While the methyl ester structure in biodiesel is responsible for lubrication improvement in base fuels with poor lubricity properties such as ultra-low sulfur diesel and non-upgraded HVO, relatively little is known about its effect on all-level blends, which would provide higher energy security for biodiesel utilization. In this study, binary blends of palm oil biodiesel (POB) with commercial petroleum diesel fuel (DF) and HVO at every 10%-v/v blend point were analyzed using a high-frequency reciprocating rig (HFRR) according to the standard method of ASTM D6079. It was found that the addition of POB successfully improved the lubricating properties of DF-CN48 and DF-CN51 and efficiently acted as a lubricity improver that showed a minimum friction coefficient and improved the specific wear rate. The adsorption of ester molecules on the metallic surfaces acted as a protective layer during the rubbing process, resulting in lubricity improvement for the diesel fuel. Interestingly, the 60–90%-v/v POB blend with HVO showed a lubricity capacity that competed determinatively and attractively, resulting in a non-ideal contribution to the changes in the friction coefficient, WSD formation, and specific wear rate.

This report is focused on comparative analysis of the impact of using biodiesel blends containing more than 30% biodiesel in diesel engine filtration systems. The objective of this study is to support the sustainability of the mandatory biodiesel utilization program by more than 30%. To evaluate filterability behavior of high-percentage biodiesel blends, namely B30 and B40 (30 and 40%-vol biodiesel on diesel fuel), the study employed the ASTM D 2068 Filter Blocking Tendency (FBT). After filter rig test, fuel filter pressure was also evaluated using the JIS 1617 standard method. It is important to note that fuel filter plays an important role in removing contaminants from fuel, and, hence, the effect of the difference in filter porosity needs to be observed with pressure difference across fuel filter monitored at the flow rate (0.03 m3/h and fuel temperature (15 ⁰C and 25 ⁰C). Furthermore, the effect of changes in temperature and surface morphology on the characteristics of filter was observed in this study. Based on FBT analysis, a polynomial regression (R2 > 0.98) was used to describe the relationship between FBT value and the effect of biodiesel blends on filterability. It was concluded that the temperature, monoglyceride content, and FAME concentration in the diesel fuel influenced their FBT. However, the rise in waxy particles at 15oC (near Cloud Point) could result in a more significant average pressure drop than at 25⁰C (ambient temperature). It was also found that a higher biodiesel mixture potentially results in a higher-pressure difference due to the lower fuel temperature and the formation of waxy contaminants that can clog filter.

Bioethanol has been considered as a more sustainable alternative for fossil fuels, and it has been used as a drop-in fuel mixture. In this paper, the autoxidation properties of real kerosene as well as single, binary and ternary surrogates with the presence of ethanol are investigated for the first time. A simplified python code is proposed to predict the pressure drop of the PetroOXY method that was used for assessing the fuel autoxidation properties. The experimental results show that the addition of an ethanol concentration reduces the induction period of real kerosene while increasing that of surrogate mixtures. Also, the maximum pressure during the PetroOXY test increases with the increase of ethanol concentration. The model is able to predict the induction period of ethanol accurately by employing an automated reaction mechanism generator. A strategy to increase the autoxidation stability of ethanol by adding 1 g/L antioxidant has been evaluated. The efficiency of the antioxidants for ethanol is in the following order: PY > Decalin > DTBP > Tetralin > BHT > MTBP > BHA > TBHQ > PG.

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.

MJT Makara Journal of Technology

Indonesia has demonstrated a firm commitment to achieving Net Zero Emissions (NZE) by 2060. The implementation of diverse strategies, such as the application of biomass co-firing technology in coal-based steam power plants, demonstrates this commitment. This study focuses on the 660 MW supercritical coal-fired boiler as the object of investigation. The key problem addressed in this research is the unstable combustion performance due to the dynamic and nonlinear interactions among operational variables under biomass co-firing conditions. These fluctuations can negatively impact boiler efficiency, CO2 emissions, and the plant’s capability factor. The study proposes a dynamic multi-objective optimization framework using a Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and a Multi-Objective Genetic Algorithm (MOGA) to enhance performance reliability and support Indonesia’s transition to cleaner energy sources. The findings indicate that the RNN model exhibited superior prediction accuracy compared to the RSM, with a Root Mean Square Error (RMSE) value of 0.1523% for boiler efficiency, 1.6993% for CO2 emissions, and 0.5284% for the capability factor. The MOGA optimization exhibited an enhancement in boiler efficiency from 86.6793% to 87.32%, a reduction in CO2 emissions from 114.213 mg/Nm3 to 53.972 mg/Nm3, and an augmentation in the capability factor from 87.9% to 89.32%. Furthermore, coal consumption is reduced to 51,524 tons per hour, which can generate operational cost savings of IDR 1.34 billion per day. The RNN and MOGA-based approaches have been demonstrated to be more effective than RSM for optimizing boiler combustion. This method is important for developing a strategy to improve the efficiency of the combustion process in boilers in coal-fired power plants. It will also help support the transition to clean energy and achieve the NZE 2060 target