Elsevier

Fuel

Volume 202, 15 August 2017, Pages 699-716
Fuel

Full Length Article
Performance and emission characteristics of a CI engine using nano particles additives in biodiesel-diesel blends and modeling with GP approach

https://doi.org/10.1016/j.fuel.2017.04.117Get rights and content

Highlights

  • Using nano-diesel-biodiesel increased the brake power, torque and decrease bsfc of CI engine.

  • The CO2 and NOx increased while the concentration of CO and HC were decreased with nano-biodiesel-diesel blends.

  • Good correlation was observed between genetic programming predicted results and experimental data.

  • GP proved to be a useful tool for correlation and simulation of engine parameters.

  • GP provided an accurate and simple approach in the analysis of the CI engine performance and emissions.

Abstract

The performance and the exhaust emissions of a diesel engine operating on nano-diesel-biodiesel blended fuels has been investigated. Multi wall carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) were produced and added as additive to the biodiesel-diesel blended fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel and biodiesel fuels, increased diesel engine performance variables including engine power and torque output up to 2% and brake specific fuel consumption (bsfc) was decreased 7.08% compared to the net diesel fuel. CO2 emission increased maximum 17.03% and CO emission in a biodiesel-diesel fuel with nano-particles was lower significantly (25.17%) compared to pure diesel fuel. UHC emission with silver nano-diesel-biodiesel blended fuel decreased (28.56%) while with fuels that contains CNT nano particles increased maximum 14.21%. With adding nano particles to the blended fuels, NOx increased 25.32% compared to the net diesel fuel. This study also presents genetic programming (GP) based model to predict the performance and emission parameters of a CI engine in terms of nano-fuels and engine speed. Experimental studies were completed to obtain training and testing data. The optimum models were selected according to statistical criteria of root mean square error (RMSE) and coefficient of determination (R2). It was observed that the GP model can predict engine performance and emission parameters with correlation coefficient (R2) in the range of 0.93–1 and RMSE was found to be near zero. The simulation results demonstrated that GP model is a good tool to predict the CI engine performance and emission parameters.

Introduction

The CI engines are widely utilized due to its reliable operation and economy. As the petroleum reserves are depleting at a faster rate, an urgent need for a renewable alternative fuel arise. Also the threat of global warming and the stringent government regulation made the engine manufacturers and the consumers to follow the emission norms to save the environment from pollution.

Among the many alternative fuels, biodiesel is considered as a most desirable fuel extender and fuel additive due to its high oxygen content and renewable in nature [1]. Among the various techniques available to reduce exhaust emissions, the utilize of fuel-borne catalyst is currently focused due to the advantage of increase in fuel efficiency while reducing greenhouse gas emissions. The influence of cerium oxide additive on ultrafine diesel particle emissions and kinetics of oxidation was studied by Jung et al. [2]. It has been detected that addition of cerium to diesel cause significant reduction in number weighted size distributions and light-off temperature and the oxidation rate was increased significantly.

The structural and morphological characterization of a Ce-Zr mixed oxide supported Mn oxide as well as on its catalytic activity in the oxidation of particulate matter arising from diesel engines has been studied by Escribano et al. [3]. Mn-Ce-Zr catalyst shows high activity in the soot oxidation producing CO2 and CO as a byproduct in the range 425–725 K. Idriss investigated the complexity of the ethanol reactions on the surfaces of noble metals/cerium oxide catalysts [4]. The hazard and risk assessment with the use of nano-particle cerium oxide bases diesel fuel was studied by Barry Park et al. [5]. Effects of cerium oxide nano-particles addition in diesel and diesel-biodiesel-ethanol blends on performance and emission characteristics of a CI engine has been studied and results showed that the cerium oxide acts as an oxygen donating catalyst and provides oxygen for the oxidation of CO or absorbs oxygen for the reduction of NOx. The activation energy of cerium oxide acts to burn off carbon deposits within the engine cylinder at the wall temperature and prevents the deposition of non-polar compounds on the cylinder wall results reduction in HC emissions. The tests revealed that cerium oxide nano-particles can be used as additive in diesel and diesel-biodiesel-ethanol blend to improve complete combustion of the fuel and reduce the exhaust emissions significantly [1]. Carbon nano-tubes (CNTs) are as useful additives for increasing the octane number. Functionalized carbon nanotubes containing amide groups have a high reactivity and can react with many chemicals. These compounds can be solubilized in gasoline to increase the octane number. In a study, the amino-functionalized carbon nano-tubes were added to gasoline. Research octane number analysis showed that these additives increase octane number of the desired samples [6].

