Prediction of performance, combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificial neural network

Abstract

This paper explores the use of artificial neural networks (ANN) to predict performance, combustion and emissions of a single cylinder, four stroke stationary, diesel engine operated by thermal cracked cashew nut shell liquid (TC-CNSL) as the biodiesel blended with diesel. The tests were performed at three different injection timings (21°, 23°, 25°CA bTDC) by changing the thickness of the advance shim. The ANN was used to predict eight different engine-output responses, namely brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), carbon monoxide (CO), oxide of nitrogen (NO_x), hydrocarbon (HC), maximum pressure (P_max) and heat release rate (HRR). Four pertinent engine operating parameters, i.e., injection timing (IT), injection pressure (IP), blend percentage and pecentage load were used as the input parameters for this modeling work. The ANN results show that there is a good correlation between the ANN predicted values and the experimental values for various engine performances, combustion parameters and exhaust emission characteristics. The mean square error value (MSE) is 0.005621 and the regression value of R² is 0.99316 for training, 0.98812 for validation, 0.9841 for testing while the overall value is 0.99173. Thus the developed ANN model is fairly powerful for predicting the performance, combustion and exhaust emissions of internal combustion engines.

Keywords

cashew nut shell liquid (CNSL) / artificial neural networks (ANN) / thermal cracking / mean square error (MSE)

Introduction

Automobiles are the prime movers of the economic growth of any country. With the rapid industrialization of many of the third world countries, the requirement for conventional source of oil has increased enormously. Coupled with this, the inability of the oil producers to meet the demand has evived the interest in non-conventional oil resources such as non-edible oil such as Pungamia oil, cotton seed oil, Jatropha oil, and etc. Of these non-conventional sources, the least explored oil resource is cashew nut shell liquid (CNSL). Even though CNSL undergoes all the conservative responses of phenols, CNSL aldehyde concentration products and CNSL-based phenolic resins are used in applications such as surface coatings, and adhesives. It can also be used as an alternative fuel in internal combustion engines after suitable treatment. One of the very simple methods is to directly use the CNSL in the engine by pre-heating it. But this method is afflicted with problems of gum formation and corrosion of cylinder liner due to the sulfur content in the fuel. Some researchers [ 1] have used the concept of mixing CNSL with diesel in varying proportions and preheating the oil before injecting directly in the engine cylinder. With these modifications, they reported improvements in brake thermal efficiency (BTE). Efforts are made to utilize CNSL as the raw material for producing biodiesel is rare, compared to other non-edible oils which have gained much popularity. Vedharaj et al. [ 2] have stated that it is very essential to use the renewable source of energy from CNSL which is extracted from cashew nut shell biomass and to utilize it as another fuel for diesel engine. CNSL, a valuable raw material for the petrochemical industries, has been used as a potential source for producing biodiesel and has also been directly used in diesel engine without any modification of the engine [ 3]. The research in IC engines has been conducted over the many years by using various methods to find the engine performance for different fuels and operating conditions. These methods attempt to reduce the time consumption and complexity. The artificial neural network (ANN) is a technique used for modeling of the physical phenomena in complex systems without need of mathematical representations. The carbon buildup, deposit formation, and lubricating oil contamination result in durability problems for continuous running of engine with vegetable oils [ 4].

ANN is a simple technique, with powerful, more information processing characteristics [ 5]. ANN is also used as a tool in automotive industry to predict numerous engine-output parameters. When new operating conditions are tested and is more attractive as an engine optimization tool because it is less expensive in terms of required time and resources [ 6].The engine emission and performance of a diesel engine fuelled with diesel and biodiesel fuels are modeled by using the ANN technique [ 7]. This technique can be applied to predict the desired output when enough experimental input data are given [ 6]. The statistical approach of square error values (MSE) and correlation coefficient (R) are utilized to determine the accuracy of the predicted data from a formulated ANN model. The difference between the true values of the ANN and the predicted values of the parameters are quantified by using the MSE. But the R is the proportionality values between the experimental and predicted data [ 8].

In the ANN, the multiple input variables can be used to predict multiple output variables. The prediction by an ANN is much faster than the conventional mathematical models as no lengthy iterations are needed to solve differential equations [ 9]. The hyperbolic tangent, linear functions and sigmoid are the commonly used transfer functions. The neural network can be interpreted as a form of an input/output model [ 10]. The engine performance, exhaust emissions, brake specific fuel consumption (BSFC) and A/F equivalence ratio of a diesel engine can be predicted by using the ANN technique [ 11].

