Statistical and experimental investigation of single fuel reactivity controlled compression ignition combustion on a non-road diesel engine

Energy Conversion and Management 199 (2019) 112025

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Statistical and experimental investigation of single fuel reactivity controlled compression ignition combustion on a non-road diesel engine

T

⁎

Ganesh Duraisamy, Murugan Rangasamy , Ayyappan Punamalai Ramasankaran Internal Combustion Engineering Division, Department of Mechanical Engineering, College of Engineering – Guindy, Anna University, Chennai 600025, Tamil Nadu, India

A R T I C LE I N FO

A B S T R A C T

Keywords: Reactivity controlled compression ignition Multi-factor optimization Response surface methodology Isobutyl alcohol Cetane improver Polyoxymethylene dimethyl ether

In the present work, an attempt is made to implement the single fuel RCCI combustion on a non-road diesel engine using oxygenated renewable non-fossil fuel. In this study, isobutanol (high octane fuel − low reactivity fuel)/isobutanol-polyoxymethylene dimethyl ether blend (medium Cetane fuel − medium reactivity fuel) based reactivity controlled compression ignition combustion has been investigated on a modified single cylinder nonroad diesel engine to control its engine out emissions and to improve the brake thermal efficiency. Various operating parameters have been optimized for a wide range of engine loads (i.e. from 1.3 bar BMEP to 3.9 bar BMEP) using response surface methodology and statistical significance of the used models have been recognized by analysis of variance. Then, numerical optimization has been performed using the desirability approach of response surface methodology. The predicted solutions are validated by engine experiments. Design of experiments approach saved many experimental trials and helped in understanding the effects of various operating parameters such as PODE energy ratio, the start of injection of a pilot and main fuel, fuel injection pressure of medium reactivity fuel, pilot injection mass, intake air temperature, and EGR on combustion, performance and emission characteristics. The predicted and actual experimental results show that a maximum 97% reduction in soot emission and 83% reduction in NO emission was achieved in single fuel RCCI combustion compared to conventional diesel combustion along with a 10.4% percentage increase in brake thermal efficiency.

1. Introduction Fuel energy has become an essential for human beings like air and water. However, unprecedented exhaustion of petroleum products [1] and implementation of strict emission regulations [2] may restrict the use of diesel engines in industries, urban & rural transportation and electricity generators. Hence, there is an immediate prerequisite to discover sustainable renewable alternative fuels with the advanced combustion technologies for internal combustion engines. So far, many studies were attempted to develop low emission diesel engines with better fuel economy using a variety of renewable alternative fuels

[3–7]. Even though, these studies reduced the use of petroleum-based fuel, the occurrence of mixing controlled combustion leads to emission of higher NOx and soot from the engine. Hence, in recent year’s engine research is focused on cleaner combustion strategies referred as low temperature combustion (LTC) instead of conventional mixing controlled combustion. In LTC strategy, the formation of soot has been suppressed from the elimination of the local rich regions by prolonging the mixing time of air and fuel [8] or by multiple direct injection technique [9]. Besides, the formation of oxides of nitrogen (NOx) has been eliminated by reducing the in-cylinder temperature using lean airfuel mixture and exhaust gas recirculation (EGR) [10].

Abbreviations: ANOVA, Analysis of Variance; aTDC, After top dead center; bBDC, Before Bottom dead center; BMEP, Brake mean effective pressure; bTDC, Bottom top dead center; bTDC, Before Top dead center; BTE, Brake thermal efficiency; CA, Crank angle; CDC, Conventional Diesel combustion; CO, Carbon Monoxide; CO2, Carbon dioxide; COVIMEP, Coefficient of Variation of indicated mean effective pressure; CRDI, Common Rail Direct Injection; DI, Direct Injection; DME, Dimethyl Ether; DoE, Design of experiment; DTBP, Di-tert-butyl peroxide; E10, 90% Gasoline and 10% Ethanol; ECU, Electronic Control Unit; EGR, Exhaust gas recirculation; EHN, Ethyl Hexyl Nitrate; H2, Hydrogen; HC, Unburned Hydrocarbon; HCCI, Homogeneous Charge Compression Ignition; HCII, Homogeneous Charge Induced Ignition; HCNG, Hydrogen Compressed Natural Gas; IAT, Intake air temperature; IMEP, Indicated mean Effective Pressure; IS, Indian Standard; LRF, Low Reactivity Fuel; LTC, Low temperature Combustion; MPRR, Maximum Pressure Rise Rate; MRF, Medium Reactivity Fuel; NO, Nitric Oxide; NOx, Nitrogen Oxide; PCCI, Premixed Charge Compression Ignition; PER, POD Energy ratio; PFI, Port fuel injection; PIM, Port injection mass; Pinj, Injection Pressure; PM, Particulate Matter; PODE, Poly Oxy Methylene Dimethyl Ether; RCCI, Reactivity Controlled Compression Ignition; RoPR, Rate of pressure rise; RSM, Response Surface Methodology; SCCI, Stratified Charge Compression Ignition; SOImain, Main Start of Injection; SOIpilot, Pilot Start of Injection ⁎ Corresponding author. E-mail address: [email protected] (M. Rangasamy). https://doi.org/10.1016/j.enconman.2019.112025 Received 3 July 2019; Received in revised form 1 September 2019; Accepted 4 September 2019 Available online 13 September 2019 0196-8904/ © 2019 Elsevier Ltd. All rights reserved.

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observed in EHN mixed high reactivity fuel. In addition, few other researchers also performed single fuel RCCI experiments using onboard reformers. Chuahy et al. [51] demonstrated single fuel RCCI combustion by producing syngas (CO and H2) from the high reactivity diesel fuel and this was able to achieve lower emissions nonetheless with lower thermal efficiency. Another investigator Hwang et al. [52] has done a partial reformation of high reactive diesel fuel into low reactive products using a thermally integrated reforming reactor and used it as a low reactivity fuel. Through this method, RCCI like NOx and soot emission were achieved. However, the thermal efficiency is decreased below the conventional diesel combustion (CDC). Hence, in the present investigation, instead of using Cetane improvers such as DTBP and EHN, poly oxy-methylene dimethyl ether (PODE) fuel added with Isobutanol are used to form a medium reactivity fuel (MRF). This Isobutanol doped with PODE (i.e. MRF) is injected directly into the cylinder instead of high reactivity diesel fuel. Neat Isobutanol fuel is injected in the port and is used as a low reactivity fuel. PODE is an alternative fuel which has a higher Cetane number (> 78) and higher volatility which means it is more reactive and volatile than diesel [53]. Further, isobutanol has a Cetane number of nearly 15 which is higher compared to other low reactivity fuels like gasoline, methanol, ethanol, natural gas and hydrogen. Hence, adding a small portion of PODE with isobutanol can make single fuel RCCI operation a viable one. Based on the literature and previous experimental work [40], a numerical and experimental investigation have been attempted in this work, to study the effect of Isobutanol (low reactivity)/Isobutanol doped with PODE (medium reactivity) single fuel RCCI combustion on the non-road diesel engine. The author’s previous study was performed with dual fuel RCCI combustion (Isobutanol/diesel) on non-road diesel engine which demonstrated with the higher thermal efficiency and lower emissions [40].

