The modern era of sustainable development and emission-free operations has motivated researchers to look for alternative fuel sources, particularly for automotive applications, as they are a major source of emissions.
Due to the incorporation of several additives, various blends of automotive fuels are available. Particle analysis methods provide invaluable insights into the composition and attributes of different fuels, allowing for a greater comprehension of their distinct features. This article focuses on applications of particle analysis techniques for determining essential features related to various blends of automotive fuels.
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Near Infrared Spectroscopy for Automotive Diesel Improvers
The release of greenhouse gases from the burning of fossil fuels has become a major concern over the past decade. As a consequence of these concerns, global petroleum restrictions have become more stringent, especially in terms of minimizing pollutants. To maintain the beneficial characteristics of diesel fuels while assuring optimal engine efficiency, the economical incorporation of additives is required.
As per an article published in ACS Omega, fuel additives appear in a variety of forms, including liquids, gases, and solids, and are classified into three categories. Diesel additives are applied before, during, and/or after blending. However, no precise, swift, and non-invasive analytical technique is utilized during the manufacturing process of diesel fuel to identify the precise quantity of the various types of additives.
The researchers concentrated on simultaneously determining the concentration of multiple improvers in diesel matrices. The cold-flow improver (CFI), the conductivity-lubricity improver (CLI), and the Cetane number improver (CNI) were identified simultaneously using near-infrared (NIR) spectroscopy, multivariate statistical analysis, and the partial least squares algorithm.
CLI 4.2 (mg kg-1), CFI 4.6 (mg kg-1), and CNI 5.3 (mg kg-1) were the satisfactory values of the root mean square error of estimation for the forecasting algorithms. The calculated excess standard deviation of the reproducibility was approximately 8%. These findings demonstrate the prospect of NIR spectroscopy as a rapid, inexpensive, and effective method for determining diesel additives concentrations. Furthermore, this technique is applicable during petrochemical operations, particularly for the goal of continuous online monitoring to avoid additive excesses and reduce costs.
Fourier Transform Spectroscopy for Biodiesel Property Prediction
Predicting the attributes of biodiesel to aid in the meticulous selection of feedstock for the production of biodiesel for use in automobile engines requires robust mathematical models. Biodiesel reduces CO emissions from the turbine. The benefits of reducing particulate matter emissions differ considerably between biodiesels produced from various feedstock.
The type and relative percentage of methyl esters significantly impact the biodiesel's characteristics, which in turn impact the engine's efficiency and emission generation. Therefore, predicting properties is a crucial endeavor, and the article published in Energy & Fuels utilized a Fourier transform infrared spectroscopy (FTIR)-based method is employed to forecast the density, kinematic viscosity, higher calorific value, and cetane number of biodiesels.
Current biodiesel composition-based algorithms have been adjusted with a restricted spectrum and variety of methyl esters found in biodiesels, which is one of their main limitations.
Using multilinear regression (MLR) and artificial neural networks (ANNs), only a handful of factors from the FTIR spectrum of biodiesel are associated with the characteristics of interest in this investigation. Five biodiesels with substantially distinct chemical compositions, namely coconut, camelina, Karanja, palm, and linseed, were selected and mixed in various quantitative proportions to produce seventy biodiesel blends to simulate a broad spectrum of fuels.
Two prominent peaks at 1743 and 1169 cm-1 revealed the existence of an ester functional group, demonstrating that vegetable oils were successfully blended into biodiesels.
The accord between the forecasted properties obtained by MLR and ANN models with a few parameters based on biodiesel functional classifications was superior to the standard full-spectrum PLS regression. The FTIR-based MLR model proved to be more precise than other models in the literature for predicting the kinematic viscosity of biodiesel, with a mean absolute percentage error (MAPE) of less than 4%.
Applications of Spectrophotometry for Petrol-Ethanol Fuel
In the wake of the 1973 oil crisis, the replacement of traditional petroleum-based fuel with a petrol bioethanol hybrid commenced. Purified bioethanol is unsuitable with standard spark-injection combustion engines, necessitating the use of engine modifications or flexible-fuel vehicles. Forecasts for the United States and Europe indicate that biofuels will satisfy 33% of the demand for petroleum by 2030.
The article published in Green Analytical Chemistry states that bioethanol ignites cleanly with a 35% oxygen concentration and can significantly reduce petroleum and CO releases. As a constituent, bioethanol increases the octane ranking, resulting in an improved compression rate and power generation. In addition, the incorporation of bioethanol improves brake thermal efficacy, volumetric performance, heat dissipation rate, and braking torque.
