Monitoring biodiesel production (transesterification) using in situ viscometer

Ellis, Naoko; Guan, Feng; Chen, Timothy; Poon, Conrad

doi:10.1016/j.cej.2007.06.034

Cited by 56 publications

(27 citation statements)

References 14 publications

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“…The measurements provide valuable information about the operating conditions and simultaneously give an estimate for critical variables by monitoring the state of a process and verifying that performance is optimal. There are methods to monitor the progress of the reaction, such as gas and liquid chromatography [9][10][11][12], Fourier transform infrared (FTIR), near-infrared (NIR), and nuclear magnetic resonance (NMR) spectroscopy methods [10,[12][13][14][15][16][17], measurement of refractive index [18], and viscosity [19]. Most of these methods require samples to be taken and offline analyses to be performed which include a pre-treatment of the sample.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensors for Biodiesel Production in a Batch Reactor

et al. 2017

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Fossil fuel combustion produces around 98% of coal emissions. Therefore, liquid and gaseous biofuels have become more attractive due to their environmental benefits. The biodiesel production process requires measurements that help to control and supervise the variables involved in the process. The measurements provide valuable information about the operation conditions and give estimations about the critical variables of the process. The information from measurements is essential for monitoring the state of a process and verifying if it has an optimal performance. The objective of this study was the conception of a virtual sensor based on the Extended Kalman Filter (EKF) and the model of a batch biodiesel reactor for estimating concentrations of triglycerides (TG), diglycerides (DG), monoglycerides (MG), methyl ester (E), alcohol (A), and glycerol (GL) in real-time through measurement of the temperature and pH. Estimation of the TG, DG, MG, E, A, and Gl through this method eliminates the need for additional sensors and allows the use of different types of control. For the performance analysis of the virtual sensor, the data obtained from the EKF are compared with experimental data reported in the literature, with the mean square error of the estimate then being calculated. In addition, the results of this approach can be implemented in a real system, since it only uses measurements available in a reactor such as temperature and pH.

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Section: Introductionmentioning

confidence: 99%

Virtual Sensors for Biodiesel Production in a Batch Reactor

et al. 2017

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“…However, it is very sensitive to the quality of the feedstock, requiring vegetable oil, animal fats or other sources of raw material with a very low amount of FFA and water content. The reaction mechanism for the base catalysed transesterification process [18], formulated in three sequential steps, is shown in Fig. 5.…”

Section: Base Catalysed Transesterificationmentioning

confidence: 99%

Production of Biodiesel from Waste Cooking Oil: A Review

Bhavani¹,

Sharma²

2018

JACS

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“…One example is in the case of using viscosity measurements. In order to monitor the progress of the transesterification reaction, Ellis et al (2008) used an in-situ viscometer to correlate the viscosity measurement with the rate of biodiesel production. To extend the work done by Ellis et al (2008), the rate of biodiesel production, can be coupled with the kinetic model, which can then be used to infer the concentration of the five main components measured.…”

Section: Engineering Perspectives -Use Of the Kinetic Models In Enzymmentioning

confidence: 99%

“…In order to monitor the progress of the transesterification reaction, Ellis et al (2008) used an in-situ viscometer to correlate the viscosity measurement with the rate of biodiesel production. To extend the work done by Ellis et al (2008), the rate of biodiesel production, can be coupled with the kinetic model, which can then be used to infer the concentration of the five main components measured. Use of the viscosity meter along with the soft sensor provides a relatively inexpensive way to have real time monitoring of the system; giving to the operator quick feedback on the progress of the reaction and on the on the activity of the enzymes.…”

Section: Engineering Perspectives -Use Of the Kinetic Models In Enzymmentioning

confidence: 99%

Application of Uncertainty and Sensitivity Analysis to a Kinetic Model for Enzymatic Biodiesel Production

Price

Nordblad

Woodley

et al. 2013

IFAC Proceedings Volumes

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This paper demonstrates the added benefits of using uncertainty and sensitivity analysis in the kinetics of enzymatic biodiesel production. For this study, a kinetic model by Fedosov and co-workers is used. For the uncertainty analysis the Monte Carlo procedure was used to statistically quantify the variability in the model outputs due to uncertainties in the parameter estimates; showing the model is most reliable in the start (first 5 hours) of the reaction. To understand which input parameters are responsible for the output uncertainty, two global sensitivity methods (Standardized Regression Coefficients, and Morris screening) were used. The results from both sensitivity analyses identified that only 10 of the 32 parameters are influential to the model outputs. The model was then simplified by removing the non-influential parameters. A parity plot of the simplified model vs. the full model gave a R 2 value of over 0.95 for all the model outputs.

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Monitoring biodiesel production (transesterification) using in situ viscometer

Cited by 56 publications

References 14 publications

Virtual Sensors for Biodiesel Production in a Batch Reactor

Virtual Sensors for Biodiesel Production in a Batch Reactor

Production of Biodiesel from Waste Cooking Oil: A Review

Application of Uncertainty and Sensitivity Analysis to a Kinetic Model for Enzymatic Biodiesel Production

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