Until now, different modes of LTC concepts have been developed by various researchers such as, homogeneous charge compressions ignition (HCCI) [11–13], premixed charged compression ignition (PCCI) [14–16], stratified charge compression ignition (SCCI) [17,18], reactivity controlled compression ignition (RCCI) [19–21] and proved as viable through a series of experimentation. LTC concept has the potential to breakdown the trade-off relationship between NOx and soot emissions. Even so, HCCI and PCCI combustion modes are challenging due to misfire and higher ringing intensity problems at low and high loads respectively [22]. Hence, to avoid such issues in HCCI and PCCI, various strategies like intake air preheating [23], higher injection pressure [24], EGR rate [25] and multiple injections [26] have been implemented and studied. In fact, these strategies were expensive and also leads to a complicated structural design. In this scenario, Bessonette et al. [27] revealed that, the fuel for LTC should have auto-ignition qualities between those of diesel and gasoline fuel. Jiang et al. [28] conducted a study on gasoline homogenous charge induced ignition (HCII) by admitting the gasoline in the intake port and injecting the small quantity of diesel directly into the cylinder. Jiang et al. concluded that, HCII has a superior impact on both NOx and particulate matter (PM) emissions. In another study, Inagaki et al. [29] investigated a dual fuel PCCI operation on a diesel engine by injecting two different reactivity fuels such as isooctane and diesel by port and direct injection respectively. They reported that, combustion phasing can be controlled by adjusting the fuel reactivity inside the cylinder by varying the energy ratio between low to high reactivity fuels. To accomplish this, Kokjohn et al. [30] performed an experiment on a single cylinder diesel engine by altering the in-cylinder reactivity by varying the ratio between gasoline and diesel and the approach is referred as reactivity controlled compression ignition. They compared the obtained results with conventional diesel combustion and found that the gross indicated thermal efficiency has been increased with a significant reduction in NOx and soot emissions. In continuation of this, Curran et al. [31] performed RCCI experiments on a multi-cylinder diesel engine and found an analogies trends with efficiencies and emission patterns at entire load and speed conditions. Based on this, numerous investigation has been done by various researchers on dual fuel RCCI combustion with an aim to implement the concept on the onroad diesel engines [32–35]. Further, considerable investigations have been done using variety of fuel combinations such as gasoline/diesel [36–37], gasoline/PODE [38], gasoline/biodiesel [39], Isobutanol/ diesel [40] ethanol/diesel [41], methanol/diesel [42], 2-butanol/diesel [43], Dimethyl ether (DME)/ethanol [44], compressed natural gas (CNG)/diesel [45], (hydrogen + CNG) HCNG/diesel [46], biogas/ diesel [47] to understand the fuel effects on RCCI combustion. In dual fuel RCCI combustion, two different reactivity fuels are required to generate reactivity difference inside the cylinder. The potential limitation of RCCI combustion is the possession of two fuel supply system that could increase the cost of the vehicle. To address this issue, some researchers have conducted single fuel RCCI (SFRCCI) experiments using gasoline and gasoline doped with cetane improvers such as 2-ethylhexyl nitrate (EHN) and di-tert-butyl peroxide (DTBP). Splitter et al. [48] investigated the single fuel RCCI combustion on a heavy-duty diesel engine using gasoline as a low reactivity fuel (LRF) and Cetane improved gasoline (gasoline + DTBP) as a high reactivity fuel. This approach is able to achieve RCCI combustion by mixing 2% of DTBP with the gasoline fuel. Similarly, Kaddatz et al. [49] also performed a single fuel RCCI experiments using E10 (90% gasoline and 10% ethanol) as a low reactivity fuel and 3% of EHN mixed E10 as a high reactivity fuel. They found that, E10 doped with 3% EHN has lower reactivity than diesel fuel. In addition, more E10 + EHN mixture is required to achieve similar combustion phasing which results in increased oxides of nitrogen emission due to fuel bound nitric oxide (NO) from EHN. Dempsey et al. [50] also performed a single fuel RCCI experiments using EHN and DTBP as reactivity improver and concluded that EHN is superior to DTBP. However, higher NOx emission was

2. Experimental methodology 2.1. Test setup and facilities Experiments have been conducted on a single cylinder, naturally aspirated, Genset diesel engine with suitable modifications. The key specifications of the test engine are given in Table 1. The required instruments and facilities of the test setup for achieving single fuel RCCI combustion and its specification are listed in Table 2. The schematic and photographic view of the experimental setup is shown in Figs. 1. Test engine has been coupled to a water-cooled eddy current dynamometer and open loop digital controller for loading the engine. Isobutanol fuel has been injected using alcohol compatible fuel injector placed in the intake port. The port injection fuel pressure has been controlled and maintained by manually operated gate valves. Injection quantity (injection duration) of port injected Isobutanol has been controlled using the Arduino based microcontroller. Cetane number improved Isobutanol referred here as medium reactivity fuel (Isobutanol + PODE blend) has been directly injected into the cylinder using the common rail direct injection (CRDI) system. Direct injection of medium reactivity fuel has been controlled using the NiRa i7 open electronic control unit (ECU) which takes inputs from various sensors like crank, cam, manifold absolute pressure, mass air flow, rail pressure and oil temperature sensors. Both port and direct injected fuel Table 1 Key specifications of the test engine.

2

Make and model

Kirloskar TAF1

Bore and stroke (mm) Rated output (kW @ rpm) Compression ratio Air charging system Cooling system

87.5 & 110 4.4 @ 1500 17.5:1 Naturally aspirated Air-cooled

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Smoke [% of Opacity] N = 0.12 FSN3 + 0.62 FSN 2 + 3.96 FSN

Table 2 Instruments details.

