Ethanol Content Estimation Research Articles

Quantitative estimation of ethanol content in eribulin mesylate injection using headspace gas chromatographic with flame ionization detector [HS-GC-FID

A specific HS-GC method has been developed, optimized, and validated for the quantitative estimation of ethanol content in eribulin mesylate injection. Chromatographic separation was achieved on DB-624 column (30 m x 0.32 mm, 1.8 μm), consisting of 6% cyanopropyl and 94% polydimethylsiloxane as stationary phase and passing nitrogen carrier gas. The performance of the method was assessed by evaluating the specificity, linearity, precision, and accuracy of experiments. The correlation coefficient value of the linearity experiment was 0.9999. The average recoveries for the accuracy were in the range of 98.7 to 102.4%. The results proved that the method is suitable for the quantitative estimation of ethanol content in eribulin mesylate injection.

Accurate Estimation of Ethanol Content in Fruit Juices using CIELab Color Space and Chemometrics via Smartphone-based Digital Image Colorimetry

Accurate Estimation of Ethanol Content in Fruit Juices using CIELab Color Space and Chemometrics via Smartphone-based Digital Image Colorimetry

Control-oriented engine model for online flex-fuel identification

In this contribution, we present an abstract second-order mean value engine model suitable for model-based control and parameter estimation methods. Furthermore, we introduce an online capable algorithm for flex-fuel identification, i.e., ethanol content estimation, based on air-to-fuel (AFR) control. Simulation results show, that the algorithm can reliably estimate the ethanol content, and provides additional monitoring functionality about deviations in the amount of injected fuel.

Development of Novel ICH Compliant Method for Estimation of Ethanol Content in Traditionally Fermented Ayurvedic Formulations

Background: Due to lack of stringent regulations and standards from regulatory authorities, Ayurvedic formulations available in the market are not properly standardized and assessed for their quality. Asava and arishta are very important fermented and commonly used dosage forms of Ayurveda. Information on the quantitative parameters of the ayurvedic fermented preparations is very poor. These formulations contain naturally selfgenerated alcohol that acts as the medium for extraction of active ingredients of the herbs. Hence alcohol content becomes a point of concern as well as an important parameter for standardization of the asava and arishta and it becomes important for manufacturers to mention alcohol content on the label. In addition, as per ASU GMP guidelines, it is mandatory to assess the accurate ethanol content to ensure the quality of the product. However manufacturers used to escape to comply with the guidelines because of complexity, cost and time consuming available methods. Hence there was a need to develop a novel method. Objective: This study is intended to develop a simple, efficient, less time consuming, economic and accurate gas chromatography method and estimate the ethanol content in marketed ayurvedic formulations of asava and arishta. Methods: The GC system (Nucon 5700 Gas Chromatograph) fitted with flame ionization detector was used in the present study. A capillary packed column (2m length and 3.170mm diameter) with stationary phase made up of WHP having 0.14mm in mesh size with nitrogen carrier gas was, used and the data were acquired through Winchrom software. Results and Conclusion: The retention time for ethanol was found to be 1.53min. The correlation coefficient (r2) of ethanol was found to be 0.9990. The ethanol content obtained by the proposed method was found to be between 3.05% and 11.53%. Conclusion: The study presents a simple and validated gas chromatographic method for estimation of ethanol content in herbal formulations. The developed method was found to be specific, accurate, precise and rugged. The method has been successfully applied for the analysis of marketed ayurvedic formulations.

AFR-Based Fuel Ethanol Content Estimation in Flex-Fuel Engines Tolerant to MAF Sensor Drifts

Flexible fuel vehicles (FFVs) can operate on a blend of ethanol and gasoline in any volumetric concentration of up to 85% ethanol (93% in Brazil). Existing FFVs rely on ethanol sensor installed in the vehicle fueling system, or on an ethanol estimation based on air-to-fuel ratio (AFR) regulation via an exhaust gas oxygen (EGO) or λ sensor. The EGO-based ethanol detection is desirable from cost and maintenance perspectives but it is known to be prone to large errors during mass air flow sensor drifts. Ethanol content estimation can be realized by a feedback-based fuel correction of the feedforward-based fuel calculation using an exhaust gas oxygen sensor. When the fuel correction is attributed to the difference in stoichiometric air-to-fuel ratio (SAFR) between ethanol and gasoline, it can be used for ethanol estimation. When the fuel correction is attributed to a mass air flow (MAF) sensor error, it can be used for sensor drift estimation and correction. Deciding under which condition to blame (and detect) ethanol and when to switch to sensor correction burdens the calibration of FFV engine controllers. Moreover, erroneous decisions can lead to biases in ethanol estimation and in MAF sensor correction. In this paper, we present AFR-based ethanol content estimation, associated sensitivity and dynamical analysis, and a cylinder air flow estimation scheme that accounts for MAF sensor drift or bias using an intake manifold absolute pressure (MAP) sensor. The proposed fusion of the MAF, MAP, and λ sensor measurements prevents severe misestimation of ethanol content in flex fuel vehicles.

