Evaluation of the performance and gas emissions of a tractor diesel engine using blended fuel diesel and biodiesel to determine the best loading stages

Utilization of renewable biodiesel blends with different proportions for the improvements of performance and emission characteristics of a diesel engine

This work investigated and compared the impact on performance and emission characteristics of diesel engine fueled with five different proportions of biodiesel blends. Firstly, the three-dimensional simulation software CONVERGE was used to create a 3D simulation model of in-cylinder combustion for a diesel engine. Secondly, the experimental data of cylinder pressure and NOx emissions at 50% and 100% loads were employed to verify the simulation model. Finally, the combustion processes of blends with proportions of 0%, 5%, 10%, 15%, and 20% biodiesel were simulated and compared by using the model. The study showed that the brake thermal efficiencies (BTEs) of biodiesel blends with 5%, 10%, 15%, and 20% of biodiesel were increased by 1.24%, 1.89%, 3.13%, and 3.82% at 50% load, respectively, compared with pure diesel. In addition, the soot emissions were decreased by 1.20%, 2.64%, 3.88%, and 4.65%, respectively. However, as the proportion of biodiesel in the biodiesel blends increased, the brake specific fuel consumption (BSFC) and NOx emissions increased. At 50% load, the BSFCs of biodiesel blends with 5%, 10%, 15%, and 20% of biodiesel increased by 0.61%, 1.34%, 1.42%, and 2.17%, respectively, compared with pure diesel. Additionally, the brake powers (BPs) were decreased by 0.64%, 1.31%, 1.88%, and 2.62% at 100% load, respectively.

Engine performance and emissions evaluation of surfactant-free B30 biodiesel-diesel/water emulsion as alternative fuel

Malaysia is one of the top exporters of palm oil, and although currently facing fierce resistance towards palm oil imports in some parts of the globe, one of the ways to utilize this commodity is by increasing palm biodiesel content in local commercial diesel. However, due to the oxygen-rich nature of biodiesel, its utilization suffers from increased nitrogen oxides (NO_x) emission compared to conventional diesel. To mitigate this issue and improve diesel engine performance and emissions using biodiesel-diesel blends, this study attempted to investigate implementation of a real-time non-surfactant emulsion fuel supply system (RTES) which produces water-in-diesel emulsion as fuel without surfactants. NO_x reducing capability of water-in-diesel produced by RTES has been well documented. Therefore, in this study, 30% biodiesel-diesel (B30) was used as the base fuel while B30-derived emulsions consisting of 10 wt%, 15 wt% and 20 wt% water content were supplied into a 100 kVA, 5.9-L common rail turbocharged diesel engine electric generator. Fuel consumption and exhaust emissions were measured and compared with commercially available Malaysian low grade diesel fuel (D2M). Evidence suggested that emulsified B30 biodiesel-diesel produced by RTES was able to increase brake thermal efficiency (BTE) up to a maximum of 36% and reduce brake specific fuel consumption (BSFC) up to 8.70%. Furthermore, B30 biodiesel-diesel emulsions produced significantly less NO_x, carbon monoxide and smoke at high engine load. In conclusion, B30 biodiesel-diesel emulsions can be readily utilized in current diesel engines without compromising on performance and emissions.

The effect of biodiesel production method on its combustion behavior in an agricultural tractor engine

In this study, biodiesel fuel was produced from waste cooking oil (WCO) using a heterogeneous catalyst under microwave (MB) and conventional (CB) heating, and fueled in an agricultural tractor engine to evaluate the engine performance as well as emissions. The biodiesels presented different fatty acid methyl ester (FAME) profiles where MB had lower unsaturated FAME chains. Beyond the transesterification reaction time, the energy consumed for MB biodiesel production diminished by around eight times compared to that required for CB production. The engine results confirmed the positive influence of blending net diesel fuel with biodiesel for enhancing the engine performance and reducing the emissions. More than 20% increment in the engine power and torque was detected at all engine loads (the engine speed was adjusted at 1500 rpm). The hydrocarbon (HC), carbon monoxide (CO), and smoke opacity (SO) indicated significant reductions compared to when net diesel fuel was used. According to statistical analysis, CB25 and MB25 fuels presented a suitable combination as fuel where MB25 provided better engine performance, lower HC and SO emissions, with CO emissions reaching the minimum amount by CB fuels.

Impact of Plant Spacing and Nitrogen Rates on Growth Characteristics and Yield Attributes of Egyptian Cotton (Gossypium barbadense L.)

This current study was performed to determine the influences of plant spacing, Nitrogen (N) fertilization rate and their effect, on growth traits, yield, and yield components of cotton (Gossypium barbadense L.) cv. Giza 97 during the 2019 and 2020 seasons. A split plot experiment in three replicates was utilized whereas the cotton seeds were planted at 20, 30, and 40 cm, as main plots and nitrogen at 75, 100, and 125%, was in subplots. The results revealed that the planting spacing at 40 cm significantly (p ≤ 0.01) increased plant height, number of fruiting branches per plant, number of bolls per plant, boll weight (BW), lint percentage (L%), seed cotton yield (SCY), lint cotton yield (LCY), seed index and lint index by 165.68 cm, 20.92, 23.93, 3.75 g, 42.01%, 4.24 ton/ha, 5.16 ton/ha, 12.05, 7.86, respectively, as average in both seasons. The application of N fertilizer rate at 125% caused a maximum increase in growth and yield parameters i.e., plant height (169.08 cm), number of vegetative branches (2.67), number of fruiting branches per plant (20.82), number bolls per fruiting branch (1.39), number of bolls per plant (23.73), boll weight (4.1 g), lint percent (41.9%), seed index (11.8 g), and lint index (8.2), while the plants treated with 100% N rates exhibited highest seed cotton yield (4.3 ton/ha) and lint cotton yield (5.6 ton/ha), as average in both seasons. Combining plant spacing at 40 cm between plants with a 100% N fertilizer rate recorded the highest lint cotton yield (5.67 ton/ha), while the highest seed cotton yield (4.43 and 4.50 ton/ha) was obtained from 125% N fertilizer rate under planting spacing 20 and 40 cm, respectively. Conclusively, a wide density (40 cm) with 125% N is a promising option for improved biomass, cotton growth, yield, physiological traits, and fiber quality.

Modeling and prediction for diesel performance based on deep neural network combined with virtual sample

The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.