(1)

Instruments

Details

Dynamometer

Benz, Eddy Current dynamometer, 1500–6000, Water cooled. SAJ, Digital Controller, Constant speed type AVL, GO31D, Air cooled, Range 0–250 bar AVL Indismart 512, 14 Bit/800 kHz per channel AVL 437C opacity meter AVL Digas analyser 444 MACS gravimetric balance Range : 0–10 kg, accuracy: 0.001 g AVL 365CC optical encoder Nira i7 hardware and Simulink based software Arduino Uno Bosch HFM5, 0–80 kg/h

Dynamometer Controller Pressure Transducer Combustion analysis system Smoke meter Gas analyser (HC, NO, CO, CO2) Fuel consumption meter Crank angle encoder Direct injection fuel control Port fuel injection control Air flow meter

4.95 FSN (0.38 ∗ FSN) e 0.405

(2)

Soot(B) ∗ (mair ∗ mfuel ) ∗ 3.6 1000 1.165 ∗ BP

(3)

Soot (B) [mg/m3] = Soot [g/kWh] =

where N – Opacity in %, FSN – Filter smoke number. A cooled EGR system has been integrated with the test setup to study its effect on the single fuel RCCI combustion. Cooled EGR system consists of EGR cooler, vacuum operated valve, pulse width vacuum modulator and a vacuum pump. The required EGR rate was supplied by actuating a vacuum operated valve. The valve lift is directly proportional to the rate of supply of EGR and the lift is controlled by a pulse width modulator. The pulse width modulator is actuated by a NiRa i7 open ECU depending upon the demand. EGR rate is estimated by measuring the concentrations of CO2 in the intake and the exhaust manifolds using AVL Digas analyser.

consumptions have been measured using MACS gravimetric balance. The intake air temperature has been maintained and controlled using a 1.5 kW tubular heater with Shimax MAC5D temperature controller. The combustion parameters have been analysed using 2 channel AVL Indi smart 512 data acquisition system. Flush mounted 0–150 bar AVL GO31D piezo-electric pressure transducer was used to measure the in-cylinder pressure. The AVL 365C optical encoder has been used to measure the crank angle position. 100 consecutive cycle average of incylinder pressure and crank angle data have been used to calculate the combustion parameters. AVL 444 di-gas analyser has been used to measure the concentrations of an engine out gaseous emissions such as hydrocarbon (HC), carbon monoxide (CO), carbon dioxide (CO2) and nitric oxide (NO) and AVL 437c opacity smoke meter has been used to measure the smoke emission. The measured smoke emission was converted into soot concentration using the following correlation (1)–(3) [10]

EGR percentage Concentration of CO2 in intake air − Concentration of =

CO2 in atmosphere Concentration of CO2 in exhaust gas − Concentration

× 100

of CO2 in atmosphere (4) 2.2. Test fuels The fuels (Diesel, Isobutanol and Polyoxymethylene dimethyl ether (PODE)) used in the present work have been procured from the local fuel stations, M/s Alpha Chemika, India and M/s Beyond Industries (China) Limited respectively. PODE has been used in small proportions with the Isobutanol to improve its reactivity and used as a medium

Fig. 1a. (a) Schematic diagram of single fuel RCCI experimental setup. 3

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Fig. 1b. (b) Real photographic view of the experimental setup (i) Modified head (ii) Back view (iii) Side view and (iv) Front view.

methodology for modelling and analyzing the engineering problems. RSM is used to develop a functional relationship between a number of various input factors A (A1, A2, A3,…, Ak) and the response (B) using relationship denoted in Eq. (5)

Table 3 Test fuel properties. Properties

Standards

Iso-butanol

Diesel

PODE

Lower heating value [ MJ/kg] Cetane number [-] Density @ 25 °C [kg/m3] Kinematic Viscosity @ 20 °C [mm2/s] Molecular mass [g/mol] Mass fraction [%] Oxygen [%] Hydrogen [%] Carbon [%]

– ISO 5165 ISO 3675 ISO 3104 – –

18.7 <5 790 0.98 32.04

41.7 48 840 3.2 190

21.8 60.7 1047 2.577 –

49.9 37.5 12.6

0 13 87

47.3 8.8 43.9

B = f ' (A)·β + ε

(5)

where f (A) is a vector function, β is vector constant-coefficient and ε is an error in the experiments which is assumed to have a zero mean. In the present study, 1st order degree polynomial model is used to predict the response that is indicated in Eq. (6). 7

B = β0 +

∑ βi Ai

+ε

i=1

reactivity fuel which enabled single fuel RCCI combustion operation. The properties of the test fuels were measured as per the Indian standard IS 15607/2005 and listed in Table 3.

= βo + β1 A1 + β2 A2 + β3 A3 + β4 A 4 + β5 A5 + β6 A6 + β7 A7 + ε

(6)

Seven operating parameters such as PODE energy ratio (PER), direct injection pressure (Pinj), exhaust gas recirculation (EGR), start of main injection timing (SOImain), start of pilot injection timing (SOIpilot), pilot injection mass (PIM) and intake air temperature (IAT) have been selected for DoE approach. The main responses such as nitric oxide (NO), hydrocarbon (HC), carbon monoxide (CO), soot, brake thermal efficiency (BTE), indicated mean effective pressure (IMEP), maximum pressure rise rate (MPRR) and coefficient of variation of IMEP (COVIMEP) were studied. A central composite rotatable design has been used to create the test matrix. Because, this design provides accurate predictions compared to other designs [54]. In addition, the factorial portion of central composite rotatable design has a small factorial design with zero center points which is helpful in reducing the number of test runs. Table 4 shows the input variables selected for the study with its lower and higher values. A commercially available Design-Expert 11 statistical software has been used to develop a RSM model. The central composite rotatable design provides 36 experimental data set consists of a

2.3. Methodology First, the engine was tested under the conventional diesel combustion mode to collect the baseline data for comparing the combustion, performance and emission characteristics of single fuel RCCI combustion. The baseline test was performed as per the operating conditions prescribed by the stock engine manufacturer (Pinj: 200 bar, SOI: 23° CA bTDC). Then, the test engine was modified to operate in single fuel RCCI combustion. From the baseline data and previous work [40], the operating parameters for single fuel RCCI combustion were optimized using a design of experiment (DoE) approach called response surface methodology (RSM). RSM is a statistical technique used for design, prediction and optimization of the responses (Output) that is influenced by various factors (operating parameters). Wilson and Box introduced and used RSM 4

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between CDC and single fuel RCCI combustion mode to show the benefits and limitation of single fuel RCCI combustion over the conventional diesel combustion.

