Biodiesels from three feedstock : The effect of graphene oxide (GO) nanoparticles diesel engine parameters fuelled with biodiesel
- Authors: Hoseini, Seyed , Najafi, Gholamhassan , Ghobadian, Barat , Ebadi, Mohammad , Mamat, Rizalman , Yusaf, Talal
- Date: 2020
- Type: Text , Journal article
- Relation: Renewable Energy Vol. 145, no. (2020), p. 190-201
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- Description: Physicochemical characteristics of three type biodiesel feedstock and diesel engine parameters fuelled with graphene oxide (GO) nanoparticles addition in diesel/biodiesel blends have been investigated. Three types of oilseeds, namely Evening primrose (Oenothera lamarckiana), the fruit of Tree of heaven (Ailanthus altissima) and Camelina (Camelina sativa), were selected as suitable resources for Iran. The result showed that the Tree of heaven contains 38% oil which is higher than the Evening primrose (26%) and Camelina (29%). Physicochemical properties of the oils showed that the viscosity of the Camelina oilseeds was less than the Tree of heaven oilseeds and Evening primrose oilseeds. Therefore, in terms of viscosity, the Camelina oilseeds is preferable. Experimental results showed that the biodiesel from all three types of oilseeds are consistent with the ASTM biodiesel standards. However, Camelina biodiesel has better physicochemical properties than another feedstock. Therefore, biodiesel of Camelina oil can be an appropriate alternative to diesel fuels in Iran. Performance and emission parameters of diesel engine fuelled with graphene oxide (GO) nanoparticles addition in three biodiesel resources compared with diesel. A reduction in UHCs, CO, and BSFC with a penalty of increased NOx emissions was realized with all graphene oxide (GO) nanoparticles addition in diesel/biodiesel blends. Also, with Camelina biodiesel, the power increased.
Artificial neural network modeling and sensitivity analysis of performance and emissions in a compression ignition engine using biodiesel fuel
- Authors: Jaliliantabar, Farzad , Ghobadian, Barat , Najafi, Gholamhassan , Yusaf, Talal
- Date: 2018
- Type: Text , Journal article
- Relation: Energies Vol. 11, no. 9 (2018), p. 1-24
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- Description: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.