The utilization of liquefied petroleum gas (LPG) as an alternative automobile fuel in Nigeria was studied, focusing on varying different blend ratios of propane and butane as an alternative fuel in a single-cylinder, ...The utilization of liquefied petroleum gas (LPG) as an alternative automobile fuel in Nigeria was studied, focusing on varying different blend ratios of propane and butane as an alternative fuel in a single-cylinder, four-stroke, and spark ignition (SI) engine. Ricardo WAVE, 1-Dimensional engine simulator was used to model the internal combustion engine where the different blend ratios of propane and butane (P100, P90B10, P80B20, P70B30, P60B40 and P50B50) were tested and compared with a gasoline engine operating under same conditions. From the simulation results for the different LPG blends, there was no significant difference in the engine performance and emissions, but when compared with pure gasoline, it was observed that the LPG showed improved engine performance and lower emissions. The engine power output in using the blends was 25% higher compared to using gasoline;CO emission was 50% less, UHC was 20% less while NO<sub>x</sub> at low speed was significantly lower.展开更多
The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies.However,supersonic separators have seen minimal field appli...The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies.However,supersonic separators have seen minimal field applications despite the multiple benefits over other gas dehydration techniques.This has been mostly attributed to the uncertainty in ascertaining the design and operating parameters that should be monitored to ensure optimum dehydration of the supersonic separation device.In this study,the decision tree machine learning model is employed in investigating the effects of design and operating parameters(inlet and outlet pressures,nozzle length,throat diameter,and pressure loss ratio)on the supersonic separator performance during dehydration of natural gas.The model results show that the significant parameters influencing the shock wave location are the pressure loss ratio and nozzle length.The former was found to have the most significant effect on the dew point depression.The dehydration efficiency is mainly dependent on the pressure loss ratio,nozzle throat diameter,and the nozzle length.Comparing the machine learning model-accuracy with a 1-D iterative model,the machine learning model outperformed the 1-D iterative model with a lower mean average percentage error(MAPE)of 5.98 relative to 15.44 as obtained for the 1-D model.展开更多
文摘The utilization of liquefied petroleum gas (LPG) as an alternative automobile fuel in Nigeria was studied, focusing on varying different blend ratios of propane and butane as an alternative fuel in a single-cylinder, four-stroke, and spark ignition (SI) engine. Ricardo WAVE, 1-Dimensional engine simulator was used to model the internal combustion engine where the different blend ratios of propane and butane (P100, P90B10, P80B20, P70B30, P60B40 and P50B50) were tested and compared with a gasoline engine operating under same conditions. From the simulation results for the different LPG blends, there was no significant difference in the engine performance and emissions, but when compared with pure gasoline, it was observed that the LPG showed improved engine performance and lower emissions. The engine power output in using the blends was 25% higher compared to using gasoline;CO emission was 50% less, UHC was 20% less while NO<sub>x</sub> at low speed was significantly lower.
文摘The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies.However,supersonic separators have seen minimal field applications despite the multiple benefits over other gas dehydration techniques.This has been mostly attributed to the uncertainty in ascertaining the design and operating parameters that should be monitored to ensure optimum dehydration of the supersonic separation device.In this study,the decision tree machine learning model is employed in investigating the effects of design and operating parameters(inlet and outlet pressures,nozzle length,throat diameter,and pressure loss ratio)on the supersonic separator performance during dehydration of natural gas.The model results show that the significant parameters influencing the shock wave location are the pressure loss ratio and nozzle length.The former was found to have the most significant effect on the dew point depression.The dehydration efficiency is mainly dependent on the pressure loss ratio,nozzle throat diameter,and the nozzle length.Comparing the machine learning model-accuracy with a 1-D iterative model,the machine learning model outperformed the 1-D iterative model with a lower mean average percentage error(MAPE)of 5.98 relative to 15.44 as obtained for the 1-D model.