Table 4 Input factors and its range. Operating parameters

Notation

Symbol

Unit

Lower value

Higher value

PODE energy ratio Start of main injection Start of pilot injection Pilot injection mass Intake air temperature Direct injection pressure Exhaust gas recirculation

PER (SOImain) (SOIpilot) PIM IAT Pinj

A B C D E F

% °CA bTDC °CA bTDC % K bar

5 10 30 10 303 300

15 30 45 60 333 600

EGR

G

%

0

20

3. Results and discussion The results of the present work are organized and presented in three sections. The first section explains the effect of input variables (factors) on output responses of single fuel RCCI combustion that is explained using contribution percentage obtained from the statistical investigation. The second section presents the results of the numerical optimization (best optimized operating parameter) and its desirability using perturbation plots. In the final section, the validation of predicted value and the comparison between CDC and single fuel RCCI combustion mode at different load conditions is presented.

different combination of input factors. Experiments have been conducted on the test engine based on the test matrix and the corresponding responses have been recorded. Then, the RSM approach was used to develop a model for each response. After developing a model for responses, numerical optimization was performed by converting each response into desirability value. Desirability value is a dimensionless value which is varied from 0 (unacceptable) to 1 (desirable). During the numerical optimization, a set of goals assigned for each response that may be either to minimize, maximize, in range, target, and/or equal along with weight and importance of the response. Weight is assigned to a goal to adjust the shape of desirability function d (A). In the design expert software, the weight of each goal can be varied from 0.1 to 10. The importance (ki) of responses is assigned to define the importance of a particular response relative to the other responses. The “importance” value varies from one plus (+) to 5 pluses (+++++) which means least important to most important. Different importance are assigned for different responses and the desirability function is estimated using Eq. (7), n

3.1. Analysis of model for single fuel RCCI combustion In this study, analysis of variance (ANOVO) has been used to validate the model developed for responses and to identify the significance of each factor affecting the responses. To reduce the number of terms in the regression model, a simple linear equation has been used in this study. Table 5 shows the linear regression equation developed for evaluating various responses of single fuel RCCI combustion derived by RSM approach. The deviation of test data is assessed by sum of squares. The mean square of the test data is calculated using Eq. (12).

Mean square (MS) =

(7)

i

where n = number of responses For achieving a different type of goals such as minimize, maximize, target, in range the desirability function is defined by following Eqs. (8), (9) and (10) [55] For minimize

1, Bi ≤ Lowi ⎧ ⎪ Highi − Bi di = [ High − Lowi ]Wti Lowi < Bi < Highi i ⎨ ⎪ 0 Bi ≤ Highi ⎩

F − ratio =

(8)

Contribution % =

(9)

(13)

Sum of the squared deviation (SSd) × 100% Total sum of squared deviation (SSt ) (14)

In range,

Bi ≤ Lowi ⎧ 0, di = 1, Lowi < Bi < Highi ⎨ Bi ≥ Highi ⎩ 0,

3.2. Interactive effect of dominant input factors on emission characteristics of single fuel RCCI combustion.

(10)

After finding the optimized operating parameters, experiments have been conducted for validation. The simplified schematic diagram of the experimental procedure is shown in Fig. 2. The percentage of absolute error has been calculated using Eq. (11). [56]

Percentage of error =

Mean square of the term Mean square of the residual

The significance of each factor on responses are checked using probability values (p-values). Generally smaller p-value means more significant with the corresponding factor. If the p-value is less than 0.005 means, the factors are significant at 95% confidence level [57]. The contribution percentage of each factor to the corresponding responses were calculated using Eq. (14) [56] and it is also given in Tables 6 and 7 for emission and performance parameters responses respectively.

For Maximize

0, Bi ≤ Lowi ⎧ ⎪ Bi − Lowi Wti Lowi < Bi < Highi di = [ Highi − Lowi ] ⎨ ⎪ 1, Bi ≥ Highi ⎩

(12)

The fitness of the regression model is checked using “R-square” (R2) value. The R2 value of the developed regression equation is near to 1 and if predicted R square (Prec. R2) has a reasonable agreement with the adjusted R square (Adj. R2) (i.e. if the difference between Adj. R2 and Prec. R2 is less than 0.2) which means the model is well fitted. The accuracy of the model is checked using F- ratio and it is calculated using Eq. (13),

1

D = ( ∏ dir1) ∑ ri

Sum of square degree of freedom

In this section, the effect of input factors on emission characteristics of single fuel RCCI combustion is discussed using an interactive plot between two dominant input factors with another input factor as constant. Table 6 shows the results of ANOVA for the emission responses of single fuel RCCI combustion obtained from the linear model. It is observed that, among the seven given input factors, SOIpilot, PER, PIM and EGR have major impact on NO emission. Whereas, the other input factors such as SOImain, IAT and Pinj have minimum impact only on NO emission. Fig. 3 shows the effect of dominant input factors on NO emission of

Experiment result − predicted result × 100% Experiment result (11)

The similar procedure has been followed at different load conditions at a rated speed of 1500 rpm. Finally, the comparison has been made 5

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Fig. 2. Schematic diagram of the experimental methodology. Table 5 Regression equation for developed model. Responses

Developed regression equation

NO [g/kWh] HC [g/kWh] CO [g/kWh] Soot [g/kWh] IMEP [bar] COVIMEP [%] RoPR [bar/deg] BTE [%] Where, A – PER, B – SOImain, C – SOIpilot , D

+0.7531 + 0.1631 A + 0.0062 B + 0.1660 C − 0.1367 D + 0.0267 E − 0.0534 F − 0.0472 G +7.24–1.04 A − 0.2840 B − 1.00 C + 0.6146 D − 0.5648 E − 0.0954 F + 0.5503 G +16.64–0.7662 A − 1.25 B + 0.8919 C + 0.1550 D + 0.6523 E − 0.1331 F + 1.33 G +0.0001 + 0.0000 A − 0.0000 B − 3.531 E − 06 C − 0.0000 D + 9.166E − 06 E − 7.466E − 06 + 0.0001 G +3.09 + 0.0965 A − 0.0196 B − 0.0275 C − 0.0362 D − 0.0325 E + 0.0231 F − 0.0056 G +3.38–1.74 A − 0.4150 B − 0.0593 C − 0.0197 D − 0.1323 E − 0.0389 F − 0.1407 G +3.84 + 1.76 A + 0.1973 B + 0.0998 C − 0.1604 D + 0.0790 E + 0.0950 F − 0.1190 G +23.64–0.1419 A − 0.1419 B + 0.0615 C − 0.0735 D − 0.2385 E − 0.1158 F − 0.0346 G – PIM, E – IAT, F – Pinj, G – EGR.

