Elsevier

Applied Thermal Engineering

Volume 37, May 2012, Pages 217-225
Applied Thermal Engineering

Prediction of engine performance for an alternative fuel using artificial neural network

https://doi.org/10.1016/j.applthermaleng.2011.11.019Get rights and content

Abstract

This study deals with artificial neural network (ANN) modeling to predict the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of the methanol engine. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm was developed. Then, the performance of the ANN predictions was measured by comparing the predictions with the experimental results. Engine speed, engine torque, fuel flow, intake manifold mean temperature and cooling water entrance temperature have been used as the input layer, while brake specific fuel consumption, effective power, average effective pressure and exhaust gas temperature have also been used separately as the output layer. After training, it was found that the R2 values are close to 1 for both training and testing data. RMS values are smaller than 0.015 and mean errors are smaller than 3.8% for the testing data. This shows that the developed ANN model is a powerful one for predicting the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of internal combustion engines.

Highlights

► The applicability of ANN was investigated for the performance of a methanol engine. ► It was found that R2 values are closely 1 for both training and testing data. ► RMS values are smaller than 0.015 for both training and testing data. ► Mean errors are smaller than 3.8% for both the training and testing data. ► Methanol was used as fuel without any modifications on a gasoline automobile engine.

Introduction

The requirement of energy increases due to industrialization and continuously growing population in the world. Therefore, developing and newly developing countries tend to the new energy sources to compensate their energy necessity. Currently, the main energy source of the motor vehicles is petroleum products. It is expected that the petroleum reserves will be consumed away in the near future. In addition, one of the main causes of air pollution in the cities is harmful emissions of the motor vehicles which are operated with petroleum products. As a result, a lot of researchers have started to search for cheap, renewable and environmentally friendly alternative fuels such as ethanol, methanol, hydrogen, and liquefied petroleum gas (LPG), liquefied natural gas (LNG), compressed natural gas (CNG), electric and vegetable oil based fuels [1]. Calisir and Gümüs [2] analyzed the usage of mixture of methanol and gasoline in spark-ignited engine and studied the effects of methanol ratio on engine performance in different ignition advances. The best performance at standard ignition advance value of engine is acquired by mixture of 15% methanol and 85% petroleum. Celik et al. [3] investigated the effects of use of pure methanol as fuel at high compression ratio in a single cylinder gasoline engine on engine power, brake thermal efficiency and emissions. By increasing the compression ratio from 6/1 to 10/1, the engine power and brake thermal efficiency increased by up to 14% and 36%, respectively. Moreover, CO, CO2 and NOx emissions were reduced by about 37%, 30% and 22%, respectively. Li et al. [4] experimentally investigated the effects of injection time and ignition stroke on engine performance and emissions through a methanol engine with high pressure, direct injection and spark ignition. It was found that both injection time and ignition stroke have significant effects on methanol engine performance, combustion process and exhaust emissions. The best compromise values between thermal effectiveness and exhaust emissions were acquired at the determined optimal injection time and ignition stroke. Sayin [5] investigated the effects of mixtures of methanol-diesel (M5-M10) and ethanol-diesel (E5-E10) on engine performance and exhaust emissions. For the experiments, an ordinary diesel engine with single cylinder and four-cycle was used. The results showed that break specific fuel consumption and nitrogen oxide emissions increased while thermal effectiveness, smoke opacity, carbon monoxide and total hydro carbon decreased.

ANN is the algorithms which are developed in order to explore and generate new knowledge via learning without taking any external help [6]. In other words, it is a synthetic network which imitates biological neural network. ANN and biological neural network have significant differences in terms of both architectures and capabilities. ANN constitutes of a mathematical model for prediction of new problems [7]. Recently, ANN has been a notable and commonly used method for engine performance tests, cutting mechanics, signal processing, data decomposition and image processing [8]. Besides, the method is able to produce new solutions for some problems. ANN was preferred as control strategy due to its high reliability and efficiency [9]. The studies of literature show that ANN has a powerful modeling technique [10], [11], [12], [13]. Khandelwal and Singh [14] studied on the prediction of macerals contents of Indian coals from proximate and ultimate analyses using ANN. It was found that coefficient of determination between measured and predicted macerals by ANN was quite higher as well as mean absolute percentage error was very marginal as compared to conventional multivariate regression analysis prediction. Rajendra et al. [15] predicted the optimized pretreatment process parameters for biodiesel production using ANN and GA. Balabin et al. developed an ANN model for analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared spectroscopy. The lowest root mean squared errors of prediction of the developed ANN for density, viscosity, water percentage, and methanol content were 0.42 kg m−3, 0.068 mm2 s−1, 45 ppm, and 51 ppm, respectively [16]. Ghobadian et al. [17] developed an ANN model to estimate diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel. In their study, it was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. Kiani et al. [18] dealt with ANN modeling of a spark ignition engine which used ethanol- gasoline blends to predict the engine brake power, output torque and exhaust emissions (CO, CO2, NOx and HC) of the engine. Results showed that the ANN provided the best accuracy in modeling the emission indices with correlation coefficient equal to 0.98, 0.96, 0.90 and 0.71 for CO, CO2, HC and NOx, and 0.99 and 0.96 for torque and brake power respectively.

In this study, the changes in engine performance have been observed by using methanol as fuel without any modifications on a gasoline automobile engine, and the impact of the fuel on engine performance has been examined. An ANN model was developed by considering the torque, engine speed, fuel flow, intake manifold mean temperature and cooling water entrance temperature in the input layer. By this way, prediction of some parameters such as effective power, exhaust gas temperature, average effective pressure and specific fuel consumption was aimed. Also, the mathematical models for outputs were obtained using MATLAB program.

Section snippets

Experimental set up and measurements

The engine used in this experiment was a 4 cylinder, 4 strokes, 1.3 L volume Ford-Escort automobile engine. In the hydraulic brakes, a DPX1A type braking system which has 100 kW power and 750 rpm rates that can rise up to 200 Nm torque maximum was used. The technical specifications of the test engine are given in Table 1. Electronic mean rotation tachometer was used for measuring engine rotation. The sensitivity of the device for measuring number of rotation is ±0.04 rpm. During experiments,

Network structure

ANN consists of artificial neural cells (neurons). ANN has three main layers, namely, input, hidden and output layers. Neurons (processing elements) at input layer transfer data from external world to hidden layer [6]. The data in input layer do not process as the data in the other layers. In the hidden layer, outputs are produced using data from neurons in input layer and bias, and summation and activation functions. There can be more than one hidden layer. In this case, each hidden layer

Variations of Pe, Ape, Tex and BSFC depending on engine speed and torque

The experimental results are shown in Fig. 3. The effective pressure increases proportionally with increasing torque at constant engine speed (Fig. 3a). Engine power increases as well as the engine speed at constant torques. This behavior is the general characteristic of internal combustion engines. Depending on engine speed, while rate of increase of power is small at low torque values, it is found to be higher at high torque values. These two cases can be explained by the lowest values of

Conclusions

The applicability of ANNs has been investigated for the performance, average effective pressure and exhaust gas temperature values of a methanol engine. To train the network, N, T, m, Tin and Tcw are used as the input, while the outputs are BSFC, Tex, Ape and Pe. This study deals with ANN modeling of a methanol engine to predict the brake specific fuel consumption, effective power, average effective pressure and exhaust gas temperature of the engine. Using some of the experimental data for

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