Model-based ethanol blend estimation in flexible-fuel vehicles

This paper presents a model-based strategy estimating the ethanol content of an ethanol–gasoline blended fuel in flexible fuel vehicles. A steady-state parametric model relating engine speed, throttle angle, and air–fuel ratio to the fuel injector pulse-width is developed from physics. The parameters of this model are adapted and linked to percentage of ethanol content via a suitably defined metric. The proposed steady-state model structure is experimentally validated on a 2005 5.4L V8 Ford engine. The developed ethanol content estimation methodology is justified based on the combustion chemistry and physics involved. The methodology developed has a distinct advantage over previously proposed methods as it uses only the existing sensor set on a production vehicle.

Simultaneous determination of fermented milk aroma compounds by a potentiometric sensor array

The paper reports on the application of an electronic tongue for simultaneous determination of ethanol, acetaldehyde, diacetyl, lactic acid, acetic acid and citric acid content in probiotic fermented milk. The αAstree electronic tongue by Alpha M.O.S. was employed. The sensor array comprised of seven non-specific, cross-sensitive sensors developed especially for food analysis coupled with a reference Ag/AgCl electrode. Samples of plain, strawberry, apple-pear and forest-fruit flavored probiotic fermented milk were analyzed both by standard methods and by the potentiometric sensor array. The results obtained by these methods were used for the development of neural network models for rapid estimation of aroma compounds content in probiotic fermented milk. The highest correlation (0.967) and lowest standard deviation of error for the training (0.585), selection (0.503) and testing (0.571) subset was obtained for the estimation of ethanol content. The lowest correlation (0.669) was obtained for the estimation of acetaldehyde content. The model exhibited poor performance in average error and standard deviations of errors in all subsets which could be explained by low sensitivity of the sensor array to the compound. The obtained results indicate that the potentiometric electronic tongue coupled with artificial neural networks can be applied as a rapid method for the determination of aroma compounds in probiotic fermented milk.

Ethanol Content Estimation in Flex Fuel Direct Injection Engines with Fault Detection Under Fuel Injector Drifts

Estimation of the ethanol content in flexible-fuel vehicles (FFVs) has attracted increasing attention given the possibility of a variety of fuel blends, beyond E10 and E85, at the pumping stations. In this paper we develop an ethanol estimation algorithm for FFVs equipped with cylinder pressure sensing and direct injection. The algorithm utilizes the cylinder pressure measurements to infer the charge cooling effect associated to evaporating fuel with various ethanol concentrations [Oliverio et al. (2009)]. The ethanol detection feature using the charge cooling effect is named LHV estimation due to the fact that it relies on the Latent Heat of Vaporization characteristics of the fuel. The LHV estimation is shown to be more robust to fuel injector drifts than the traditional ethanol content estimation based on the stoichiometric air-to-fuel ratio (SAFR) identified through the closed-loop air/fuel regulation using the exhaust gas oxygen (EGO) sensor [Ahn et al. (2010)]. In this paper, a method is presented to enable robust ethanol estimation in the presence of fuel injector drifts by integrating the LHV and SAFR based detection features. Dynamometer testing and simulations of a 2.0L turbocharged spark ignition direct injection (SIDI) engine has demonstrated the feasibility of implementing the proposed method at specific operation conditions.

Ethanol Detection in Flex-Fuel Direct Injection Engines Using In-Cylinder Pressure Measurements

Flexible fuel vehicles (FFVs) are able to operate on a blend of ethanol and gasoline in any volumetric concentration of up to 85% ethanol (93% in Brazil). The estimation of ethanol content is crucial for optimized and robust performance in such vehicles. Even if an ethanol sensor is utilized, an estimation scheme independent of the ethanol sensor measurement retains advantages in enhancing the reliability of ethanol estimation and allowing on-board diagnostics. It is well-known that an exhaust gas oxygen (EGO) sensor could be utilized to estimate the ethanol content, which exploits the difference in stoichiometric air-to-fuel ratio (SAFR) between ethanol (9.0) and gasoline (14.6). The SAFR-based ethanol estimation has been shown to be prone to large errors with mass air flow sensor bias and/or fuel injector shift. In this paper, an ethanol estimation scheme is proposed by additionally using measurement of in-cylinder pressure, which essentially exploits the difference in latent heat of vaporization (LHV) between ethanol and gasoline, and hence detects the charge cooling effects of ethanol. Due to the additional and independent information introduced by the new algorithm, the proposed ethanol content estimation is more tolerant to air flow sensor drifts and injector shifts.