Table 6 ANOVA results of single fuel RCCI mode emission characteristics. Source

Model A-PER B-SOImain C- SOI Pilot D-PIM E-IAT F- Pinj G-EGR R2 Adj.R2 Prec. R2 A. Precision

NO

Soot

CO

HC

Linear model

Linear model

Linear model

Linear model

F ratio

p-value

%

33.32 87.11 0.12 90.63 61.23 2.35 9.33 28.1 0.89 0.87 0.85 18.5

< 0.0001 < 0.0001 0.7271 < 0.0001 < 0.0001 0.1365 0.0049 < 0.0001

31.2 0.01 32.5 22.0 0.8 3.3 10.0

F ratio

p-value

%

65.59 31.73 10.25 0.71 7.79 4.79 3.17 387.07 0.94 0.92 0.91 19.9

< 0.0001 < 0.0001 0.0034 0.4063 0.0094 0.0371 0.0860 < 0.0001

7.1 2.3 0.2 1.7 1.1 0.7 86.9

F ratio

p-value

%

21.55 16.37 43.70 22.26 0.67 11.90 0.49 49.71 0.92 0.90 0.89 18.9

< 0.0001 0.0004 < 0.0001 0.0001 0.4200 0.0018 0.4881 < 0.0001

11.3 30.1 15.3 0.5 8.2 0.3 34.3

F ratio

p-value

%

24.11 64.60 4.79 60.04 22.44 19.01 0.54 18.04 0.95 0.93 0.92 21.08

< 0.0001 < 0.0001 0.0371 < 0.0001 0.0001 0.0002 0.4682 0.0002

34.1 2.5 31.7 11.8 10.0 0.3 9.5

Table 7 ANOVA results of combustion and performance parameter. Source

Model A-PER B-SOImain C- SOI Pilot D-Pilot Mass E-IAT F- Pinj G-EGR R2 Adj.R2 Prec. R2 A. Precision

IMEP

COVIMEP

RoPR

BTE

Linear model

Linear model

Linear model

Linear model

F ratio

p-value

%

103.8 486.8 20.1 468.8 334.4 55.2 27.8 1.6 0.96 0.95 0.93 23.47

< 0.0001 < 0.0001 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.2094

34.8 1.4 33.6 23.9 3.9 2 0.1

F ratio

p-value

%

626.5 411,401 234.5 4.8 0.52 23.93 2.05 1327 0.99 0.99 0.93 42.2

< 0.0001 < 0.0001 < 0.0001 0.0369 0.4741 < 0.0001 0.1623 < 0.0001

72 4.1 0.08 0 0.4 0.03 23.2

6

F ratio

p-value

%

404.1 369.3 1251.26 8.81 22.68 5.5 7.9 1688.45 0.977 0.973 0.95 38.07

< 0.0001 < 0.0001 < 0.0001 0.006 < 0.0001 0.0259 0.0086 < 0.0001

11 37.3 0.26 0.67 0.1 0.2 50.3

F ratio

p-value

%

112.1 280 43.5 250.72 32.18 30.8 12.7 142.5 0.97 0.96 0.924 31.02

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0013 < 0.0001

35.3 5.5 31.6 4 3.9 1.6 17.9

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Fig. 3. Effect of dominant input factors on NO emission of single fuel RCCI combustion.

From Table 6, it is inferred that, only EGR and PER have statistical significance on soot emission of the single fuel RCCI combustion at 95% confidence level. The contribution of EGR on soot emission is nearly 87% followed by PODE energy ratio of 7.1%. All other input factors have minimal effect on soot emissions. Fig. 4 shows the combined effect of EGR and PER on the soot emission of single fuel RCCI combustion. It is observed that, soot emission is increased at higher EGR and PER. This may be due to the substitution of EGR and PER which reduces the oxygen level and the mixing period of air-fuel mixture respectively to cause the increased soot emission. In diesel engine, generally the formation of CO emission is mainly due to lack of oxygen and lower in-cylinder temperature. The increase in EGR reduced the oxygen concentration inside the cylinder due to the dilution effect of EGR. In addition, the in-cylinder gas temperature also

single fuel RCCI combustion. Fig. 3(a) shows the combined effect of PIM and SOIpilot on NO emission of single fuel RCCI combustion. It is observed that, lower PIM with advanced SOIpilot of MRF (Isobutanol doped with PODE) resulted in higher NO emission due to higher incylinder pressure and temperature. The in-cylinder pressure and temperature are increased due to the availability of more premixed fuel-air mixture inside the cylinder. Fig. 3(b), 3(c) and 3(d) show the interactive effect of PER with PIM, SOIpilot and EGR. It is observed that, increase in doping ratio of PODE alters the reactivity gradient inside the cylinder, which results in increased NO emission due to increase in in-cylinder temperature. In addition, it is observed that lower PER with higher percentage of EGR exhibited a lower NO emission due to decreased incylinder temperature instigated by the higher specific heat capacity of H2O and CO2 present in the exhaust gas. 7

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Fig. 6. Effect of PER and SOIpilot on HC emissions of single fuel RCCI combustion.

Fig. 4. Effect of EGR and PODE energy ratio on soot emissions of single fuel RCCI combustion.

cylinder due to altered reactivity gradient, which results in enhanced oxidation of fuel-air mixture occupied in the crevice and squish regions. Further, advanced SOIpilot fuel may assist in oxidizing the fuel-air mixture accumulated in the squish region by extending the oxidation duration. It is clearly observed in Fig. 6. It is also observed from Table 6 that, direct injection pressure and intake air temperature have only negligible impact on the emissions of single fuel RCCI combustion. However, these two factors show minor impact on the CO (8.2%) and HC (10%) emissions. On the whole, PER and EGR are the two influential factors showing major impact on the emissions of single fuel RCCI combustion.