Many engine fuel studies have shown that ANN is a very powerful modeling technique [ 12– 20]. In this research, the engine is operated by the CNSL-diesel blend and the input layer load, blend ratio, injection timing and injection pressure are taken into account by developing an ANN model to estimate parameters such as BTE, BSFC, exhaust gas temperature (EGT), CO, HC, NO_x, P_max and heat release rate (HRR). The equations for the parameters obtained from the output layer have been optimized using the Matlab 10 program.

Materials and methods

Production of thermal cracked cashew nut shell liquid (TC-CNSL)

The distilled technical CNSL (DT-CNSL) is the first stage of processed raw CNSL. The DT-CNSL consists of 2% polymeric material, 8% cardol and 78% cardanol while the remaining material are other substances in volume basis. The TC-CNSL is derived from Cardonal at temperatures ranging from 180°C to 380°C, under atmospheric pressure. The schematic diagram of the apparatus used for producing the TC-CNSL is shown in Fig. 1. It consists of a reactor, a pressure gauge, a safety valve, a thermocouple, a temperature controller, a condenser, a heating coil and an electrical panel. The vapor temperature was measured using calibrated K-type thermocouples. Initially, five liters of DT-CNSL (cardanol) was filled into the cracking reactor. Then the reactor was heated by the electric coil until the CNSL reached the temperature of 180°C–380°C.This temperature was maintained by a temperature controller for the electric heater. The vapor formed due to heating was condensed in the condenser. The condensate was collected in the beaker. This organic fraction (light dark color) is called TC-CNSL. Water was used as a coolant and circulated in the condenser at a constant flow rate. The pressure gauge and the safety valve were fitted with the reactor to measure and prevent the excess pressure inside the reactor respectively. The properties of TC-CNSL and diesel fuel were tested in the laboratory (Sargam laboratory, Chennai) and given in Table 1.

Experimental setup and data pre-processing

The experimental setup consists of an engine, an air intake system, a fuel blending setup, loading and measurement devices. This setup was equipped with all the necessary instrumentation, devices and controls to acquire the different operating parameters like fuel flow, airflow, cylinder pressure, exhaust gas temperature and exhaust emission measurements during the experimentation.

A single cylinder, four-stroke water-cooled DI diesel engine developing 3.7 kW at 1500 r/min was used for the research in this paper. The test engine was equipped with a constant speed governor and has a compression ratio of 17.5. The details of the engine are listed in Table 2. The engine had a bowl-in-piston combustion chamber, particularly hemispherical shaped open combustion chamber. The engine also had an overhead valve arrangement. The valves were operated by push rods and a camshaft. The water required for the engine cooling was forced by a water pump through the water jacket. To measure the pressure, a piezo electric transducer was mounted on a typically located groove on the cylinder head. The crank angle encoder was connected to the camshaft by a belt drive. The pressure transducer and crank angle encoder were connected to the digital data acquisition system. A computer based digital data acquisition system was used for analyzing the pressure crank angle data and to obtain the heat release rate. A K-type thermocouple was used to measure the exhaust gas temperature from the engine. The engine exhaust emission parameters were measured by an NDIR (non-dispersive infrared) exhaust gas analyzer. The injection timing recommended by the manufacturer was 23°bTDC and the injector opening pressure was 20 MPa. Further, the motor was tried with different mixes such as B20, B40, B60, B80 and B100 (TC-CNSL) on a volume premise. The performance, emission and combustion qualities were assessed and compared with the slick diesel fuel operation.

1)€ Initially, the engine was run with neat diesel for the compression ratio of 17.5 (normal value) at a injection time of 21°, 23° and 25° bTDC. The engine propelled from an empty load of 0% to a full load of 100% with a gradual increase of 20% of load in each run.

2) €When the steady-state was touched, the parameters such as cylinder pressure, HRR, CO, NO_x, HC, fuel consumption rate and brake power were measured.

3) €The engine was then kept running on mixes of TC-CNSL blended with diesel (by volume) at 20%, 40%, 60%, 80% and 100%. The combustion, emission and performance were dignified.

4)€ The entire set of experiments were recurrent for injection pressures of 18 MPa, 20 MPa and 22 MPa.

5)€ The injection timing was progressed by 2°bTDC by evacuating a shim, and the above trials were rehashed for all the three injection pressures. The injection timing was retarded by 2° than the typical esteem by including an extra shim and the above arrangement of trials were rehashed. The combustion, emissions and performance at dissimilar operative conditions at only a load of 100% were deliberated.

