Determination of vegetable oils and fats adulterants in diesel oil by high performance liquid chromatography and multivariate methods

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Development and validation of a simple and reliable alternative method for process monitoring and final product quality control during fatty acid ethyl esters production

As the production of biofuels increase, there is an urgent need to easily analytically control their production at the plant level as well as to assess the quality of the final products. Especially method capable of determining fatty acid ethyl ester content of 96.5% is crucial for utilization in praxis. In this work, a refractive index method with required sensitivity was developed and validated by means of a standard reference gas chromatography method. Validation with a considerable set of real unique samples obtained at pilot scale was performed for both purposes - process monitoring at high conversions and final product quality control. The results confirmed a favourable degree of accuracy with a relative deviation lower than 3.5% from the reference value given by the gas chromatography. Moreover, application of the method for quality control of fresh and long-term stored samples revealed that the deterioration of final products during storage can be detected. The developed refractive index method is thus suitable for the simple and rapid evaluation of the quality of produced fatty acid ethyl esters and for analytical monitoring of their production process.

A Review on Machine Learning Application in Biodiesel Production Studies

The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

Determination of biodiesel and used cooking oil in automotive diesel/green diesel fuels through high-performance liquid chromatography

This study reports a simple and convenient analytical method for the simultaneous determination of biodiesel and vegetable oils or used cooking oils in petrodiesel and green diesel (hydrotreated vegetable oils or paraffinic diesel). The approach is based on normal-phase high-performance liquid chromatography with refractive index detection. It employed silica stationary phase, n-hexane mobile phase with isopropanol modifier to achieve optimum separation between hydrocarbons (petrodiesel or green diesel), fatty acid methyl esters (biodiesel) and triglycerides (vegetable oils and used cooking oil). In addition to determining vegetable oils or used cooking oils as adulterants in diesel, this method is also proposed as a better alternative to the standard method ASTM D7371, which is currently recommended for determining fatty acid methyl esters in petrodiesel. The method development involved screening of various stationary and mobile phases, with and without modifiers, to achieve acceptable chromatographic resolutions between analytes. Under the optimized method conditions, silica column, and n-hexane containing 0.6% isopropanol as the mobile phase provided the best results. The real-world scenario was simulated for the method validation carried out by fortifying Jatropha seed oil, soybean oil, and used cooking oil in the biodiesel blended petrodiesel and green diesel. Measurement of all analytes was accompanied by high precision, low limit of detection/quantification and linear response range of 0.05 to 50% for biodiesel, and 0.05 to 30% for vegetable oils. The proposed method is simple, fast (runtime 7 min), and does not require sample pre-treatment and backflushing.

Laser-Driven Calorimetry and Chemometric Quantification of Standard Reference Material Diesel/Biodiesel Fuel Blends

Requirements for blends of drop-in petroleum/bio-derived fuels with specific thermophysical and thermochemical properties highlights the need for chemometric models that can predict these properties. Multivariate calibration methods were evaluated using the measured thermograms (i.e., change in temperature with time) of 11 diesel/biodiesel fuel blends (including four repeated runs for each fuel blend). Two National Institute of Standards and Technology Standard Reference Material® (SRM®) pure fuels were blended by serial dilution to produce fuels having diesel/biodiesel volumetric fractions between (0 to 100) %. The fuels were evaluated for the prepared fuel-blend volume fraction and total specific energy release (heating value), using a laser-driven calorimetry technique, termed 'laser-driven thermal reactor'. The experimental apparatus consists of a copper sphere-shaped reactor (mounted at the center of a stainless-steel chamber) that is heated by a high-power continuous wave Nd:YAG laser. Prior to heating by the laser, liquid sample is injected onto a copper pan substrate that rests near the center of the reactor and is in contact with a fine-wire thermocouple. A second thermocouple is in contact with the sphere-reactor inner surface. The thermograms are then used to evaluate for the thermochemical characteristic of interest. Partial least squares (PLS) and support vector machine (SVM) models were constructed and evaluated for SRM-fuel-blend quantification, and determination of prepared fuel-blend volume fraction and heating value. Quantification of the fuel-blend thermograms by the SVM method was found to better correlate with the experimental results than PLS. The combination of laser-driven calorimetry and multivariate calibration methods has demonstrated the potential application of using thermograms for fuels quantification and analysis of fuel-blend properties.

Comparative phytochemical profiling of different soybean (Glycine max (L.) Merr) genotypes using GC-MS

This study aimed to estimate the proximate, phenolic and flavonoids contents and phytochemicals present in seeds of twenty four soybeans (Glycine max (L.) Merr) genotypes to explore their nutritional and medicinal values. Crude protein composition ranged between 35.63 and 43.13% in Argentinian and USA (Clark) genotypes, respectively. Total phenolic content varied from 1.15 to 1.77 mg GAE/g, whereas flavonoids varied from 0.68 to 2.13 mg QE/g. The GC-MS analysis resulted identification of 88 compounds categorized into aldehydes (5), ketones (13), alcohols (5), carboxylic acids (7), esters (13), alkanes (2), heterocyclic compounds (19), phenolic compound (9), sugar moiety (7) ether (4) and amide (3), one Alkene and one fatty acid ester. Indonesian genotypes (Ijen and Indo-1) had the highest phenolic compounds than others genotype having antioxidant activities, while the Australian genotype contains the maximum in esters compounds. The major phytocompounds identified in majority of genotypes were Phenol, 2,6-dimethoxy-, 2-Methoxy-4-vinylphenol, 3,5-Dimethoxyacetophenone, 1,2-cyclopentanedione and Hexadecanoic acid, methyl ester. The presence of phytochemicals with strong pharmacological actions like antimicrobial and antioxidants activities could be considered as sources of quality raw materials for food and pharmaceutical industries. This study further set a platform for isolating and understanding the characteristics of each compound for it pharmacological properties.

Chemometric Approach to Fatty Acid Profiles in Soybean Cultivars by Principal Component Analysis (PCA)

The purpose of this study was to investigate the fatty acid profiles in 18 soybean cultivars grown in Korea. A total of eleven fatty acids were identified in the sample set, which was comprised of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1, ω7), stearic (C18:0), oleic (C18:1, ω9), linoleic (C18:2, ω6), linolenic (C18:3, ω3), arachidic (C20:0), gondoic (C20:1, ω9), behenic (C22:0), and lignoceric (C24:0) acids by gas-liquid chromatography with flame ionization detector (GC-FID). Based on their color, yellow-, black-, brown-, and green-colored cultivars were denoted. Correlation coefficients (r) between the nine major fatty acids identified (two trace fatty acids, myristic and palmitoleic, were not included in the study) were generated and revealed an inverse association between oleic and linoleic acids (r=-0.94, p<0.05), while stearic acid was positively correlated to arachidic acid (r=0.72, p<0.05). Principal component analysis (PCA) of the fatty acid data yielded four significant principal components (PCs; i.e., eigenvalues>1), which together account for 81.49% of the total variance in the data set; with PC1 contributing 28.16% of the total. Eigen analysis of the correlation matrix loadings of the four significant PCs revealed that PC1 was mainly contributed to by oleic, linoleic, and gondoic acids, PC2 by stearic, linolenic and arachidic acids, PC3 by behenic and lignoceric acids, and PC4 by palmitic acid. The score plots generated between PC1-PC2 and PC3-PC4 segregated soybean cultivars based on fatty acid composition.