3.3. Effect of input factors on combustion and performance characteristics of single fuel RCCI combustion. Table 7 display the ANOVA data for combustion and performance parameters of single fuel RCCI combustion. It is observed that, indicated mean effective pressure of single fuel RCCI combustion dominantly influenced by PER (34.8%), SOIpilot (33.6%) and PIM (23.9%). Li et al. [58] documented that, higher in-cylinder pressure is observed while operating the engine using PODE blended diesel fuel compared to diesel fuel due to better combustibility characteristics and an improved expansion process. The higher latent heat of vaporization of PODE is helpful in reducing the in-cylinder temperature and pressure during the compression stroke which results in reduced compression work and increased IMEP in single fuel RCCI combustion. Advanced SOIpilot fuel along with an increase in PIM resulted in advanced start of combustion and it may increase the in-cylinder pressure and compression work of the engine. Interactive effect of PODE with SOIpilot fuel and PIM is depicted in Fig. 7. It is observed that, increase in PODE energy ratio, retarded the SOIpilot fuel and lowers the PIM which increased the IMEP nearly by 3.2 bar. The major limitation of dual fuel RCCI combustion is higher cycle to cycle variation of IMEP (unstable engine operation) due to variation in the intake air temperature and pressure caused by the port injected fuel. The similar issue is also observed in the present investigation of single fuel RCCI combustion. From Table 7 it is noticed that, PODE energy ratio and EGR are the two major factors influencing the COVIMEP of single fuel RCCI combustion. Substitution of increased

Fig. 5. Interactive plot of SOImain and EGR on CO emissions.

reduced due to the heat capacity effect of EGR. This also shown in Table 6 and Fig. 5. From the ANOVO result, it is observed that EGR contributes 34% followed by 30% of SOImain and other input factors showing a minor impact on CO emission in the case of single fuel RCCI combustion. Fig. 5 shows the combined effect of EGR and SOImain on CO emission of single fuel RCCI combustion. It is observed that, higher percentage of EGR with retarded SOImain increased the CO emission due to decreased in-cylinder temperature and retarded combustion phasing respectively. From Table 6, it is inferred that HC emissions of single fuel RCCI combustion is mainly influenced by two factors (i) PER (34.1%) and (ii) SOIpilot (31.7%). In general, the formation of HC emission is mainly due to the trapped fuel-air mixture in the crevice volume and reduced oxidation. Increased PER, improves the radical formation inside the 8

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Fig. 7. Effect of dominant input factor on IMEP of single fuel RCCI combustion.

Fig. 9. Effect of dominant input factor on RoPR of single fuel RCCI combustion.

Fig. 8. Effect of dominant input factor on COVIMEP of single fuel RCCI combustion.

4.1 to 3.6 bar/deg CA. Brake thermal efficiency of Single fuel RCCI combustion is dominantly influenced by PER and SOI of pilot fuel. Fig. 10 shows the combined effect of PER and SOIpilot on BTE. It is observed that, lower PER with advanced SOIpilot fuel results in increased BTE. This may be due to, increased mass of port injection of isobutanol and advanced direct injection of pilot fuel which may be increased the ignition delay. The increase in ignition delay enhanced the air–fuel mixture formation and produced a well homogeneous air-fuel mixture, which leads to shorter combustion duration and improved BTE. Again it is observed that, direct injection pressure and inlet air temperature have only minor impact on the performance and combustion characteristics of single fuel RCCI combustion. Whereas, other factors are marginally influencing the performance and combustion characteristics.

percentage of EGR and PODE increases the ignition delay (better homogeneous charge) and improves the reactivity inside the cylinder which results in lower percentage of coefficient of variation as shown in Fig. 8. The maximum thermal efficiency of the engine generally realized with shorter combustion duration and maximum peak pressure. However, which leads to higher RoPR and engine damage. Hence, limiting the RoPR is very important while introducing new combustion concepts. From Table 7 it is observed that, RoPR of single fuel RCCI combustion is dominantly influenced by EGR and SOImain. While increasing EGR with late SOImain fuel. Further retarding the start of combustion which results in retarded combustion phasing and lower RoPR and it is clearly observed in Fig. 9. Also, while increasing the percentage of EGR from 0 to 20% and retarding SOImain from 20 to 10° CA bTDC, the RoPR of single fuel RCCI combustion is decreased from 9

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Table 9 Best optimized set of operating conditions for single fuel RCCI combustion. Parameters/Load

Unit

1.3 bar

2.6 bar

3.9 bar

PER SOImain SOIpilot PIM EGR Pinj IAT Desirability value

% °CA bTDC °CA bTDC % % bar K %

12.4 20 31 10 7.8 478 310 0.86

8.6 12 22 7 14 512 306 0.88

5.3 8 Nil 0 20 586 303 0.87

conditions, because it is structurally unsafe. The perturbation plots generally used to compare the effect of all the input factors at a particular point in the design space [59]. The response was plotted by changing only one factor over its range while holding all other factors as constant. A steep slope indicates that, that factor is very sensitive to the response and flat line indicates that, the factor is insensitive. Fig. 11 shows the perturbation plot of emission characteristics of single fuel RCCI combustion at 1.3 bar BMEP load condition. From Fig. 11(a) it is observed that, operating parameters such as PER, SOIpilot, PIM are dominantly influencing the target NO emission. From the optimized point, slightly decreasing PER and increasing PIM, significantly reduces the NO emission. Similarly, an advance in SOIpiot significantly increases the NO emission. However, other input parameters such as SOImain, IAT, EGR, Pinj have marginally impact only on the target NO emission. In the case of soot emission, EGR and PER have more sensitive with the optimized point. Small changes in the EGR drastically increases or decreases the soot emission and it is clearly observed in Fig. 11(b). Fig. 11(c) and (d) shows the perturbation plot of HC and CO emission concentration. It is observed that, SOImain and EGR are more sensitive in achieving the goal. It is observed that, advancing SOImain and decreasing EGR from the optimized point, the HC emissions of single fuel RCCI combustion is drastically increased. Formation of CO emission in single fuel RCCI combustion is more sensitive with SOIpiolt and less sensitive with the other factors. Overall it is observed that, except IAT and Pinj. all other input factors have more sensitivity towards achieving the emission goals of single fuel RCCI combustion. Fig. 12 shows the sensitivity of each factor on the combustion and performance characteristics of single fuel RCCI combustion. It is observed that, IMEP and COVIMEP are more sensitive with PER. The slight variation in the PER significantly affecting the IMEP and COVIMEP. Similarly, the RoPR and BTE are more sensitive with PER and minor sensitive with other parameters.

Fig. 10. Effect of PODE energy ratio and SOIpilot on brake thermal efficiency.