Artificial neural networks

Artificial intelligent structures are generally utilized as an innovation offering an option approach to take care of complex issues. Neural systems are a kind of manmade brainpower frameworks that endeavors to emulate the way the human cerebrum lives up to expectations. It is constructed from interconnected basic handling gadgets called neurons. They find themselves able to handle loud and inadequate information, manage nonlinear issues and once prepared can perform speculation and expectation at high speeds [ 4]. In this paper, the steady-state experimental data were used for ANN modeling and independent models were also developed for engine combustion, emission and performance characteristics. Appropriate numbers of epochs were decided in training to overcome the difficulty of overfitting and under-fitting, hence the errors were minimized. In each model, a single hidden layer was adapted. The training function was used to decide the mapping of output and input values to minimize the training and testing errors [ 21]. The errors during testing and training were evaluated by the index called MSE in percentage while the accuracy of predication between actual and predicted values were evaluated using the correlation (R²) defined as

(1) \mathrm{M}\mathrm{S}\mathrm{E}\left(\mathrm{\%}\right)={\displaystyle \frac{\mathrm{1}}{n}}{\displaystyle \sum _{j=\mathrm{1}}^n\left[{\left({{\displaystyle t}}_j-{{\displaystyle o}}_j\right)}^{\mathrm{2}}/\mathrm{100}\right]},

(2) R^2=\mathrm{1}-{\displaystyle \frac{{\displaystyle \sum _j\left({{\displaystyle t}}_j-{{\displaystyle o}}_j\right)}}{{\displaystyle \sum _j{\left({{\displaystyle o}}_j\right)}^2}}},

where t is the target value, n is the number of data sets, and o is the output value [ 22]. In this model, 70% of the data set is assigned for the training set, while the remaining 15% data are put away for prediction, and another 15% for validation.

ANN generally comprises three types of neuron layers, namely input, hidden and output layer. The injection pressure, injection timing, blend in percentage and load in percentage were used as the input parameters. Eight engine-out responses, consisting of HRR, P_max, BTE, BSFC, EGT, HC, CO, NO_x, were recognized as output neurons. The eight engine-out reactions were shown as a purpose of four engine control limit using the ANN as illustrated in Fig. 2. Generally, in ANN, the indication from the input layers is moved to the neurons in the hidden layers. Here, the enactment elements of the shrouded layer were used to rough the non-direct conduct of the information set. This was finished by considering the weighting elements of interconnected neurons obtained amid the ANN preparing and acceptance stage.

The settings of the ANN design formulated and utilized in the study in this paper were listed in Table 3. A popular back-propagation algorithm, with dissimilar variations is used for training the ANN model. Back propagation (BP) training algorithms incline descents with impetus are often too slow for practical problems because they require small learning rates for constant learning. Faster algorithms such as quasi-Newton, Levenberg-Marquardt (LM) and conjugate gradient use standard numerical optimization techniques. LM method is, in fact, an approximation of the Newton’s method [ 9]. Steady-state experimental data were used for ANN modeling. Initially, independent models were developed for engine combustion, emission and performance characteristics. Out of 294 patterns, 15% (44 patterns) were used for testing, 15% (44 patterns) in the validation, and 70% (205 patterns) were applied in the training set. Engine load is one of the important parameters affecting engine combustion, emission and performance. In the study in this paper, the engine was run from a load of 0% to a load of 100% with an increase of 20% in each run. The percentage of blend is an additional input to the network. The experiments were conducted with designated blend percentages diesel of B20, B40, B60, B80 and B100 which were taken as the input to the network. The initial dynamic fuel injection in terms of crank angle before TDC is referred to as injection pressure and injection timing [ 4].

The transformation functions are to be designed depending on the nature of the training data variables. The performance, combustion and emission model of the network using various training algorithms are given in Table 4. From the assessment of the results of various training functions, the yielding least error for the validation was selected for predication purposes. LM (trainlm) is the most effective function than all other training algorithms as the MSE% value is lesser [ 11]. Besides, it converges more quickly than all other algorithms. Once the training of the ANN is over it is tested to predict the results for the unknown values by providing them as inputs. MSE% are the performance constraints used for testing the network performance. For evaluating the testing operation of the network model, the MSE% value should be closer to the testing and training data. The Matlab 10 was used for simulating the ANN model and the standard training functions defined in the neural network toolbox were used in the study in this paper.

The operation of the ANN model is noticeably affected by the number of neurons in each hidden layer and number of hidden layers. Three neural networks were trained for different injection timings viz 21°bTDC, 23°bTDC and 25°bTDC. For the chosen network, the performance was achieved with a minimum error of 0.00565 error rate with 30 epochs as depicted in Fig. 3.