Experimental investigations to measure the performance and emission parameters of internal combustion engines are complex, time consuming and costly. To predict the parameters from the engines, one approach is to utilize numerical models [7]. The alternative to a mathematical model is the experiment-based approach. Genetic algorithm (GA), which is based on solutions of fixed length chromosomes, usually consisting of binary genes, organized into sequences, often termed schema is the most commonly used evolutionary-computation algorithm [8], [9]. Evolutionary computation (EC) is drawing attentions for solving real engineering problems. This approach is to be robust in delivering global optimal solutions and coping with the restrictions encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimization tools [10], [11], [12], [13]. This is very applicable to different problems in the manufacturing industry [11], [12], [13], [14], [15], [16], [17].

One of most important EC methods is genetic programming (GP). GP is a similar technique as genetic algorithm, an evolutionary computation method for imitating biological evolution of living organisms. Genetic Algorithms (GAs) and genetic programming (GP) have been found to offer advantages dealing with system modeling and optimization, especially for complex and nonlinear systems. GP has been applied to a wide range of problems in artificial intelligence, engineering and science, chemical and biological processes and mechanical issues [18], [19], [20], [21], [22]. Pires, et al. [23] used GP method to predict the next day hourly average tropospheric ozone (O3) concentrations. The results showed very good agreement between predicted and measured data. Prediction of compressive and tensile strength of limestone was carried out via genetic programming as reported by Baykasoglu, et al., [24]. Another interesting genetic programming application was conducted by Cevik and Cabalar [25] for prediction of peak ground acceleration (PGA) using strong-ground-motion data. In this research, they demonstrated a high correlation between PGA and predictions. Multigene genetic programming is a recently developed approach for improving accuracy of GP that was developed by Hinchliffe, Willis, Hiden, Tham, McKay and Barton [26] and Hiden [27] and have been utilized in some recent research works [28], [29]. Kiani et al. [30] studied the application of genetic programming to predict an SI engine brake power and torque using ethanol-gasoline fuel blends. At this study, the optimum models were selected according to statistical criteria of root mean square error (RMSE) and coefficient of determination (R2). The values of RMSE and R2 for brake power were found to be 0.388 and 0.998. It was observed that the GP model can predict engine torque with correlation coefficient in the range of 0.99–1 and RMSE was found to be 0.731. The simulation results demonstrated that GP model is a good tool to predict the engine brake power and torque under test [30]. Numerous studies have been under taken by using GA for optimization of engine characteristics [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41]. Numerous studies have been undertaken by using genetic programming (GP) [42], [43], [44], [45], [46]. GP has been utilized to construct prediction model for diagnosing the engine valve faults. Kalogirou [47] reviewed ANN and GP for the modeling and control of engine combustion. A GP based mathematical model developed for the prediction of SI engine torque and brake specific fuel consumption in terms of spark advance, throttle position and engine speed [48], [49].

The properties, combustion- and emission parameters of some common bio-fuels used in diesel engines as used under both steady-state and transient conditions, has been investigated [65], [66].

In the present research study, the stable diesel biodiesel blends are prepared using vegetable waste oil methyl ester as additive and the emission reduction potential are investigated using nano silver and carbon nano tubes particles as fuel borne additive with neat diesel and diesel-biodiesel blends on the compression ignition engine. Parallel a multi-gene genetic programming (GP) algorithm based mathematical model for predicting an CI engine performance parameters and emission parameters in relation to input variables including engine speed, and nano-particles in diesel-biodiesel fuel blends.

The innovative characteristics of the present study compared to existing similar studies is utilize the new nano particles and additives with diesel-biodiesel blended fuels to investigate the performance and emission parameters of CI engine. This study also presents genetic programming (GP) based model to predict the performance and emission parameters of a CI engine in terms of nano-fuels and engine speed. Experimental studies were completed to obtain training and testing data.

Section snippets

Description of the experimental setup

In this study, the experiments were performed on a CI engine, 6 Cylinder, naturally aspirated, direct injection; fuel injection system of engine was solid and mechanical injection with distributer system. The shape of combustion chamber was Shallow depth chamber. Fuel injector type was single hole nozzle with hole diameter of 0.2 mm and spray cone angle obtained ranges from 5 to 20 degree, it requires high injection pressure in the range of 150–180 bar. The engine specification is given in Table 1

Brake power and torque output

Fig. 7 shows the effect of various fuels on engine brake power. When the nano content in the diesel fuel and diesel-biodiesel blended fuel is increased, the engine brake power slightly increased for all engine speeds. The gain of the engine power can be attributed to the increase of the indicated mean effective pressure for higher nano content blends [54]. The heat of evaporation of nano-diesel is higher than that diesel fuel, this provides fuel–air charge cooling and increases the density of

Overview of genetic programming

Genetic programming (GP) is a sub-branch of evolutionary algorithms (EAs) emulating the natural evolution of species. Genetic programming (GP) technique is an extension to Genetic Algorithm (GA). The main difference between GA and GP resides in the nature of the individuals: in GAs the individuals are symbolic strings of fixed length (chromosomes); in GP the individuals are nonlinear entities of different sizes and shapes (parse trees) [49].

