The use of a high concentration of biodiesel blends has been implemented nationally in Indonesia as part of the government’s program to increase energy security and improve environmental quality. However, a high concentration of biodiesel, specifically a blending volume of 30% (B30), leads to a shorter fuel filter lifetime compared with pure diesel fuel (B0), due to the precipitation of impurities from biodiesel and the presence of contaminants from the environment. A study was conducted involving a rig test to evaluate the effect of using B30 on filter lifetime, referred to as JIS D1617:1998. The results showed that the temperature and cleanliness of the biodiesel had a strong influence on filter blocking. B30 with an ISO cleanliness of 22/21/17 without added standard dust contaminants at 15 °C for 48 h produced larger amounts of deposits compared to B0 with an ISO cleanliness of 16/13/7 with the addition of 1 g of contaminant for the same treatment. B30 with 1 g of additional contaminants soaked at 15 °C produced a larger amount of deposit than B30 with 2 g of added contaminant soaked at ~27 °C. The weighing of the used filters showed that deposits that originated from biodiesel impurities and precipitations were the dominant material causing a reduced fuel filter lifetime. In addition to the cleanliness factor, a decrease in the micron rating of the filter resulted in a shorter filter lifetime.

This paper discusses an effect of rhodinol and turpentine-based bio-additives to improve pure plant oil characteristics. Rhodinol and turpentine were added to PPO at 1% by volume. Testing was conducted on single cylinder diesel engine for agriculture application referring to SNI 0119 : 2012. The results of fuel characteristics show that bio-additive+PPO could fulfill the quality standards of SNI 7431 : 2015. Both rhodinol and turpentine showed capability to reduce water and particle content in PPO. Engine test results showed that at engine speed of 1800 rpm, turpentine+PPO could reach a maximum power of 4.53kW, maximum torque of 24.06 Nm, smoke opacity of 6.87 FSN, and specific fuel consumption of 145.62 g/kWh; while the PPO had maximum power of 4.39 kW, maximum torque of 23.31 Nm, smoke opacity of 7.56 FSN, and specific fuel consumption of 150.12 g/kWh. Here, rhodinol+PPO were also slightly higher on power, torque and lower on smoke emission, and specific fuel consumption compared with PPO without additive. In general, rhodinol and turpentine were slightly higher on engine power and torque with lower specific fuel consumption and lower smoke emission. However, reduction of water and particle content must be further investigated to have better understanding on its effect on engine components lifetime.

The depletion of petroleum reserves as the basic raw material for gasoline production has driven studies into alternative fuels. One of the alternative fuels is alcohol, both ethanol and methanol. Due to their liquid form and physical-chemical properties similar to gasoline, small modifications to the engine are required. This paper will explain the effect of using a mixture of gasoline (in this case, RON 98 gasoline with methanol or methanol on engine performance and emissions. The fuel mixtures are as follows: G100, E10, E20, E30, M10, M20, M30. AVL Boost simulation software was used as a tool for 1-dimensional engine modelling, where the modeling is based on engine testing with G100 fuel. The results show that with increasing ethanol-methanol composition, torque and power decrease, and SFC increases. On the emission side, CO, CO2, and HC were decreased and NOx increased.

Heavy-duty diesel engines are essential for transportation and industrial applications but face challenges related to high fuel consumption and carbon emissions. This study aims to develop a hybrid optimization framework combining Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Multi-Objective Genetic Algorithm (MOGA) to enhance engine efficiency and sustainability. Experimental tests were conducted on a 7.7 L heavy-duty diesel engine operating with commercial diesel with at speeds ranging from 800 to 2400 rpm. Three input parameters—engine speed, fuel mass flow, and air mass flow—were examined with respect to Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), and CO ₂ emissions. Predictive models were established using RSM and ANN, followed by multi-objective optimization via MOGA. The RSM models exhibited strong predictive performance with R ² values of 0.95 (BTE), 0.98 (BSFC), and 0.99 (CO ₂ ). ANN models achieved regression coefficients of R = 0.99 (training), 0.97 (validation), and 0.98 (testing). Optimization results indicated the most sustainable operating region at mid-speeds (1200–1400 rpm), achieving a maximum BTE of 42.5%, minimum BSFC of 0.187 kg kWh ⁻¹ , and CO ₂ emissions between 0.0128–0.0185 kg s ⁻¹ . The ANN–MOGA Pareto front revealed a narrow optimization surface dominated by fuel consumption, whereas the RSM–MOGA approach exhibited broader trade-offs reflecting variable interactions. The findings show that the RSM, ANN, and MOGA framework helps analyze efficiency and emission trade-offs, providing a valuable tool for advancing sustainable diesel engine operation.