Utilizing a probing methodology that harnesses particle transitioning and ensuing variation in the transitioning potential of the probe molecule in polar and non-polar media, the researchers have proposed a low-cost particle analysis approach for the exact measurement of fuel mixture composition.
The standard procedure recommended by the American Society for Testing and Materials (ASTM International) is D5501, which outlines gas chromatography. The method requires the use of extended columns, temperature control, and specialized gravity computations, as well as high apparatus procurement and operating costs.
Blends of gasoline and ethanol were created by combining the appropriate amounts of both substances. The mixtures were labeled E0 to E20, where the suffixes indicated the percentage of ethanol in the mixture.
Using the hyper-chromic shift of a probe (RhB) in a region of 500-800 nm devoid of hydrocarbon disruptions, an LED spectrometer precisely measured the ethanol concentration in gasoline. Two models of calibration were demonstrated. A model based on hyper-chromic shift produced LOD, LOQ, and RMSEP values of 0.02, 0.08, and 0.23%, respectively.
Study of Effect of Methanol Addition in Particulate Production
As per the article published in ACS Omega, both methanol and biodiesel are substitute diesel engine fuels. Methanol and biodiesel comprise 50% and 11% oxygen, respectively, and are derived from an extensive range of sources. Methanol and biodiesel have high solubility and are simple to combine. The addition of methanol to biodiesel significantly altered the fuel's chemical and physical characteristics.
Coagulation and aggregation of cylindrical fundamental particles dominated the formation of particulates. The SEM examination was performed to examine the micromorphology of the collected particles. Utilizing thermos-gravimetric examination, the sample's quality was evaluated in the combustion furnace as a function of time and temperature.
Observations indicate that particulates are formed primarily by a buildup of quasi-spherical basic carbon particles of varying particle sizes, producing clusters of particles with varying densities.
The calculated average diameters of B100, BM10, and BM20 were 35, 32.6, and 31.2 nm, respectively. The TG curve revealed that the mass of particles decreased as the reaction temperature rose.
What are Synthetic Automotive Fuels?
An article published in Combustion Engines informs us regarding synthetic fuels. Potential renewable energy sources can be incorporated with mobility thanks to synthetic fuels. E-fuels are synthetic petroleum products created through a blend (synthesis) of "green hydrogen" generated through electrolysis of water (e.g., utilizing seawater) fueled by renewable electricity and CO2 collected from a concentrated reservoir or directly from the atmosphere via carbon capture. Thus, e-fuels are defined as fuels that are produced using renewable electricity and typically synthetic raw materials. E-fuels consist of liquid and gaseous hydrocarbons, including methane, etc.
Particle analysis revealed that the decrease in particulate exhaust emissions observed with e-fuels is most likely due to their low aromatics content. However, the current technology for the production of e-fuels is still in the demonstration phase, and significant research is necessary shortly.
In short, particle analysis techniques are crucial for the automotive industry. They have found vast applications for the determination of fuel properties and are applied extensively during research studies.
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References and Further Reading
Frost, R., 2023. What are e-fuels and can they really make Europe’s cars emissions-free?. [Online]
Available at: https://qrius.com/the-future-of-fuel-a-look-at-alternative-energy-sources-for-cars/
[Accessed 12 June 2023].
Qrius, 2023. The Future of Fuel: A Look at Alternative Energy Sources for Cars. [Online]
Available at: https://qrius.com/the-future-of-fuel-a-look-at-alternative-energy-sources-for-cars/
[Accessed 11 June 2023].
Stępień, Z. (2023). Synthetic automotive fuels. Combustion Engines. 192(1). 78-90. Available at: https://doi.org/10.19206/CE-152526
Hradecká, I. et. al. (2023). Near-Infrared Spectroscopy as a Tool for Simultaneous Determination of Diesel Fuel Improvers. ACS Omega. 8(4). 4038-4045. Available at: https://doi.org/10.1021/acsomega.2c06845
Bukkarapu, K. R., & Krishnasamy, A. (2021). Fourier-transform-infrared-spectroscopy-based approach to predict engine fuel properties of biodiesel. Energy & Fuels, 35(9), 7993-8005. Available at: https://doi.org/10.1021/acs.energyfuels.0c03927
Vijayan, A., & Prakash, J. (2022). Probe-based spectrophotometric quantification of petrol-ethanol fuel blends for field-able applications. Green Analytical Chemistry, 3, 100043. Available at: https://doi.org/10.1016/j.greeac.2022.100043
Li, R. et. al. (2021). Study on particulate structure characteristics of diesel engines fueled with a methanol/biodiesel blend. ACS omega, 6(9), 6081-6087. Available at: https://doi.org/10.1021/acsomega.0c0437
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