3.4. Numerical optimization The complete analysis of the input factors on the performance and emission characteristics of single fuel RCCI combustion is discussed in the above section and it has been found that all the input factors having its own advantage and disadvantages with regard to performance improvement and emission reductions in the case of single fuel RCCI combustion. Hence, it is inevitable to optimize a set of input factors for maximizing the performance and minimizing the emission aspects with some targets. Based on this, in this study, to identify the optimized operating parameter for single fuel RCCI combustion, the numerical optimization is performed by using set of criteria in the design expert software. The set of criteria (goal and weight of each response) used for the present optimization are listed in Table 8. Numerical optimization provides 100 sets of solutions with the maximum desirability value of about 0.86. It is observed that, the combination of PER-12.4%, SOImain – 20° CA bTDC, SOIpilot – 31° CA bTDC, PIM – 10%, IAT-310 K, Pinj – 478 bar and EGR rate of 7.8% provides the maximum desirability value of about 0.86. Hence, it is considered as an optimum set of input factor for this particular speed (1500 rpm) and load (1.3 bar BMEP) condition. Similar procedure was followed for 2.6 bar BMEP (50% load) and 3.9 bar BMEP (75% load) load conditions and optimized set of operating conditions were obtained to achieve the desired goals. The best set of optimized operating condition for each load conditions is listed in Table 9 with the maximum desirability value. In single fuel RCCI combustion, experiments have not been performed at 100% load

3.5. Validation and comparison of single fuel RCCI combustion with CDC To validate the predicted value and to explore the benefits of single fuel RCCI combustion, experiments have been conducted at an optimized operating condition as shown in Table 9. Fig. 13 shows the

Table 8 Goal and importance of responses for numerical optimisation. Responses

NO HC CO Soot IMEP RoPR BTE COVIMEP

Unit

g/kWh g/kWh g/kWh g/kWh bar bar/deg % %

Goal

Target Minimize Minimize Target In range Minimize Maximize In range

Importance

Range

– – – – 3.1–3.5 – – 0.8–5

+++++ +++ +++ +++++ +++ +++ +++ +++

10

Target

0.7 – – 9e−5 – – 23.5 –

Target Range Lower

Higher

0.3 3.4 12.04 2.8e−5 2.85 2 23 0.8

1.8 11.05 24.9 2.9e−4 3.3 4 24 6.2

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Fig. 11. Perturbation plot of emissions characteristics of single fuel RCCI combustion.

/isobutanol doped with PODE) have better LHV than conventional diesel fuel and thereby decreases the NO significantly. The comparison of soot emission between CDC and single fuel RCCI combustion at different load conditions is also shown in Fig. 13. It is observed that, soot emission of CDC increases with increase in engine load due to heterogeneous diffusion combustion caused by an increased local equivalence ratio. Further, it is observed that compared to CDC, single fuel RCCI combustion reduced the soot emission concentration nearly by 93–97% and this is attributed to better homogenous fuel-air mixture inside the cylinder. Generally, above 1500 K only the oxidation of carbon monoxide (CO) happens and results in carbon-di-oxide (CO2). In single fuel RCCI combustion due to lean homogeneous air-fuel mixture and lower incylinder temperature, the oxidation of CO to CO2 is reduced. In addition, the contact of port injected fuel with crevice and squish region

comparison of emission characteristics between the predicted and actual value of single fuel RCCI combustion and CDC. It is observed that, except HC emission, the average percentage of error between the predicted and actual value of emission concentration is less than 5%. From, Table 10 and Fig. 13, It is noticed that, NO emission is reduced nearly by 2.5 g/kWh, 3.9 g/kWh and 5.9 g/kWh at 1.3 bar, 2.6 bar and 3.9 bar BMEP load conditions respectively in the case of single fuel RCCI combustion which is about 77%, 83% and 87% lower than the CDC. It is also noticed that, as the engine load increases, the percentage reduction in NO emission is increased due to reduced PODE energy contribution for the combustion. Further, Port injected isobutanol and well advanced PODE doped Isobutanol form a lean and homogeneous air-fuel mixture which eliminates the local high temperature regions. In addition to that, PODE also have good latent heat of vaporization (LHV) and volatility, the fuel combination used in this study (isobutanol 11

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Fig. 12. Perturbation plot of combustion and performance characteristics of single fuel RCCI combustion.

Indicated mean effective pressure (IMEP) is used to measure the actual net-work output of the engine. In RCCI combustion process, port injection of low reactivity fuel during the suction stroke reduces the incylinder temperature during the compression stroke which means the compression work of the engine is reduced. Further, the higher in-cylinder pressure and appropriate combustion phasing result in improved work output of the engine. Hence, on the whole, nearly 8%, 8.7% and 9% higher IMEP is observed at single fuel RCCI combustion compared to CDC at 1.3 bar, 2.6 bar and 3.9 bar BMEP conditions respectively as shown in Fig. 14 and Table 10. Unstable combustion/misfire has been indicated by the coefficient of variation of indicated mean effective pressure (COVIMEP). Fig. 14 shows the actual percentage variation of COVIMEP for both CDC and single fuel RCCI combustion along with the predicted value. It is observed that, the error between the predicted and actual value is below 3%. The higher percentage variation of COVIMEP is

also result in CO emission. Hence, overall, higher CO emission is observed at single fuel RCCI combustion compared to CDC as shown in Fig. 13. The percentage increase in CO emission in the case of single fuel RCCI combustion is nearly 74%, 70% and 66% respectively at 1.3 bar, 2.6 bar and 3.9 bar BMEP load conditions compared to CDC. It is noticed that, as the load increases the percentage increase in CO emission is decreases due to better thermodynamic conditions and enhanced oxidation process triggered by the higher in-cylinder temperature. Generally, the formation of HC in a diesel engine is due to incomplete combustion. In single fuel RCCI combustion, contact of port injected isobutanol with the crevice region and lower in-cylinder temperature reduces the oxidation process and results in incomplete combustion which results in higher HC emission. Similar to CO emission, the percentage increase of HC emission decreases with increasing engine load due to increase in-cylinder temperature. 12

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Fig. 13. Emission comparison between SFRCCI combustion and CDC.

Fig. 14. Combustion and performance characteristics of SFRCCI combustion compared with CDC.

Table 10 Comparison of Emission and performance characteristics between CDC and SFRCCI combustion. Responses

Operation

CDC

SFRCCI (Actual)

SFRCCI (Predicted)

% Error

NO (g/kWh)

1.3 bar 2.6 bar 3.9 bar

3.21 4.70 6.80

0.78 0.82 0.94

0.75 0.79 0.91

4.1 3.8 3.2

Soot (g/kWh)

1.3 bar 2.6 bar 3.8 bar

0.0150 0.0220 0.0350

0.0009 0.0009 0.0010

0.0009 0.0010 0.0010

2.1 2.1 1.9

HC (g/kWh)

1.3 bar 2.6 bar 3.8 bar

0.3 0.2 0.1

6.9 5.5 4.6

6.4 5.1 4.3

6.7 6.5 6.1

CO (g/kWh)

1.3 bar 2.6 bar 3.8 bar

3.5 2.6 2.3

13.4 8.9 6.8

12.8 8.5 6.5

4.2 4.1 4.1

BTE (%)

1.3 bar 2.6 bar 3.8 bar

21.6 28.5 31.8

23.5 31.5 35.5

23.3 31.2 35.1

0.8 0.9 1.1

Fig. 15. Comparison of in-cylinder pressure and RoHR between single fuel RCCI combustion and CDC.