Koza [61] was one of the scientists who first

Conclusions

The present work demonstrates that the use of nano-diesel-biodiesel blended fuel will increase the brake power and torque and decrease the brake specific fuel consumption. It was also found that the CO2 and NOx concentrations were increased while the concentration of CO and HC were decreased when nano-biodiesel-diesel blends are used. The multi-gene genetic programming results are very good, R values in this model are very close to one, while root mean square errors (RMSE) were found to be very

References (84)

  • M. Asadi et al.

    Evaluating the strength of intact rocks through genetic programming

    Applied Soft Computing

    (2011)
  • C. Fonlupt

    Solving the ocean color problem using a genetic programming approach

    Applied Soft Computing

    (2001)
  • B. Can et al.

    Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems

    Comput Ind Eng

    (2011)
  • H.C. Tsai

    Using weighted genetic programming to program squat wall strengths and tune associated formulas

    Eng Appl Artif Intell

    (2011)
  • J.C.M. Pires et al.

    Prediction of tropospheric ozone concentrations: application of a methodology based on the Darwin’s Theory of Evolution

    Expert Syst Appl

    (2011)
  • A. Baykasoglu et al.

    Prediction of compressive and tensile strength of limestone via genetic programming

    Expert Syst Appl

    (2008)
  • A. Cevik et al.

    Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming

    Expert Syst Appl

    (2009)
  • M.H. Baziar et al.

    Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: an evolutionary approach

    Comput Geosci

    (2011)
  • H.M. Chen et al.

    Genetic programming for predicting aseismic abilities of school buildings

    Eng Appl Artif Intell

    (2012)
  • U. Kesgin

    Genetic algorithm and artificial neural network for engine optimization of efficiency and NOX emission

    Fuel

    (2004)
  • K. Atashkari et al.

    Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms

    Energy Convers Manage

    (2007)
  • Z. Yang et al.

    Investigation into the optimization control technique of hydrogen-fueled engines based on genetic algorithms

    Int J Hydrogen Energy

    (2008)
  • G. Cai et al.

    Performance prediction and optimization for liquid rocket engine nozzle

    Aerosp Sci Technol

    (2007)
  • X.Y. Tong et al.

    Optimization of system parameters for gas generator engines

    Acta Astronaut

    (2006)
  • J.J. Wang et al.

    Optimization of capacity and operation for CCHP system by genetic algorithm

    Appl Energy

    (2010)
  • B. Saerens et al.

    Minimization of the fuel consumption of a gasoline engine using dynamic optimization

    Appl Energy

    (2009)
  • I. Al-Hinti et al.

    The effect of boost pressure on the performance characteristics of a diesel engine: a neuro-fuzzy approach

    Appl Energy

    (2009)
  • C. Evans et al.

    Application of system identification techniques to aircraft gas turbine engines

    Control Eng Pract

    (2001)
  • A.E. Ruano et al.

    Nonlinear identification of aircraft gas-turbine dynamics

    Neuro Computing

    (2003)
  • V. Arkov et al.

    System identification strategies applied to aircraft gas turbine engines

    Annu Rev Control

    (2000)
  • G.J. Gray et al.

    Nonlinear model structure identification using genetic programming

    Control Eng Pract

    (1998)
  • S.A. Kalogirou

    Artificial intelligence for the modeling and control of combustion processes: a review

    Prog Energy Combust

    (2003)
  • N. Togun et al.

    Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine

    Appl Energy

    (2010)
  • H. Bayraktar

    Experimental and theoretical investigation of using gasoline–ethanol blends in spark-ignition engines

    Renew Energy

    (2005)
  • M.B. Celik

    Experimental determination of suitable ethanol–gasoline blend rate at high compression ratio for gasoline engine

    Appl Therm Eng

    (2008)
  • M. Al-Hasan

    Effect of ethanol-unleaded gasoline blends on engine performance and exhaust emissions

    Energy Conv Manage

    (2003)
  • W.D. Hsieh et al.

    Engine performance and pollutant emission of an SI engine using ethanol–gasoline blended fuels

    Atmos Environ

    (2002)
  • A.K. Agarwal

    Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines

    Prog Energy Combust Sci

    (2007)
  • C.W. Wu et al.

    The influence of air–fuel ratio on engine performance and pollutant emission of an SI engine using ethanol–gasoline- blended fuels

    Atmos Environ

    (2004)
  • B. Can et al.

    A comparison of genetic programming and artificial neural networks in meta modeling of discrete-event simulation models

    Comput Oper Res

    (2012)
  • D.C. Rakopoulos et al.

    Impact of properties of vegetable oil, bio-diesel, ethanol and n-butanol on the combustion and emissions of turbocharged HDDI diesel engine operating under steady and transient conditions

    Fuel

    (2015)
  • D.C. Rakopoulos et al.

    Butanol or DEE blends with either straight vegetable oil or biodiesel excluding fossil fuel: comparative effects on diesel engine combustion attributes, cyclic variability and regulated emissions trade-off

    Energy

    (2016)
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