Results and discussion

A sum of 30 arbitrary experimental data points comprising of different sorts and mixes of TC-CNSL-diesel fuel were presented as obscure test information as delineated in Table 5. These information focuses were never acquainted with the ANN amid its formative stage and were in this manner viewed as “inconspicuous” information focuses. This test was directed to focus on the capacity of the created ANN display in anticipating the obliged engine out reactions within the predetermined ranges of information set utilized amid its improvement as indicated in Table 6.

Based on the experimental work, an ANN model was developed to predict BSFC, BTE, EGT, CO, HC, NO_x, P_max and HRR. The input parameters were load, blends, fuel injection pressure and fuel injection timing. The prediction of output parameters using ANN for the experimental engine yielded impressive correlation statistics i.e., the predictive ability of the developed network for BSFC, BTE, EGT, CO, HC, NO_x, P_max and HRR was commendable. The comprehensive correlation coefficient R of the selected network architecture is demonstrated in Fig. 4. The performance model was trained with 334 sets of experimental data and tested against 30 sets of experimental data. The predicated values of outputs were compared with the actual experimental results.

Performance parameters

Figures 5, 6 and 7 show the variation between ANN and experimental inferred values for BTE, BSFC and EGT respectively. The test points taken for ANN program are 30 points. The fluctuations between estimated and experimental results are very small and negligible for all the parameters. These are evaluated with measures of correlation coefficients (R²).

The BTE for experimental and predicted values are presented in Fig. 5. The predictions for the BTE yield a regression of 0.9894 for testing and 0.9794 for training. The maximum difference between the experimental values and predicted values is 6.707%. The ANN experimental and predicted values for the BSFC are declared in Fig. 6. For the BSFC, the ANN gives an R² value of 0.9984 for testing and training. The maximum difference between the experimental values and predicted values is 250.56 g/kWh. These values indicate that the ANN predicts the BSFC satisfactorily in all operating conditions. Figure 7 exhibites the experimental and predicted values for the EGT with an R² value of 0.9984 for testing and 0.9984 for training. The maximum difference between the experimental and the predicted values is 53°C. It is observed that the ANN performance model can predict engine performance with a regression value of R² very close to unity. The deviation between the experimental and predicted values is very small for any performance parameter. The correlation is 0.99 in the analysis of the whole network, therefore, the model succeeds in predicating the engine performance [ 9, 11]. From the above results, it is noted that there is a good relationship between the ANN experimental and predicted data in the performance model.

Emission parameters

The ANN experimental and predicted values of emission parameters are presented in Figs. 8, 9 and 10 in which a good correlation exists. A plot of the experimental and predicted values of CO is given in Fig. 8. In predicting CO emissions, the ANN result gives an R² value of 0.9948 for testing and 0.9822 for training. The deviation between the experimental and predicted values is 0.1123 g/kWh. Figure 9 indicates the experimental and predicted values of HC emission. The ANN predictions for the HC yield an R² value of 0.9821 for testing and 0.9896 for training. The deviation between the experimental and predicted values is 0.001 g/kWh. HC emissions during combustion are caused by a sequence of difficult and wild chemical reactions. Figure 10 shows the plot of ANN predicted and experimental values of NO_x. The regression value of R² is 0.9936 for testing and 0.9914 for training. The deviation between the experimental and predicted values is 2.0386 g/kWh. As the combustion process is closer to flame temperature and stoichiometric, the NO_x emission increases [ 20].

Combustion characteristics

Figures 11 and 12 show the plot of the experimental and ANN predicted values for combustion pressure and heat release rate, respectively [ 23]. The ANN predictions of the R² value of P_max is 0.9936 for training and 0.9842 for testing. Figure 11 indicates that the maximum difference between the predicted and experimental values is 21.346 bar. Figure 12 indicates that the experimental R² value of the HRR is 0.9818 for training and 0.9821 for testing. The deviation between the predicted and the experimental values is 14.4439 J/°CA.

Conclusions

In this paper, the experimental values of engine combustion, emission and performance are compared with ANN predicted values for the TC-CNSL-diesel blended fuel. Parameters that are used in the ANN are injection timing, injection pressure, engine load and blend percentage. The training and testing values of regression (R²) are compared and the errors between them are also presented. The predicted values by the ANN and experimental values are compared for engine performance, combustion and emission for blended fuels.

1) From the error analysis, it is evident that the ANN predicted data matches the experimental data with a high overall accuracy and with correlation coefficient (R²) values ranging from 0.99316 to 0.99173.

2) A BP neural network model with a 4–9–8 configuration is developed to predict BTE, BSFC, EGT, CO, HC, NO_x, P_max and HRR of a diesel engine.

ANN can be used as a tool for the TC-CNSL blend fuel operated for predicting the unknown values from the given input values. This tool can reduce the number of experimental data necessary for evaluation.

