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

Kumar, Shiva; Pai, P. Srinivasa; Rao, B. Rama

doi:10.1016/j.apenergy.2010.12.030

Cited by 201 publications

(65 citation statements)

References 25 publications

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“…The use of ANN for the prediction of the cetane number of biodiesel has been presented in [52]. Furthermore, a based prediction of performance and emission characteristics of a variable compression ratio CI engine, using WCO as a biodiesel at different injection times by means of ANN was examined by Shivakumara et al [53], whereas Najafi et al [54] have applied ANN in the performance and exhaust emissions of a biodiesel engine, and Najafi [55] carried out a combustion analysis of a CI Engine Performance employing waste cooking biodiesel fuel with an ANN Aid. Another application includes not only biodiesel production from soybean oil [56] and waste frying palm oil [57], but also the prediction of engine performance for an alternative fuel [58] and the simulation of biodiesel production from waste olive oil [59].…”

Section: Introductionmentioning

confidence: 99%

Brette Pearl Spar Mable (BPSM): a potential recoverable catalyst as a renewable source of biodiesel from Thevetia peruviana seed oil for the benefit of sustainable development in West Africa

et al. 2018

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Background The energy requirements are globally on a rapid escalation, as technology advances, which is also true for a developing country like Nigeria, which is dependent on fossil fuels and its derivatives. Apart from its adverse effect on its economy, it has also negative impacts on the health and the environment, in general. However, investments in renewable energy are faced by the competitive oil prices, the very high investment cost for renewable energy, and high local electricity prices. This paper appraises the attractiveness of investing in renewable energy sources over the continued use of non-edible oil for electricity generation. Methods This paper explores the application of biomass seed oil to produce a renewable fuel (biodiesel) using heterogeneous base catalyst. Meanwhile, two-step processes were employed to produce the biofuel. In the first step (esterification), the acid value of the oil was reduced to the recommended limit (FFA ≤ 1.5) using H2SO4, while in the second step (transesterification), the catalyst calcination of grounded Brette Pearl Spar Mable (BPSM) pre-soaked in methanol was used as a biobase for biodiesel production. For the optimization, minitab response surface (MRS) and artificial neural network (ANN) were employed to model and optimize the process variables responsible for the optimum production of the oil and the biodiesel. Results The result presented showed that T. peruviana seed was found to be rich in oil with an average yield of 44.00% (w/w), and the oil was highly unsaturated with a high FFA. The maximum experimental biodiesel yield obtained was 86.00% at a catalyst amount of 4 g, a reaction time of 70 min, and a methanol/oil ratio of 0.1(v/v). This result was validated in triplicate under the same conditions, which yielded 85.70% (v/v) for MRS and 85.98% (v/v) for ANN. Furthermore, the optimization results also indicated that the p values (p < 0.05) of the model terms were significant, and the accuracy of the models achieved by MRS and ANN based on R2 depict that both optimization tools gave good predictions of R2 (MRS: R2 = 99.98% and ANN: R2 = 99.97%). The properties of the biodiesel, as described in other earlier reports using the same feedstock with different catalysts, indicated that the produced biodiesel had properties which agreed to those reported in the literature. Conclusions T. peruviana seed has proved to be a good biomass raw material for oil production, and its conversion to biofuel using a heterogeneous biobase catalyst showed its suitability as a renewable environmental friendly fuel. Government should invest in more sustainable sources of energy by imposing law for the use of non-edible oil or decreasing the price of electricity.

