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