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This study provides a meta-analysis of renewable landscape energies in Iran. In order to do this effectively, we identify and estimate the amount of wind, solar, geothermal, biomass and tidal energy in Iran, identifying the potential for exchanging RE with neighboring countries, and methods for utilizing these energies in architectural design and landscape design. Data used are collecting from local and world databases and presented for analysis using descriptive and inferential statistics. The results show that the level of achievements of the stated goals is generally low. The plurality of RE documents and offices, lack of operational programs and sub-optimal use of private sector capacity are the main weaknesses. It is further concluded that if conditions were more conducive to private sector participation and appropriate operational and action plans were adopted, Iran could supply solar energy to Azerbaijan, wind energy to Turkmenistan and Afghanistan, and can supply Iraq with hydropower. Ultimately it is proposed that the adoption of a comprehensive and integrated RE policy which minimizes the number of policy documents and concentrates RE management in a small number of offices is an important factor in facilitating the success of renewable energies in Iran.

In the present study, the effects of graphene oxide (GO) nano-particles on performance and emissions of a diesel engine fueled with Oenothera lamarckiana biodiesel was investigated. Biodiesel was used in the blend of B20. The GO nano-particles with concentrations of 30, 60, and 90 ppm were considered for each fuel blend. Experiments were performed at a constant speed of 2100 rpm at loads of 0%, 25%, 50%, 75%, and 100%. Various parameters, such as power, exhaust gas temperature (EGT), carbon monoxide (CO), carbon dioxide (CO2), unburned hydrocarbons (UHCs), and nitrogen oxides (NOx), were investigated. Results showed that by using GO, power and EGT significantly increase. Furthermore, by using GO nano-particles, significant reductions in CO (∼5%–22%) and UHCs (∼17%–26%) were observed. However, under similar conditions, a slight increase in CO2 (∼7%–11%) and NOx (∼4%–9%) emissions observed. Finally, it can be concluded that nano-graphene oxide can be introduced as a suitable alternative fuel additive for Oenothera lamarckiana biodiesel blends.

The present study investigated the effect of graphene oxide (GO) nanoparticles, as novel fuel additives, on the engine performance and emission characteristics of a diesel engine. The GO nanoparticles were mixed with B0, B10, and B20 Ailanthus altissima biodiesel blends. The graphene oxide nanoparticles, at concentrations of 30, 60, and 90 ppm, were dispersed into each fuel blend by ultrasonication. Throughout this study, engine testing was performed at a constant speed of 2100 rpm and loads of 0%, 25%, 50%, 75%, and 100%. Performance and emission characteristics, including power, torque, specific fuel consumption (SFC), exhaust gas temperature (EGT), and CO, CO2, unburned-hydrocarbon (UHC), and NOx emissions, were investigated. study revealed that, under the same conditions, most performance characteristics, such as power, torque, and EGT, significantly increased through the addition of GO to fuel blends, while SFC significantly decreased. The GO-nanoparticle additives facilitated significant reductions in CO and UHC emissions (approximately 7–20% and 15–28%, respectively); however, under the same conditions, slight increases in CO2 and NOx emissions (approximately 6–10% and 5–8%, respectively) were observed. On the basis of the experimental results, we conclude that fuel blends composed of nanographene oxide, Ailanthus altissima biodiesel, and diesel can be introduced as environmentally friendly fuels.

Microalgae biodiesel has captured the attention as a clean renewable fuel for diesel engines due to their positive characterizations such as high productivity, fast growing rate and their ability to convert CO2 to fuel. This work investigates the use of microalgae biodiesel from Chlorella Protothecoides (MCP-B) as alternative fuel for Compression Ignitions (CI) engines. Engine performance and emissions along with the fuel properties of the MCP-B100, MCP-B50, and MCP-B20 were evaluated and compared with petroleum diesel (PD). Analysis of variance statistical test (ANOVA) was conducted to evaluate the significance of the differences between the parameters means. The results showed that MCP-B100 produces less emission compared to PD. Statistically significant differences were found in the engine brake power, torque, BSFC, exhaust gas temperature, CO, O2 and NOx when MCP-B100 and its blends were used compare to PD. MCP-B100 showed a reduction of 7, 4.9, 6.1, 28, 4.2 and 7.4% in brake power, torque, exhaust gas temperature, CO, CO2 and NOx, respectively. Contrarily, the use of MCP-B100 resulted in an increase of 10.2 and 15.8% in BSFC and O2, respectively compared to PD.

This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm for the engine. Multi layer perception network (MLP) was used for non-linear mapping between the input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient ( R ) 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model.

Diesterol is a new specific term which denotes a mixture of fossil diesel fuel (D), vegetable oil methyl ester called biodiesel (B) and plant derived ethanol (E). In the context of the present paper, this term refers specifically to the combination of diesel fuel, bioethanol produced from potato waste, dehydrated in a vapor phase using 3A Zeolite, and sunflower methyl ester produced through transesterification. The mixture of DBE, i.e. diesterol, was patented under the Iranian patent No. 39407, dated 12-3-2007. The main purpose of this research work was to reduce engine exhaust NOx, CO, HC and smoke emissions due to application of biofuel and the increase of fuel oxygen content. It was needed to prepare suitable low cost and renewable additives. The diesterol properties such as pour point, viscosity, flash point, copper strip corrosion, ash content, sulfur content and cetane number were determined experimentally. The optimum ratio of bioethanol and biodiesel was found to be 40/60 considering fuel oxygen content, fuel price and mixture properties. Bioethanol was added to enhance the oxygenated component in the fuel, while the sunflower methyl ester was added to maintain the fuel stability at low temperatures. The parameters considered for investigation are the engine power, torque, specific fuel consumption and exhaust emissions for various mixture proportions. The experimental results showed that bioethanol plays an important role in determining the flash point of the blends. By adding 3% bioethanol to diesel and sunflower methyl ester, the flash point was reduced by 16 °C. The viscosity of the blend was also reduced by increasing the amount of bioethanol. The sulfur content of bioethanol and sunflower methyl ester is very low compared to diesel fuel. The sulfur content of diesel is 500 ppm whereas that of bioethanol and sunflower methyl ester is 0 and 15 ppm, respectively. This lower sulfur content is another factor enhancing the use of fuel blends in diesel engines. The bioethanol and sunflower methyl ester combination has sulfur content less than 20 ppm. The maximum power and torque using diesel fuel were 17.75 kW and 64.2 Nm at 3600 and 2400 rpm, respectively. Adding oxygenated compounds to the new blend seems to slightly reduce the engine power and torque and increased the average sfc for various speeds. The experimental measurement and observation of smoke concentration, NOx, CO and HC concentration indicated that both of these pollutants reduced by increasing the biofuel composition of diesterol throughout the engine operating range.