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Section: Introductionmentioning

confidence: 99%

Brette Pearl Spar Mable (BPSM): a potential recoverable catalyst as a renewable source of biodiesel from Thevetia peruviana seed oil for the benefit of sustainable development in West Africa

et al. 2018

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“…Zweiri and Lakmal studied a diesel engine to predict the indicated torque by means of a BP neural network (NN) [15]. Shivakumar et al predicted the brake thermal efficiency (BTE), brake specific energy consumption (BSEC), T exh , NO x , smoke, and HC of a diesel engine fueled with a biodiesel blend using an ANN BP algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Kökkülünk¹,

Akdoğan²,

Ayhan³

2013

Turk J Elec Eng & Comp Sci

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Exhaust gases have many effects on human beings and the environment. Therefore, they must be kept under control. The International Convention for the Prevention of Pollution from Ships (MARPOL), which is concerned with the prevention of marine pollution, limits the emissions according to the regulations. In Emission Control Area (ECA) regions, which are determined by MARPOL as ECAs, the emission rates should be controlled. Direct injection (DI) diesel engines are commonly used as a propulsion system on ships. The prediction and control of diesel engine emission rates is not an easy task in real time. Therefore, in this study, an artificial neural network (ANN) structure using the back propagation (BP) learning algorithm and radial basis function (RBF) has been developed to predict the emissions and exhaust temperature for DI diesel engines with emulsified fuel. In order to show the ANN performance, the network outputs and experimental results of the BP and RBF have been compared in this paper. The experimental results were obtained from a real diesel engine. The results showed that the emissions and exhaust temperature were estimated with a very high accuracy by means of the designed neural network structures and the RBF is more reliable than the BP.

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“…Shivakumar et al [32] studied the ANN-based prediction of performance and emission characteristics of a variable compression ratio engine using waste cooking oil as a biodiesel at different injection timings. The tests were performed at three different injection timings by varying the thickness of the advance shim.…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel

Muralidharan

Vasudevan

2014

J Braz. Soc. Mech. Sci. Eng.

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The intention of this study is to predict the performance, emission and combustion characteristics of a single-cylinder, four-stroke variable compression ratio engine fuelled with waste cooking oil methyl ester and its blends-standard diesel with the aid of artificial neural network (ANN). The tests were performed with fuel blends of 20, 40, 60 and 80 % biodiesel with standard diesel, with an engine speed of 1,500 rpm and at compression ratios of 18:1, 19:1, 20:1, 21:1 and 22:1 under different loading conditions. Three different ANN models based on standard feed-forward back-propagation algorithm have been developed to predict the performance, emission and combustion characteristics of VCR engine. To train the network, compression ratio, blend percentage and percentage load were used as input parameters whereas engine parameters such as brake thermal efficiency, specific fuel consumption, brake power, indicated mean effective pressure, mechanical efficiency and exhaust gas temperature were used as output parameters for the performance model and exhaust emissions such as carbon dioxide, carbon monoxide, hydrocarbon and NO x were used as output parameters for emission model. Separate model is developed for combustion characteristics in which compression ratio, blend percentage, load percentage and crank angle were used as the input parameters whereas combustion pressure, heat release rate, ignition delay, combustion duration and mass fraction burnt were used as the output parameters. This study shows that there is a good correlation between the ANN-predicted values and the experimental data for different engine performance, emission parameters and combustion characteristics. Keywords Biodiesel Á Variable compression ratio engine Á Performance Á Combustion Á Emission Á Artificial neural networks List of symbols ANN Artificial neural networks B20 20 % Biodiesel ? 80 % Diesel B40 40 % Biodiesel ? 60 % Diesel B60 60 % Biodiesel ? 40 % Diesel B80 80 % Biodiesel ? 20 % Diesel BP Brake power BTE Brake thermal efficiency CA Crank angle CO Carbon monoxide CO 2 Carbon dioxide CNG Compressed natural gas CR Compression ratio HC Hydro carbon IMEP Indicated mean effective Pressure LM Levenberg-Marquardt (algorithm) MLP Multi-layer perception MSE Mean square error NO x Nitrogen oxides O 2

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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

Cited by 201 publications

References 25 publications

Brette Pearl Spar Mable (BPSM): a potential recoverable catalyst as a renewable source of biodiesel from Thevetia peruviana seed oil for the benefit of sustainable development in West Africa

Brette Pearl Spar Mable (BPSM): a potential recoverable catalyst as a renewable source of biodiesel from Thevetia peruviana seed oil for the benefit of sustainable development in West Africa

Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel

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