Introduction
In Asia, two-wheeled agricultural tractors predominantly use single-cylinder two-stroke diesel engines, which are characterized by high fuel consumption and substantial air pollution. At the same time, the severe environmental impacts of energy production from diminishing fossil fuel reserves are increasingly evident. Therefore, it is essential to develop sustainable and clean energy sources to meet these needs. Biodiesel is an alternative fuel that can be blended with conventional diesel to help reduce environmental pollution. In this study, diesel-biodiesel blends produced from rapeseed, soybean, and palm oil were evaluated for their effects on engine performance metrics, including power (P), torque (T), and specific fuel consumption (SFC). Furthermore, the emissions of pollutants (NOx, HC, CO, and CO₂) from these fuels were measured and modeled using linear and non-linear regression, as well as the adaptive neuro-fuzzy inference system (ANFIS).
Materials and Methods
To leverage the benefits of palm oil biodiesel, known for its high calorific value, along with the low kinematic viscosity of biodiesel derived from soybean and rapeseed oils, pure diesel was blended with 10% and 20% mixtures of rapeseed, soybean, and palm biodiesel, as well as 10% and 20% combinations of all three biodiesels. These nine fuel blends were tested at four engine speeds (1800, 2100, 2400, and 2700 rpm) under full load conditions. The diesel-biodiesel blends were produced at Sari Agricultural Sciences and Natural Resources University (SANRU) and transported to the engine laboratory at Tarbiat Modares University in Tehran, Iran, for detailed analysis. A total of 36 treatments were evaluated using a randomized complete block design (RCBD), incorporating four engine speeds and nine fuel types. The measured outputs included engine power, torque, specific fuel consumption, and pollutant emissions such as NOx, HC, CO, and CO₂. The collected data were used as input for modeling through both linear and non-linear regression in SPSS software, as well as ANFIS in MATLAB software.
Results and Discussion
This study evaluated nine diesel-biodiesel blends derived from palm, rapeseed, and soybean oils using a diesel engine in a controlled laboratory setting. Tests were carried out at four engine speeds—1800, 2100, 2400, and 2700 rpm—under full load conditions to assess engine performance and exhaust emissions. The results showed that for all tested fuel blends, power, specific fuel consumption, and pollutant emissions increased with engine speed, while torque decreased. Based on the findings, a blend containing 20% palm biodiesel can be used as an alternative fuel in diesel engines without requiring any modifications. The modeling results indicated that non-linear regression provided better accuracy than linear regression. However, ANFIS demonstrated a much higher correlation between actual and predicted values, with R² exceeding 0.98 for both performance parameters and emissions, compared to R² values below 0.47 for linear regression and below 0.92 for non-linear regression. The ANFIS model achieved its highest and lowest R² values at 0.99 for specific fuel consumption (SFC) and 0.98 for power (P), respectively; substantially higher than those from linear regression, which yielded 0.47 for torque (T) and 0.00 for power. Non-linear regression resulted in R² values of 0.92 for SFC and 0.60 for carbon monoxide (CO), still lower than those achieved by ANFIS. Overall, the highest R² value recorded was 0.7525 for torque, and the lowest was 0.6112 for power.
Conclusion
Single-cylinder diesel engines, which have high fuel consumption and contribute to air pollution, are commonly used in two-wheel agricultural tractors across Asia. One approach to reducing the environmental impact of fossil fuels is to use biodiesel in these engines without requiring any modifications. The results of this study showed that a 20% blend of palm biodiesel can be an effective alternative fuel for diesel engines, with no need for engine modification. Furthermore, the modeling results indicated a significantly higher correlation (R² > 0.98) between actual and predicted values of performance variables and emissions using ANFIS, compared to linear regression (R² < 0.47) and non-linear regression (R² < 0.92). Therefore, ANFIS can be effectively used to accurately predict engine performance and emission parameters. |
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