IMEP (bar)

1.3 bar 2.6 bar 3.8 bar

2.80 3.85 4.90

2.99 4.15 5.30

3.04 4.22 5.37

1.8 1.6 1.4

COVIMEP (%)

1.3 bar 2.6 bar 3.8 bar

0.8 0.5 0.4

2.8 2.3 2.0

2.9 2.4 2.1

1.5 2.1 2.9

RoPR (bar/ deg)

1.3 bar 2.6 bar 3.8 bar

5.8 6.4 7.3

5.0 5.5 6.1

5.2 5.6 6.3

3.8 2.4 3.1

BTE of single fuel RCCI combustion is nearly 8%, 9.5% and 10.4% higher than CDC at 1.3 bar, 2.6 bar and 3.9 bar respectively as shown Fig. 14. Further, it is observed the error between the predicted and actual value is less than 1% only. Fig. 14 also shows the comparison of rate of pressure rise between CDC and single fuel RCCI combustion at various engine load conditions. It is observed that, at single fuel RCCI combustion, RoPR is decreased by 13%, 14% and 16% at 1.3 bar, 2.6 bar and 3.9 bar BMEP respectively compared to CDC. Control of rate of pressure rise was important while implementing the new combustion concepts, otherwise engine damage might have occurred. In RCCI mode of operation, due to fuel reactivity gradient and staged combustion process, the rate of pressure rise is lower compared to CDC. Fig. 15 shows the comparison of in-cylinder pressure and rate of heat release between CDC and single fuel RCCI combustion at different load conditions. The maximum and retarded peak in-cylinder pressure was observed in single fuel RCCI combustion compared to CDC. The peak in-cylinder pressure observed in the case of single fuel RCCI combustion is 66 bar, 70 bar and 74 bar at 1.3 bar, 2.6 bar and 3.9 bar BMEP respectively which is 9%, 11%, 12% higher than the CDC. Two peak heat release patterns, i.e. premixed and diffusion combustion is

observed with single fuel RCCI combustion compared to CDC. Generally, RCCI combustion operation is based on the reactivity gradient between the port and direct injected fuel, initial charge temperature and injection timing of direct injected fuel. While using the higher mass of port injected isobutanol fuel, local reactivity gradient inside the cylinder is reduced which may lead to an increase in the COVIMEP. Brake thermal efficiency (BTE) is used to evaluate how much heat energy is derived from the fuel and converted into mechanical energy (shaft output). The combustion duration of single fuel RCCI combustion is lower than CDC as shown in Fig. 14. Reduced combustion duration means, reduced heat transfer losses during the power stroke. Hence, 13

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soot emission is reduced to nearly about 93 to 97% compared to CDC.

observed in CDC combustion due to the presence of heterogeneous fuel–air mixture. However, only premixed combustion is observed in the case of single fuel RCCI combustion, due to more premixed isobutanol and isobutanol doped with PODE fuel. In CDC, a negative heat release pattern is observed due to the local cooling effect created by the absorption heat by the direct injected diesel fuel from the compressed hot air. Delayed start of combustion (SOC) is observed with single fuel RCCI combustion compared to CDC, due to the presence of more amount of octane fuel (isobutanol) and less amount of cetane number enhanced fuel (PODE) which takes more time to initiate the combustion. In single fuel RCCI combustion due to the presence of more homogeneous air–fuel mixture, combustion has been completed earlier and results in reduced combustion duration. In CDC, the SOC advances with increase in engine load due to increase in-cylinder temperature. Whereas, in single fuel RCCI combustion, the SOC is delayed due to the presence of more premixed high octane isobutanol fuel.

Overall, it is observed that adding a small quantity of PODE in Isobutanol is enough to run the engine under single fuel reactivity controlled compression ignition without compromising the benefits of dual fuel RCCI combustion. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.enconman.2019.112025.

4. Conclusions

References

In the present study, single fuel RCCI combustion was performed on a single cylinder, Genset, diesel engine with an aim to achieve lower fuel consumption and emissions. The DoE approach using RSM tool was used to identify the significant input factors that have more influence on single fuel RCCI combustion, performance and emission characteristics. Then, numerical optimization was performed to get a set of optimized operating parameters to maximize the efficiency and minimize the engine out emissions. Finally, at the optimized set of operating conditions obtained from the numerical analysis (i.e. input factors), the experiments were conducted on the test engine and the results were compared with conventional diesel combustion and the following important conclusion were drawn

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1. The tested non-road diesel engine was able to run under single fuel RCCI combustion mode using isobutanol and isobutanol doped PODE. PODE was used as a cetane improver. 2. The linear prediction model developed by RSM successfully covers and predicted the changes of the engine combustion, performance and emission characteristics using experimental operating variables, including PODE energy ratio, SOImain, SOIpilot, PIM, IAT, EGR and Pinj. 3. The Design of Experiments using RSM is highly helpful in identifying the significance of input factors which are most influential on the combustion, emission and performance characteristics of single fuel RCCI combustion. Unlike previous study (experimental parametric study), the time required to achieve the desired targets is less due to design of experiment approach which is significantly reduced the number of experiments. 4. Auto ignition of isobutanol/isobutanol-PODE fuel-air mixture is initiated by PODE. Hence, PODE energy ratio is significant in achieving single fuel RCCI combustion on the test engine. It is observed that only 12%, 9% and 5% PODE energy ratio is required to achieve better single fuel RCCI combustion at 1.3 bar, 2.6 bar and 3.9 bar BMEP respectively. 5. Desirability approach was used to optimize the input operating parameters of single fuel RCCI combustion by targeting a decrease in emissions and an increase in performance of the engine. A desirability value of 86%, 85% and 84% is achieved 1.3 bar, 2.6 bar and 3.9 bar BMEP respectively at 1500 rpm condition. Further, the predicted model was validated using the actual engine test results and it is found that, the error between the predicted and actual value is less than 4% except HC emission. 6. Single fuel RCCI combustion experiments performed with optimized operating conditions obtained from the numerical analysis showed a percentage increase in BTE of about 8.3%, 9.6% and 10.4% for 1.3 bar, 2.6 bar and 3.9 bar BMEP respectively at the rated speed of the test engine. The NO emission is reduced about 77 to 83% and 14

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