. In contrast to that, the fuel should have a longer ignition delay at a high engine load, where particulate matter emissions become a

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dominant role. (3) With a mixture of 70% 2-MTHF and 30% din-butylether, it was possible to identify a tailor-made fuel that meets the defined requirements at high part load points. With this fuel blend, particle emissions can be avoided almost entirely even at the greatest EGR rates. Disadvantages are slightly higher noise, HC, and CO emissions at lower part load.

’ AUTHOR INFORMATION Corresponding Author

*Telephone: +49-241-80-98362. E-mail: [email protected].

’ ACKNOWLEDGMENT This work was performed as part of the Cluster of Excellence “Tailor-Made Fuels from Biomass”, which is funded by the Excellence Initiative by the German federal and state governments to promote science and research at German universities. Additional information about the TMFB research approach is available at http://www.fuelcenter.rwth-aachen.de. ’ NOMENCLATURE 2-MTHF = 2-methyltetrahydrofuran °CA BTDC = degrees crank angle before top dead center CFR = cooperated fuel research CO = carbon monoxide CO2 = carbon dioxide CSL = combustion sound level DGM = diethylenglycoldimethylether EAVL = mass flow particles according to AVL correlation EGR = exhaust gas recirculation FSN = filter smoke number HC = hydrocarbon IMEP = indicated mean effective pressure ISCO = indicated specific CO emissions ISHC = indicated specific HC emissions ISNOx = indicated specific NOx emissions ISPM = indicated specific particulate matter MPAB = mass flow exhaust gas (humid) n = revolutions per minute NEDC = new European driving cycle NOx = nitrogen oxide NTC = negative temperature coefficient PIND = indicated power PRF = primary reference fuels RAFB = gas constant humid exhaust gas RAVL = “smoke concentration” according to AVL correlation TMFB = tailor-made fuels from biomass ’ REFERENCES (1) Janssen, A.; Jakob, M.; M€uther, M.; Pischinger, S.; Klankermayer, J.; Leitner, W. MTZ 2010, 71, 922–928. (2) Janssen, A.; Muether, M.; Pischinger, S.; Kolbeck, A.; Lamping, M.; Koerfer, T. SAE [Tech. Pap.] 2009, DOI: 10.4271/2009-01-1811. (3) Pischinger, S.; M€uther, M.; Janssen, A. Tailor-made biofuels— Results from the Cluster of Excellence at RWTH Aachen University. Proceedings of the 31st International Vienna Motor Symposium; Vienna, Austria, April 2930, 2010. (4) Liebig, D.; Krane, W.; Ziman, P.; Garbe, T.; Hoenig, M. SAE [Tech. Pap.] 2008, DOI: 10.4271/2008-01-2471. (5) Hosseini, V.; Neill, W.; Guo, H.; Dumitrescu, C. E.; Chippior, W.; Fairbridge, C.; Mitchell, K. SAE [Tech. Pap.] 2008, DOI: 10.4271/ 2008-01-2471. 4743


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Tailor-Made Fuels from Biomass for Homogeneous ... - ACS Publications

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