References

[1] Vedharaj S, Vallinayagam R, Yang W M, Chou S K, Chua K J E, Lee P S. Performance emission and economic analysis of preheated CNSL biodiesel as an alternate fuel for a diesel engine. International Journal of Green Energy, 2015, 12(4): 359–367

[2] Vedharaj S, Vallinayagam R, Yang W M, Chou S K, Chua K J E, Lee P S. Experimental and finite element analysis of a coated diesel engine fuelled by cashew nut shell liquid biodiesel. Experimental Thermal and Fluid Science, 2014, 53: 259–268

[3] Vallinayagam R, Vedharaj S, Yang W M, Saravanan C G, Lee P S, Chua K J E, Chou S K. Impact of ignition promoting additives on the characteristics of a diesel engine powered by pine oil-diesel blend. Fuel, 2014, 117: 278–285

[4] Shivakumar,  Srinivasa Pai P, Shrinivasa Rao B R. Artificial neural network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Applied Energy, 2011, 88(7): 2344–2354nbsp;

[5] Çay Y, Korkmaz I, Çiçek A, Kara F. Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy, 2013, 50: 177–186nbsp;

[6] Mohamed Ismail H, Ng H K, Queck C W, Gan S. Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, 2012, 92: 769–777

[7] Betiku E, Omilakin O R, Ajala S O, Okeleye A A, Taiwo A E, Solomon B O. Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: a case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesis. Energy, 2014, 72: 266–273

[8] Arumugam S, Sriram G, Shankara Subramanian P R. Application of artificial to predict the performance and exhaust emissions of diesel engine using rapeseed oil methyl ester. Procedia Engineering, 2012, 38: 853–860 

[9] Ghobadian B, Rahimi H, Nikbakht A M, Najafi G, Yusaf T F. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy, 2009, 34(4): 976–982

[10] Moradi G R, Dehghani S, Khosravian F, Arjmandzadeh A. The optimized operational conditions for biodiesel production from soybean oiland application of artificial neural networks for estimation of the biodiesel yield. Renewable Energy, 2013, 50: 915–920

[11] Najafi G, Ghobadian B, Tavakoli T, Buttsworth D R, Yusaf T F, Faizollahnejad M. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy, 2009, 86(5): 630–639

[12] Çelikten I, Mutlu E, Solmaz H. Variation of performance and emission characteristics of a diesel engine fueled with diesel, rapeseed oil and hazelnut oil methyl ester blends. Renewable Energy, 2012, 48: 122–126 

[13] Parlak A, Islamoglu Y, Yasar H, Egrisogut A. Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Applied Thermal Engineering, 2006, 26(8-9): 824–828nbsp;

[14] Sayin C, Ertunc H M, Hosoz M, Kilicaslan I, Canakci M. Performance and exhaust emissions of a gasoline engine using artificial neural network. Applied Thermal Engineering, 2007, 27(1): 46–54

[15] Canakci M, Erdil A, Arcaklioglu E. Performance and exhaust emissions of a biodiesel engine. Applied Energy, 2006, 83(6): 594–605

[16] Çelik V, Arcaklioglu E. Performance maps of a diesel engine. Applied Energy, 2005, 81(3): 247–259 

[17] Can O, Celikten I, Usta N. Effects of ethanol addition on performance and emissions of a turbocharged indirect injection diesel engine running at different injection pressures. Energy Conversion and Management, 2004, 45(15-16): 2429–2440 

[18] Yusaf T F, Buttsworth D R, Saleh K H, Yousif B F. CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Applied Energy, 2010, 87(5): 1661–1669

[19] Oğuz H, Saritas I, Baydan H E. Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 2010, 37(9): 6579–6586nbsp;

[20] Kiani Deh Kiani M, Ghobadian B, Tavakoli T, Nikbakht A M, Najafi G. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends. Energy, 2010, 35(1): 65–69nbsp;

[21] Sayin C, Ertunc H M, Hosoz M, Kilicaslan I, Canakci M. Performance and exhaust emissions from a gasoline engine using artificial neural network. Applied Thermal Engineering, 2007, 27(1): 46–54

[22] Canakci M, Ozsezan A N, Arcaklioglu E, Erdil A. Predication of performance and exhaust emissions of a diesel engine fuelled with biodiesel produced from waste frying palm oil. Expert Systems with Applications, 2009, 86: 630–639

[23] Velmurugan A, Loganathan M, James Gunasekran E. Experimental investigations on combustion, performance and emission characteristics of thermal cracked cashew nut shell liquid (TC-CNSL)-diesel blends in a diesel engine. Fuel, 2014, 132: 236–245 

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